The rise of developers — as buyers, as influencers, as a creative class — is a direct result of “software eating the world”, and of key shifts in IT from on-prem to cloud & SaaS to the API economy, where application programming interfaces are essentially building blocks for innovation. Developers therefore not only play an outsized role in high-performing tech companies — but managing and motivating them is actually critical in ALL companies, since every company is a tech company (whether they know it or not).

As every industry turns digital, and a company’s interface to their customers IS software, “asking” one’s developer is the key to solving business problems and to thriving not just surviving, argues Jeff Lawson, CEO and co-founder of cloud communications platform-as-a-service company Twilio, in his new book, Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century. So in this episode of the a16z Podcast in conversation with Sonal Chokshi and David Ulevitch (who previously argued “the developer’s way” is the future of work), Lawson shares hard-earned lessons learned, mindsets, strategies, and tactics — from “build vs. buy” to “build vs. die”, to the art and science of small teams (“mitosis”) — for leaders and companies of all sizes.

But what does it mean to truly treat developers as creatives within an organization? What does it mean to be “developer first”? And how does this affect customers, product, go-to-market? All this and more in this episode.

Show Notes

• How software has become an essential part of the supply chain [1:40]
• Discussion of whether companies should build their own software or buy it [4:10]
• Including developers in the sales process [8:10], seeing them as influencers within a company [14:08], and having a culture of asking developers for input [17:13]
• Defining the “developer way,” or the culture of work that developers prefer [20:00]
• The importance of small teams [23:47] and how to keep teams small as a company grows [28:37]
• Lessons from Twilio’s IPO and expanding the role of sales [31:17]

Transcript

The new software supply chain

Sonal: Since I hate starting with the “why’d you write the book” question, I’d love to instead start with the big picture. We’ve seen 3 eras of software: from on-prem to cloud and SaaS to the API economy; you not only outline these shifts in the book, but you go further and argue that software is the new supply chain. So, I’d love to hear more about how you think about that to start us off.

Jeff: Yeah, thank you for asking, Sonal. I mean, it’s interesting, I come from Detroit, it’s the automotive capital of the world. I grew up around cars, and so many people that I knew, if they didn’t work directly for the automaker, they worked for a company that supplied the automakers. And it was very easy to understand the very sophisticated supply chain that allowed them to manufacture a complex thing like an automobile: General Motors doesn’t have to manufacture every little piece of that car. There’s companies who specialize in speedometers, and headlights, and seats, and all this kind of stuff — they just pick the vendors that are going to help them get to market as quickly as possible, with the best product.

And that’s what finally the software industry is developing. Software’s obviously been around in some form or another for 50 years, and in its modern internet-enabled form for close to 30 years. I was a developer starting in the 90s, writing code; back then, you basically had to write it all yourself, and you could pull in some open source stuff here and there. But building software has gotten easier and easier and easier, because of sophisticated APIs, and abstractions, and all this kind of stuff.

However, the scale of the internet, and building applications at internet scale, has gotten so much harder. And so, if you think about the progress that’s happened over the last decade or so of APIs that run in the cloud — if you need a piece of functionality, whether it is compute storage, payments, communications, maps, you name it — all you need to do is sign up, put in a credit card, and plug in this few lines of code, and now your app is supercharged with these powers… What that really is, is the development of a supply chain for building software.

Sonal: That’s great. And so, I mean, you’re basically saying software is innovation in that context. And we totally agree, obviously. The obvious next question that begs is, what do you build versus buy? In your book, you had a really neat rule of thumb, which is, that software that faces your customers, you should build; anything where your customers will be saying, why doesn’t it do X, and your answer is, well the thing we bought doesn’t do X, et cetera, et cetera — You basically argue that you can’t buy differentiation, you can only build it.

So, talk to me a little bit more about that, and really, where do people then compete? Because if everyone has access to the same APIs, like, where’s the differentiation?

Whether to “build or buy?”

Jeff: Absolutely. You know, something happened over the last 15 years, which was software went from the back office to the front office. It went from being something customers don’t care about to something they experience every day.

David: The remote control in our pocket — it’s how we interface with the companies we do business with!

Jeff: You know what I called my iPhone for a while, I called it my “summoner”.

David: Yeah, exactly.

Jeff: Think about your bank. Twenty years ago, your bank was a storefront that you walked into; it was clean, the teller was friendly, and they gave your kid a lollipop — okay, I like my bank. And now your bank, of course, is a mobile app. Suddenly, the interface you put in front of that customer is the perception of your product and of your value as a company. You like your bank if the mobile app is fast; if it is bug free; and if it has a lot of features and functionality to make your life a little bit easier.

Back in the days when it was back office, it would be common for IT departments to say, “Okay, should we build versus buy?” And a vendor would inevitably come in and say, “Don’t reinvent the wheel”, and you just bought something off the shelf. But now in a world where the software you use is your source of competitive differentiation, the act of building is the act of listening to your customer — and so now, the question has gone from build versus buy to build versus die!

Because one participant in that market starts listening to customers, and using the agility of software to innovate faster and faster and faster. And then the incumbents in that industry start listening, and they say, “Oh wow, we’ve got to do the same thing” and so they start becoming builders of software as well — this Darwinian evolution is going on in every industry. And so the buy act is one enabling you to be the best builder in your industry. That’s how I think about it.

David: That is great.

So, you know, if you are a company, you’re thinking about, “Hey, I do want to start building out developers; I want to have teams. But do I just use all the AWS kind of APIs? Do I go to Microsoft and use Azure?” There’s all these new startups that have APIs, how do I think about those things, how do I make those decisions?

It seems like AWS is creating an equal API for every startup that’s out there… how do you talk to business executives about those decisions?

Jeff: Well, it’s like any competitive dynamic in any supply chain. Which is, you want to pick the vendors and the partners who are going to enable you to go build as fast as you can. Every company has their areas of strength, their areas of expansion. And APIs offer you the ability to pick the services that are going to serve you best.

And one of the things I talk about in the book, is actually trusting your developers to help you navigate the vendor choices that you have. I remember back to the early days, when I was a developer, I would do a Google search; oh, this looks good; okay, how do I get started? And you click and it would say, “Contact sales. And, you know, if you sign an NDA, you can read the documentation.” You’d be like, okay, back, never mind, I don’t want that.

Because for a developer, documentation is the ultimate marketing. Yes, every company has a marketing website that’s pretty and hand-wavy. But at the end of the day, the documentation of an API is the perfect description of what that product does — it literally describes every in and out of what the product does. And you don’t have to believe a salesperson, you don’t have to look at a slide deck; which is like faster than in the old world, you couldn’t even get a meeting with the salesperson, right?

That’s why they are becoming so influential in adoption cycles and sales cycles, because a developer can — for free — read the documentation, make an evaluation of what they think the product is. And then for literally dollars, build the prototype, and test those hypotheses, and put it in front of customers, and actually do a beta. And that is a completely revolutionary way to de-risk these projects and take them from a bunch of hypotheticals — with a lot of budget and energy put into signing contracts with vendors and taking meetings — to actually just getting hands on a keyboard, building the thing, and putting it in front of customers.

David: That empowered developer really transforms the go-to-market for API companies, right? I mean it changes the way you do customer success, and the way you do onboarding, if they’re going to build that prototype before they maybe even talk to you — it must really radically transform the way you think about what an enterprise go-to-market organization looks like in an API world.

Seeing developers as customers

Jeff: I’ll tell you a true story; WhatsApp is a very large customer of Twilio and has been for a long time. And like, literally, that is a Yahoo email address — of Jan, signing up for a Twilio account back in 2011 or ’12, or whenever it was — and this is one of the big differences between API-economy companies, and other companies, who say they serve developers. At other companies — who regularly launch APIs and say hey, we’ve got a platform — the developers are a strategy. At API economy companies, developers aren’t a strategy… They’re our customer. They are our revenue. You’re never going to pull the rug out from under them, because you are dependent on them for the health of your company. And that’s a very different world than other companies where the customer is an advertiser or somebody else; for the API economy, you have to treat the developer as your customer.

David: Yeah, the example of the WhatsApp story of having an individual developer sign up, means that you really rethink marketing, communications, how you engage with those customers, how you measure the metrics, how they’re using the product.

Lots of companies don’t have great visibility into how their customers are using their product. But by definition, an API company has incredible visibility into how their product’s being used. And that has never been possible before. You know the nice thing about an API company is you don’t have to track: Did they build a prototype? Are they going into production? Are they making one or two calls a week, are they now making thousands of calls a week? Maybe we should reach out, see what they need, what features are missing, have a product manager engage them, and you know, keep those people close to the customer, whether it’s developers or product managers.

And so the order of operations of the traditional enterprise go-to-market HAS shifted. What used to be a whole bunch of pre-sales, marketing material, and brochures, and websites; now, as Jeff said, it’s the documentation. But then after that, you do want to come in with that white-glove kind of a service and really embrace your customer, understand what their needs are, understand what the opportunities are; you know, maybe rethink your roadmap and all these things, based off of how people are using the products.

And I think that creates lots of other opportunities for startups to actually support this new kind of a go-to-market motion.

Sonal: I think the most under-discussed, but most important aspects of this conversation IS this notion of keeping developers close to customers. That’s a really novel idea for a lot of traditional companies; it’s actually probably even a novel idea for a lot of established software companies, frankly.

You both mentioned the documentation. But what really struck me, is it forces developers to be better communicators. Because you’re essentially having to explain (even if you don’t write all your own documentation), what is this value, what is this thing you’re doing? And that is another segue to this topic of how does one keep developers close to customers? Does that mean you literally tactically put them in front of the customer; are they now the front interface to customers? Are they taking the customer success calls? Are they taking, you know, reports?

Like, what does it actually mean to keep your developers close to customers; and, how should this happen (or not happen)?

Jeff: That’s a great question, Sonal. I think the answer starts with my assertion that being a developer is fundamentally a creative exercise; it’s not merely a technical exercise.

And I think that’s something that is really misunderstood about software developers. You know there’s this pop culture myth about developers that’s propagated by Hollywood; and, look, there may be some truth to that, but, really, developers are not just, like, calculus, you know, math nerds. In fact, we did a survey of software developers, and we found more than half of them played a musical instrument, and it was like three quarters of them did some sort of artistic thing on the side. And the act of writing software is creative problem-solving.

But that creative problem-solving skill doesn’t end with writing an algorithm — it really goes all the way to the types of problems that you throw at developers. And so one of my biggest statements in the book is, instead of sharing solutions with developers, share problems. Instead of handing a product-requirements document that was written by some MBAs, and throwing it over the wall, and build it to the spec — you know, having a developer basically be a digital assembly line worker — share the problem with them: Hey, we’re trying to make it so customers can sign up for our product and get productive in 30 seconds instead of the 20 minutes which it takes today. NOW you unlock the ability for that developer to use the full creative energy they have.

David: You and I both self-identify still as software developers; I still write code and I’m sure you write code as well. The reality is, as you said, these developers are creatives — and like any real creative, they want people to use their work, their art.

I actually believe that there’s like a selfish reason why people are open source developers, which is that they just get a much wider audience much more quickly. And then inside of a company, you want to know that there are people that are going to pay tens of thousands, or hundreds of thousands, or millions of dollars to use the code that they wrote — and, find that extremely satisfying. One of the greatest tropes that always bothered me was this idea that developers need to be protected from the customer.

Jeff: Don’t get me wrong; you don’t want your software developers handling every support ticket and every sales cycle. However, if you don’t poke holes in those siloed walls — and you treat them like these precious things that can’t be bothered by such trivial matters, like customers – well then you are doing a huge disservice, ‘cause you’re essentially blinding the developers to why they’re writing the software in the first place.

And so you need to intentionally poke holes in those walls, and I think product managers are actually the key to this. At a lot of companies, product managers see their jobs as shielding developers; and I think the job of product managers is to figure out how do I facilitate the right interactions between the developers — who I want to be able to have instinctive understandings of my customers and their problems, and the jobs-to-be-done by those customers — and the development team who’s there to solve problems. Because when you have an instinctive understanding of the customer, well so many other ways of solving problems arise, and so many other ways of thinking.

Developers as internal influencers

Sonal: It’s super important to treat your creative class that way. And it’s so funny because we also talk about the rise of design a lot; and this is a similar shift that’s happening with designers when it comes to designing technology products as well.

I’ve noticed that people often do the same thing with writers and editors. Like they give you this specs doc, and I’m just bring ‘em more upstream, like, embed into your flow… ‘cause we’re going to hear things that you don’t know to ask us or tell us.

Jeff: Sonal, one thing that comes to mind is the parallel between the shift that’s happened because of personal computers and the internet for other creative classes — we’re all aware of the fact that you can use GarageBand, or Pro Tools, and a musician in their own home can record a song with basically the same tooling that the professionals use. And if your music is any good, you can develop an audience of millions of people, as a creative. The same thing for film production or video, right, like you used to have gatekeepers, who were studios, and you needed millions of dollars of equipment to make a movie; now, anyone with an SLR can make a movie, can edit it on Final Cut Pro (the same software that they use in Hollywood), and upload it to YouTube.

And so people well understand what’s happened to those creative disciplines. But really, the same exact thing has happened for software developers. Which is a software developer can take the same infrastructure that’s used by the largest companies in the world; can build a software app on the internet; and get distribution with Google AdWords, or Facebook Ads, or any of the stuff. And a developer with the right idea is also liberated to be able to build just about anything they need — in that exact same way that musicians or video artists, or storytellers do. And that’s an amazing thing that’s happened.

Sonal: Combining that with what David said about open source, it does create this sort of composability — build on top of each others’ building blocks. I mean, the best thing about TikTok is remix culture; like the fact that you can remix all these bits. And that’s exactly the same thing you’re talking about.

You know this notion of developers want an audience, developers are a creative class — what does it mean for developers to become influencers more broadly within a company; with the question being, how to make developers more influencers across the company?

Jeff: Well to me, really, that comes down to giving developers a voice. And an environment where you embrace experimentation — experimentation is the prerequisite to all innovation. You enable THAT as opposed to more hierarchical, top-down, highest-paid- person’s-ideas wins, and all that kind of stuff.

David: You know Jeff, so many companies have not embraced the Ask Your Developer mindset. Sometimes what they do is they sort of find their way into the shallow end of the pool by… sort of having hackathons. And then magically, they find out that really good ideas come out of these hackathons. And hey wait maybe, maybe we should involve the developers earlier in that product-roadmap process.

You know they have these good ideas, but they never get prioritized, they never get surfaced; like you said, they come from elsewhere in the organization, but maybe they shouldn’t. How do you think about hackathons? You know, when you’re talking to those business executives, how should they think about hackathons? And then how can they take that catalyst of sort of an event inside the organization and actually institutionalize that into their culture and workflow and process?

Jeff: You know, I like hackathons, not necessarily because every hackathon results in the next giant innovation or whatever it is — you’re right, it often does end up proving the hypothesis that, oh wow, there are some things that we could do relatively quickly, that are very impactful, if we let our teams kind of go wild thinking about what are the things — but I like hackathons because they are a practice that actually encodes Ask Your Developer.

‘Cause if you think about it, inherent in a hackathon is this idea of letting developers essentially spend a period of time self-organizing and building the things that they think are interesting and important, and using that opportunity to prove out and to test out their ideas. In an ideal world, companies would operate more normally, in more hackathon-oriented ways — i.e., small teams working iteratively, and being agile, and being tasked with problems not solutions. And a hackathon is a way to simulate that, for a short period of time, and at small scale.

Sonal: I mean, I hear what you’re saying, but I feel like hackathons are a bit performative. Because I’ve seen too many times, like a lot of companies do what you describe in the book as that “Silicon safari” effect, like animals in a cage; we must follow the same practices, and perform them essentially.

David: I don’t think they’re performative. I think that they’re like, you have pressure building up in a system, and it’s like the steam valve — you reconfigure the machinery so that that steam valve doesn’t need the release.

I don’t think there’s ever been an organization (at least that I’ve ever heard of) that’s done a hackathon. And been like wow, that was totally useless, we’re never going to do that again. They may not get that great new product that, you know, sends up their revenue for the next five years. But there’s always learnings — and those learnings are not just in the code that gets written, but in the processes that get created. So, I think hackathons are great. I would certainly not describe them as performative.

Jeff: I will play the role of peacemaker here, because I think you’re both partially right. I think that, look, if you go into a hackathon saying okay I really am waiting for these folks to come up with the thing that’s going to save the company — it’s probably not really the right expectations to walk in with. So, to some extent, it is performative.

But I think that the goal of the hackathon is not to solve the problem during the hackathon. The goal of the hackathon is actually to model what you want your organization to become; it’s like a rehearsal for really, the organizational structure and the way of operating during the regular course of business. And so I think that’s the role that hackathons play.

I actually think a better way to structure it is, if you’re an executive at a company, create a two days a week, whatever you want to do; but go in with, hey, I care about this. You’re important, I’m committed to this. #1.

#2: here is a list of the 10 biggest problems I hear from our customers; or here are the 10 biggest problems that we face as a company — and I’d love for you to be thinking about. NOW you’ve directed the energy, you’ve shared problems with those developers — and you’ve told them the stakes are high… I think that is a much more effective way to run a hackathon.

Sonal: I love that. You’ve made peace.

Developers’ unique workflow

We’ve been talking about the developer mindset quite a bit. But we haven’t actually defined what is the developer way here: It’s not just a role and a function; like, it’s a mindset. And Jeff, have you seen in your work that these habits transfer across the org? You use the word “mindset” throughout your book; and David has used the word “way” throughout his work.

I would love to hear your guys thoughts, kind of define what makes a developer.

David: Look, I think developers in general — especially open-source developers — have mastered a whole bunch of working methodologies that end up just turning out to be great working methodologies not just for developers, but for anybody.

So, that involves really having the tools to do asynchronous sort of communication and development; so in software development, that could be revision-control systems, things like GitHub, or GitLab, which allow people to collaborate. It can be ways of memorializing decisions: developers have change logs, they have issue tracking, they have pull requests — and so it’s often very easy to figure out how did this line of code get into the codebase; who signed off on that decision; who else reviewed it? And these are things that other organizations (outside the developer part of the organization), no one knows how the decisions get made; who made those decisions; when were they made; why were they made?

And then, of course, there are power users of their own computing devices. And so, you know, developers often are much more keyboard-driven, they use shortcuts, they’re much more fast to operate. And we see these things bleeding into our world today; people now use emojis as shorthand. People are now using things like a command palette, and they’ve gone way beyond the way developers use command palettes; they’re now sort of bleeding into our normal daily life.

But I do think there’s a lot to learn from the way developers organize, the way they communicate, the way that they memorialize decision making. And then, of course, the way they just use their computing tools as power users, because, you know, everyone’s effectively a digital native these days and becoming more and more of a power user.

Sonal: How do you define it? Curious for your thoughts on this.

Jeff: You know, I’m a little more hesitant to define like, the developer way. I struggle a little bit with saying, you know “here’s my definition of developers”, because there’s a lot of different ways to work.

Now that said, a lot of developers do share a lot of common traits. Like when your work involves writing Boolean logic, a lot, if you tend to be drawn to that work, you probably tend to also want to have logical thinking in other areas of life. So I do tend to see engineers as being logical thinkers. And, you know it’s interesting, because like I, as a CEO (and a software developer), bring logic to a lot of the decisions, but also to a lot of my interactions with other team members.

And I actually have noticed that it can be rather infuriating, actually — it’s one of things I’ve had to moderate as being a CEO, from being a developer — I’ve actually realized some of the ways in which the ways developers think, while they may be often right, they don’t necessarily serve you in interfacing with people who don’t think the same way.

But I would say, if you’re a business executive, a few things to think about: One is, like many other arenas, where you have a lot of concentration required to do your job, *flow* is one of the most important things for developers — so the ability to immerse yourself in a problem, be able to kind of fit it all into the working memory of your brain, and then be able to get your work done is really important. That’s why developers are really sensitive to interruptions, to taps on the shoulders, or meetings, and things like that.

And the other thing I would say is, if a developer is poking holes in the logic of your idea or your plan, they’re not being a jerk; it’s just the way they think. They’re processing whatever they’re hearing through the lens of how they think, and therefore, that’s the response you’re getting. And so, it’s maybe a way for folks to understand developers — and therefore be able to engage with them — is to think about the ways in which developers process information and make decisions.

I like to propagate this idea that developers are creative problem solvers, and much bigger, more influential parts of the team, when they’re whole human beings. Not just like, you know, code monkeys.

The importance of small teams

Sonal: Jeff, you’ve alluded to this a few times — in fact, I thought this was one of the most interesting themes in your book — is, you asserted throughout it’s about small teams, it’s about small teams; it felt like a refrain.

I’ve always heard the two-pizza rule for Amazon; I never heard the origin story until your book — and you describe having a dozen bagel team — so tell us a little bit about small teams, why they matter, how to grow them, how to make them work? I feel like the title of the book should also be, “Small Teams”!

Jeff: Yes, “Ask Your Developer: Small Teams Are Right for You.” <Sonal laughs>

So, back to, we were starting Twilio — at the very beginning, in this very small team that you are, you kind of do everything; everything from like having talked to customers that day, to handled support tickets, to writing code, to understanding the architecture of everything that’s going on. Like, you can hold the whole business in your head at the scale of several people.

And as we started growing Twilio, one of the most momentous things that happened to me was I was talking to my friend — his name’s Dave Schappell (not the comedian, different Dave Chappelle) — he was actually the person who hired me at Amazon. And he had started at Amazon in, I think, ’97, so when the company was about 100 people. I joined in 2004, Amazon was about 5000 people. And Dave, he quit; that was my first week, he was like, “I’m sorry, I couldn’t tell you before. I’m out of here.” He went and started a company called TeachStreet, that was acqui-hired back into Amazon about seven years later. So, Dave found himself back at Amazon, but now the company was 75000 people.

So, Dave saw Amazon at 100 people, 5000 people, and then again at 75000 people. And so, as I was starting to scale Twilio’s culture, and thinking about how we were going to structure ourselves, I called Dave and I said “Hey, Dave, can you compare and contrast Amazon at 100 people, Amazon at 5000 people, Amazon at 75000 people?” And he said, “Hunh, let me think about that for a second.” And he said, “You know what? It’s exactly the same. It’s the same bounce in people’s step, the same sense of urgency, the same intellect that everybody here has. It feels like the same company.”

And to me, THAT is the outcome of the two-pizza team, as they call it at Amazon. Because as the company is growing, there’s a natural tendency for every company, as they get bigger, to slow down, to insert more bureaucracy, to create walls between customers, to create politics and things like that.

And what small teams do is they keep a small group of people who are very tight, and focused on — what my definition is, the small team is defined by — a customer they’re serving, a problem they’re solving for that customer, and then metrics of success that say whether or not they’re succeeding. And there’s a lot of advantages here:

First of all, on a very small team of say 10 people, there’s no room for a low performer to hide; on a team of 10 people, everyone’s got to carry their weight, and it’s obvious when somebody isn’t. The other thing that I think is interesting about small teams, is that people’s willingness to go along with decisions is proportional to how involved they were in that decision, and how close they are to the decision maker. And so if you’re on a small team of 10 people, and there’s a single-threaded leader to lead that team, then you want to push as many decisions as possible to that leader. And when you do, it’s likely that they’re going to be involved in that decision, and if the person’s managers would have made a decision that maybe they disagree with, they’re probably going to be more inclined to disagree and commit. Or, they’re more likely to be able to question it; hey, can you explain to me why you made this decision? Like you can’t do that when it’s someone five levels up; usually you don’t even know the person, or it’d be hard to get the meeting, or you’d be afraid to express yourself. And even when there’s disagreement, those disagreements get resolved. So instead of having this like us vs. them, you get this sense of: okay, you know we’re all on the same team here; you know let’s go, let’s do this.

David: You know, one additional benefit of small teams that I’ve always observed is that there are some people that like to work on very small projects with rapid iteration, where they sort of have the dopamine rush of shipping a release and getting something done very quickly. There’s also other kinds of engineers that like really loooong, hard projects, that take months and months and have very little to show for it for a long period of time. And by having small teams, you can actually let people sort of work in an environment that works best for them. Like those people that want to close out a ticket to help win a deal, or save a renewal — like those people like to be on this fast, close-to-the-customer kind of teams; and there’s those infrastructure people.

And, by having small teams, you allow people to end up gravitating towards the kind of work that ends up allowing them to work at their highest and best sort of potential. People can find where they fit in best, and I’ve always found that as an organization scales, to be a really, really valuable component.

Jeff: The other interesting thing by the way, about the infrastructure people you mentioned — great engineers love building for the other builders, right? — but they’ve got to see it as I’m serving a customer with a mission and metrics.

And so, even internally focused teams, it works the exact same way. And I think that’s one of the beautiful things about structuring yourself that way, is reminding everybody: Like, if a team exists and has no customer, internal or external, then, man, I’d wonder why they exist.

Managing growth and team size

Sonal: I want to probe into like, what happens when companies scale and grow, and, small teams can’t really stay small — You argue for a really interesting concept called “mitosis”, which obviously is borrowed from cells, that split as you grow. And I thought that was a really interesting idea.

Jeff: Yes. So for us — I’ll give [an] example — our first product was Twilio Voice, the ability to make and receive phone calls with Twilio. And you know that was built by the founding team, we built it, we started hiring people, we grew. And suddenly the team that was working on that product became like 15 people. And we said okay, this is getting too big. If we believe in small teams, we need to split this. How are we going to do that?

And so what you do is you take the problem domain, and you say okay, if I want to divide this problem domain in half — so I can have two teams instead of one big team — how would I do it? And there’s no one answer to it, but the best thing you can do is align the people, the technology, the code itself, and the customers. And when you can figure out how to actually divide the problem so that the customer, the technology, and the team can actually stay together, that’s the best way to do it.

And so for like our voice product, we realized that the voice product really consisted of two things: One was the connectivity layer into the carriers of the world; and the second was all the programmable APIs that allowed you to do things with that connectivity. And so we divided those two teams. And initially, the code was completely intertwined, and it was like a complete mess. And we sat out and we said, okay, we need to decouple those two systems; and we need a technical leader for the connectivity side, we need a technical leader for the API side. And… over the course of about six months, we untangled the code bases, we untangled the teams. If we didn’t have a leader we needed for the next team, we would hire the leader. And after about six months, we were able to decouple the two, and take one very big team and turn it into two small teams again.

And that’s basically the process that Twilio has used to grow from, you know the three engineers that we were when we founded the company, to now… several thousand engineers. We just keep doing this mitosis process.

In the act of that, one of the key enablers of that is itself, APIs — and those APIs can be used internally, but they could also be exposed externally (if we wanted). And so we actually ended up doing that. We productized — we call it SIP trunking — that’s the connectivity layer, that is now its own product; and that product itself has undergone mitosis now many times, as well as our API layer, which is its own product.

Do you throw away the notion of small team and say well, that’s only for the early stages, once it gets big, so be it. I think that’s exactly the wrong answer.

Sonal: That’s fantastic, and I have to say, people really should read your book, because you say a lot more about the types of leaders that are needed, and I love that you have this line about — a phrase that you’ve coined — “The fallacy of better collaboration” — because that’s one of my pet peeves — where when you have too many small teams, how do you coordinate and collaborate? And it’s a wonderful, wonderful chapter.

Lessons from Twilio’s IPO

Last question. One thing that I’ve been dying to ask you, just super quick, which is, what would you say is your biggest personal evolution, pre- and post-IPO? That’s top of mind for a lot of people right now, so I’m very curious about that.

Jeff: For me, it has been — the biggest evolution has been — really thinking more holistically about the intersection of product and go-to-market.

You know, we went public, and we had about 12 sales people in the company. And we really loved our developer-first approach, our self-service model; developers sign up and start building. And… you know, we were very happy with that, and as such, like really had under-invested in sales. You know like 12 sales reps to manage a quarter billion in revenue, that’s an underinvestment, right?

Sonal: Wow… yeah!

Jeff: But what I came to realize was, empowering a developer to get started with Twilio is amazing. But once a company starts spending hundreds of thousands or millions of dollars, you can’t rely on a relationship with a developer to maintain that level of spend, because now you’ve got so many more stakeholders inside of the company.

Developers want to do great work, but when the CFO is saying, “Hey, how come we’re spending this much on Twilio?” like “I don’t know, I… ” I mean, you know, that someone else’s job, right? And so we now we call it the “developer-first” approach, where, developers start the relationship, but then we build a mature relationship with many stakeholders inside the company — and that’s essentially what salespeople often do, is they understand the org chart of the company; and they understand who the stakeholders are; and they really build deep relationships with the customer (the customer, meaning the company, not just the individual.)

You know so that’s probably I think one of the biggest things that I’ve come to you know, evolve, in my thinking is the holistic nature of what it takes to build a company.

Sonal: It’s so funny, we have a whole series of podcasts called How to Go from a Technical to Product to Sales to Go to Market CEO, because it’s exactly the journey.

That’s fantastic, Jeff Lawson, author, CEO of Twilio, and author of Ask Your Developer: How to Harness the Power of Software Developers and Win in the 21st Century. Thank you so much for joining!

Jeff: Thank you, Sonal, it’s been a pleasure.

• 

  Jeff Lawson
• 

  David Ulevitch is a general partner at a16z where he invests in enterprise and SaaS companies. Prior to joining the firm, he was the founder and CEO of OpenDNS (acquired by Cisco).

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  Sonal Chokshi is the editor in chief as well as podcast network showrunner. Prior to joining a16z 2014 to build the editorial operation, Sonal was a senior editor at WIRED, and before that in content at Xerox PARC.

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Social Strikes Back is a series exploring the next generation of social networks and how they’re shaping the future of consumer tech. See more at a16z.com/social-strikes-back.

The intersection of social and finance—as well as shifting attitudes around what we share about money online—have given way to an ambitious new wave of financial products.

While revealing one’s financial information was once considered taboo, now people are more apt than ever to openly discuss money online, particularly Gen Z and millennials. That’s evident on both ends of the spectrum, whether people are bemoaning their crushing levels of student debt on Twitter and Instagram or bragging about their latest stock trades on WallStreetBets. The repercussions extend far beyond social media, fueling a wave of new social-fintech products like Public, Commonstock, and Doji, among others.

In this conversation between fintech partner Anish Acharya, formerly a product manager at Credit Karma, consumer partner D’Arcy Coolican (who himself is a former founder in this space), and host Lauren Murrow, we discuss why the “holy grail” of social plus finance is both so challenging and, potentially, so rewarding.

This episode was originally released last year and been resurfaced as part of Social Strikes Back, a16z’s new series exploring the many ways social networks are shaping the future of consumer tech.

Transcript:

Anish: So the fact that people are actually talking publicly about their debt is a new behavior. In the past, spending was public but debt was private. For the first time, debt is starting to become a public conversation. What’s new is that this generation is living in a completely different socioeconomic context. That’s not “flighty millennials” and Zoomers or whatever, that’s a completely different financial world that they’re growing up in and that’s driving a different set of conversations.

D’Arcy: You see it across all of the platforms, but you see certain categories that people are now talking about that they didn’t talk about before. Salary is something that a certain generation is much more comfortable talking about, student debt is a category that people are much more comfortable talking about. Trading is a category that people are much more comfortable talking about.

Across the spectrum you see sharing on social of financial stuff going up. You see it on Twitter, you see it on Facebook, you see it in blogs. There are a bunch of pockets.

Lauren: Why do you think this shift is happening?

D’Arcy: I think it’s driven by a few factors. One is generational, so every generation’s relationship with sharing and every generation’s relationship with money is different. So what Boomers did versus what Gen X did versus what millennials do versus what Gen Z does is different, and I think you see this macro trend around increased sharing.

Lauren: And that’s driven by historical changes, that’s driven by the financial crisis.

Anish: Yes, exactly. They have to take nontraditional paths to achieve financial progress and dreams. For a long, long time, buying a home was not only the American dream but something you achieved through the traditional financial system. So, everyone had a mortgage. Today, mortgages are less accessible than they’ve ever been. Will you talk to your peer set about, “How am I ever going to buy a home?” That’s really the catalyst behind many of these things.

D’Arcy: And I think you see that also with the massive increase in student debt over the last 10, 15 years. It’s reaching unsustainable levels and that’s forcing a conversation that breaks down the stigma around talking about student debt. Once you break the stigma, then it’s like, hold on, and everything comes flooding to the forefront.

Lauren: We’ve talked about how money is inherently private. Do you not think that that is becoming less so? There’s the generational piece of it. Then, yes, we’re sharing more of our lives in general. And then there’s a political angle to it, this idea of radical transparency to affect change. So that’s why we’re posting more about student debt, about medical debt, about our salaries.

D’Arcy: Definitely there is a long-term trend line towards sharing more rather than sharing less. But you see it happening at the category level and, to a certain extent, at the subculture leve. Let’s take student debt as like one category. When people start talking about it, then everybody feels empowered to talk about it, right?

I think you need catalysts for walls to come down around certain categories, like the student debt crisis, the financial crisis, there’s a lot of external events that have led to some of these things coming down. But it’s happening inch by inch and category by category. The question is: what pieces are going mainstream?

Anish: I think the hacker mindset has pushed outside of software and into finance. There was always a small number of people who were excited about “hacking their money,” but now that’s becoming a more mainstream concept. So the idea of being someone who arbitrages rewards across credit cards used to be a pretty niche, edge thing, and now more and more people are doing it. To the point where a lot of card companies are having to pull rewards back because there’s Points Guy and a million other sites that tell you how to hack the system. And credit scores are very similar. It’s not a destiny, it’s a game—or it’s at least closer to a game than a destiny—and more people are talking about the ways that you play it. When I say it’s a game, I say that in a hopeful way, not in a dismissive way, in terms of the importance of it.

D’Arcy: What are the things people like to do on social? Three of the core functions are bragging, complaining, and rubbernecking. And I think you’ve seen that where social and finance intersect, they’re coalescing around those three use cases as well. At the end of the day, social and finance, a lot of it is just content. It’s content that’s anchored around some financial transaction, but it’s still just content, so the usual rules of social apply.

Another way to think about it is: when you’re building something in social plus finance you have an interaction layer and you have a transaction layer. And the interaction layer is built around the emotional and cognitive pieces—that is content creation, that is messaging, that is all these social things that we see pop up—they appeal to these cognitive and emotional levers. And then you have a transactional layer, which is whatever your actual financial transaction is. That’s generally much more of a functional use case.

The magic in social plus finance happens when the transactional piece and the interactive piece are mutually reinforcing. That’s where the flywheel on social plus finance really starts to spin aggressively.

Lauren: Can you give me some examples of particular products in which you’ve seen this magic happen?

D’Arcy: The easiest example is probably Venmo back in the day. You had messaging apps and money transfer apps like PayPal that existed—and chat existed—but the idea that you could attach your transaction to an emoji just made the transaction easier, it made the emoji more fun, it made the whole thing more self-reinforcing. It’s a really challenging problem to be able to do that, but when you do it, it’s magic.

Anish: I actually think that those products are fascinating. I still like to scroll through the global feed on Venmo, which now is capped, I think, at the last 50 transactions. But it’s just so fascinating to see all of these people all over the country sending each other money. There’s something that is just vicariously thrilling about it. And because money does touch all of us and it’s so private, the products that can start to invert that touch a nerve in an interesting way.

By the way, it doesn’t have to only be online—there are a couple of interesting offline examples. SoFi, which is really in the business of refinancing mispriced student debt, built this whole community of HENRYs—High Earning Not Rich Yet. They did a ton of parties and events and made it feel special to be a SoFi member. And, really, they were a lender. So I think at least in the early days, they’ve had a lot of success combining the two. I imagine what’s less successful is, you know, Capital One opening coffee shops where you can hang out and get coffee and do your banking. It’s easy to dismiss that as clumsy, but I do think that they’re trying to touch the same nerve.

D’Arcy: There’s also this long legacy of companies starting out at the nexus of social and fintech and then eventually moving one way or the other, generally towards the fintech/transactional layer. So a lot of people build either social features or community in the early days and really use it as a way to bootstrap their product, but then over time they migrate more towards a transactional fintech product, rather than a truly social product.

Lauren: What are some of those examples?

D’Arcy: SoFi is a great example of that. It’s functionally a lender, which is not a multiplayer social game, but they were able to build this early community which was able to get them a lot of traction. You look at like Wealthfront. Before it transitioned into Wealthfront, I think it started as KaChing, which was a social fintech product. If you look at Robinhood, originally it was a much more social product, then became a much more transactional product. Prosper started out as a much more social product, then became more of a peer-to-peer lending platform.

So a lot of these things start social and are able to bootstrap in their early days off of some of those networks. Then you end up at a decision point where you try to thread this needle and continue down this social plus finance angle, or do you move into a more single-player fintech product? And I think a lot of the more successful fintech companies started social, but then eventually transitioned.

Lauren: Why are they making that transition?

D’Arcy: It’s hard.

Lauren: Well, let’s talk about it. What’s so hard about social plus fintech?

Anish: The most direct manifestation of social plus fintech is: we have messaging, plus we have payments or some other shared accounts, shared ledgers, joint accounts, etc. I think that is very difficult for a number of reasons. Because money is so private, people are less likely to send invites to each other and bootstrap a social product in the way that you would bootstrap other social products.

I think there are a lot of other examples, though, where the experience may not directly represent social plus money, but it very much plays to that. So I think the example D’Arcy brought up is great, which is Robinhood. There’s been a ton of talk about how Robinhood is doomed because others have cut fees and adopted their business model. But in truth, Robinhood is a game and it’s a game that people like to talk about. It works because it feels like adulting when you actually have a stock portfolio, not because active trading is something that’s smart for almost anyone to do. So I really see it as addressing a different consumer need than Schwab is addressing, and it’s really not threatened as much by players like Schwab. So that’s an example where the fintech product is addressing a social consumer need, but at first blush, it may not appear to be the combination of social plus money.

Lauren: And some of these products are really tapping into the trend towards gamification. Do you think more products will go that route and design around that impulse?

D’Arcy: I think the thing you will likely see is that social plus fintech products will actually come much more from the consumer side of things. There are some things like Robinhood, where you’re able to build a fintech and community and it comes from the fintech side of things. Another encouraging angle is the things that are coming from the social sites, whether it’s a bunch of the chat apps that now have wallets and payments installed in them or even something as weird as Fortnite, which is technically a game, but they have V-Bucks and they have economies built into them. It’ll be fascinating to see what happens with those types of products, because that could be the place where we see social plus money take off.

Anish: I do think, by the way, there have been a bunch of past attempts which maybe seemed naive at the time, but now just seem like bad timing. So Blippy is a famous example of this, where it tweeted everything that you bought. You’d link your credit card and every time you swiped it, it tweeted. Okay, like there’s obvious reasons why that might not be a good idea. And yet I think you’re like the fact that…

Lauren: Just too soon.

Anish: …that Dave Ramsey exists and people are talking about debt and spending, you know, there’s the nugget of truth in all of these things. And as Marc says, it’s rarely that the idea is wrong, it’s usually that the timing is.

D’Arcy: One of the interesting things about this category of companies is that if you just take a step back and you’re looking for broader consumer trends, you can often look to little emergent behaviors that are happening somewhere on the internet and try to figure out: is that going to actually go into the mainstream at some point? One of the interesting and challenging things about like social plus fintech is that so much of it is driven by norms. So much of it is driven around what’s taboo and what’s stigmatized, and that actually exists at the subculture level.

You can grow up in the same town at the same age, and if you grew up on one side of town, your norms around money and sharing are very different from the person on the other side of town. And so that leads to a lot of very distinct subcultures within different pockets on the internet. One of the more entertaining one is WallStreetBets on Reddit, where people are posting some mix of fake and real trades and explosions and everything like that. And so then you can look at these things and say, “Oh here’s this crazy emergent behavior that’s happening. I think this is gonna go mainstream.” In some cases it will, or in some cases it’s just part of that subculture, because the norms and taboos will never translate into the mainstream. But when those stigmas fall then, you know, everything happens and everybody runs for the entrance at that point.

Anish: It is interesting, you know, if you think about crypto. So there’s crypto as a computing platform, which is how we talk about it a lot internally, but then there’s also the sort of socio-political, perhaps anarchist thread of crypto, and I think the historical example of that was mostly gold. You know, though at the end of…

D’Arcy: But nobody was, like, screenshotting their Boolean collection and sharing it on Twitter.

Anish: Well, depending on what Facebook group you were in. So I think, again, there is a past precedent. But you’re right, there’s a functional aspect of hedging against things that may go badly wrong in the future, and then there’s the cognitive-emotional and sociopolitical, to your point, Lauren.

D’Arcy: Crypto’s fascinating because it’s a subculture that has a totally different relationship with transparency and anonymity and all of these different dimensions. Just changing the form factor of value from a dollar to some sort of token has freed an entire segment of people to talk about it and have a different relationship with it. It’s one of the most entertaining parts of social, what’s happening in crypto. And again, the concept of crypto versus the concept of money created a psychological shift in some people that then made the norms around it much different.

Lauren: So you’re saying there are these subgroups, little niche categories, but it’s difficult to build a business around them until they reach that tipping point.

D’Arcy: I actually think you can build great businesses around some of these subcultures. There’s a lot of this “niche,” but they can be massive niches, right? Like, WallStreetBets has something like 800,000 members.

Anish: People always want to talk about how they’re making money. It’s having debt that’s always been private. So the hardest problem in terms of social and money is having people talk about their debt, which is why people don’t want to have a relationship with their lender or talk in too much detail about their credit card debt. They feel bad about it, they feel like it reflects poorly on them. I was just checking Instagram right now, and there’s 675,000 posts for #debtfreejourney. This has become a public conversation, and a lot of it is happening on Instagram. I think that’s the hardest problem, the hardest segment to actually unlock. So I actually think we’re pretty far ahead right now.

Lauren: Well, and to your point, WallStreetBets is not just about, “I made a bunch of money,” it’s also people posting, “Shit, I just lost a bunch of money.”

Anish: Though the subtext is: look at all the swagger I’ve got, I can lose all this money and it’s all good, you know.

Lauren: Not always.

Anish: Fair. Where this gets a lot more interesting is looking beyond social media and social networks and starting to talk about how this stuff drives an emergent set of products and how products are designed. Lauren and I have both talked about this, which is the concept that as a product, you can create value in a functional way, which is, “Hey, my credit score was X and now it’s X plus Y.” You can create value in a cognitive way, which is, “Hey, I now better understand my credit score,” or you can create value in an emotional way, which is, “I feel better about my credit score and my financial situation.” Historically, most products have been designed with a complete focus on the functional. And now we’re seeing the next generation, not just in fintech, but in consumer products that think more about the cognitive and emotional.

There are also more offline examples than we’re all typically aware of. So one I learned about over the last few years is called ROSCAs, Rotating Savings and Credit Associations, which are these offline communities, mostly immigrant communities, that are managed by an individual. Everyone contributes, let’s say, $1,000 a month. And then each month if there are 10 members, one member receives $10,000. And typically these are folks in your community, you might meet them at church. It’s really hard to save $10,000, it’s a lot easier to contribute $1,000 a month. And then when you receive the lump sum, there’s always some big thing you want to do with the $10,000. There are tons of examples of these micro-communities that have not yet successfully been brought online. So, you know, not everything is starting from zero when it comes to digital products.

D’Arcy: And those are interesting because there is a different iteration in every single culture and every single country.

Anish: That’s right.

D’Arcy: It is this robust offline behavior. And the question is, how do you bring it online? And how do you bring it online in a way that is culturally specific enough that it reflects the norms of that culture, but also in a way that’s scalable?

Anish: So there’s the example of ROSCAs in a lot of communities all over the world, and then I think if you look at the flip of that, what’s the extreme San Francisco version? A lot of people here do things like invest in restaurants. Why would you ever invest in a restaurant? You’re probably not going to get your money back and there’s no liquidity. At best, it’s sort of cool to tell your friends maybe that you’re an investor there. Maybe you skip a reservation.

D’Arcy: It goes to your emotional versus transactional. It’s not a transactional piece, it’s the emotional piece, right?

Anish: Exactly. But the proof point of actually investing in something versus just frequenting something is very different. People want to participate, they want to express these preferences, and money is the strongest way to do so.

Lauren: Well, and another example of something that’s inherently social—you’re investing in something that is then has a built-in social network.

Anish: Exactly.

D’Arcy: There’s also this amazing trend around fractional ownership. There’s a category of companies that includes Rally Rd., and Otis, and Mythic. They will take some asset—be it a classic car, be it a culturally significant item, be it a Magic card, be it a case of wine—and they will take that asset and they’ll functionally securitize it. And then you, as a user, can purchase shares of that asset. And in some cases, depending on the kind of investment that you make, you get certain levels of access or swag or other things that are associated with ownership.

So on the one hand, you actually have a piece of equity, a share in something that is theoretically valuable because it’s a hard asset that has value. On the other side, you have this status of owner within this piece that is of value in a more emotional sense. You’re investing in cultural pieces, which may or may not be a good financial investment. But from an emotional/cognitive side it can be really, really rewarding. So I think that’s another version where this idea of social plus fintech is taking off.

Anish: I love this example. And, you know, we’ve talked about this internally as perhaps the future of museums. I think that vision is really interesting, and it’s much more emotional than rational.

Lauren: What’s the potential there? Are there areas where you see opportunity in some of these niche groups?

D’Arcy: I think social and finance is like the holy grail, right? The social version of most products is the best version of most products. Engagement is higher, retention is higher, customer acquisition costs go down. All these things that most consumer fintech companies struggle with are solved by building the social product. To the extent that you can get something that threads that needle between social and fintech, it’s amazing, it’s magical, it’s incredible when it actually happens. It’s really hard to do, but when it does happen, it’s phenomenal.

I think the biggest opportunity comes from finding the emergent behavior within niche groups at the social level, at the community level, and then figuring out how fintech or a transaction layers into or on top of that. The saying is “every company is eventually going to become a fintech company.” And I think that is probably the direction it goes, in which you have a weird social behavior that has some ability to layer a transaction inside of it. That’s how social plus money takes off.

Anish: In my mind, the most direct way to start seeing this play out is just having more fintech products address emotional needs, as well as functional and cognitive needs. There are some fintech products like Joy, which is an app where you rate every transaction on how it made you feel. The goal of the game, of course, is to only spend money on things that make you feel good, which is kind of interesting. So I think that’s a product that’s completely designed around a set of emotional needs, with perhaps a set of functional outcomes as a happy side effect.

I think there’s probably a middle ground where a lot of products that are focused on helping you buy your first home or reduce your debt or invest in stocks can actually start to design for these emotional needs when it comes to money. And that’s how we actually start to see this achieve scale.

Lauren: Are there companies right now that you see making strides in that direction?

Anish: I mean, I think an example of a company that’s really gotten this right is Credit Karma. And granted, I was at Credit Karma, but if you look at the tone of the emails, if you look at the ads that are on TV, if you look at the way the product is positioned, it plays as much to one’s curiosity and to taking some of the heaviness out of credit. And I think that’s been a really successful strategy for them. So I think this is a company that’s gotten it right when it comes to how you talk to your customer about these otherwise really heavy things.

Lauren: And as people share more, it becomes less intimidating

D’Arcy: Or if you can see yourself relative to other people. That’s the other way that Credit Karma works. It’s like, I know where I stand relative to other people. And maybe it makes me stressed or maybe it makes me feel more comfortable, but at least there’s some level of transparency.

Lauren: Right. There’s some freedom in that transparency that perhaps is driving customer acquisition.

Anish: That’s right. In terms of the products that have not worked, I think the product category that hasn’t really seen success is personal financial management tools. There’s two reasons. The first is that there’s a very small number of people who are super excited about budgeting and trying every budgeting app, which is why when a lot of these products launch, they get great growth in their first 18 to 24 months. You can get a couple of million users who are really engaged. That’s not actually representative of the wider market, where most people hate budgeting. And it’s not just because it’s a pain to keep a budget, it’s because it’s mostly bad news.

So I look at a lot of these PFM and budgeting apps like calorie counting apps, they mostly make you feel bad and it’s easier to uninstall the app than it is to actually stick with the budget or the diet. So I think that’s a great example of a product category that, despite the fact that there’s real functional value there, it hasn’t taken off because it didn’t address the emotional challenge that the consumer is facing.

D’Arcy: I think another category that has not worked super well is products that are designed to be social, but only transactional. So I think there’s been this long history of people trying to get people to be more public about what their portfolio is. And then other people can invest based off of that portfolio, and it benefits the portfolio manager who’s sharing it. That’s one where it’s an almost purely transactional relationship with purely financial incentives. And I think there’s been a lot of attempts at that. As far as I’m aware, none of them have really taken off. But I think that’s another category where when you just stick within one bucket, within the transactional side, it’s really hard to layer social into that.

Lauren: So we agree that social meets fintech is really hard to do. But I’ve also heard you both say it’s the holy grail. Why is that? What makes it so powerful, if we can get there?

Anish: I think if you just look at the most narrow lens, from a core business perspective— stickiness, cross-sell, acquisition—all of these things that are super hard problems for most fintech companies become dramatically easier if there’s a strong social layer. So that’s the most narrow lens.

And then I think the broadest lens is ending this dynamic where we’re alone together. You know, everyone’s in a dark room feeling bad about their money with everyone else in that same dark room. And I think if you can turn the light on, all of a sudden it is an opportunity to uplift everyone a little bit and normalize the situation that folks are in. We talked about the good side of Instagam but Insta is also a very public place to talk about your spending. And I think that drives a sort of perverse set of expectations around what’s normal, and we should try to change that

D’Arcy: Yes, there are multiple levels to why social plus money is this holy grail. Another lens is it broadens the solution space a founder can operate within, because now you’re not just on the transactional level or you’re not just on the emotional and cognitive level. You’re now across all three, if you actually have social plus finance or social plus fintech or whatever it is. So you can now design things that have some combination of those three levers. If you’re competing against a purely transactional thing or you’re competing against a purely emotional thing, you now just have more factors that you can operate across. The flip side of that is it’s combinatorially more complicated to do. But if you do it, you’re in a class of your own.

Lauren: Thank you for joining us on the a16z Podcast.

Anish: Thanks, Lauren. Thanks, D’Arcy.

D’Arcy: Thanks, Anish.

Anish: Cheers.

• 

  Anish Acharya is a general partner at a16z. Prior to joining the firm, he served as a GM at Credit Karma. He also founded SocialDeck (acquired by Google) and Snowball (acquired by Credit Karma).

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  D’Arcy Coolican is a deal partner at a16z where he focuses on marketplaces, social networks, and consumer technology companies. Prior to joining the firm, he co-founded Frank, a social lending platform.

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  Lauren Murrow is an editor at Future. She oversees posts, podcasts, & special projects for a16z's consumer and fintech teams. Previously, she was a senior editor at WIRED, where she edited op-eds and features.

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There’s a few ontologies for describing the phases leaders — and their startups — go through, whether it’s product-sales-etc. or pioneer to settler. In any case, as companies evolve, so must the leaders — but can the same person transition across all these phases? When and when not; what are the qualities, criteria, and tradeoffs to be made?

In this episode of the a16z Podcast, originally recorded as an internal hallway-style chat (pre pandemic!) a16z general partner Martin Casado, who co-founded but decided to remain CTO of Nicira — and previously shared his own journey, lessons learned, and advice for founders about bringing in an external CEO and the question of “to CTO or not to CTO” — and Armon Dadgar, co-founder (with Mitchell Hashimoto) and CTO of HashiCorp, chat with Sonal Chokshi about both managing their past psychology through these common questions and decisions. They also share their strategies on managing the specific tactics behind it all: Everything from the “dating” process of finding an external CEO to figuring out swim lanes; handling debates and decisions; who presents, who sells. And while the conversation is a brief glimpse into their longer personal journeys, there’s lessons in it for startups and leaders of all kinds on the art of hiring and sales, managing credit and conflict, and more…

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  Martin Casado is a general partner at a16z where he invests in enterprise companies. Prior, he was cofounder and CTO of Nicira (acquired by VMware) and is the creator of the software defined networking movement.

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  Armon Dadgar
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  Sonal Chokshi is the editor in chief as well as podcast network showrunner. Prior to joining a16z 2014 to build the editorial operation, Sonal was a senior editor at WIRED, and before that in content at Xerox PARC.

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This episode features Q&As with two artists who are exploring crypto-powered auction sites and marketplaces – this is part of our ongoing series on the creator economy. The big picture is that emerging “tokenization” models, including non-fungible tokens, or NFTs, are creating new ways for collectors and investors to buy, sell, and trade digital art. More broadly, these innovations open the door to the tokenization of any products or collectibles that can be captured and owned digitally, and many new business models for creators.

Marketplaces powered by NFTs open up new revenue streams for creators, because anytime digital work is resold or their tokens traded on these platforms, the creator automatically gets a percentage of those secondary sales. It’s all transparent and governed by code on the blockchain, and it’s a big shift in creator economies.

Our first guest is one of the biggest names in crypto art, and one of the most mysterious. Pak is the artist and designer who created the AI-powered image sharing site Archillect. Pak has made it a policy to separate their personal identity from their online work, and prefers to keep their quote-unquote real identity hidden, so we conducted this interview by email and converted Pak’s answers to audio using text-to-speech software. As Pak has expressed in other interviews, it’s really the work that matters.

And we do know a lot about the work, Pak has sold more than 60 pieces of digital art this year on the crypto-based auction site SuperRare, for more than $350,000. And that’s just one of the several platforms on which Pak’s work is sold.

In this Q&A, Pak talks with a16z’s Zoran Basich about NFTs. These “non-fungible tokens” are unique assets that are not interchangeable. Dollar bills are fungible — each dollar bill is worth exactly the same as every other one. But works of art, for example, or any collectible, can be non-fungible — their value varies based on the market for that particular asset. With crypto, these assets carry digital ownership rights that can be easily exchanged.

We start by discussing the whole concept of digital art.

Why would someone pay a lot of money for something that seems like it could be easily copied? 

PAK: You see, that’s a tricky question. Because the newcomer assumes that it can be copied but in reality, the collector of the NFT does not obtain a digital file, they get a unique and signed token that cannot be copied or owned by anyone else. So, assuming that NFT’s can be right-click-save-as copied is very similar to the assumption of going to the Louvre to take a picture of Mona Lisa to own it, or taking a picture of a plane ticket to copy it. NFT is not about the visible object, it’s about the permission and access to a thing.

It’s only a matter of time until it becomes accepted in a wider sense. Every new medium for art had this struggle for acceptance. Having this struggle is not bad, it’s good. When the argument is over, this conflict and resolution will be the thing that will make crypto-based art valid. From my personal perspective, this is not a conflict or questioning of “is this art or not.” It’s more of a questioning of “is this unique enough or not.” And sooner or later it will be understood. 

What was the dominant commercial model for artists in the past, and how might crypto change that? 

I’ve never categorized myself as an artist even though a portion of what I do, design, surely touches art. Therefore, I believe, a better word for a “crypto-artist” should be “crypto-creator.” 

From a design point of view, the options are working full time for studios, working as a freelancer, or having your own studio.  

For crypto art it’s currently closer to the second one, the freelancer model, however, there is an important detail. A design client needs your work, but an art collector wants your work. Sometimes it feels better to be wanted than needed.  

What was your first exposure to crypto, and how did your interest develop? 

I was able to meet bitcoin and make moves before its initial explosion. Being interested in value concepts as long as I can remember, I was instantly charmed by the idea of how value is transformed to this new exchangeable form. 

NFTs, on the other hand, are new to me. The first time I met with NFTs was when CryptoKitties contacted me to create an Archillect kitty when they were forming. 

Today, NFT’s are more than just little fun pieces. I believe in the technology it defends, therefore, I am happy to make waves in this branch of technological revolution, and evolution. 

How do NFTs make it more attractive for creators to become crypto-creators? 

It’s similar to cars becoming electric cars. “Crypto creator” is not a term that defines a special group of creators in my opinion. It’s only this now-new, soon-to-be-norm term for digital tradables. In other words, when it’s so easy to make it happen for any creator, why not?

Most of the activity around crypto is financial — trading, borrowing, lending, and investing in cryptocurrencies. What are the characteristics of crypto that might hold appeal for artists, designers, and other creators? 

Creation and destruction of value is always charming for any kind of creator.

What advice would you give creators who are interested in exploring crypto as a new model for selling their work?

Experiment!

You have been working at the intersection of technology and art for some time. Apart from their possible financial advantages for artists, do crypto and the blockchain, or the idea of a decentralized technology in general, hold some deeper cultural meaning or symbolism, in your view? 

Anything can carry symbolism, it depends on the receiver rather than the source. I value innovation, I value creation of value and I try to exist on that line, where something new pushes the limits of what’s widely known. Of course, decentralization of things holds a future of a new culture of technology, many new norms and standards, and many branches in unknown directions, and that’s exactly the reason to be there. Almost like the internet just before it went mainstream. 

Let’s talk about revenue for artists. Will NFTs lead to a relatively small number of creators making good money, as has generally been the case in traditional art-business models, or do you think the distribution of wealth might become more equitable with crypto? 

Art auctions are a completely different world, and it would not be fair to estimate NFTs based on that. Of course, NFT-based and supported art will have its own audience, and I expect to see similar dynamics with the traditional art world in terms of how things are evaluated. 

On the other hand, NFTs can be used for many other things — this is what makes it powerful. Anything that can be digitally owned can be supported with an NFT. It has a lot of uses in media and entertainment, real estate, gaming, identification, or any kind of asset or collectible. It’s a technology that’s slowly going mainstream for art, but it’s not limited to that. Therefore, I do not think it will be only a small number of creators making good money. It’s going to be the base of many new business models of the future norm.  

OUR NEXT GUEST is Signe Pierce, a visual, digital, and performance artist whose work has appeared in major galleries in Paris, Los Angeles, and New York. She’s currently featuring her artwork on the creator marketplace Foundation. On that site, the price of tokens associated with limited-edition works of art is something like you’d see on a stock market – the pricing is real-time and dynamic, fluctuating according to demand by buyers, who might be investors, collectors, or fans. (Signe recently opened NFT-based auctions of single-edition works as well.) Signe discusses why she went from working exclusively with galleries to trying crypto marketplaces, how this move affects her work and her business, and how crypto could change the way she engages with her fans. She also offers advice for creators interested in getting into the world of crypto.

First she talks about how social media popularity several years ago opened her eyes to the idea of new monetization models for creators. 

SIGNE PIERCE: I really had a big swing on Tumblr in the years 2014-2017 when Tumblr kind of popped off. And my pictures were just getting, like, hundreds of thousands of re-blogs.  

And I just kept really returning to this idea of, why is there no valuation around this energy? And maybe how could there be? How could we turn Likes into money? How can I sort of flip the attention economy into an actual monetization form? 

And that was what really got me thinking big about, yeah, I actually really can understand why this blockchain technology and tokenizing artwork could potentially be a really major disruption to the art world way of doing things as well as married to my vision for just a more prosperous world for artists and for people to enjoy art. 

So how did you come to start working with Foundation?  

I was put in touch with the curator, Lindsay Howard. We talked at length about our vision for wanting to find some new ways of doing things and make it so that artists have a little bit more stake in their work. I think partially because there’s a curator that I really trust who was inviting me in, as well as the fact that I could just sense that they had a finger on the pulse, all of that was what swayed me into wanting to work with them.  

Which of your art is there now? Walk us through what that looks like and what’s being presented there at the moment. 

So I have done a specific run of three works which are from what I call the Jangular Lilies series. I’ve offered two prints at editions of 100 and then one video at an edition of 10. I wanted to create a run that serves as this kind of cool, crypto training wheels for people who might be interested in this but that are nervous to get their feet wet. (Note: Foundation enables trading of limited-edition works via redeemable ERC20 tokens, along with its NFT-based auctions for single works.)

Let’s see what happens, you know? And at the end of the day, you can experiment with the crypto. And it’s been really exciting to watch the trading aspect of it, when they trade in their tokens and they get to have the exchange. It’s just kind of been a new frontier for me personally. 

And the interesting wrinkle is that people can come, fans, collectors, and they can buy your artwork at the price at which it’s listed. And when you are ready to produce that artwork they will get a physical copy of it, right? 

Yes. 

Or, while they’re waiting for you to complete the artwork and fulfill that order, they can also trade the token that is associated with that artwork. And so the price of that artwork is going up and down in value, or it’s dynamic, right? It can go up and down. 

Exactly. 

So that goes to the point of what you were saying about people being able to trade it. And that’s really what makes it interesting here because now you’re talking about not this static price but rather a dynamic price that can lead more people to perhaps get involved with this from sort of a purely trading or investment aspect on top of the collectible or fan aspect of it. 

I know not all artists are gonna like this, and not all artists are going to find this to be their jam. But I see this as kind of an interesting way to think about our works like stock, in a way. I’m interested in watching the way that that’s playing out in this sphere because it’s new and it needs to be pioneered in order for it to become normalized. 

So from the buyer’s perspective or from the collector’s perspective, they can wait for the price to go higher and trade it and make a profit on that and not end up owning your physical piece of art, correct? 

Yes, exactly. And I think that’s kind of also a cool way for people to support the artist. You might not necessarily end up with a print if you don’t decide to redeem your token. You’re still supporting me, the artist, in a little way just by engaging with the trading. And I think that’s, again, a cool way to get people into the world of crypto and supporting the arts.  

And one thing we should mention, you just touched on it, is that every time anyone trades one of these tokens you receive a cut of that. The artist receives a cut of that. 

Exactly. That, plus the fact that as the editions decrease, the more editions that are sold, the price increases. And that is also a really exciting aspect of this.  

And how do you think about pricing? How do you decide what the initial price will be? And how big the limited edition will be? 

I’ve spent a lot of time thinking about this. I’m going to kind of keep my works that are dealt through gallery systems and the art market, I’m going to purposefully keep them low and, you know, rare. They come with the full package of framing and serious fabrication. With these works, they’re high quality, digital C prints. It’s just larger editions, and therefore the price kind of matches the fact that there isn’t as much scarcity attached to them. 

But again, I’m finding there’s a sweet spot for me wherein I’m still able to work with the fine art market, but I’m also able to make it so that the work is accessible to more people. I think my work is valuable. I take it very seriously, and I want it to be valued appropriately. And I want it to appreciate over time. And I think the fine art market finesses that opportunity. 

But I don’t want it to be completely forbidding to everyone else in the world because that goes against my general ethos of art being for the people. So to me that’s my personal approach. Again, not everyone’s approach, but this is my way of making it so that we can kind of play both fields. I’m not giving up on the fine art market or the fine art world, but I’m also really interested in trying new things and inviting more people into that, into the opportunity to become a collector. 

So when you go to the site and your artworks are there, it’s like a stock market, right? There’s a price, and it shows how much it’s risen or fallen. I think I looked this morning and all of yours were up over 100%. Congratulations. 

Yeah, we’re doing pretty good.  

How often do you look at that? And what’s that like? It’s just a whole different kind of insight. It’s a real-time look at how people are responding to your work, right? 

Absolutely, and that’s exciting. It feels very modern. I’ll be super honest and say I’m not, like, a finance guy, you know? I’m an artist. But I’m still, you know, I’m a modern artist, and I really, I’m interested in modernity and the future as well as looking at the way it works, understanding it in order to better think about it. So when I get to see in real time the valuation of the work, that’s kinda thrilling because, again, I’m so used to this kinda Tumblr culture where you get to see your numbers going up. You’re watching Likes and re-blogs happen. But there’s nothing actually attached to it for me other than minor dopamine bursts. So this is actually creating a little bit of capital to it to make it a little more fun at least, rewarding.

Is it an experiment where, yeah, you might make a little bit of cash? Is it a significant amount of cash that’s possible? Like, how much of a revenue stream does this represent for you? 

At the end of the day, I’m making money making my work, and that’s exciting. And there is profit. I’m making a profit from this, and it’s another avenue for revenue for me to make my art and get it out to the people. To me these are all wins. While the profits are not as gigantic as if it was on, you know, the fine art market immediately, but that’s for one work, you know? This is multiple works that, over time, it is similar numbers, you know? In fact, sometimes more. 

So maybe it doesn’t make that much sense for me to only be having, you know, extremely high prices, and there’s only one period, and then you have to wait for that appreciation and valuation. And then if it flips on the secondary market, I don’t get to really see, I often don’t get to see that unless I’ve contractually negotiated it.  

How do you think about digital art versus physical art? Because I think in this case right now you are gonna produce physical pieces that you’re gonna send to people. There’s, like, a reproduction cost involved there, which presumably cuts into your profit, whereas digital work is more easily reproduced. 

Totally. 

But then also, digital art historically has been hard because, like, how do you stop it from being copied? But with crypto there’s this kind of additional thing of the token, where you know the provenance, and you know it’s this unique piece of art that cannot really be owned in that same way. So how do you think about those things as part of a business model? 

That’s a really great and important point/question. And I think that people are and have been struggling with getting their head around the idea of, like, paying for a video or paying for a GIF. And how can it have value if it’s not tangible, if it’s not a physical artwork? And it’s taken a lot of thinking and pioneering from a lot of artists and, you know, technologists. And to me, like you said, the provenance of blockchain technology, which guarantees authenticity of the work, is essential to make this possible. 

Once we can kinda get the collective consciousness head around that, I think it could be really revolutionary for how art continues to be made and traded and collected. 

I want to talk about fan engagement too because this opens up really interesting possibilities. And it’s happening now with your artwork where people are engaging with it in a financial way, but tokens also potentially enable other kinds of engagement as well. People can become “super fans” or have access to certain things in your life or in your work that non-token holders may not have. So have you thought much about the evolution of fan engagement as it relates to crypto? 

I think that there’s totally so much potential for this. Accessibility, the access points are what’s gonna be a really big part of that fan engagement generation. There’s all kinds of different ways to approach this. 

I just want to do it in a way that I think is actually cool. That’s honestly one of my big things about what I’m trying to design is, like, I want it to work in a way that I would actually want to do it. I’m interested in figuring out how we can work with fan engagement in a way that I really would feel comfortable asking my fanbase to participate in, when I kinda, like, flip the script and I think about an artist that I love, what would I be interested in paying to gain more access to their work? And that really is kind of the decision of the artist to configure what the different tiers of value are. 

So I’m interested in your perspective on what artists who are kind of interested in this, who are trying to break free of this rut, this traditional kind of commercial system around art, what should they know as they look to explore crypto? And how much do they need to know about crypto before they get started? 

I think it’s always helpful to have someone hold your hand a little bit. If you don’t have a natural direct resource to introduce you to this, throw yourself into some research and teach yourself about it. I mean, I’ve really had to do that for myself with this. I barely scratched the surface of fully understanding how all of this works because, again, I’m an artist. So it’s not necessarily my 9-to-5 attention span to be reading about advanced blockchain technologies. But, because I’m an artist who’s interested in future models and methods, it’s important for me to sit and focus on these ideas to get it into my head. So that’s part of it. 

But another part of it I think is just this fearless entrepreneurial spirit of, what do I have to lose? If you’re currently not making much money or any money wheeling and dealing your art, what do you have to lose but to throw yourself into something new and see if it sticks, you know? And I think that’s a really important energy for people to hold in general is just this kind of, “Let’s go. Let’s try something. Let’s try something new.” And if enough people host that innovative spirit, that’s when things start to crackle and spark and change.  

Awesome Signe. Thanks so much for being with us. It’s been a pleasure to talk to you. 

Absolutely. I had a great time. Thank you for this opportunity. 

Featured image: Pak, “The Balance”

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  Signe Pierce
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  Zoran Basich is an editor at a16z & Future, focusing on crypto and corporate development/ finance. Previously he covered venture capital and the startup ecosystem at the Wall Street Journal and Dow Jones, and was the banking editor at NerdWallet.

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Lakes v. warehouses, analytics v. AI/ML, SQL v. everything else… As the technical capabilities of data lakes and data warehouses converge, are the separate tools and teams that run AI/ML and analytics converging as well?

In this podcast, originally recorded as part of Fivetran’s Modern Data Stack conference, five leaders in data infrastructure debate that question: a16z general partner and pioneer of software defined networking Martin Casado, former CEO of Snowflake Bob Muglia; Michelle Ufford, founder and CEO of Noteable; Tristan Hardy, founder of Fishtown Analytics and leader of the open source project dbt, and Fivetran founder George Fraser.

Their conversation covers the future of data lakes, the new use cases for the modern data stack, data mesh and whether decentralization of teams and tools is the future, and how low we actually need to go with latency. And while the topic of debate is the modern data stack, the themes and differing perspectives hit on an even more longstanding question: how does technology evolve in complex enterprise environments?

Show Notes

• The future of data lakes [1:07] and specific operations that may impact their usefulness [6:01], including AI/ML [8:55]
• The evolution of two-stack architecture [9:35] and Arrow as a potential solution [11:32]
• The pros and cons of a data mesh [16:18], future use cases for the modern data stack [20:07], and data apps [22:05]
• Discussion of latency and ways to reduce it [22:46], and predictions for a future data platform [25:41]

Transcript

The future of the data lake

George: I’m going to kick this off with a spicy topic, at least spicy in this crowd, which is data lakes. Data lakes is a blurry term used by different people to mean different things, but for the purposes of this discussion, let’s define data lakes as tabular data – so tables, rows and columns – stored in an open source file format, like Parquet or ORC, in a public cloud object storage, like S3 or Google Cloud storage.

In a world where we have data warehouses that use object storage to store their data and give you some of the advantages of data lakes, do data lakes still have a place? Let’s start with you, Martin, does the data lake have a future?

Martin: One of the biggest fallacies that we do as an industry is we look at an architecture, and we’re like, oh, that can do all of these things, therefore it will be pushed into service to do all of these things. And that’s just not how technology evolves. We make decisions in the design space based on the primary use cases that technology is being used for.

If you look at the use cases that data warehouses are being used for, they’re largely driven by analytics, which is a certain workflow, it’s a certain query pattern. And if you look at data lakes, it’s actually quite different. They tend to have more unstructured data, focused on operational AI, compute intensive. If you look at the respective technologies, they’re just being optimized in this massive design space for different use cases.

Architecturally, sure, they can both do what the other one does, but in the end, you’ve got products and companies optimized around use cases. And I think the operational AI use case is the larger one, and it’s growing faster. So I actually think over time you can argue that it’s the data lake that ends up consuming everything, not the data warehouse.

George: You’re just trying to provoke Bob there, Martin.

Bob: You succeeded.

Martin: I’m watching Bob’s face.

George: All right, Bob. Let’s hear from you. The data lake, does it have a future?

Bob: No, I see these things very largely converging onto a relational SQL-based model. Five years from now data is going to sit behind a SQL prompt, and SQL data warehouses will replace data lakes.

From the perspective of storing structured and semi-structured data, the cloud SQL data warehouses already do everything that is necessary, and there really is no reason for people to have a separate data lake except for historical precedent. A lot of companies come from environments where they had a lot of semi-structured data in a Hadoop environment, and having a data lake is a natural transition. And in a sense, the data lake, which is really S3 storage together with any tools you want to put on top of it, is a very generalized platform.

But, over time, infrastructure evolves to take on more and more of the use cases. SQL relational data warehouses have evolved to the point that for structured and semi-structured data, storage and query, they subsume pretty much all of what needs to be done today. What remains is images, video, documents, PDFs.

Now I don’t call that unstructured data. I think that’s a misnomer. There is no such thing as unstructured data. All data has structure of some kind. Structured data is tables, rows and columns. Semi-structured data is like JSON. It’s hierarchical in its nature. And I think there’s a third category of data, which is what I call complex data: images, documents, videos. Most things that are streaming fall into this category, and more and more machine learning can be applied to the content of those data sources that turn it into semi-structured data that can be used for building complex data applications and for doing predictive analytics.

So what’s missing in the case of the data warehouse today is the support for complex data. But that’s going to come. That’s called a feature. Can you imagine if you could transact, fully transact all of these types of images, videos, and things together with any source of semi-structured data in a data warehouse? The applications that open up are remarkable, and that’s going to come in the next two to three years.

Michelle: I could see images being easily retrieved from the database. But do you actually see all of the image processing or the video processing taking place in the database as well?

Bob: Not with SQL. SQL can’t do that. You’ll use procedural logic and Python, or something else to do that, at least for now. In the long run, relational will win, too, but that’s probably more like 8 to 10 years away.

Martin: I think we’ve been waiting for that for 40 years, Bob.

Bob: We have, but look what’s happened. Over time, navigational and hierarchical in the 1980s was replaced with SQL. OLAP was replaced with SQL over the last 10 years or so. We’ve replaced MapReduce with relational. All of these things, relational always wins.

Michelle: Well relational wins for the actual retrieval, but what about the processing? The technology that you need to process images is fundamentally different than you do to retrieve data records.

George: Tristan, what are your thoughts on this?

Tristan: So, I completely agree that SQL is going to dominate data processing, at least a very large chunk of data processing, but there’s different APIs that the data lake and the data warehouse expose. There’s the file storage layer, and for a lot of reasons I believe that an organization will store their files one time. You will not have a data warehouse copy of the file and a data lake copy of the file, which, in some architectures today, that’s what you see. And that requires you to have an open source file format that is shared between your data warehouse use cases and your other use cases.

Above that you have indexing and meta data that is a core part of the data warehouse, but it’s also a core part of the data lake. I think those have to also start to converge so that different use cases can take advantage of the same stuff. And then you have the SQL prompt, and maybe, at the SQL prompt layer, the data warehouse dominates, but I think you need to allow different access patterns as well because one closed source firm is never going to accomplish literally all data processing use cases in the world.

Bob: All of these things should interoperate in an open source and an open format way. But the issues of format have kind of gone away because you can input and output any kind of format and export into any kind of format very easily.

The question is: what are the operations that actually need to be performed against data that sits in a data lake? Today anything associated with complex data, the data warehouse can’t help you, and so there’s a huge reason to have a data lake today. In 2025, I don’t think so.

I think that we really have five platforms being created globally: Snowflake, Databricks, and then the three clouds. Both Snowflake and Databricks, while they will come from very different places – Snowflake will always be SQL and declarative in its approach, and Databricks certainly historically has been procedural and code-based, so it’s a version of SQL versus code in some sense – you’ll see both companies and pretty much everybody else in the industry offering both within their platforms.

Martin: So, you’ve got two technologies that start with different use cases, somewhat different architectures, but they’re clearly going to a converged point, which is you have some declarative something, and you have some procedural something. Whether one is on top of the other at the end of the day, they can both do both. But, in the meantime, you have this decade-long journey, and in that decade-long journey, there is an architecture that’s optimized around use cases. The amount of tradeoffs and decisions you make when building one of these systems is…

Tristan: Yeah, like TimescaleDB has very different characteristics than Snowflake, and they are characteristics that are optimized for workflow.

Martin: Yeah, entire companies focusing on different points in the design space with different optimization parameters. It’s the use case that drives the technology because of all of the gravity around it. And so, again, if it turns out that AI/ML and an operational use is growing quicker, which it seems to be, that is going to dictate the technology from an architectural standpoint.

Tristan: Martin, you’ve said a couple times now that the AI/ML space is appearing to grow faster. I’ve actually not heard that assertion before.

Martin: Let me clarify. So broadly, there are two use cases. There’s the analytics use case, which is driven by queries and dashboarding. The other one is creating a complex model from a data scientist and then serving that in production. That does things like wait time prediction. That does things like fraud detection. That does things like dynamic pricing. These were folks in R building complex models on existing data and then coming up with a bespoke way of serving that. That is very clearly now turning into a pattern that’s being served by a data lake.

Now it’s on a much smaller base, but if you actually look in the industry, it’s a very rapidly growing use case.

George: Michelle, you’ve spent time in both the data science community and the analytics community, and notebooks in many ways are the place where these things sometimes come together. I’m curious to hear your thoughts about how the two stacks have evolved. Maybe they’re converging. Maybe they’re building each other’s features and getting more similar, but where does that take us? Do we still have two stacks five years hence?

Michelle: I think we’re going to continue to see greater and greater specialization because we’re not going to have the ability or the budget to hire enough data scientists. Those stacks are going to continue to evolve, and it’s going to be specialized based upon what it is that they’re trying to do.

The data lake will have a place. Your images, your blob storage, all of those things are probably going to remain in the data lake and have a home there for a long time to come. I just think it’s not going to look like how it looks today. Today, it’s just been a lack of understanding around what data do we really need to collect? We went from one extreme to the other. We weren’t collecting any data. Now we’re collecting everything because we don’t know what’s valuable. And the reality is that’s not necessarily a good idea either.

The movement of data, I think we’re going to see that stop, but format is going to be really important. We need that interoperability because reprocessing data at scale is just cost prohibitive. It’s time prohibitive. It’s not something we want to do if we can avoid it.

And I think you’re going to see decentralization here, at the lower levels, where you’ve got either the business units embedded, or you’ve got your new product teams, you’ve got your data science teams embedded in those product teams. You’re going to need a unifying layer at the very top the form of technologies that make it easier for everybody to query or be able to serve information.

I think that the notebook is probably the best suited for that because it does have the language agnostic approach. It gives you the ability to look at both data and code and have all of that context, that rich business context, the visualizations. We’re going to see that evolve as this modern data document, and we can use that as part of our unifying layer because your data scientists can then work with R, your data analysts can work with SQL, but we can, at the end of the day, really hide all of the code and really get to: what is the business implication of these things that we’re doing?

Will two stacks become one?

George: This really brings us to the second major topic that I wanted to discuss, which is: how do we bring the machine learning, Python, Scala world, and the analytics, SQL, BI tool world together? There really are two stacks and two communities who sync the exact same data sources to Delta Lake and to Snowflake simply for operational reasons. There’s not a fundamental technological reason, but it’s just the way the tooling has evolved. It’s too inconvenient to cross that boundary.

And there’s essentially three visions of that world. One is that you’re going to put machine learning into SQL, and probably BigQuery is the furthest along in pursuing this. You basically create a bunch of UDFs that do your linear algebra stuff. The other is more the Databricks vision where you put SQL into Python or SQL into Scala and you use data frames to do that. And then there’s maybe a third vision where you use Arrow, the interchange format, and everything can just talk to each other, and you can arrange it any way you want.

Which of these visions do you think is going to win?

Michelle: What I would like to see win is something like Arrow, so that you have the interoparability. You’re going to see machine learning moving into SQL because you’re going to have data engineers who are perfectly capable and have the need to do some anomaly detection or some logistic regression, and it’s within their ability to do that. Feature engineering is just another data transformation for them. But they don’t have the same background in stats, and so they can only take it so far.

And then you’re going to see, on the other side of the spectrum, your data scientists where they have all of this really great math background, and they understand how to do more advanced deep learning, but they don’t have the technology skills. SQL is the most successful language for working with data, so you’re really going to see both of them really become capable of supporting both use cases. Ultimately, you’ll continue to see specialization where the things that you want to do if you’re trying to do deep learning are just fundamentally different than the types of things if you’re just trying to do predictive models.

Tristan: I think a lot about the Arrow vision of the world, and I think that will end up in the fullness of time dominating for the same reason that Martin has been talking about: tools end up evolving to the personas that they serve and the use cases they serve.

I want to do all the data prep and feature engineering. And then I want machine learning models to be trained on top of that. People do that, certainly. But the fact that the infrastructures to do those two different things are generally separate creates this big slowness. It’s purely a technical slowness. Arrow doesn’t solve all of that. Arrow certainly helps, but, there’s dumb things like the servers that do those things are in different clouds. And the interchange fees, what do you, do you call them interchange fees?

George: Egress fees.

Tristan: Egress fees are expensive.

George: They’re criminal. They’re not just expensive. They’re ridiculous.

Tristan: As more people do this, it’s going to be become smoother. they’re going to become more localized.

Martin: There’s a reason why you’ve got multiple languages, and it’s not because one is Turing complete and the other isn’t. The reason is because people build their entire workflow around languages and all of the tools, and so you’re going to have a heterogenous, fragmented system. Therefore you do need to have open interfaces.

George: Bob?

Bob: I’m a big believer, at this time, in the approach of having multiple systems that interact with common formats.

Arrow is a huge step forward for that, not just because it’s an efficient format, but because it provides a consistent in-memory layout for people to do advanced analytics in their Spark environments. It’s the way the world is working right now because most customers actually have a data warehouse and an analytics platform separately, and they are connecting them together.

Now, I’m going to continue to be the ultimate radical, however, and declare that the approach that we’re taking today in terms of machine learning is still roughly the approach of the internal combustion engine in the automobile. The approach that’s happening where Arrow ties together those predictive systems with declarative databases, that’s really the creation of the hybrid, or the Prius era.

Hybrid will dominate for the next, say, three to five years. You will see hybrid systems being built by every major vendor, and all of them will have a full predictive stack and a full declarative, relational, SQL stack built in using some kind of interface like that. But that’s only until relational actually solves the broader set of problems.

George: Does that mean that you’ll be using SQL functions, PredictX, or…?

Bob: No. Ironically, I think that while SQL will dominate well into the 2030s for doing data modelling and data transformation, there’s another step beyond that which is business modelling, and that needs to be represented in a knowledge graph. Knowledge graphs are how we’ll do predictive analytics in the 2030s. And what needs to happen is a whole new generation of data system that’s based on relational knowledge graphs to create that.

Data mesh: decentralized teams, unified architecture?

George: Michelle, you brought up a term earlier that I wanted to follow up on, which is data mesh. And I wonder if you could define that briefly for everyone because similar to data lakes versus data warehouses, there’s a question whether going forward that’s more of a historical phenomenon or an actual, good architecture that we want to continue.

Michelle: Data mesh is really a concept of decentralizing the data processing and the ETL and the analytics into each individual business unit and then having some sort of unifying solution at the top. To do this well requires having specialized data teams, having specialized roles, having infrastructure as a service available to them for data processing, and then having some overarching standards board, almost like a federation, of your data engineers to ensure that all of your ETL looks consistent so that as you are trying to do data retrieval on some common, query tool, you’ll have that familiarity that you need.

We are going to see things like Arrow really come to the forefront sooner rather than later. I think customers are going to demand it because of all the challenges that we’re currently having. You’ve got all of the cost of the storage and the processing. Your teams that are trying to do the processing don’t have the business context that they need. As a result, you have this back and forth and a lot of wasted time. You’ve got a lot of data quality errors. You have data multiple times. Ultimately, we want to take that body of knowledge and put the technology where that body of knowledge lives. The data mesh is an attempt to do that.

Bob: One part of what the data mesh folks are talking about is how to organize and how to structure a team to manage data across a large enterprise with very disparate and important data sources. That’s very, very important, and there’s some good ideas in data mesh for that.

Architecturally, data mesh has this sort of odd idea that data is basically streaming, and you can use facilities, like Kafka, to do transforms as the data is in flight. And I don’t believe that.

While there is streaming data, and you can do quite a bit with data that’s simply streaming — in other words, append-only data — to me, another critical source of data is transactional data coming out of business systems. The streaming solutions have no answer for that, and they just pretend that data consistency is unimportant. I don’t understand that because I put data consistency at the top of the issues that I think about when I think about managing data.

Martin: Mesh has historically been one of these terms that conflate architecture with administrative domains, and distant service mesh, and distant Wi-Fi mesh, and mesh networking, etc. I think actually Bob is exactly right, which is there is a very real issue with separate administration domains, separate processing domains, separate access to tool sets. That’s very, very different than building a fully distributed architecture, which just tends to be hard and inefficient. And it’s often not the people that promote the mesh idea, but when people hear the term mesh, they default to full distribution, which tends to be just a bad way to build systems.

George: Said like a networking guy.

Martin: Having seen this exact same thing happen in other domains for a couple of decades.

Tristan: All of us are very technology-focused human beings, so when we think about data mesh, we tend to think about the architecture part of it. Bob, I’m glad you pointed out the distributed teams and the people aspect of this. My constant question for data mesh is: why can’t you enable the distributed nature of what you’re talking about with a unified architecture?

Michelle: My preference is always to have one data set that is very clean and well understood that we do not have to move anywhere, that is performant alongside our large batch analytical processing, which is also working with our data science. That’s the nirvana. That’s the goal is to just have one data storage and then having something that sits over top of it, and each of those different things are specialized for each of the different use cases but you have one data store.

Next use case for the modern data stack

George: The modern data stack keeps swallowing up more and more use cases. It killed cubes a while ago. It’s mostly killed Hadoop at this point. It keeps pulling more use cases into its orbit because it’s fundamentally so flexible and so capable of doing many different things well enough that you don’t really want to buy another system, build another system for one use case. What are some of the most interesting, surprising, significant use cases that may start to get pulled into the orbit of the modern data stack in the next couple years?

Bob: Complex data. We now have all this very, very interesting stuff that’s happening in predictive analytics. And to me we’ve gone from semi-structured data as being the most interesting data sources to now having a wide variety of data sources. I was talking to a company involved in the medical field yesterday, and just the rich amount of data that exists, and the images, and the doctors’ notes, all of that is opaque to our systems today. It will not be in five years. That will all become part of the modern data stack, and to me that’s a gigantic transformation for the types of applications that will be created in the years to come.

Tristan: My last job was I ran marketing for a company, and I was deep into growth marketing. The problem that you run into there is that you’re constantly writing code to push data back and forth between systems because the different operational systems do different things, and you need the same data in all of them.

No one has yet rearchitected the systems to, in the modern data stack, just take all of the work that you’ve ingested and now push it back out to your operating systems or your operational systems. But I think we’re at the beginning of that.

Bob: What you’re really talking about Tristan is the advent of the modern data app, which basically is an operational application that autonomously can make decisions for the business. We’ve seen very few of those and very trivial examples, but boy will they be significant in the future.

George: There’s really two visions of the data app that I’ve seen. One of them is the data app is a separate system, and you take the important data from your data warehouse, and you push it. Then the other vision is the data app is just natively built to run on top of the data warehouse. I’m curious whether people have opinions about those two models and where they see that going.

Bob: It’s really the same conversation we’ve been having about how these things are built. A data app is predictive analytics that actually takes autonomous action. It takes the data that would otherwise be presented to a person and instead leverages that to actually take actions within the business. They’re being built every which way today because there are few good tools to build data apps. That will not be true in a few years.

Latency: How low do we need to go?

George: One of the things that you run into when you try to build data applications and take action automatically is latency becomes incredibly important. Everybody in the ecosystem is battling this right now. I think there’s a lot of different visions of how we’re going to crush the latency problem and how low we need it to get. How low does the latency need to be? At what point do we have most of the interesting use cases

Bob: People have dozens to hundreds or even thousands of operational systems. More and more, they’re SaaS operations. They’re outside of your organization. They’re always a source of truth now. They are the present, and a data warehouse or a data lake is about historical or the past.

What does that latency need to be? Does it need to be zero seconds? I don’t think so. There are applications where zero seconds or instant is required, mostly having to do with eventing and alerting of some sort. Most of the time, if you can get it in a minute or two, you can leverage that data inside your historical system with predictive analytics to begin to perform actions on it.

Martin: This is a very complicated topic that I think is very use case specific. But there tends to be serious trade-offs that systems designers make between latency and throughput. If you want higher throughput, you batch. And the reason that you batch is that you don’t have as many domain crossings.

However, if you look at most systems, you can make the tradeoff. Meaning you could do low latency in a data lake, and you could do high throughput in a data warehouse, or vice versa. These are not architectural limitations. They just tend to be the tradeoffs that were made as a result of serving whatever the primary use case is. I’ve heard a number of these latency-throughput tradeoff discussions, and you actually get down to a machine level, they are just a result of the tradeoffs that were made on the system going into it.

George: One of the interesting things that we see is that the point at which you start to have to spend a lot more to get the latency lower is actually lower than people think. I suspect you can get down into the 10 second range with the throughput optimized architecture. Basically, the throughput optimized architecture I suspect will go lower than we expect.

Michelle: What do you imagine will happen with the serving layer? Your website still needs to operate over that data. Are you imagining that there’s just going to continue to be a caching layer? Or is that going to be a separate system?

Bob: It depends on what the characteristics of the system need to be. If something needs to be really low latency, today’s data warehouses are not always the right solution for it. It just depends on the application. Latencies will go down in these products, but to Martin’s point, some of the architectural choices make the latency characteristics of a Snowflake somewhat different than, for example, the latency characteristics of a MemSQL.

Tristan: One of the things that I would like to see more of in the future is Lambda architectures, but with off-the-shelf tools. So my data is flowing into a more streaming-like system and a more batch-like system so that I can get the best of both worlds. You’re making tradeoffs when you build these systems. As a user, I want to be able to choose and have both of them.

George: Well, we have one minute left. I’d like to ask a yes or no question for everyone: will there emerge another major data platform alongside Snowflake, Databricks, Google, AWS, and Azure? We’ll start with you, Michelle. Yes or no?

Michelle: Yes.

George: Bob?

Bob: What’s your timescale?

George: In the next five years.

Bob: Yes.

Michelle: Yes.

Bob: But the new one may be relatively small relative to those guys.

George: Well I said major. That sounds like an in-between…

Bob: Snowflake was small five years ago.

George: Tristan?

Tristan: I think no.

George: Martin?

Martin: Yes.

George: All right. Thank you very much, everyone, for joining. This has been a really fun conversation. I really appreciate all of you being here. I know our audience does as well.

• 

  Bob Muglia
• 

  Michelle Ufford
• 

  Martin Casado is a general partner at a16z where he invests in enterprise companies. Prior, he was cofounder and CTO of Nicira (acquired by VMware) and is the creator of the software defined networking movement.

  Follow Twitter
• 

  Tristan Handy
• 

  George Fraser is co-founder and CEO of Fivetran.

  Follow Twitter Website

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How to moderate good, productive discussions and navigate tricky conversations is top of mind — whether doing a panel, conducting a live event, presenting a talk (or even hosting a podcast), managing (or just participating in!) a meeting. Especially in a world where remote and virtual work is increasingly become the norm for many knowledge workers (given the pandemic and even beyond) — one in which we’re increasingly communicating through little “Hollywood Squares, Brady Bunch”-like boxes.

So how to translate physical and nonverbal presence in such virtual environments, or voice-only modes? How to manage unruly discussions? Do parasocial vs. social interactions change things? And beyond these broader contexts, how do the things inside us — whether agendas, tics, anxiety — manifest outwardly, and can we better control them?

In this episode of the a16z Podcast, Matt Abrahams — lecturer at Stanford’s Graduate School of Business (where he also has a podcast, “Think Fast Talk Smart”); principal and co-founder of Bold Echo (a company that helps people with presentation and communication skills); and author of Speaking Up Without Freaking Out — shares frameworks and best practices, in conversation with Sonal Chokshi. The discussion offers many concrete tips for moderation and communication for anyone, across all kinds of mediums and modes.

image: Paul Hudson / Flickr

Show Notes

• The importance of planning when moderating an event [1:45], and how to deal with unruly discussions [3:37]
• Using paraphrasing to guide the conversation [5:21] and techniques for bridging and linking ideas [7:41]
• Further discussion of preparation [12:50], setting event ground rules [20:07], and how to be the voice of the audience [26:40]
• All about virtual communication [27:56] — visual issues [29:22], vocal issues [31:47], and reducing verbal tics [33:34]
• Managing anxiety through breath control [39:20]
• Discussion of new social audio platforms, such as Clubhouse [46:34]
• How to structure a talk [49:12], and best practices for introductions and conclusions [52:02]
• Concrete tips for reducing speaker anxiety [56:52]

Transcript

Sonal: Hi, everyone. Welcome to the “a16z Podcast.” I’m Sonal — and I’m here today with an episode all about one thing (but also many things), which is, How. To. Moderate. And I don’t mean moderate in life, like “everything in moderation”; I mean it in the sense of moderating when you’re speaking — whether managing or participating in a meeting, presenting a talk, speaking on a panel or live discussion, even doing a podcast, and more.

We especially go deep on something that’s top of mind right now given the pandemic — which is that many knowledge workers, who have the privilege and ability to work from home — are now working and communicating entirely online and virtually, and many will probably continue to do so well beyond. So how does that change moderation? Where do the differences between in-person and remote — as well as the evolution of tech & tools — come in?

Our special guest for this episode is Matt Abrahams, who’s a lecturer on strategic communication and virtual communication at Stanford’s Graduate School of Business, where he also has a podcast called “Think Fast Talk Smart”; he’s the principal and co-founder of Bold Echo, a firm that help executives (and anyone, really) who wants to improve their communication, learn new skills, or just improve upon and sharpen their existing skills.

Our conversation offers frameworks — and lots of concrete tips — for moderation all kinds of modes and mediums, including covering how to manage unruly discussions, how to prep (and the tensions between being scripted vs not); how to manage tics; how to translate physical and nonverbal presence, even in virtual environments; differences between parasocial and social interactions, does that change things?; tips for managing speaker anxiety; and how to structure a panel, talk, or discussion from intro to conclusion. But we begin with the role of pre-work (and post-work) around all kinds of conversations.

The importance of planning

Matt: As somebody planning a communication interaction — be it online or in person — you need to think about the *things you do in advance of it happening, *what you do during, and *what you do after.

So in terms of what you do in advance: You’re figuring out who your audience is, what’s important to them? What themes do you want to get across as part of this communication? What’s your goal — and to me a goal is very specific, a goal is about *information, *emotion, and *action: *What do you want people to know; *how do you want them to feel; *what do you want them to do? Are there ground rules you want to establish?

In the midst of moderation, when it’s actually going on: Your biggest skill sets are: *your ability to listen, *your ability to paraphrase, and *link and bridge ideas. That’s what helps a smooth interaction take place.

At the back end, when it’s over: You know just because the interaction has ended (the meeting is over, the presentation is over, whatever) — you then have to think about how do I follow that up; and how do I make sure the information is acted upon; and set myself up, and the others, for success for the next interaction.

So it is a process that starts way before people ever enter into the call or the room, and it continues long after they’ve left.

Sonal: So, what’s the difference then between sort of planned meetings — like presentations and panels — versus spontaneous, more organic sessions.

Matt: The preparation piece I think is the same, but as it’s going on, if it is a free-flowing activity — maybe a brainstorming meeting, a feedback session — your job as a moderator is really to just guide and steer it in the direction that the participants are taking it.

In a more formal situation — like a panel, or a decision-making meeting — you have to be much more directive: You have to keep things on track; you have to be monitoring the agenda, and the time, and the different types of contribution.

Managing unruly discussions

There might be power dynamics at play: It may be the case that somebody is acting the way they’re acting, because they have additional information that they can’t share. It may be that the person had a bad interaction before they came into the situation. So it’s also very important to — while moderating, while facilitating — to take a step back and try to understand at a meta level, what’s going on in the interaction, and perhaps decide to act on it — give some direct feedback or guidance — or perhaps pull back, and do some of that either on the side… or later.

Sonal: It’s so fascinating, because there’s a psychological component here, which is, it’s the difference between whether you go into an interaction — any kind, whether one-on-one, a group, whatever — seeking to understand, or seeking to be understood. That’s where I see the fundamental dynamic of where many communications break down, is when both people have very different, conflicting agendas:

So, a good segue to one of the questions I wanted to ask you, which is: How do you manage… <sure> — and this to me is one of the most top of mind things in this environment today — online, virtual, in person — how do you manage tricky communications? Just at a very high level like, you know you’ve done sessions with me and some of the team on how to manage like at a live event — if you have someone on your panel who’s kind of going in a different tangent; or, you have a spontaneous questioner who comes up and kind of throws a different vibe into the dynamic. Let’s break all of that down, starting with having an unruly panel, if you’re running a discussion, live event, moderating a room… whatever.

Matt: Sure. So, in all those tricky situations, again, pre-work matters; anything you can do to set yourself up for success: Talking to people in advance, so you set their expectations; giving some ground rules for what you expect.

If it gets unruly, your biggest friend is paraphrasing.

The power of paraphrasing

I really think the ability to paraphrase is THE most essential tool a facilitator needs to have in his or her back pocket. Let me explain first what I mean by paraphrasing then give you some examples of how to use it:

So, when I’m speaking about paraphrasing, I’m talking about listening to hear what IS the bottom line — the critical gist of what somebody is saying. And this requires a very different type of listening; most of the time when we listen, we’re just listening to get a vague idea of what someone’s saying, and then we begin formulating our response or rehearsing it. But when you’re listening to paraphrase, you’re really trying to figure out, what’s the bottom line.

And here’s how paraphrasing can really help you: If somebody is going off on a tangent, or if somebody is just bloviating, or they’re trying to figure out what it is they want to contribute — extract something of value (to you or to the conversation that you’re trying to facilitate), highlight it, and then, link or bridge to a different topic.

So, imagine that you’re about to take us further on a tangent, I can simply say, “Hey, that point you just made about X, that’s really important. And in fact, it ties nicely to…” — and all of a sudden, I’ve taken control back, I’ve validated that you said something useful, and I’ve moved on.

Sonal: You’re in control.

Matt: Yeah, it gives you the opportunity to reassert your control in the politest way possible. Because the reality is this: If you’re charged with being the moderator/ the facilitator/ the leader of the interaction, and somebody goes on a tangent, or somebody gets aggressive, or starts really rambling, people are going to look to you to manage that situation. And every moment that you’re not managing it, your credibility is at risk. So you need to step in, but you need to do so politely. And I think paraphrasing — highlighting something somebody said, questioning it in a polite way, whatever that is — is your wedge to get YOU back in control, and then you move it to somewhere else.

That’s why paraphrasing is often partnered with bridging and linking to the next topic or theme.

Sonal: It reminds me so much of a podcast host — the #1 thing I think of is that they are a shepherd for the audience. <mhm!> And their job is to do precisely that, the bridging — the signposting is what I call it: what’s happening, stitching things together — and you have to do that a lot in real time.

So, now tell me more about the bridging and linking!

Bridging and linking

Matt: Yeah, so if you have solid themes that you are driving towards — and these are either ones you’ve created yourself or co-created with the other participants — those are the cornerstones or the anchors to which you bridge or link back to. So, if we’re really trying to drive a decision on a particular feature or product, as I am facilitating the interaction, as different points come up, I will always come back to that and say, “how is that”; or, or either ask how it is; or show, and demonstrate, how it is linked to the theme that we are striving towards.

So it means in advance, you have some guideposts of where you’re going — those are the themes that you’re driving towards — and then you bridge and link back to them. And you can bridge and link back through questioning, “How does that link to our goal”; you can do it directly by saying, “That links to our goal in these ways”; or you can ask somebody else, you could say “Okay Sonal, now how do you think that helps us achieve the goal that we’re striving for?”

All of those are techniques for bridging and linking back to the central ideas.

Sonal: You know it’s a lot like a host at a cocktail party, where people are kind of meeting each other for the first time, and you’re like “Oh, you know, Matt, you just mentioned this, well it turns out that so and so is also really into this, and you guys have that in common.” And while that’s more in the sense of get-to-know each other, <mhm> this is exactly the same thing but in the sense of get to know this idea and let me help you kind of connect all these dots.

Matt: Right and the key word you said there is “connect” — and that’s really what a good facilitator and moderator does; it’s all about connecting. And connecting is just another word for bridging and linking — that’s really the task.

And it’s a mindset — you have to go into the situation thinking that way — and that’s why I like your host analogy. You know for many of us when we host a party, we have to get into that role and say, I’m a host, it’s my job to make sure everybody’s talking and enjoying themselves and connecting.

Same too, with a moderator: Many of us go into our role as facilitator or moderator with that contributor’s mindset. And that’s very different than when you are actually in the role of moderating. So that linking/ bridging/ connecting matters, a lot.

Sonal: It’s so funny because in the early days of moderating on the podcast, I often struggled with, I shouldn’t speak up; I’m here to only set up my host. And then I had all these people (fans, others) messaging me like, speak up more, we wanna hear more from you… and I realized like, oh my god, the orientation point is the voice — they’re the GPS for the episode, the themes that cut across things — and the connecting is key, because in audio in particular, the intimacy you have is SO exquisite. And this is really relevant to communities: like, let’s say you have a club; or, a group of people in the workplace, a team, a department, a meeting, a project — that idea of connecting, I agree, is critical.

It’s about the thing that the listener wants, the audience wants, that’s top of mind, making it about what you said about why is this relevant to YOU? That is another great orienting technique. Because one of my biggest pet peeves when I go into a conversation, especially in podcasts (or a newsletter blurb, or any kind of editorial product) is not knowing why does anyone care? <Right> Like, that is the first thing that I want to know out the door. Period.

Matt: I love the analogy of GPS. And, I think that’s a great way to look at it, is: you have a destination; your job is to get there; there are multiple paths to get you there — as a moderator you have to decide, do we take the most direct route, are we going to take some more scenic routes to get there; but you’re really driving towards that goal.

And I have to say as a listener to your podcast, Sonal, I love when you contribute, and I think there is a role for the moderator and facilitator to share his or her points of view. But you do so in a very… thoughtful way, so it doesn’t just become about your point of view and your direction. And that’s a skill. It’s a skill to learn when and how much to contribute.

Sonal: It is not easy, and it’s something that I also constantly learn and evolve…

But just also — because all the listeners of the show know I can’t resist a damn good analogy! — if you take the human GPS analogy even further, and you’re saying you have to know are you taking the scenic route or this route? — in much the same way, when someone’s in the car seat with you giving you directions, you wanna kind of know the map and the terrain ahead of time. Like “by the way in three streets, we’re gonna turn right”. <right> Because you don’t want to suddenly turn right, right? <correct> And, similarly, you want to know if there’s like a lake that you don’t want to drive into by accident <Matt chuckles> like, hey we may want to avoid that traffic jam. So, as a moderator, you’re kind of rerouting around people are going too long on this thing; or, oh man that’s like a- I don’t want to jump into this lake, like, that’s going to tank this conversation. Let me redirect this. So I totally love that analogy, taking it even a step further.

Matt: Yes, it works really well. For sure.

Sonal: One of the tactics — you talked about always having the bottom line in mind, as a way to kind of help with the paraphrasing, the bridging, and the linking (it is both the way to summarize the paraphrase, as well as a way to then signal that you’re about to take a turn) — I have to give you credit, because I just realized (I don’t even know if I remember this, but I think) one of my signature lines on one of our other shows, 16 Minutes, which is our news analysis show, <mhm… yeah> I end every episode with “bottom-line it for me”. And I just remembered in this conversation, like oh my god, I think I got that from you, when you were helping me prep for a live panel years ago.

Matt: It’s definitely a mantra of mine. But you deploy it expertly, so I’m not going to take any credit.

Sonal: Well, you deserve the credit!

Preparation and ground rules

So on the note of prep, one of the only ways to do a lot of this stuff is to do it in real time, frankly. And if you’re live, like a live community room or a live town hall, or anything else. So… tell me a bit more about what goes into that prep, a little bit more concretely? Is it a script? Is it just knowing your guests really well? Is it a prep call? <Matt chuckles> Like, how do you kind of thread that needle?

Matt: So, to me, it starts first and foremost, by getting an understanding of what it is that I need to accomplish. Is it really about collaboration? Is it decision making? Is it just getting people to know each other? And from that, it’s really important to then think about the audience. And you have to do reconnaissance, reflection, and research. So it might be looking at people’s social media profiles and postings; it might be talking to people who have interacted with these folks. Or, just talk to the folks themselves — and get a sense of what’s important to them, what their attitudes are, etc. That’s part of the pre-work that you need to do just to understand who’s going to be in the space and part of the communication.

Next, you have to think about, again, the goal; what is it I’m trying to achieve? Now that I know the people, and where they’re coming from, and the purpose I have, I can then craft the goal: know/ feel/ do > information, emotion, and action. A lot of us are really good at focusing on the information: here’s what I want us to be talking about. And, we’re also pretty good at saying, okay we’re driving towards this kind of action.

We don’t often think about the feeling, the tone — <yes!> what tone do I want the interaction to have? Maya Angelou is famous for saying I might not remember what you said, but I’ll remember the feeling. So, you need to think about that up front.

Sonal: I am so glad Matt, that you talked about not just the know but the feel. That to me is the thing that I care about the most as a moderator. And I don’t mean that in only a mushy-gushy way like “Oh I want people to feel good.” But I want people to come out of a conversation feeling smarter, and feeling empowered, or more knowledgeable, or that anything is possible, or that they can find a way that’s relevant to them. And also that I’m their advocate, because I genuinely believe I am.

I think for me — there’s no like systematic technique or at least one that I’m aware of — is trying to find kind of the person’s guiding light. <mhm> Like, what is the thing that drives them or makes them passionate about what they do? <yeah> And then how do you really draw that out? And we never talk about that actually, overtly.

Matt: Right. The way we have to actually do it often is much more subtle and nuanced.

If you feel that the thing that is most important is to convey those feelings, as part of the interactions you’re facilitating, then the question and challenge for you becomes, what do you do in preparation of the participants — during the interaction and even after — to really bring those emotions, those feelings to life? You know it’s so much easier to think about the knowing piece — Here are the bullet points I need to get across, <exactly> here are the questions I need to ask. But what is it that you can do to really call out or invoke those feelings that you want? And it could be simple things: Non-verbally acknowledging what somebody said; it could be thanking somebody and expressing gratitude.

You then need to stockpile questions. And these are questions that you can use to ask the participants, to get them communicating, to move it in the direction you want. These can be what I call “back-pocket” questions — emergency questions that YOU deploy, if silence comes in — you know, you can throw out a question that says “something I’ve been wondering about,” or “think about how this applies to” these situations. So, having questions you can ask others and having questions you can use to get the conversation moving: really important.

Sonal: You mentioned “stockpiling”, I want to probe on that one a little bit, because, frankly, I am actually not a big believer in… So okay, Margit calls bullshit on me on this, which I actually really love. But where I’m like, “I don’t believe in prep.” And she’s like, “What are you talking about? Your whole lifetime is prep. Like, you read all the time. You absorb things all the time. Blah, blah, blah.” <right> Which, okay, that’s fair.

By prepping, I mean like having a script in front of me <mhm> because I want things to be very organic and very free flowing: I’m going on the same journey as my listeners. However: I had one person a few years ago say, “Oh I love being a naive questioner.” And I’m like, Oh, no no no no; you’re not a naive questioner, because that is also bad; <right> like, don’t make that mistake.

On the flip side, other people go so far with the stockpiling, as you described, <yeah> that they go to the point where they almost lose their way if things don’t kind of stick perfectly, <mhm> and it feels very constrained and scripted. What would your advice be on how to thread that one?

Matt: So, you’re highlighting a really important point: You want to feel as if you have a direction, and tools to help you get to where you’re going; but you don’t want to have it SO scripted, and SO structured, that free-flowing, spontaneity is stripped from it.

So, everybody needs to find their level of comfort. People who might be newer to a topic, newer to a language: Doing a little extra prep and scripting could help them. For people who are more comfortable, more extroverted, it might be better to have less of those guidewires. But the point is, you would never go into a situation totally unprepared. You have ideas, themes; you have some boundaries.

I love this research (it came out of the U.K.), what they did is they took children and they brought them to an empty field and they said “go play.” And the children played. And the researchers evaluated how playful the play was, how creative the play was, how much time was spent playing versus planning. And then they brought a similar group of kids to a similar field, but the difference was, in the second field there was a play structure. And they said go play. And they rated the same things (amount of play, quality of play, creativity) — and it turned out, the play with the play structure was much more creative, much more engaging, more time spent playing. I like that as an analogy for planning interactions: Having some structure, some tools, some idea of content, direction, etc., can really, really help you focus on what you’re trying to do. If it’s TOO open, if it’s TOO spontaneous, you can get lost in that spontaneity.

So, finding the right balance is hard (each person is different), but using that as a guide — knowing you have to have some structure, some tools, some things in that stockpile — can really help.

Sonal: I found that research so fascinating, because I was in the world of early education and developmental psychology as you know back in the day <right> — and one of the one of the concepts (the phrase in the education world, this constructivism idea) — was “scaffolding” versus structure. And the idea is that it’s like the bones — it’s not like a full- built structure, but the scaffolding that sets something up, but it’s not fully filled in, and it’s also not like fully free-for-all — so, that’s an idea that applies there.

And then two, the other thing is the importance of ground rules. Because one of the things that you learn with early childhood education and any kind of play, is all the kids going into it know the ground rules: Like, you cannot hit, you cannot fight, you cannot pull so-and-so’s hair, or you know, wear sun block <chuckles>; whatever the rule is! <chuckles> <Matt: right> So, I’d love to hear you tell me more about how you think about the ground rules to make these goals, and intentions, and scaffolding more explicit — versus only in the moderator’s head — to the audience and the panels.

Matt: So first and foremost, there’re two different types of types of ground rules: There are behavioral ground rules, that’s what you do, how you act; and then there are content- specific ground rules, <mmm!> what’s acceptable to say and what’s not. Just creating those two categories can be helpful for people.

Now, to the question of how do you share them: So first and foremost, you can take time to collaborate together to create them. So you can start by saying, hey let’s figure out how we want to best interact. By virtue of co-creating them, that’s how you’re disseminating the information. If you want to do them in advance, come in with them… then, you can put them in the invite to the meeting, or in some communication that happens in advance, and then just remind people of them when you start.

What you want to avoid with any rules that you set up is getting bogged down in the rules. If you have ever watched young children (and I know you have experience with this), young children interacting, they spend a tremendous amount of time just dealing with the rules — so much so, that they don’t actually get to playing whatever it is they’re trying to play. And adults can do the same thing. <yeah> So, it’s make them explicit; maybe create them with others; and then just get moving on, from them.

Sonal: One other question about knowing the audience’s intent in a live event, where you may not have the ability to know — like, for example, parasocial versus social interactions, where you’re interacting with strangers, often, in a group of people — so how do you then think of aligning the goals and knowing your audience when you have groups of strangers interacting in the same room? This is the case that’s common when you go to a conference and there might be unknown people who can just come and join the Q&A section; you don’t have registration, it’s an open event or, it could be in online audio social places like Clubhouse… it plays out in many different ways.

Matt: Wouldn’t it be great just to be psychic and be able to know that stuff? That would be fantastic! So, I mean look for contextual clues: what’s the title of the event; what’s the motivation for people to be there — and that can often give you cues as to what’s important to people.

The other way is just to inquire, ask questions; observe what people seem to be saying and how they’re saying it — gives you insight into what’s important for them. But again, that means your approach is different than coming in as, I’m a contributor and I’m gonna share what I have <yes> on my mind. Versus, I need to understand what’s going on and taking that time just to reflect and look around and see what others are doing can be very helpful to figuring it out.

And then, being comfortable adjusting on the fly <yes!> — I can’t tell you the number of interactions I have gone into where I thought we were going one way with this group of people, and it turned out to be different. And you just have to be flexible and say okay, that’s what this is going to be about, or that’s how we’re going to make this conversation move forward. And, you know improvisation — the notion of “yes and” — take what you’ve got and move it forward, rather than coming in and say this is what “this conversation is going to be about”.

And certainly there’re times that you have to drive the conversation to a particular point; but a lot of the time, we can just see what happens organically and move with it, within the structure and confines of what we’re talking about.

Sonal: This goes to me to how I think about prep docs, ‘cuz while I don’t stockpile questions in advance, I do have like a quick-list of topics that I want to make sure to hit. <sure> And it’s really helpful, because I know the three that I absolutely want to hit no matter what, but then I also have like a couple others that may come up, that I can go into and pull (or double-click on so to speak) — if it’s more interesting. And if it’s not so interesting, then you quickly can move into something else, because, you kind of want to always think about what’s maximally interesting to keep people engaged.

So, the way I structure my prep docs: I make ‘em modular chunks, so that I can go out of order very easily. And I know this is a piece of advice that you probably also have given. But for me, that’s like the #1 thing is, I have an arc in mind <mhm> but I keep it very modular chunks so that I can quickly rearrange it on the fly if necessary; I’m not wedded to that.

Secondly, like a quick topic, I might have like a one word or two words for like a probe <mhm> — like angle, or twist, or nuance — because that’s kind of the thing that makes it more differentiated from like the same way of having that conversation.

So, I have like a particular template that I’ve made up over years of doing a lot of these, that works very well for me in this vein.

Matt: I would love to see the template… I absolutely agree that “chunking” or being modular is really important. And, having just key topics that you want to address can work very well for many people.

The only thing I would add to that is try to have some prioritization among those, because if time gets crunched, or, if some topic heats up and takes you in a different direction — know the prioritization, so you can adjust. So on the fly, you’re not having to make those decisions, you’ve already thought about this is the most important, this is second and third most important.

Sonal: Oh you’re absolutely right. And sometimes I, in my template, conflate arc-order with priority. But in fact, sometimes the last thing is the most important thing to get across. And so having that prioritization is really critical.

I will also add that I don’t map it out like time-wise, but I put percentages next to each modular chunk in order to kind of figure out the weighting of it: So, I want 50% of the conversation to be about this; and then like 20%, like takeaways — that’s not quite the same as priority, but it does tell you how much you want to get across.

Matt: I am smiling as you are speaking. Not only do I like that idea, but I, like Margit, am gonna call bullshit that you don’t plan and prepare. <Sonal laughs out loud> I mean everything you have just described is planning and preparing to an extent that most people don’t — even if it doesn’t feel that way… so! <chuckles>

Sonal: Okay but to be very clear, I only do that for live events. I do NOT do that for podcasts; I’ll tell you what I do for podcasts: I quickly, at the very beginning, spend five minutes — and we have obviously the general theme because of the guests, and the lineup, and the angle — so what happens is, when I get people together, and it’s usually multiple people, we quickly talk about — and I say very clearly, I want topics, I don’t want you to tell me what you’re gonna say <mhm> —

And in fact, one of my fundamental rules of live events, is I do not believe in putting people in the same green room beforehand. Because speakers reference something — they always do this, like “oh yeah, we were talking about this in the green room” — and the audience is left feeling like they were cheated out of the idea. And so I don’t want any rehearsal. I actually cut people off when we do this, in the first five minutes, where I’m like “No no no no no — save that for the actual discussion. I don’t want you to tell me what you’re going to say. I just want the topics.” Because nothing ever sounds as good as the first time someone says it raw, and real-ly.

Matt: I agree. And as a facilitator and moderator, your job is to bring out that fresh conversation. And if people do talk about private, or previous conversations, you have to call it, and you have to bring it forward to make it relevant to everybody.

One of one of the best mindsets or frames that a moderator/facilitator can have is that YOU are the voice of the audience. <yes!> So if there’s something that is inside baseball, if there’s some insider information, you have to call it, you have to pull it out so others can participate.

And there are things you can do that are very simple linguistically: You can say, “as we’re curious”, or “as you know”, or “as many of us are interested” — using that inclusive language brings the audience IN. Not only does it help the audience feel like they’re part of that conversation, but it reminds the others — the panelists, the people that you’re helping facilitate — that there’s an audience they need to be talking to, it’s not just themselves.

Sonal: It’s not talking to each other — I love this. So this goes back to the host being a shepherd.

But actually, you talk about the linguistic aspects — this is one of my favorite technique that I’ve specifically learned from you (in some of the live event preparation) which is: How to change the exact same question, but in a way that it’s very much phrased as advocating on behalf of the audience. And you went so far as to even show me physical, nonverbal things that I can do to bring the audience along, where, I literally open up my hand like “listen, I think everyone in this room” — kind of hug the room in <right!> — “wants to know like, what do you mean by that”? <right> That was SO useful.

On virtual communication

Matt: Yeah, it’s not just verbal stuff that you can do using words — using inclusive language, using analogies that everybody relates to; ALL of that’s a way to do that verbally — but nonverbals matter a lot. Now the fact that we’re virtual, it’s harder.

The equivalent to what you mentioned — where you actually open up your body and angle it towards the audience, as you say “as many of us in this room are wondering,” before you turn to the person and ask the question <mhm> — the way we have to do that virtually is you have to look at the camera. And it’s SO tempting to look at notes or to look at the faces on the screen, but you need to look at the *camera* so that people feel like you’re connecting TO them, talking TO them, and including them. And that’s hard.

Sonal: I am so glad you brought up the online/ remote environment. Because a) I don’t think this is an important skill just for the duration of the pandemic — let’s face it, a lot of knowledge work in particular is gonna be remote-first — we’ve definitely shifted the baseline on this. But secondly, I don’t believe we’ve seen the first big wave of companies that are all built in an all remote-native way — culturally, interaction-wise, etc. — it’ll be really interesting to see a lot of the learnings that come out of that, because we are in an unprecedented age of online communication and collaboration.

So, can we really dig deep into both nonverbal and in-person, and then let’s go into nonverbal and the differences online. Like, how does one optimize techniques, like, I open up my arms in a room — but in Slack, nobody even sees my arms. How do you… think about all that.

Matt: So there are three major components to nonverbal presence: There is *the visual, *the vocal, and *the verbal. And these play out differently depending on the channel through which you’re communicating (in person, online, et cetera).

The importance of visuals

So visually is what people see of you; it’s how you hold your body. We have to make sure that we come across as confident and composed. So we want to be big (that is, not hunched or crouched); we want to be balanced (head straight, shoulders square); and we want to be still.

Now everybody has to find what’s comfortable to them; you know I always give the analogy, we could ask every one of your listeners to show how they swing a baseball bat, a tennis racket, a golf club — how they look for each person is going to be slightly different because of their build, their experience, their injuries. And that’s what we strive for in our nonverbal presence: You follow some foundational principles, and then you adapt them to who you are and your experience.

So, visual is what we see. And virtually or in person — big, balanced, and still is what it’s all about.

Sonal: How do you do big in virtual though?

Matt: Ahh, great question. So when you’re in that little box — whatever the tool is you’re using, we’re all in our little Hollywood Squares, Brady-Bunch boxes — you want to pull your scapula, your shoulder blades down, away from your neck. And in so doing, it broadens out your shoulders. So you look bigger, and you sit straighter. It also will tense the muscles in your neck so your head doesn’t tilt; head tilting in a virtual environment might compromise your credibility and confidence (or at least appearance of that).

So, when you’re in the box: Pull those shoulder blades down, broaden the shoulders, hold your head straight; really important.

The other thing that’s important is gesturing: When I’m up in front of people, I want my gestures to be broad; I want them to go beyond my shoulders. Now when I’m virtual in the box, if I were to do that, you’d never see my hands.

Sonal: It looks weird too, when people even wave goodbye.

Matt: Yeah, no it is weird! But gesturing is important. Gesturing helps your audience, it also helps you.

So bringing your hands up higher, putting them about your shoulder level — so if I were to see you in person doing this, you would look like a caricature, a puppet — but online, in a virtual meeting, it actually looks okay to have your hands up. And then again, broader than your shoulder — we want to avoid any gestures that are in front of our chest for too long, because it makes you look tight and nervous. <right!>

On vocals

So that’s the visual part. The vocal part is varying your voice. You know this so well; I mean, with podcasting as a medium — if. I. talked. like. this. for even. just a few seconds, folks are gonna tune out. <Yeah, Ferris Bueller effect! chuckles> Exactly! Our brains are wired to look for and seek out novelty and change, anything that stays the same, we habituate to very quickly. So you need to make sure that your voice has variation in it. <yes> And a great way to bring that variation is to use emotive words, adjectives and adverbs. So I would never say “I’m really excited to be here Sonal.” I would say, “I’m really excited to be here!” <Sonal laughs> So really in the “excited”, invoke that emotion.

So you want to have variation. And really, what it comes down to in person or virtually, you have to work on your breath; your voice is a wind instrument. And if you don’t have vocal stamina, you’re gonna be in trouble: Your voice is gonna trail off, you’re gonna start speaking fast. So I encourage everybody, before you have a big event — I don’t care if it’s a presentation, meeting contribution — you should be building vocal stamina. And the best way I know to do that is reading out loud.

So if I know next week I’m doing a 30-minute whatever, I’m reading out loud the week before 5-10 minutes each day to build stamina. I equate it to, if you want to run a run a marathon, you don’t start at that distance; you start by doing gradually more and more mileage. The same thing has to be true with your voice. That way you can support your voice — and therefore your ideas — as you speak.

So, breath control is critical.

Sonal: I’m definitely gonna come back to that one, because I have a lot of thoughts on that one!

So: so far, we covered the visual and the vocal. So let’s do the third one.

Verbal patterns

Matt: So let me talk about the verbal. So clearly, the words you say are important. What I really like to highlight are the words that get in the way, what I call the “verbal graffiti” — so it’s the ums, the uhs, the likes, I means — my favorite, “honestly”, that one bothers me so much, because it implies everything else you said prior was dishonest — we use those fillers. And, it is really hard to get rid of them. The best thing you can do is just try to build your own awareness. And based on that, then, eventually over time, they will decrease.

The other part of verbal that I want to add is hedging language; this stuff, it is rampant: kind of, sort of, I think — that language undercuts your credibility. If I were to say, “Sonal, I kind of think we should do this” versus “we should do this”, it just sounds very different. Now there are times, if I’m leading a meeting, and I’m the head honcho and I want to avoid people just doing what I say because I’m the big boss, then I might say “I kind of think we should do this” — because that invites them to share their opinion. But when you are running a panel, when you’re giving a presentation, and you say “kind of” and “sort of” and “I think” all over the place, you are reducing your credibility.

Sonal: Oh my. So first of all, I love the framework, super helpful; because you’re actually reminding anyone, in any speaking engagement — you are visual, vocal, and verbal — it feels like it’s obvious, but it’s really not; because when you go into any session, it’s so important to tease them apart, so you keep all three in balance.

So let’s start with the first one, which is visual. One question I wanted to just check in with you about is, when it comes to Zoom meetings is like visual fatigue — <mhm> no one looks at each other in a meeting where you’re literally looking eye to eye the entire hour — and so there’s a visual exhaustion that happens. And then secondly, it’s very hard to tell where to look. So can you give me a few more specifics about where the eyes should go and land? Because one of the techniques that you’ve taught me in live events is to land your eyes. <mhm> But, how do you even do that when you don’t know; it’s like a black hole!

Matt: Eye contact virtually is really challenging. It’s challenging because, where the camera is and where you want to look are two different places: So we want to look at people’s images, if people are showing their video; and that’s usually below the camera. And what it looks like to the audience, if you’re actually looking at the pictures, is that you’re talking to their feet. And we know that that’s rude in person, and part of us says, hey look at me. And we attribute a whole bunch of negative thoughts to people who don’t look us in the eye: They’re nervous, they’re not prepared, <totally> they’re lying. So you really do need to train yourself to look at the camera.

So, a couple things you can do to help: One, some of the virtual tools allow you to physically move people’s images; so you can actually move the images under or closer to where the camera is. Other times (what I recommend people do) is take a picture of people you know, or maybe even a pet you own, and put it right behind the camera — we as humans are wired to look at living things, so put a picture right behind it — and that will help you remember to look and connect to it.

The other thing that’s really tricky here Sonal is, we are not used to seeing ourselves when we speak. There’s research that shows it activates areas of our brain regarding self-awareness, that we typically don’t have active when we’re communicating <right!> — and it drains cognitive resources. So, some of these tools actually allow you to mute your own image; I know somebody who takes a post-it, sticks it right over her image.

But just know that seeing yourself speak is hard.

Sonal: You’re absolutely right. I use it unfortunately, as a mirror, <mhm> <Sonal chuckles> where I’m constantly checking myself, like wait my hair’s out of place — and the other thing is when you go to a live event, you know they have confidence monitors; and in this case, it’s like the opposite of a confidence monitor: it’s like an un-confidence monitor because it’s really distracting.

So, I love that tip of putting a post-it. And I also forgot that some tools allow you to turn that view off — but it IS incredibly different — because when you’re on stage, you’re not that close up. It’s a new level of intimacy and I actually think we’re going to see some new behaviors come out of it, and maybe with new technologies, even better <mhm> — but it is not easy, for sure.

Matt: Yeah, no, it’s not easy. And I think as we do more and more of this, we will get more used to it.

Sonal: Yeah, I agree. Okay, so then that’s for the visual. So now on vocal — the second part of the framework <mhm> — we talked about varying cadence. And god, as an podcast editor, what’s really fascinating to me is how most of the time, people are off in their cadence, like it’s misaligned. So for instance, the moment they should be slowing down, they’re speeding up; and the moment they should be speeding up, they’re taking too long to get it out. And I do this too, for the record. But I noticed when I interviewed Guy Raz — who’s obviously a very seasoned radio <mhm> and voice personality — the edit was kind of easier than other edits, because every sentence he gave was so clean. <mhm!>

And I was like oh my god, this is a technique of a really trained voice personality, essentially — and that’s a new type that’s emerging in this modern era of audio: “voice personas” — where, the better you are at varying your cadence — Like he would do things, like he’d slow down… when… it’s about to get really intimate… and… special. <yeah…> And that immediately, instantly makes you viscerally respond — both as the guest and the audience — so it’s really fascinating how that plays a role.

I also love that you talked about using an adjective, like something that makes it emotive. Because you’re right, you can’t say the word excited, like “I am so excited” you know <laughs>.

Matt: You have to work at it.

Sonal: Right, you have to work harder to NOT do that. I will also say though that this goes back to your earlier point about the feeling, and the tone of the room, and setting up that how you want people to feel — because the better you are a master of that, then the better you can actually control that. <Matt: Absolutely>

Breath control and anxiety

And then the final thing is on the breath now. And we’ll come back to this on the anxiety part but it is very tied, as you know Matt, to anxiety. And it’s really hard when you get anxious about public speaking to manage your breath. I often feel, when I go on stage (for live events, this is, because that’s what I’ve worked with you on) where, I feel like I can’t get my breath. Like, I’m going to have a panic attack or something.

So, can you say more about the breath? I mean, you gave some for proactive planning, but can you give us some in-situ, like reactive things to do to control your breath?

Matt: Certainly. And you are not alone. Being nervous and having it affect your breath happens a lot.

So one of the cool things about being virtual, is you can mute yourself. Taking deep breaths to help calm yourself down has been known for millennia. And, I can just mute myself, take a deep breath; nobody’s the wiser. Much harder to do in person — so there are some advantages that the virtual world brings us.

If you find that in the midst of communicating, your breath is getting away from you — because you’re nervous, or because you’re getting excited — we human beings sync up three things: the rate with which our eyes move, the rate with which we speak, and, how quickly we gesture. It is very hard to change your eye movements; it’s reasonably hard to change your breathing; it is pretty easy to change how fast you gesture. So, if you find yourself breathing quickly and out of breath, slow down your gestures, make them a little broader — it will slow down your breathing. And that’s something everybody can do, in the moment, that can help a lot.

So taking a deep breath before; working on your vocal stamina (way in advance of ever doing a communication event); and monitoring and managing your gesture rate can all help you breathe more evenly and less rapidly.

Sonal: I have one more from you, and one of mine.

One from you is — and this goes back to your earlier point of having an emergency question — which is how to have that in your back pocket, so that if I do find myself– not only is it useful if you kind of lose your train of thought (which does happen a lot in real time), but, it’s really great when you’re feeling like that anxiety coming on. Because you can get that question out, and then it lets you catch your breath while people answer.

And the other one that I — this is going to sound so funny — but it’s just taking a sip of water. It’s huge; because it’s another way that you can kind of slow down and catch your breath. I always tell audio platforms that one of my favorite features that I want everyone to build is a “drink water” button, and everyone kind of chuckles but I’m like no I’m serious, <Matt chuckles> this is what I really think is important.

Matt: Absolutely. Taking a breath, actually physically just moving — you don’t have to speak as you move, and you can take a breath as you step — it’s a great way, especially if it’s a transition point.

Sonal: So we covered the nuances that you outlined in the framework of visual, vocal… now let’s go into verbal. One thing I wanted to talk about here, with what you brought up, is, the verbal tics: So first of all, I agree with you; they are very weakening words. But: I do not believe in eliminating every single tic — I actually think that’s very bad practice, because we’re wired to hear people sound real and raw. And as you know, everyone has them. <right> My rule of thumb that I tell the audio editors is, try to remove as many tics as possible that are disruptive to the listener’s experience <mhm> — so if it’s like a “That’s right. That’s right. That’s right. That’s right.” — it’s almost like annoying to get the point across, then cut those. But otherwise, keep ‘em, so it’s not like robotic either, you know.

However… of course, I have a lot of vanity tics. And so I tried to get rid of them. Early days of podcasting, I was always behind the scenes; so I hated hearing my own voice, all of that. I always noticed only the tics — I like, I like, I like. Got it, got it, got it. Right, right, right — I have a million, and they’re so freaking annoying <mhm>. So I’d like systematically try to work on not saying them. And as you note, one of the ways to do that is to record yourself and hear yourself.

Guess what happened?

Matt: What?

Sonal: Another one popped in its place! <Sonal chuckles> <Matt: uh-oh!> So I got rid of “I like” and the next one was, “got it”. I got rid of that one, and then “right” came up, and then something else came up, like, “uh-huh, uh-huh”. And I think they serve some neurological purpose — I don’t know if you have a thought on this, but I think it’s impossible to get rid of tics.

Matt: Well I know it’s not impossible, because I’ve done it and I have helped other people do it — <Aww, dammit!> <Sonal laughs> but you’re right, they don’t ever go away completely. They don’t go away completely, but you can reduce their frequency.

I believe that they are remnants of our thinking, and in-the-moment feeling like we need to be saying something because we are in front of people; <Yes> we’re filling the space. And that’s why they’re called filler — <filler words>

So, there is a trick, there is a trick — it is hard — but there is a trick where it is a breathing issue. So, speaking is an exit-only event: You can only speak when you’re pushing air out, not when you’re taking air in. So if you happen to know that you say “got it” or “right” at the end of all your sentences or phrases, if you can train yourself to be completely out of breath when you are done speaking that phrase, you must inhale before you can say your next phrase. <ahh!> Which precludes you from saying anything such as right, um, got it.

Now that’s hard… <mhm> But as you were referring to earlier with Guy Raz, you can train yourself to really end and finish your sentences. <mhm!> And then you start another one. And by training yourself to land a phrase — to finish a phrase completely out of breath (now I’m not saying get <quiet at the end>, I’m just saying finish a phrase) — you then have to inhale, builds a pause (pauses are good), and doesn’t allow you to fill it with anything.

Sonal: I am going to try that.

People complain all the time about how we are all very fast talkers. <yes> And it is true, I talk the way I think, and maybe I could slow down on that. <chuckles>

Matt: Well, it’s interesting — ‘cause I don’t find you a fast talker — but what I find is sometimes <mhm> you won’t pause as long as you could. I speak very quickly too, but if I pause… people can catch up. The problem is, the listeners get fatigued <yes> because there’s no rest.

Sonal: I find that too; I also notice that and it drives me a little nuts that I do that, some of my speakers do that.

You know what is funny? — people don’t know this — a lot of people think we cut all the breaths out of our podcasts; it’s actually the opposite.

Matt: Oh really?

Sonal: Many times in an edit, we are often going in and adding breaths, because, I needed to slow it down to give the listener a split second to take it in. Exactly to your point. And I don’t do it myself.

The other thing is just I want to make a note, with the filler words: Sometimes I think it has to do with representation, sometimes I think it has to do with just societally; in fact, one of the edits I make often, for a lot of my expert guests, is NOT having them say an acknowledging statement at the beginning, “Well, you know, Tom, I agree with you, Jim. But here’s what I think.” And I just go right to the “I think”, which is such an important thing.

Matt: I agree with everything that you’ve said. And it’s — the kind ofs, sort ofs, I thinks creeps into everybody’s language — I hear it more and more across <yeah> everybody I work with.

Sonal: Yep. I hear this across very established, privileged, powerful people <yes> — all the time, everybody has them; so it’s not at all disproportionate in that sense. (I do think it’s dangerous when we judge the speech of people, like no vocal fry, or women shouldn’t do this, or uptalk and whatnot — which you’re not doing at all; it’s really about how to make the authority come across.)

New audio platforms

So one last thing on the visual, vocal, and verbal — there’s been an emergence of social audio and new forms of audio-interaction platforms, like Clubhouse, and you know there’s a whole wave of other types of tools for different interactions; gaming contexts, others. And I, I have to tell you, it’s completely changed how I think about communication — that framework you outlined, if you’re in a voice-only medium, you almost have to caricature-like, exaggerate some of the things that we’re talking about to make up for the lack of visual.

Matt: It’s really interesting you bring that up; that is going on concurrently with people wearing masks, where we also have to exaggerate nonverbal behavior <oh yeah… totally!> to communicate information. So, we are in a position now where nonverbal presence — both in vocalics, what you do with your voice and what you do with your face, etc., — are really being highlighted.

And for most of our lives, we really haven’t thought about that. For some people, this is exciting and liberating; for other people, it’s really, really challenging. But, you’re right, <yeah> we are having to focus on… emphasizing things very consciously… to get our points across because something in our situation is different: We’re covered up, we don’t have the visual cues.

Sonal: The other thing that’s happening in a lot of these new interaction paradigms is, it’s often more social-first, by default, than content-first, necessarily — even though it is about content and interaction. And so one of the things that I’m kind of learning is, how to navigate that. And so the question I have for you along these lines, is — we’ve talked already about how to deal with like navigating tricky panelists, navigating tricky audience members — what have if actually want to proactively, offensively engage a tricky conversation, socially, oftentimes with strangers. I’d love to know if you have any thoughts on that, and which best practices may or may not apply.

Matt: So I find that very intriguing, to actually be an instigator of some tension and conflict. That’s very provocative.

You know, I am a big fan of using questions to invite engagement, participation, and in this case, perhaps challenges. People can come in with declarative statements that can be seen as, as offensive and really make people defensive. But if you’re really inviting, I think questions are the best way to invite. So for example: When I give people advice on giving feedback, a component of feedback is an invitation to collaborate to fix the problem. And that invitation is best delivered as a question, I believe.

And for what you’re talking about, using *questions* is a great way to do that rather than come in with some exclamation or declaration.

Structuring a talk

Sonal: Great. So the other key thing that I’ve noticed in these kinds of dynamics when you have parasocial and social mixed — you know strangers and familiars — is intent matters. And to me, one of the greatest sources of conflict is when you have two competing intents: One being, I just want empathy; and the other being, I don’t want an echo chamber, I want to hear other competing viewpoints. And so to that point — now I want to ask you about how that plays into concretely, how do you then design the beginning, middle, and end of a session; whether it’s a live event, a room, a panel, a meeting. How do you think about structure in that?

Matt: So structure is something I spend a lot of time thinking about. And, I think about it from an overarching event structure — so the meeting itself, the panel, the presentation — but also the specific content that gets discussed in that: be it a contribution you’re making, a presentation you’re delivering, or in an interaction you’re facilitating.

So, at the macro level, it’s all about the arc — this is where we can look to artists, look at playwrights, look at movies — look at how do people weave… that? What do I want the beginning to feel like? What information do I want at the beginning? Where do I want to land this? And then there’s the actual content that gets spoken in the actual interaction. And for that, I can give very concrete examples. <please, yeah>

So I am a huge fan — a huge fan — of structure. And the structure that I like the most for information is what I call *the what, *so what, *now what structure.

And, let me explain how it works: It starts by defining what it is you’re talking about — could be your idea, your product, your process. You then talk about why it’s important, that’s the “so what”. And you get to pick the level of relevance here — it could be to the individual you’re talking to, could be a group, could be a company, it could be society in general. <Yes!> And then “the now what” is the next step, what comes next? Maybe it’s signing up for a particular offering; maybe it’s calendaring another meeting; perhaps it’s looking at a demo, or having somebody else come onto the stage.

But if you can package your information in a way that is clear and concise and connected, then it’s going to be more valuable. And this structure really helps do that. And you can move things around. So if I’m talking to a hesitant or resistant audience, I might move the “so what” first. Start by saying, imagine what it would be like if we could save money, or time, or lives? <Yes!> And people are like yes, I like that. Then you say well, here’s what we need to do: Here’s the what, and here’s the now what that comes after it.

And it applies not just to information you’re disseminating, it could be feedback you’re giving, it could be emails you’re writing — a structure like what/ so what/ now what can help. So when you put the micro-level structure — the what, so what, now what, into the macro-level structure — where you’re worrying about the flow and the arc, that’s where you get rich, engaging, memorable communication happening.

Introductions and conclusions

Sonal: That’s fantastic, Matt. And I love what you said about that you could reorder it based on resistance, because, <yeah> that is exactly how I think about every podcast episode or event is — it is not just about the topic, it’s actually about broadening the potential audience for the topic. And so you can actually bring more people in if you orient things in a broader way — like, hey this conversation seems like it’s about DevOps <right> but it’s really about innovation and all of you care about this, actually. And “the now what”, I think of as how do you know bridge theory to practice, or, make something more concrete — like, you were talking about abstract software system — what do people DO with this information? <right> Or what do people act on? And I think that’s a very, very useful, framework.

And in fact, it frees you up! Because one of the techniques that very good playwrights (to use your example), use is the technique of “in medias res”, like starting something in the middle of the action <mhm> — you know like the way Star Wars began; <yeah!> it doesn’t begin with like, episode one, it begins with episode four — and in that way, we can actually start the conversation by picking the right place: And the way we orient it, is the what/ so what/ now what!

Matt: Yeah, and I’ll just make one other comment — I totally agree with the notion of starting with action, starting in the middle — there are a few things I get up on a soapbox for, and I really really want to see changed in people’s communication — I would love for presentations, meetings, and panels to avoid starting with “Hi, my name is; today I’m going to talk about”. The analogy that I use is every action movie starts with Action. And then they put up the Title. And then they put up the Credits. <Exactly!> And I would much prefer that you start with something provocative, intriguing, interesting — and then say who you are and what you’re going to cover. And it gets right to that point you talked about: start in the middle.

So, not only do you have to think about how you structure the event, and how you structure your content, but think about how you structure the START.

Sonal: My biggest pet peeve is when people have the guests introduce themselves <mhm>, because a moderator is literally conceding control of how to begin the conversation in the most boring way possible. Even if you tell them, do it in 30 seconds or less, it does not set the tone that you want — in fact, I very strongly believe a moderator needs to do the intro for their guests — you can get that bullet point across in like two words <yeah> instead of wasting like three minutes on it. It’s the worst use of time, to begin any conversation.

Matt: I absolutely agree.

Sonal: So on the intros, you said it’s really important to understand your audience — and one of the techniques is to understand their context or cues — what do you make up the technique of polls… especially in a parasocial community where you don’t really know everybody and you want to sort of understand. How does that fit in or not fit in? What do you think about polls and polling your audience?

Matt: So I think anything that gets your audience interacting is a good thing — rhetorical questions, questions the way they answer — polls are very useful. But polls work in a limited way; you can’t keep polling your audience.

Two rules for using polls: You have to tell people how to respond. And second, you have to comment on whatever response you get. If you just throw out a question, and people don’t know am I thinking the answer, am I raising my hand, if it’s virtual do I push on a button? — so you have to tell them how to do it; and then comment: Say, oh, that’s what I thought most of you have; or oh, I’m surprised only half of you have. That recognizes the contribution and makes people more likely to feel that it was useful and they’ll do it again.

Sonal: Great. And then, conclusions! This is one of the techniques I learned from you because I used to be very front-loaded, like only focus on, when live events, on the intro and the middle. And I’d kind of be sloppy at the end like — okay, we’re done — <laughs> I mean, I wasn’t quite that sloppy, but, you know <laughs>

Matt: Most people think, you know, if I can get the beginning down, then it’ll all follow. But the reality is it doesn’t. Most meetings and presentations end very poorly. In fact, people will just say “Uhh, I guess we’re out of time,” <yeah…> and then they’re done. That’s it.

Sonal: …Very abrupt and useless. How do you recommend people conclude?

Matt: Very concisely. I like endings that express gratitude, and then, have a quick wrap up. Quite frankly, if you define a goal up front, then the way you end is simply by stating your goal: “Thank you for your time today. I hope you’re leaving knowing this, feeling this, and likely to do this.” And then you’re done.

You know as a teacher, I see this all the time. When I signal to my students that we are done or coming close to wrapping up, they are packed up and halfway out the door before I’m done. So that’s why I like ending in a concise and clear way, and being very thoughtful about it in advance about how you want to end.

Sonal: And I would add one thing, that I’ve learned from editing written text. I don’t like it when conclusions introduce new information. <oh yeah> It’s almost like giving people a teaser that you don’t get to pull that thread. <yeah> It’s okay to allude to something coming, to say we’re going to cover this next time or, <right> stay tuned for the next event on so-and-so date; that’s fine, but I can’t stand it when people bring up a new point in the conclusion.

Matt: Totally agree. It’s all about concision in the end.

Managing speaker anxiety

Sonal: Last question. We’ve threaded through this a little bit throughout the conversation, which is how do people manage anxiety — and that’s of course a psychological question — what would your best tips and advice be (kind of universalities for) how to manage anxiety in both public speaking, written communication, etc.?

Matt: So I could spend a lot of time talking about this point. I spend a lot of my life helping people become more comfortable and confident speaking; I’ve written a book Speaking Up without Freaking Out on the topic — and it’s something that I think is so critical, because I know we miss valuable input, voices, and ideas because people are just too afraid to share them.

When it comes to managing anxiety, at the highest level it’s about doing two things: managing symptoms, and managing sources. Symptoms are the things that your body experiences: <mhm> Your hands get shaky? Does your mouth get dry? Do you get sweaty in your brow? And then it’s sources, things that actually exacerbate the anxiety; it’s: Am I worried about trying to get it right? Am I concerned that I might not achieve my goal? Is it that I’m feeling soo intensely evaluated? Those are sources.

And, with both symptoms and sources, there are things that you can do, that over time, will help you feel more comfortable and confident. It takes work; it’s not a light switch — it’s not like boom, all of a sudden, you’re not nervous. But gradually, you will feel better.

Sonal: So give us — and I agree it’s a whole longer conversation — but give us a few tips for both symptoms and sources. Some concrete things that people can just do out the door, right away.

Matt: Sure. So we’ve already talked about deep breathing. Deep breathing will slow down the fight-or-flight autonomic nervous system response that happens.

People who get shaky — that’s the adrenaline coursing through their bodies — doing big broad gestures when you begin a presentation invokes muscles, big muscles that then dissipate some of that adrenaline.

If you get sweaty, that’s because your core body temperature is going up — it’s as if you’re exercising — the same thing’s going on: your heart’s beating faster, you’re tighter, your blood vessels are more constricted, your blood pressure goes up, your temperature goes up: You can cool yourself down, simply by holding something cold in the palm of your hand; your hands are <ooh!> thermal regulators for your body, just like uh your forehead and the back of your neck are.

Sonal: Water bottle saves the day again. Water to the rescue.

Matt: It does! So those are symptomatic relief. In terms of sources, so, many of us put a lot of pressure on ourselves to do it right. I’ve been doing this kind of work for three decades now, and I’m here to tell you, there is no right way to communicate. There are better and worse ways; if you can remove the pressure to do it right, you actually free up cognitive resources to do it better.

So rather than seeing your communication as a performance where perfection is the goal, see it as a conversation where understanding and collaboration are the goal. And that takes a lot of pressure off of you.

Now it’s very easy for me to say that, and it’s harder to do; but with work and practice, you can do that.

Sonal: I have to tell you, one of the things that you’ve helped me with, as an anxiety- management technique, for big events and prep — one of the techniques you gave me is like having three keywords <mhm> as a way to kind of orient my identity before I go on stage. <right> And it is amazing how that helps me. And it’s funny, because my three words are: “energy, light, and shepherd”. <great!> And the reason is, because I’m a shepherd for the audience; energy, which goes to the point about feeling; and light, because I want people to feel enlightened — which I know sounds really mushy-gushy but, those are literally the three words that really ground that I’m collaborating with the audience: It’s not this oppositional, adversarial, dynamic.

Matt: I think those are really empowering.

Many of us are worried about a potential negative future outcome. The entrepreneurs that come to your firm are afraid they’re not going to get funding. My students are afraid that they’re not going to get a good grade. The people we coach to be better speakers are afraid they’re not going to get support for their ideas.

That fear is a future fear. And because of that it makes it worse. So if you can short-circuit that, become present-oriented, focus on the moment, you by definition won’t be as nervous. So how do you do that? Well: Do something physical before you communicate, take a walk around the block. <Yes> You can listen to a song or a playlist; you see athletes do this all the time. The one I always joke about, but works really well: Start at 100 and count backwards by some difficult number. Try right now, start at 100 and count backwards by 17s… the only way you can do that is by getting really present oriented.

Sonal: My therapist has given me a technique where, what’s the worst thing that could happen? <yeah> And we actually make it very concrete, it’s like oh, the worst thing that’s gonna happen is I run out of breath. <yes> And I’m not stopping breathing (which is what it feels like when you’re having a panic attack), and also, people are actually more friendly <right> then I think. And so all of that is extremely helpful.

And knowing, it’s not weakness — but the better you know yourself, the better you can then plan and even reroute around or address it head on. I think a lot of times what happens is people deny it, they act like it’s something they have to run away from — because when you feel anxious, you just want to run away from the feeling, you don’t want it. But it’s far worse to be surprised by it on stage <that’s right> than to lean into the fact that you’re going to have it, so prepare for it.

Matt: That’s exactly right. What I want people to take away is that — with practice, with commitment; giving yourself permission to take risks to try some of these things out — you can actually learn to be more comfortable and confident in high-stakes communication situations.

Sonal: Matt, thank you for your time today. <chuckles> I hope this leaves everyone feeling empowered to be a moderator in whatever form. And, those that are more interested should go check out boldecho.com, your book, your podcast. And, I hope this is a helpful resource. Thank you for joining the “a16z Podcast.”

Matt: Awesome.

<fadeout>

Sonal: Matt, there was so much insight per minute packed into what you just said…

Matt: <chuckles> Well thank you… 

• 

  Matt Abrahams
• 

  Sonal Chokshi is the editor in chief as well as podcast network showrunner. Prior to joining a16z 2014 to build the editorial operation, Sonal was a senior editor at WIRED, and before that in content at Xerox PARC.

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“The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they’re indistinguishable from it.” That quote from computer scientist Mark Weiser is from a 1991 paper where he outlined the vision of ubiquitous computing; in it, he also referenced “seamlessness”… We just can’t get away from textile metaphors: we catch airline “shuttles”, we “weave” through traffic, we follow comment “threads” — the metaphors are as ubiquitous and abundant and threaded throughout our lives as the textiles (and computing) all around us.

In fact, argues author and columnist Virginia Postrel, the story of textiles IS the story of technology and science (across all kinds of fields, from biology to chemistry); of commerce (as well as management, measurement, machines); but most of all, of civilization (vs. just culture) itself. That’s what her new book, The Fabric of Civilization: How Textiles Made the World is all about. But it’s really a story and history of innovation, and of human ingenuity… which is also the theme of the a16z Podcast — and of this special, inaugural book launch episode with the author in conversation with showrunner Sonal Chokshi.

The discussion both dives deep and lightly dips into a wide range of topics: fabrics, from the genetics of cotton to the supply chain of silk (including pre-Industrial Revolution factories, early payment and incentive alignment, “maestre” and notions of expertise); knowledge, from the storage and transmission of it to sharing tacit and explicit code (including manuals, notation, measures); and math as the science of patterns, origins of mathematics (including early education and getting paid for it). The touch on the NASA space program, knitting and AI, and the environmental impact of dyes. Throughout, they discuss the what and the why — the warp and weft of this episode! — of HOW innovation happens, from incremental improvements to sudden leaps, also taking a closer look at the demographics and images involved. And finally, they cover the evolution and meaning of kente cloth (as well as other patterns) in Ghana and beyond… Because the story of textiles — and of technology — is not just a story of one culture or time or place: it is a universally human story, woven from countless threads and wires.

Links & other articles mentioned in this episode:

• YouTube & Instagram from the author, featuring cited images among others
• The Computer for the 21st Century, Mark Weiser, Scientific American, 1991
• Every topological surface can be knit: a proof, Sarah-Marie Belcastro, Journal of Mathematics and the Arts, 2009
• How an AI took over the an adult knitting community, Alexis Madrigal, The Atlantic, 2018
• Portrait of a Man, Portrait of a Woman, Maarten van Heemskerck, Rijks Museum, 1529
• In Ghana, pandemic inspires new fabrics, Kent Mensah, Christian Science Monitor 2020
• Welcome to the new world civilization, Virginia Postrel, Reason, 2020

images: composite of knitting by © sarah-marie belcastro (courtesy Virginia Postrel) + magnetic core memory wires & beads, magnified 60x (photo from Virginia Postrel) — combined by Sonal Chokshi for the a16z Podcast

Show Notes

• Textiles as a metaphor for culture [2:20]
• The cultivation of cotton and how it became a worldwide industry [9:24]
• The ancient silk trade as a proto-industry [15:24] that required standard measurements [20:13], and a discussion of the critical role of the “maestra” [23:04]
• The complex mathematics of textiles [25:24] and the science of knitting [31:00]
• How information about textile creation was passed down through the generations [35:32], how we store ideas today [43:03], and a curious connection to NASA [44:26]
• The ways that dyes led to modern organic chemistry [47:27]
• Several case studies of innovation [49:57], and a discussion of traditional gender roles related to weaving [57:30]
• The story of kente cloth [1:00:25] and how textiles are a part of global culture [1:07:35]

Transcript

Sonal: Hi, everyone. Welcome to the a16z Podcast. I’m Sonal. Today we have the very first episode — for a new book, coming out November 10, by Virginia Postrel: “The Fabric of Civilization: How Textiles Made the World” — which is all about the central role of textiles in the history of technology, science, commerce, civilization itself. But it’s really a history and evolution of innovation across all kinds of fields, so there’s something for everyone in this episode.

Virginia has written several bestselling books including “The Substance of Style” — a long time favorite of mine — “The Power of Glamour,” which I excerpted when I was at Wired. She also wrote “The Future and Its Enemies” in the late 90s, and was the former editor in chief of Reason and has been a columnist for various magazines and newspapers, and currently she has a regular column in Bloomberg Opinion.

But we’re here today to talk about her soon-to-be released book. In the long discussion that follows, probably [the] longest we’ve done here, we both delve deep as well as lightly dip into a wide range of topics. The first segment covers fabrics, from the genetics of cotton to the supply chain of silk, including early machines, early management techniques, what was considered expertise. We then cover the storage and transmission of knowledge, both explicit and tacit — including artifacts and manuals to notation and measurement — and especially mathematics — touching on NASA’s space program, knitting and AI, and the environmental impact of dyes

And in the last section, we go “meta” on how innovation happens, from the zeitgeist of the times to the dynamics of codifying and sharing knowledge and the industrial enlightenment to the demographics and symbols involved. We actually go deep on the story of, evolution of, and meaning of kente cloth (and other patterns) in Ghana — to finally ending on the difference between cultures and civilizations, because the throughline that resonated most deeply with me throughout this book is that the story of textiles is one of human ingenuity — which is also what this podcast, the a16z Podcast, is all about. Please do also go ahead and rate us in your podcast app when you have a moment.

And now, let me welcome Virginia. I’m so excited to have you on. Welcome.

Virginia: Thank you.

Textiles as culture

Sonal: So, I’m not gonna ask you the obvious question, which is “why did you write this book?” First of all, I know why you wrote the book because we talked about it over many, many long dinners.

Virginia: Yeah, right. I mean, this book wouldn’t exist without you.

Sonal: One of my favorite things is you actually talk about all the metaphors we use, and you call them “heirloom metaphors” — but tell me about some of these metaphors. I mean, you basically say, “We catch airline shuttles, we weave through traffic, we follow comment threads,” like, all our listeners have done all of those things.

Virginia: Right. Well, first of all, let’s start with “heirloom.” So, heirloom is a reference to a loom. And the reason that we have this word is that a loom was a piece of capital equipment. And so it would get passed down as a valuable thing. <Sonal: Heirloom, yes> Heirloom. But some of the more fun ones are ones that we have no idea. Like, “on tenterhooks.

When people would make wool cloth, and I’ve seen this in a museum in England, after they would weave it, they would do what’s called “fulling” — which is a combination of wetting it, and pounding it, and using what’s called fuller’s earth on it — and it felted it. It made it thicker and more waterproof. And then, they would stretch the wool on these really vicious-looking little hooks — on a rectangular frame like nails sticking out — and when you see these tenterhooks, you realize, oh my goodness, that tension, that feeling of tension. I see why that metaphor came to be.

Sonal: Perfect. So, your very first opening quote in the book is, “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they’re indistinguishable from it.” And that quote is from Mark Weiser, in “A Computer for the 21st Century,” which was, like, a paper he wrote in 1991. Tell me about why you started with that quote.

Virginia: I love this quote for two reasons. The first is that it’s profoundly true. The most significant technologies are the ones that disappear. But the other is, you see what he did there?

Sonal: Well, I see what you did there.

Virginia: I don’t even think he realized what he was doing. Maybe he did. It proves my point, that this ancient, ubiquitous technology is really important. And, I want people to pay attention to it, and to pay attention to all the ingenuity that goes into the fabric that surrounds us.

Sonal: A little-known fact about that Mark Weiser quote, and I remember this when I was at PARC, and I was writing up, like, some language for [a] living museum project, and one of the things that I found fascinating is we had these quotes about how he also said that not only are the most profound technologies the ones that disappear, but that sometimes, technology should be that when you leave a pencil behind in a meeting, you never go back and feel like, “Oh no, I have to go grab that pencil.” Like, you’re fine discarding that pencil. Like, it can be discarded. And, it’s just — that is what he thought of as ubiquitous computing.

And of course, it’s the opposite with how technology has come out today in terms of, you know, we actually wanna keep our smartphones. They’re not like a pencil. But, the idea that something can become so ubiquitous as to be discarded, I love that quote also evokes that, which I don’t know if you’re aware that it did. But you yourself in the book talk about how “we suffer textile amnesia, because we enjoy textile abundance.” And I thought that was so profound.

Virginia: Yeah, that’s perfect. I didn’t know that about Mark Weiser — who also, by the way, uses, in that article, the metaphor “seamlessness,” which is another textile metaphor. And this really has mostly happened in my lifetime (I’m 60), where textiles have just become so abundant and so cheap that we really don’t think about them. I mean, obviously I’m really contrasting it to a pre-industrial period, where, to make enough thread to weave the fabric in a pair of jeans would take something like thirteen 8-hour days. And that’s the fastest spinners in the world. And that’s just making the thread, that’s not preparing the fiber to make the thread, it’s not weaving, it’s not dyeing, it’s not any of the other processes.

So, we live in this world where fabric is everywhere, and it’s really cheap — and, in fact, my friend Adam Minter has a great book called “Secondhand,” that’s in part about the secondhand-trade in clothes — among other things, also electronics. And so, we do take it for granted. We don’t think about where it comes from, and we don’t think about the labor, or the ingenuity over thousands of thousands of years.

Sonal: This is exactly where there’s parallels to technology, because you point out — I never actually even thought about this — that the agricultural revolution was as much about fiber as it was food. But the Industrial Revolution did bring all these technologies, and as you point out, like, the hours that goes into making thread, the cost reductions and everything that that then enables — that’s exactly what’s playing out with technology right now, if you think about transistors being cheap and almost so ubiquitous, as to be wasted. So abundant, as to be forgotten, which is why I love the quote you open with so, so much.

Virginia: So, driving down the same point, the other thing that I do in that intro is — Arthur C. Clarke famously said [that] any sufficiently advanced technology is indistinguishable from magic.

Sonal: Yeah, it’s one of his three laws.

Virginia: Right, exactly. And, what I say in the preface is, any sufficiently familiar technology is indistinguishable from nature. What I’m thinking about there is, we know intellectually that it’s not natural, that somebody made it, that there were machines involved, and chemistry, and all these things. But we never really think about it — so that’s what I was getting at. But there’s also — in the first chapter, which is about fiber — a very important point, I think, which is that there’s no such thing as natural fibers.

All of these — what I would call biological fibers, like cotton and wool and linen and silk — are the product of many, many small, concerted, human interventions. They’re basically genetically modified organisms. In some cases, it’s extreme (like with silk). Others like cotton, naturally, [don’t] grow in most of the major cotton-producing parts of the world because they’re above the frost line. And so, if in that natural state, it would never make bolls. It would just freeze, and that would be the end of it. But, humans gradually modified it. And there’s sort of mysteries of why they would do that.

The story of cotton

Sonal: This is actually one of my favorite stories in the book, which is, you kind of go on this genetic sleuthing mission to figure out like the origination of how we were able to turn cotton into this more reproducible type of cotton, I guess, to grossly simplify it. I’d love you to quickly tell that story.

Virginia: So, there are really two stories. One is a science story that’s about the origins of cotton, because there are around 50 cotton species around the world, and only one of them developed fiber, originally. And that was a species that geneticists called A, and it came in Africa. And then somehow — this is the great mystery — somehow it got to Mexico.

Sonal: Did it float? Did it go on a boat? Like, what happened?

Virginia: This is the mystery. So, it got to Mexico and it crossbred with a native species there that didn’t have fiber, and it produced what geneticists referred to as AD. The Mexican species was D. And this is what is known in biology as a “polyploidal” plant, which is common in the plant world. It means it has twice as many chromosomes — and so, therefore, there’s a lot more room to breed it, get more variety.

Well, I interview this cotton geneticist named Jonathan Wendell, and he says [that] when he was first studying cotton genetics, there were two theories. One was that this crossing-the-ocean thing happened back when dinosaurs roamed the earth and the continents were still stuck together. And the other was — oh, well, human beings must have brought it on boats. It was what he calls the Kon-Tiki hypothesis, after the explorer Thor Heyerdahl early in the mid-20th century. But we now know from genetic research that it was neither of those — that somehow this cotton got from Africa to Mexico after the continents split apart, but long, long before there were any human beings. It could have gotten caught up in a hurricane. It could have gotten on a piece of pumice — we don’t know, but we can use these genetic clocks to bracket roughly how long it was. So, we owe cotton to this weird, random, unusual event.

Sonal: And, by the way, so unusual that it hasn’t happened again. That was the point.

Virginia: Right, it only happened once. It’s two things that only happened once. One was that a species of cotton developed fiber, and then two was that it crossed the ocean and developed this rich genetic code that could be manipulated in various ways.

So, then people come along. And there’s old world cotton and new world cotton. In the old world, cotton was being raised in places like India that are in the tropics. But somehow we know that cotton spread with the spread of Islam — and it spread into places that were too far north for the cotton to grow. It spread in places that had frost. Why is that? And my theory is that because they wouldn’t have planted it there unless they already had cotton that would grow there — so, it must have come from places like India. And why would Indians raise cotton that could survive frost?

Sonal: It’s a tropical climate, exactly.

Virginia: It’s a tropical climate. They don’t need to. So, my theory is it might have been a commercial consideration. That essentially, if your cotton bloomed first, you could get to market earlier and get a jump on the competition. And so, over time, there would be sort of competitive pressures to get cotton to bloom earlier and earlier. And then once it bloomed early enough — like the cotton we have today — it would be good for planting farther north. Now, there is a competing theory, which is that it had something to do with controlling pests — but we don’t know. This is one of the great mysteries of cotton.

So, nowadays, most of the cotton in the world (90%) is the species that originated on the Yucatan Peninsula, which is called gossypium hirsutum. And, it didn’t survive terribly well above the frostline late into the 19th century. And so I tell the story of this not-so-savory character who discovered a cotton in Mexico and he brought it to the American South, and that could grow above the frostline.

Sonal: You know, there’s so many threads. And in fact, oh my god, I keep saying that. And I swear, it’s not a pun, Virginia. I keep saying thread and it’s actually a word I really legitimately use all the time.

Virginia: Yeah, totally, you can’t — you can’t avoid it.

Sonal: We’re just gonna notice it all the time now. So, a couple of quick notes on the story you just told about cotton. There are some details in your book about not only that Islam spread cotton, but you almost allude in that section, that maybe the adoption accelerated because of cotton. That’s one of the interesting ideas I noticed.

Virginia: It was a desire to get out from under certain landowners and sort of strike out on their own. And there were ways you could do that by raising cotton that were connected with also converting to Islam.

Sonal: So fascinating. And then the other thread, too — you talk about the British Empire in India, and people wanted Indian calico, but that the East India Company put limits on that. And then it kind of pushed other manufacturers like Italians to come up and step up to the plate. And then also — and this is so top of mind today, too — you talk about the ugly part of this history, that it’s the cotton gin, and history of slavery in the South, and even the observation that the South is not as untech-savvy as people portrayed — you know, compared to the “Yankee north.” So, I hope people pick it up and read it to find out more about this fascinating, terrifying history of all of that

Silk supply chains

What I want to focus on in this conversation — and this kind of follows how you organize your book from fiber, to thread, to cloth, to dye, to traders, to consumers — is you describe the evolution of what allowed us to mass-make cotton, and the steps into that. Let’s talk a bit about the supply chain, and vertical integration, and trade aspects. And that’s kind of a big jump — but I think it’s still a great jump to make.

Virginia: Well, one of the things that is the case in all types of textiles is that you have a long supply chain. Well, you have a lot of stages, and there’s a long delay between when you, say, raise the cotton, and when somebody gets paid for the cloth.

So, one thing that is the case throughout textile history is that you have to have a lot of innovation around working capital, and different ways of people getting paid. So, that’s one aspect. Textiles forced human beings to think, at a very early stage in civilization, about financial questions. About, how do you pay for things, where you’re providing inputs, rather than the final, finished product. Because you could raise sheep, and you shear sheep, and you spin wool, and you weave — but those things were not necessarily done in the same household <right>. In some ways, the most interesting developments around the supply chain take place in silk — for the reason that it is a luxury fiber, and also because it’s really complicated.

Sonal: So, tell us about the silk and supply chain.

Virginia: Okay. So, silk — I discovered while working on this book, a lot of people don’t know where it comes from, so let’s start there. Silk comes from silkworms (Bombyx mori), which are basically caterpillars that have been bred over many centuries, originally in China, to feed on mulberry leaves and produce cocoons. And the eggs need tending, the caterpillars need tending. They need to be fed, they need to be given sticks to build their cocoons on. And then, once they make the cocoons, they’re put into boiling water, which kills the insect inside, and keeps it from emerging as a moth and breaking — there’s a single filament that makes the cocoon.

So, once you’ve done that, you need to get that filament off the cocoons. And so, you have skilled people who know how to take filaments off of multiple cocoons, and create thread. And there’s a lot of tacit knowledge involved in that. And that’s called “reeling,” because it goes on these big reels. And then often, you — when you want to make stronger thread, you have a stage that’s called “throwing,” where you twist two threads together to make a stronger one. And then you need to get that on bobbins, and that’s just the thread.

So, one of the most amazing things that I discovered when I was working on the book, are these giant factories — 24/7, hundreds of people — that started in Italy, and had their heyday in the 16th and 17th century. So, this is before the Industrial Revolution — that was their heyday, but they started earlier — and they were organized around these very large, two-story throwing machines that were hydraulically powered. They would go down to the basement and there would be a source of water power. And these are all made of wood, they’re precision machines. It’s just amazing. There are museums throughout Northern Italy where you can see them.

So, the machines are amazing enough. But it wasn’t just the machines. They developed sophisticated management techniques. First of all, they vertically integrated. They didn’t grow the cocoons. They went from cocoon, to thread for export, and then the thread would go to Lyon, which was the silk capital in Europe. I think most of us — and, I would include myself here — think about factories, and management, as starting with the Industrial Revolution. Maybe starting with Wedgwood. And this is actually before that. First of all, they are operating 24/7, and I asked a historian who covers them, like, “What did they do for light?” And he said they had torches. And I said, “That doesn’t sound very safe.” And he said, “No, it wasn’t.”

And they developed measurement techniques, they developed standardized weights, they developed standardized tools for measuring things. One of the things they developed — which is used to this day — is the idea of measuring the fineness of thread by having a standard measure of length. And then how much the thread weighed, given that length. Which gives you some idea that ratio can tell you how fine the thread is.

I mean, when you wander into the textile world, one of the things that’s really disorienting is they have all these weird ways of measuring things, like denier. What is a denier — you know, you may have noticed that when you buy stockings. And it’s from that idea of a certain amount of weight per — for a standard measurement.

So, they developed a lot of techniques like that. And, this was proto-industry. So, then the question comes. Well, why isn’t that the Industrial Revolution? You know, why isn’t that the factory that changed? And the reason is that silk is a luxury. It’s a niche market. It can’t change the world. You don’t make sails out of silk. You don’t make sacks out of silk, you don’t clothe your armies with silk — all of these uses for other textiles don’t apply to silk. So a lot of techniques and machines were developed, and they did influence later developments, but they weren’t revolutionary in the way that transforming everyday textiles was.

Sonal: Isn’t that so fascinating, because it’s such an inversion from, kind of, revolutionary tech today. Because, in that world, you did need mass — both for the capital outlays that you described, you know, that the financing and everything required — and the mass production of things. But in today’s world, the inverse is happening, where we have, like, sort of this late-niche, kind of more long-tail-driven economy in many ways. Because of the internet, of course.

Virginia: Another way of thinking of it is, often new technologies start very expensive. I mean, who used original computers? It was big businesses. They were big, expensive pieces of capital equipment, and then they got smaller and smaller, and now we carry them around in our pockets. You sometimes have sort of luxury items as the first way into a technology because you can finance it that way, and justify [a] higher price. Now, I don’t wanna push it too far. That actually applies in terms of silk versus other things, more to the development of looms, and the drawloom, and Jacquard and his famous punch cards — the one thing that all technology people know about.

The role of the “maestra”

Sonal: So, you have this phrase, “management measure machines” — and those three words just popped out to me, because that to me is the art of technology and startups as well, or any business enterprise. But then you do point out a fourth M, which is the role of the “maestra.” Which, I didn’t know what that was. Tell us about the role of the maestra, and the — why that’s so significant in this supply chain and in this evolution from cloth to industry.

Virginia: So, the maestra (that’s the singular), is a woman with many many years of experience, at reeling — that is, taking these incredibly fine — I mean, they’re strong, but they’re, like, less than a human hair — finds off of the cocoons, and twisting them together into a single thread. And the very best could do — make thread out of just two. And keep in mind, these are natural fibers, they’re biological. They are not totally consistent. So, one of the skills is matching — the matching up these fibers so that they are as consistent as possible, because that makes the best thread. So, it’s a really, really high-skilled job.

Sonal: One might even call it pattern matching, but keep going.

Virginia: Yeah, it is pattern matching. It is a form of pattern matching, along with a lot of manual skills as well. And you learn how to do it because you spend many years as an apprentice, being the person who turns the reel that takes up the thread, while the maestra is doing this and you’re watching her. So, you might spend 15 years doing that and acquiring all of that tacit knowledge. And these women were very well paid for the time, because they were making a luxury product. But, they thought about how to supervise people, they thought about incentives. So, for example, the maestre were not paid by the amount they produced, because they wanted to ensure quality — they were paid by the day, or by the time.

Sonal: Oh, by the way, I underlined that because I thought that was such a great example of incentive alignment — like, modern management practice thinks about these very questions.

Virginia: Right, right exactly. So, they were thinking about incentive alignment.

The mathematics of textiles

Sonal: So, you mentioned the ratio — and this sort of mathematical problem-solving that was already happening with the threading that the maestra did. The thinness, the thickness, etc. You also mentioned, you know, in the book as well, you open with this idea that technology means so much more than electronics or machines, which I love. And that is exactly what the book is about, but also, to me, the theme of this book — and in fact, of the articles that you did for Aeon magazine — the one that I set you up with Ross for — is that the story of textiles is one of technology, and science.

And so, now let’s switch to talking about the math. You have this great quote, that “Spinning trains the hands, but weaving challenges the mind. Like music, it is profoundly mathematical. Weavers have to understand ratios, detect prime numbers, and calculate areas and lengths. Manipulating warps turns threads into rows, and rows into patterns, points into lines, and lines into planes. Woven cloth represents some of humanity’s earliest algorithms. It is embodied code.” I love this, Virginia. So, talk to me and tell me about the math of textiles and more of the story there.

Virginia: First of all, I have to note that in researching the book, I learned how to weave, on a hand loom.

Sonal: I did not know that. That’s so exciting.

Virginia: It challenges me, because I’m not very good in three dimensions, and you have to think in three dimensions even though you’re producing two-dimensional stuff. It is kind of complicated. One point that I make in the book is that weaving is the original binary code — by which I don’t mean Jacquard and his punch cards, that came long long after — but, it’s all up, down, on, off, one, zero. That is woven into the cloth itself.

And then also there’s this idea of math as the science of patterns. And I talk in the chapter about Andean weaving and the use of symmetry and — the people who are doing it aren’t necessarily thinking, I’m following algorithms. It’s sort of like the old thing about I’m speaking prose — but it is profoundly mathematical. And one of the really interesting speculative theories that I talk about is the idea that Greek arithmetic might have originated by the challenges of working on warp-weighted looms, because you have to know about prime numbers, especially on those types of looms, the techniques that they use.

And another thing, which I didn’t get into [in] the book, Judy Frater, who ran the place that I went to India where I learned how to do printing and dyeing — she makes the point that in creating these complex printed fabrics in India, there’s a tremendous amount of ratios because you want to get the repeats exactly right. You need to be able to think mathematically to get things to look right.

Sonal: That’s fantastic. One note in the later part of the book is, you talk about this 1976 study from Van Egmond. And it talks about “Abacus manuscripts” and books, and how he emphasizes their practicality. And the classical view of mathematics, inherited from the Greeks, was the study of abstract logic and ideal forms, whereas the Abacus books treat math as useful. Which I thought was so fascinating, because when I think of the fundamental challenge of learning and teaching mathematics — which is, like, my old world of work and research — that is the challenge, right there.

Virginia: Yeah. So, first of all these Abacus manuscripts have a very misleading name because they’re the opposite of Abacus manuscripts. In fact, they’re teaching people how to do pen-and-paper calculation — not how to use an abacus. And there’s a history, which I explain about why they’re called that.

But you had, in the early modern period, in Italy, the development of these schools, where people who were going to be merchants would go when they’re kids, to learn how to do arithmetic — sort of in between calculation and algebra. They do all these word problems, which, if you were solving them today, you would set up with variables and unknowns. And this was driven by essentially the need of textile merchants to do this new type of arithmetic, which was incorporating the Hindu/Arabic numbers and the zero, all of these things. They were moving away from using Roman numerals.

So, these are business problems. A lot of them have to do with the currency conversions — with lengths, and if you get this much money for this length, and how much would you get for this length. All of this sort of thing. It’s the cloth trade driving a kind of mathematical education. And one thing that’s interesting is that these teachers were the first Europeans to make a living, just by math <Sonal: Oh right, that’s so interesting> they would teach these schools and hire out as consultants, particularly for various construction projects, where they would be doing more sort of geometrical calculations. So, that was driven largely by the textile trade.

The science of knitting

Sonal: I love that. I can’t even tell you, on so many levels. So, knitting is such an interesting thing to me, because a lot of people describe how knitting is technology. And so, I’d love for you to say more about the science and math of knitting.

Virginia: Well, the interesting characteristics of knitting, mathematically, have to do with its three-dimensional qualities. So there’s an actual mathematical paper called “Any Topological Structure Can Be Knitted” — and there are pictures in the book of things like Klein bottles that have been knitted. From a practical, business point of view, this is very important, because we are at a technological moment where 3-D knitting — which has been around for several decades — is becoming more and more important. And it’s driven by the mathematical characteristics of knitting, the computing power, and the shift where, after millennia of being completely dominant, weaving is losing its market to knitting. Knitting is a relatively new way of making fiber. It started around 1200, whereas weaving goes back at least 9,000 years, probably more.

Sonal: And why is knitting taking over weaving? That was so surprising to me.

Virginia: So, it’s taking over weaving mostly because it’s more comfortable, because it stretches in multiple dimensions. Also, a lot of the athletic wear that drives technological innovation in the textile and apparel industries — athletic wear and outdoor wear — is knitted. A lot of it is just this drive for comfort, which has only been accelerated by the pandemic.

Sonal: You make a funny note in the book about what you’re wearing while you’re writing it — and of course I chuckled because every time I met up with you, we‘d wear, like, you know, nice clothes.

Virginia: Oh yeah, this is what I said. “Everything I’m wearing except my jeans — my underwear, shirts, sweaters, socks, even my sneakers — is made from knitted fabric.” But this incredible expansion of knitting, it feeds into the business side too, where — okay, we have this ability to do three-dimensional knitting. It has historically not been as economical as making big pieces of cloth and cutting and sewing, but it does allow us to make more to order so that your inventories are in thread rather than in finished garments or cloth. It allows more variety. There are advantages to it. And, you can knit an entire garment with no seams. Traditionally, most of the knitting that you have in your closet is pieced together, sewn together just like woven fabrics.

Sonal: I don’t know if you realize that you said that the comfort-quality of knitting is that it’s essentially seamless — and that, of course, goes back to the Mark Weiser quote — because earlier you said that the other word in his big paper was seamless.

Virginia: It can be seamless — and that is, in fact, what SHIMA SEIKI, who is the company that first innovated this three-dimensional knitting, although they’re not the only company doing it now — they sold it as “the seamless garment.”

Sonal: Got it. And by the way, because our listeners are very curious — and this is what I love about everyone — the paper, if they’re interested, that you referenced, is “Every Topological Surface Can Be Knit: A Proof.” And the journal, the full name is “The Journal of Mathematics and the Arts,” and it’s from June 2009, and the authors are Sarah-Marie Belcastro and Carolyn Yackel. And oh, it was also in “Math Horizons” in November 2006. So, those are the sources. Oh, by the way, on the 3-D notion of knitting, have you heard of this project called SkyKnit — which is basically a neural net that generates new patterns for knitting; have you read about this?

Virginia: I haven’t, no.

Sonal: Oh, you would love this — I’m gonna send you an article about it. It’s on Janelle Shane’s work, and she, you know, does a lot of creative AI-type experiments. But, she basically took the knitting forum Ravelry, and she trained a neural network on a series of 500 knitting instructions, and then she generated new instructions. And that the Ravelry community has actually attempted to knit. It’s just one of my favorite stories of online things.

Virginia: I’m not surprised it exists.

Sonal: One of my other favorite stories is, and this is an Instagrammer I follow who’s Shannon Downey, because I’m super into all the fabric textile artists — and she basically had heard about this unfinished quilt, and that, you know, they found a box full of fabric and discovered it was a massive quilting project that was just begun, and it was mapped out. And so, so many people on the internet joined up to quilt this quilt — and it’s called Rita’s quilt — and they just completed it.

Passing down techniques

In that case, it’s more technology for collaboration than for creation, but it’s a segue to my next question, which is, let’s talk about the artifacts — and really, this theme of record-keeping and the transmission of textile knowledge. That’s, like, another really interesting theme in your book, and so I’d love to probe on that a bit.

Virginia: So, traditionally, textile knowledge is transferred through apprenticeship — either formal (like, there were these apprenticeships in Europe, they were highly regulated and such) or informal (within families, or villages or whatever). But you have a shift in the early modern period, in Europe — there’s more documentation in China earlier, but you have a shift in the early modern period, where there’s this idea in the air that it’s a good thing to share knowledge, as opposed to keeping it secret.

Sonal: Yeah, it’s in the zeitgeist.

Virginia: It’s in the zeitgeist, and it’s very important in the history of technology and science, and the touchstone example is Diderot’s Encyclopedia and — which, in fact, includes a lot of documentation of how various looms work, and [the] source of some of the illustrations in my book. But in my research, I found a couple of less well-known examples. One is the first dye manual, which is called Applicto. Iit seems to come from some word for envelope. And essentially you had an Italian guy, who went around over a long period of time, prizing various recipes out of dyers, and he published the first dye manual — sort of like a recipe book. It tells you [to] use this much of this or this much of that — so that’s one.

And then the other one — which is very interesting, because it reminds us how important notation is — is the first weaving manual. Nowadays, if you go online, Ravelry has these weaving “drafts,” which is a form of notation that tells weavers how to set up a loom, which threads to raise, in what order to create a pattern. Well, somebody had to invent that notation. And, probably it was invented for personal use, and it was secret. But there was a guy who was very frustrated that they would have to import textiles to get the good patterns, and he was, like, “No, we can do just as well. The problem is people are too secretive about the knowledge, and they aren’t teaching other people.” So, he put together a manual that was the first weaving manual — and there is a site called handweaving.net, where they’ve turned a lot of these instructions into instructions for today.

But this is a theme throughout the book, that there is a tension — because making textiles requires a lot of tacit knowledge, a lot of the kind of knowledge that’s very difficult to write down and articulate — we talk about the maestre reeling cocoons, I mean, it would be very hard to convey that to another person. But there are other types of knowledge that can be codified, and so there is this move toward codification. That really helps jumpstart a lot of technological progress, because you’re able to have people from outside fields to understand what’s going on in other fields, and maybe apply that in other ways. Or you just get people better able to understand the state of the art and technology, and perhaps innovate on top of that. And then the notation that develops for organic chemistry is really important also. I don’t use it in the book, but I do talk about the development of organic chemistry out of the dye industry.

Sonal: It was funny, because when all my friends in college were taking O-Chem — you know, like, shorthand for organic chemistry, I remember just kind of enjoying the notational aspects of it. So, basically the notation, the manual encoding helped make the notation more standardized and common as well, and then there was a zeitgeist move of making the knowledge and artistry of weaving more public. You quote Ziegler, who did that original weaving handbook, that “I hold that it would be possible to produce many more artists in all branches of technology, if only there were no shortage of publishers.” Which I loved, because what it told me is that that manual was basically the printing-press moment of the textiles industry.

Virginia: Exactly, and this resonates so much with today in terms of all the riches we have available online — which includes not only printed stuff but videos, which are incredibly important in people learning how to do things — but it also goes to the tensions that we have around copyright. You know, the history of textiles is full of industrial espionage. I have a few examples in the book, but people were constantly trying to keep stuff secret, and other people were trying to get it, because it’s very valuable.

Sonal: I’m glad that you share that in the book. And I don’t know if you know this, but I actually interviewed Joel Mokyr on this podcast a number of years ago. Yeah, the podcast is called “Knowledge builds technology and technology builds knowledge,” and it was about the Republic of Letters, and that — its role in what he calls “the Industrial Enlightenment.”

Virginia: Oh, and I use that term a lot. Joel’s work is really great, and I’m very influenced in my understanding of this zeitgeist moment by his work. Because you have this moment where you have craftsmen, intersecting with codification, intersecting with scientists — and, each feeds the other in ways that were not previously happening. And that really allows a certain kind of scientific and technological takeoff — particularly technological takeoff — because it gives people where to look for new advances — and also just shares things across borders, shares things across class. You get to tap a lot more knowledge.

Modern-day idea storage

Sonal: You know, one of the things that fascinated me on the history of the knowledge transmission of patterns and record keeping is the idea of storing things in song, and in the cloth itself, and you talk about that in the book, and I thought that was so beautiful, yet so ephemeral. And so, one thing I wonder when you talked about how it’s really important that we now have videos for so many — do you worry about the artifacts of the future, and how future historians might come back into our time and look at some of this?

I don’t know if you have any thoughts on this, but I was intrigued by this idea, simply because you open the book talking about how over half of the tablets that were uncovered on Crete, when they were doing excavation of the ancient minotaur labyrinth area, they were textile tablets. And will future generations have those concrete artifacts? I’m very seduced by that idea of ephemerality and permanence.

Virginia: Well, I am a great supporter of the internet archive. I think it’s an incredibly important organization.

Sonal: We actually had Brewster Kahle on the podcast.

Virginia: It’s incredibly important. So, do we lose this kind of traditional knowledge? One thing that’s interesting is, since about the 1970s, there’s been a tremendous appreciation and recording of people’s traditional knowledge about traditional crafts. And what interests me the most about that is not just that it happened — and there’s some really interesting stories about how it happened in different places, particularly Peru — but also that when you have a living textile tradition, it’s very different from what people in developed countries sort of think it ought to be. It’s not static. It’s not producing the same thing that the ancestors produced. It’s subject to all the kinds of changes that affect all of us.

And you have people adopting and adapting their traditional techniques for the world they actually live in, which is the 21st century world. And I tell some stories from Guatemala, and I’ve also written about some stories from Chiapas in Mexico that [were] not in the book. It’s in an article, it’s online — all these things are on my website also.

Sonal: Great, I’ll link to some in the show notes as well. One last question on this thread. I do think it was interesting that you talked about how programmers first wrote code using punch cards, and this idea of how NASA produced “rope memory.”

Virginia: So there are two intersections between early computer memories and weaving. The first, which was the dominant form of computer memory, before the development of silicon chips, was core memory. And essentially, what you had was weaving. You had threads, copper wires, going horizontally, intersecting with ones going vertically, just like cloth. And then at each intersection, you would have a ferrite donut — they were tiny, tiny, tiny — and, by sending a certain electrical signal down the right thread, so to speak, you could flip the core from positive to negative, and it would be a one or a zero. And that’s how essentially RAM was done.

Sonal: RAM, as in Random Access Memory, right.

Virginia: Random Access Memory. That was before silicon — that was, like, from the 50s to the 70s. That was how computer memory for most purposes was done. In the Apollo program, they needed to do essentially, ROM — Read Only Memory — in a very stable and very compact and lightweight form. Relatively lightweight. And essentially what they did was called “rope memory.” Instead of having the intersection of the warp and weft, so to speak, with a core that could flip, they would just — they would write the program on punch cards, they would debug it, they would get it all working, and then they would code it into wires that either went over or under, depending on if it was a one or a zero. And so, it was literally software you could hold in your hand. It was this physical embodiment — I mean, I guess the punch cards are a physical embodiment too, but this was much more compact. And that was used in the Apollo program.

Sonal: What is rope memory but the code that runs the space program? It’s a perfect thread to end this section of how to store knowledge, transmit knowledge. I also love — throughout your book, there’s actually a few semiconductor analogies smattered about. I finished Andy Grove’s “High Output Management” book recently for the first time. People have been talking about it for years, and I finally read it this year, and I was so struck, though, by how much semiconductor manufacturing actually applies to creative work, too. And it’s just so fascinating, when you see all the analogies from semiconductors to textiles as well, and in some of these creative crafts as well. So, I just think that’s fantastic.

Dyes and modern chemistry

We don’t have time to go into the dye chapter. You already pointed out how the dye industry was one of the earliest applications of some of the organic chemistry notation. But the one interesting fact that I wanted to quickly flick on in this chapter, which was really counterintuitive and quite surprising to me — especially because Marc, Andy McAfee, and I did a podcast on his book “More From Less” is that — people have this assumption that the past was more environmentally conscious than now. And in fact that’s not true.

Virginia: Yeah. One thing that people don’t realize when — particularly when they talk about “natural dyes,” is — they’re very messy, very smelly. Indigo particularly, which is a wonderful, wonderful dye. But it stinks. And it’s not even the stinkiest dye that I talk about in the book. So there’s that. And then the other thing is dyeing uses a lot of water. And I was really struck by this when I took dyeing classes in India, in Adipur, which is in Kutch, which is a desert area, so —

Sonal: It’s in Gujarat, yeah.

Virginia: Yeah, it is <It’s my home region>, so it’s not like water is plentiful. But I came from California, Southern California, and we were in a drought, and so I was hyper conscious of the use of water — and they’re just using tons of water, throwing the water in the yard, throwing the water in the yard. And this was not particularly polluting, but it really struck me — the combination of the history of dyes as this kind of thing that you wanted outside town — people didn’t want to be next to dyers — with this use of water, even today.

And I think when we think that in the olden times, everything was environmentally benign, that’s partly a matter of scale. When you have large-scale production, you’re gonna use more of everything. But it’s really the very modern plants — and I talk about a dye house I visited in LA — that are using really precise measurements. And being — looking for innovative ways to save on water, save on energy, save on time, save on labor, just the — all the economic factors, as well as the environmental factors — that’s where you really get the innovation that becomes environmentally more benign. It’s not by going back to the past.

Sonal: It’s sort of this yearning for a past that never was.

Virginia: Yes, exactly.

Textile innovation over time

Sonal: And the technology is the way forward, and if we do want to do a lot of these great things. So, now some kind of meta theme questions around innovation. So, you actually have a chapter at the end called “innovation,” which made me chuckle because, actually, your entire book is about innovation.

Virginia: The whole book is about innovation. Actually, my editor — I had a great editor, but her idea, we were talking about this last chapter. She said, “Why don’t you call it ‘innovators’” — which is parallel to traders and consumers — but there are innovators in every chapter.

Sonal: It’s not orthogonal, it’s both the warp, the weft. So, let’s kind of come full circle. You say that “to weave is to devise, to invent, to contrive function and beauty from the simplest of elements.” And I thought that was profound, because I think that is exactly what innovation is. It’s transforming something into something else, or taking building blocks like Legos, or words, or code, and turning them into something else. Or Chris Dixon, in his post, described how code “is the encoding of human thought” — and in your book, you actually have a line in the the chapter on fiber, where you talk about Hardy’s work, and how when you twist things into forming ropes and knots, that it “demonstrates an infinite use of infinite means and requires a cognitive complexity, similar to that required by human language.”

So, I want to ask you about more of the how versus the what — which is, you say that this is a story of innovation, things that are both famous and forgotten. Incremental improvements and sudden leaps, repeated inventions, and once-forever discoveries. So, I’m gonna have you do — very lightning-round style — give me an example, in the meta story of innovation, of an example of something famous, even still famous today.

Virginia: The invention of nylon by Wallace Carothers. Nylon is the first polymer fiber, and in fact I — Carothers is the person who figured out what polymers, which also come in protein forms — he figured out that these really were these giant molecules and he demonstrated it. But nylon is the first synthetic fiber, and the first polymer. And I tell the story. The inventor is less famous, but nylon is famous.

Sonal: And give me an example of a forgotten — I mean I guess your whole book has a lot of these, but a forgotten one.

Virginia: So, the person that I would really love to know who invented it is, somewhere in China, there was probably a woman [who] was involved in silk production who figured out what we now know as the belt drop off. And the particular application was what is called, in spinning, a spindle wheel, as opposed to spinning wheel. She figured out how to take a big wheel, and put a thread or a belt onto a little wheel — and you could turn the little wheel many times, by turning the big wheel. Basically what this person figured out was you could turn that not-load-bearing wheel on its side, and attach it to another wheel, and then you could have it turn faster. And this is incredibly important in the history of textiles, and it’s incredibly important in the history of machines — and we have no idea who did it. Although we have a reasonable idea of what kind of person it was and where it was.

Sonal: Okay, so that’s “famous” and “forgotten.” So, now let’s do “incremental” and “big leaps.” So, give me an example of an incremental improvement that was important in advancing the fabric of civilization.

Virginia: All the incremental improvements in the quality of cotton for example, in not only breeding cotton that could grow above the frost line, but making it more disease resistant, making it — longer fibers, more per boll. All of those kinds of incremental improvements.

Sonal: Now, give me an example of an opposite of incremental improvement — a “sudden leap.”

Virginia: The invention of aniline dyes, synthetic dyes, which set off essentially organic chemistry.

Sonal: Say a teeny bit more on that one.

Virginia: Yeah. So, there was a 19-year-old student called William Perkin, who was fooling around — he was trying to actually synthesize something that would be like quinine, a malaria drug. And he didn’t get what he wanted, but he noticed that this precipitate in his beaker was kind of purple-y, and he tried dyeing silk with it, and it worked really great. And then he said, hey this is a business — and like many many entrepreneurs before have said, if he’d known what was going to be involved in taking his bright idea and turning it into something that could actually be a company, he might never have done it — but he did. Because they had to invent all kinds of machinery, and ways of producing large chemical sources, stuff like that. <I love that> And there’s actually a whole book about that called “Mauve,” but I tell the essentials of this story.

Sonal: That’s a story that I remember learning from Xerox PARC, which is, when you have material-science type innovations — like, the piece, the component is one tiny, tiny piece of the puzzle — it’s actually the broader ecosystem that you build around it that’s actually the true innovation. And I was on the frontlines of watching a lot of the architecture and orchestration and attempts at that.

Virginia: And this is also why — I mean, a lot of textile innovation falls under material science today. And this is why people publish about some cool new textile idea — and it never sees the light of day. Because it’s not viable, either as a business, or in some cases just as an actual textile. And I tell some stories about the 20s and 30s, things that people were fooling around with. Because rayon was around, and they had this idea that you could take natural materials and transform them — because rayon is made basically from wood — and turn them into fibers. And so, they were trying, like, everything. Eggs, and milk. And I talk about the milk fibers, which were very big in Italy, very backed by Mussolini’s government and stuff, and why they didn’t maintain themselves after the war.

Sonal: Well, that goes to the next lightning-round one, which is repeated inventions. Because today, there is a resurgence in kind of wood-based and, like, bamboo-based fibers as alternatives to cotton, for instance, and other fibers. So, what would you put on your list of a repeated invention — something that was invented over and over again many times throughout the history of textiles?

Virginia: Well, the simplest one is the drop spindle, which is essentially a weight with a hole in it, with a stick. And the weight maintains the angular momentum and lets you draw out fiber and spin it. And it was invented in slightly different forms, all around the world.

Sonal: And now, what is an example of a — not a once in a lifetime, but once in forever kind of discovery. And it doesn’t have to be invented by human ingenuity, it could be discovered by human ingenuity.

Virginia: I mean, certainly we talked about the idea that cotton only happened once, but that’s not really human ingenuity.

Sonal: That’s why I was giving you an out, actually, because they did discover…

Virginia: Well, I actually used my “only once,” which was the spindle wheel. <Right> Which you would think with all this spinning all around the world, you would think that people all around the world would have figured out ways to do it faster, but it actually only happened once. It happened in China, and then it spread from there.

Gender roles in portraits

Sonal: It’s interesting that you speculate that it was likely a woman. The thing that actually struck me personally, is this idea of the role of women and taking back the power of, sort of, women as some of the original coders or original scientists. A lot of critics today tend to emphasize that there’s this implied domesticity and subordination of the era’s images of spinning women, as in doing weaving. And you talk about how that may not necessarily be the case.

Virginia: Right. In the chapter on spinning, I start with a paired set of portraits that are in the Reich’s Museum, in Amsterdam. And it’s a husband and wife. The woman is spinning, and the man is a businessman, he’s got his account books, he’s got some money — symbols of their trade. And these are actual, real people. We have a pretty good idea who they were, they’re not types — but these could also be the iconographic symbols of industry and commerce. Before smokestacks became the images of industry in the, say, 19th century, <right> the symbol that people used to symbolize industry was a woman spinning. Because this was the epitome of sort of productive labor that’s making something, as opposed to commerce, which is trading something.

And so, you would have these very common images — and that’s because it was an incredibly important activity. Cloth consumes enormous amounts of thread, and there was never enough thread. That is why, in fact, it made such a big difference when you have the Industrial Revolution. Why does it start with thread? Because thread is incredibly in short supply always, and it’s this input into all these different kinds of uses of cloth. Everything from sacks to sails for the British navy, to all of these kinds of things. Really important. And so, women’s work — it was domestic, but then a lot of men’s work was domestic, too, and it’s not a symbol of subordination in these portraits. It is a symbol of equality. And in fact, if you look at the construction of the portrait, in sort of a deep art-analytical sense, like, their hands are in exactly the same positions. It’s commerce and industry as equally important.

Sonal: And by the hands, you mean that there was a certain curl. You describe this in the book…

Virginia: Yeah, exactly, yeah.

Sonal: …that shows that it wasn’t just someone posing, it was the precision of an actual weaver who knew the art.

Virginia: She is actually positioned like she knows the art. And then his hands are more artificially posed, with the coin and with the book and stuff — but they mirror the positions of her hands.

The unique case of kente cloth

Sonal: So then, the last tension is that you open the book talking about how this is so ubiquitous across cultures. And in fact, when you describe the words that we use — you talk about French words, like métier. You talk about non-European words, like in the Mayan language — the terms for weaving designs and hieroglyphics both use the same root. In Sanskrit, the word for sutra, which now refers to, like, a religious scripture, it originally denotes a string or a thread. You talk about the word tantra, which is tied to tantrum or warp or loom. The Chinese word zǔzhī, meaning organization or arrange, which is also the word for weave. And chéngjiù, which means achievement or result, which originally meant twisting fibers together. So basically, you really outline how this is so ubiquitous throughout multiple cultures.

So, now I’m gonna just have you tell me one last story, which is the story of kente cloth. I think what’s really fascinating to me about the kente cloth chapter is my mom [was] born and raised in Uganda — which is not originally where kente cloth is from, right — my whole entire family’s from Uganda. <Oh right> So, I grew up…

Virginia: Did they come to the U.S. after they got kicked out…

Sonal: So, it was the Idi Amin. Exactly.

Virginia: I remember when — I remember when that happened, yeah.

Sonal: It was — it was before I was born. My grandparents, her parents got relocated to the U.K. and my aunt went to Sweden. But, I grew up surrounded by African fabrics. And then we have this political moment, where people are using kente cloth as a symbol. The thing that I wanna focus on — and especially, by the way, because it also to me tied together your other books, and that includes both “Glamour” and particularly “The Substance of Style,” where you talk about…

Virginia: It was very resonant on that one, yeah.

Sonal: It’s very resonant on that one, because you talk about how fashion is totemic.

Virginia: So, West African weavers have for a long, long time, made cloths that are called “strip cloths” — which is that they weave 4-6” wide strips, which are then sewn together into a complete cloth that’s then wrapped around the body in various ways. And this is ancient. And in fact, I talk in the book about how strip cloths were even used as currency.

Sonal: Cloths as coin, that was so cool.

Virginia: Kente cloth is actually fairly recent — dates back just to the 18th century, probably, 19th century. We don’t know exactly when it originated. And in Ghana, there’s a great contestation between two ethnic groups — the Asante and the Ewe — about who invented it, because it’s the national cloth and source of great pride. And I talk about the research that’s been done that suggests that its distinctive weave pattern was probably developed by the Ewe, but quickly adopted by the Asante, who then put on their preference for bright colors, which is very much what we associate with kente cloth.

And what makes kente cloth distinct is not the patterns, although they are distinct and interesting — it’s the way it’s woven. It is woven in an alternating sort of squares of warp-faced and weft-faced weaving, so that it’s something — you could not produce a total kente cloth on an industrial loom. It has to be woven in these strips, and those strips have to be put together, and there’s a special loom that was invented to make this kind of pattern.

Kente cloth is very interesting technologically, or artistically, because it requires a great deal of forethought and planning. So, that’s interesting. Now, what makes it particularly interesting — and, of course, resonant in the U.S. — is how it went from being a kind of cloth for particularly Asante aristocrats, to then when Ghana became independent and you had the sort of pan-African idea, it became a symbol of sort of Africa in general, the African diaspora. Muhammad Ali went to Ghana and he wore kente cloth, it was a big deal. The president of Ghana was photographed in “Life” magazine shaking hands with Dwight Eisenhower on a state visit to the U.S., and he was wearing kente cloth. And in moving from Africa to the United States, its meaning and even its physical characteristics changed. First of all, the Asante elite were not related to the ancestors of African Americans. Their ancestors were the ones who sold into slavery. But textiles mean what people want them to mean, how people use them, and their meanings evolve.

And so, what you had was — first of all, you had this transformation in the 1960s of kente cloth from being a symbol of a tribal elite and a national elite, to being a symbol of African pride and the African diaspora. And then you started to have people who were famous — like W.E.B. Du Bois, they would put a little kente cloth in their stoles that they wore with their academic robes. And then, at Westchester University of Pennsylvania, students adopted, as black students, to symbolize their pride in their academic achievements and their racial identity. And they started wearing not a whole piece of kente cloth, but essentially something that amounted to one of the strips that it was made from. And this spread to becoming a widespread custom, particularly at graduations. And this is what a living textile tradition does. Because people want many different things out of textiles, and one of them is an expression of their identity, and also who they want to be, who they aspire to be.

And then you have kente cloth also go from being always a woven pattern, into being frequently a printed pattern for all kinds of things — where it takes on this, kind of, new meaning and new purpose. So, kente cloth has had many different incarnations, and it has different incarnations in Ghana. I mean, the top weavers of it make new patterns all the time.

Sonal: There’s a Ghanaian fabric maker that created a fabric featuring the Ghanaian president — because he made lots of frequent televised speech appearances during the pandemic — printed spectacles, you know, floating against, like, the swirling red, white, and green background, which I thought was fabulous. And it even had symbols like padlocks for signifying lockdowns, and plane propellers for the borders that are closed. It just goes to show how culture evolves so quickly, in so many ways, and people find meaning. I’ve also been fascinated growing up, you know, going to India, that patterns — the history of patterns, like, ikat. Like, it’s part…

Virginia: Oh, I love ikat.

Sonal: I do too. And it’s widely used in Turkey. But then some of the techniques are, you know, rooted in shibori in Japan. But shibori itself was something…

Virginia: They have their own kind in Guatemala.

Textiles and world culture

Sonal: Oh, I love that. But the other thing that I think is really beautiful about this, is it’s not only a story of things being reinvented or invented multiple times all across the world in different ways — but it’s also a story of borrowing, and reborrowing, and building on — because you know, shibori started in India and it went back to Japan, and it went back to China. I mean, it’s just like all over the place. I just got that all messed up, but the technique just kind of comes back. And last time I went to India, it was totally in vogue to buy, like, salwars that had, like, modern shibori in cool new colors like neon. Which I think is so cool. Like, this is the story of culture and technology. But you also talk about how this is actually not about culture, but about civilization, and how there’s actually a difference between the two.

Virginia: You can have multiple cultures within a civilization, and you can have a continuous civilization where the cultures change. And I’m not using it in a, like, “civilization good, barbarism bad” kind of way. I’m using it to describe, essentially, a survival technology that humans have as a way of transmitting ways of protecting themselves against [a] hostile world and hostile people.

And then the second idea of it is that it’s cumulative. It builds, and that cumulative nature can be broken, or it can continue. The example I use is, think about Western Europe in 1980. And think about Western Europe, or as it was known at the time, Christendom, in 1480. That is a continuous civilization, but it culturally couldn’t have changed more. The politics were different. The people’s understanding of the natural world was different. How they dressed was different. How they spoke was different. Every aspect that constitutes culture had significantly changed, but this cumulative civilization had continued. And what I’ve argued in a new article out in “Reason,” where I develop the idea about civilization in the current context, is that some time in the recent past, we — for the first time ever — developed a world civilization. We had a coming together of east and west into a single civilization that hadn’t been the case ever in human history before.

So, when I talk about the fabric of civilization, I’m talking about the continuous building up of knowledge, and techniques, and technology that helps knit together — to use a textile metaphor — different civilizations.

Sonal: So, this is a great note to end on, because while you talk about how this book is about the fabric of civilization — which is cumulative, it has layers, it’s about survival, you know, cloth protects us — what you end the book on in your afterward is this beautiful quote, which is that “This heritage does not belong to a single nation, race, or culture. Or to a single time or place. The story of textiles is not a male story or a female story. Not a European, African, Asian, or American story. It is all of these — cumulative and shared, a human story, a tapestry woven from countless brilliant threads.” And that is the essence of this book, “The Fabric of Civilization: How Textiles Made the World.” By Virginia Postrel. Thank you for joining this episode of the a16z Podcast.

Virginia: Thank you.

• 

  Virginia Postrel
• 

  Sonal Chokshi is the editor in chief as well as podcast network showrunner. Prior to joining a16z 2014 to build the editorial operation, Sonal was a senior editor at WIRED, and before that in content at Xerox PARC.

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Data, data, data — it’s long been a buzzword in the industry, whether big data, streaming data, data analytics, data science, even AI & machine learning — but data alone is not enough: it takes an entire system of tools and technology to extract value from data.

A multibillion dollar industry has emerged around data tools and technologies. And with so much excitement and innovation in the space: how exactly do all these tools fit together?

This podcast — a hallway style conversation between Ali Ghodsi, CEO and founder of Databricks, and a16z general partner Martin Casado — explores the evolution of data architectures, including some quick history, where they’re going, and a surprising use case for streaming data, as well as Ali’s take on how he’d architect the picks and shovels that handle data end-to-end today.

Show Notes

• The history of data storage architecture, from data warehouses to data lakes [1:20]
• Whether analytics and AI/ML should be thought of as two separate markets [3:35], and a discussion of using SQL to accommodate AI/ML [5:28]
• Use cases for real-time and streaming data, and a discussion of latency [10:33]
• Best practices for designing a modern data stack [13:17] and predictions for the future of data architecture [20:05]

Transcript

A brief history of data architectures

Ali: It kind of started in the ’80s. Business leaders were flying blind, not knowing how the business was doing, waiting for finance to close the books. This data warehousing paradigm came about where they said, “Look, we have all this data in these operational data systems. Why don’t we just get all that data, take it out of all these systems, transform it into a central place, let’s call it a data warehouse, and then we can get business intelligence on that data?”

And it was just a major transformation because now you could have dashboards. You could know how your product was selling by region, by SKU, by geography. That has created at least $20 billion market that has been around for quite a few decades now.

But about 10 years ago, this technology started seeing some challenges. One, more and more data types, like video and audio, started coming about, and there’s no way you can store any of that in data warehouses.

Second, they were on-prem big boxes that you had to buy. And they coupled storage and compute, so it became really expensive to scale them up and down.

And third, people wanted to do more and more machine learning and AI on these data sets. They saw that we can ask future-looking questions. “Which of my customers are going to churn? Which of my products are going to sell? Which campaigns should I be offering to who?”

The data lake came about 10 years ago. And the idea was, “Here’s really cheap storage, dump all your data here, and you can get all those insights. And it turns out, just dumping all your data in a central location, it’s hard to make sense out of that data that’s sitting there. As a result, what people are doing now is they’re taking subsets of that data, moving them into classic data warehouses in the cloud.

So, we’ve ended up with an architectural mess that’s inferior to what we had in the ’80s, where we have data in two places, in the data lake and in the data warehouse, where the staleness and the recency is not great.

In the last two to three years, there’s some really interesting technological breakthroughs that are enabling a new kind of design pattern. We refer to it as the lakehouse. And the idea is: what if you could actually do BI directly on your data lake? And what if you could do your reporting directly on your data lake, and you could do your data science and your machine learning straight up on the data lake?

Analytics & AI/ML: one market or two?

Martin: I would love to tease apart a few things that have led us here. There’s very clearly a large existing data warehouse market around BI and analytics, typified by people using SQL on structured data.

It seems like the ML and AI use case is a little bit different than the analytics use case. The analytics use case is normally human beings looking at dashboards and making decisions, whereas in the ML/AI use case, you’re creating these models and those models are actually put into production and are part of the product. They’re doing pricing, they’re doing fraud detection, they’re doing underwriting, etc.

The analytics market is an existing buying behavior and an existing customer. ML/AI is an emerging market. And so the core question is: are we actually seeing the emergence of multiple markets or is this one market?

Ali: There are big similarities, and there are big differences. Let’s start with the similarities. Roughly the same data is needed for both. There’s no doubt that, when it comes to AI and machine learning, a lot of the secret sauce to getting really great results or predictions comes from augmenting your data with additional metadata that you have.

In some sense, we have the same data, and you’re asking analytical questions. The only difference is one is backward-looking, one is future-looking. But other than that, you want to do the same kinds of things with the data. You want to prepare it. You want to have it so that you can make sense of it. If you have structural problems with your data, that also causes problems for machine learning.

The differences today are that it’s a line of business that’s typically doing AI and data science or hardcore R&D. Whereas data warehousing and BI oftentimes sit in IT. Users of the data warehouse and the BI tools are data analysts and business analysts. In the case of machine learning, we have data scientists and machine learning engineers. So, the personas are different and sit in a different place in the organization. Those people have different backgrounds, and they have different requirements for the products they’re using today.

But can’t we just use SQL?

Martin: If you talk to some folks that come from the traditional analyst side, they’ll say, “AI and ML is cool, but if you really look at what they’re doing, they’re just doing simple regressions. Why don’t we just use the traditional model of data warehouses with SQL, and then we’ll just extend SQL to do basic regressions, and we’ll cover 99% of the use cases?”

Ali: Yeah, that’s interesting that you ask because we actually tried that at UC Berkeley. There was a research project that looked at: Is there a way we can take an existing relational model and augment it with machine learning?

And after five years, they realized that it’s actually really hard to bolt machine learning and data science on top of these systems. The reason is a little bit technical — it just has to do with the fact that these are iterative, recursive algorithms that continue improving the statistical measure until it reaches a certain threshold and then they stop. That’s hard to implement on top of data warehousing.

If you look at the papers that were published out of that project, the conclusion was we have to really hack it hard, and it’s not going to be pretty. If you’re thinking of the relational Codd model with SQL on top of it, it’s not sufficient for doing things like deep learning and so on.

Martin: Is the same statement true about going from something architected for AI and ML and then having it support more of a traditional analyst relational model?

Ali: So, interestingly, I think the answer is no, because there is now a widespread data science API that has emerged as the lingua franca for the data scientists: data frames.

A data frame essentially is a way where you can take your data and turn it into tables and start doing queries on it. That sounds a lot like SQL, but it’s not, because it’s actually built with programing language support so you can do that in programming languages, like Python or R, which enables you to do data science.

So, now your data is in tables, and it turns out you can now also build SQL on top of data frames. You can get a marriage between the world of data science and machine learning and the world of BI and data analytics, using data frames.

Martin: I get what you’re saying about the data warehouse, but there’s a lot more than just the data sitting in the data warehouse. You still have this entire world of data and SQL and ETL. Is there a dissonance there or do they stay two worlds? What happens?

Ali: Every enterprise we talk to, they have the majority of their data in the data lake today, and a subset of it goes into the data warehouse.

There’s a two-step ETL that they do. The first ETL step is getting into the data lake, and then there’s a second ETL step that they use to move it to the warehouse. So, organizations are paying a hefty price for this architectural redundancy.

But the question is: do you really need two copies of it? And do you really have to maintain those two copies and keep them in sync? Are you going to have a world in which you have all your data in the data lake and then you do your machine learning and data science on it, and then subsets of it move again into a data warehouse, where you clean it up and put it in that structured form so you can do SQL and BI, or can we do it all in one place?

Martin: Let’s actually ask that specific question. Because even though the AI-ML is a large market with a lot of value, there’s a ton of existing workflow around BI.

You’ve got all the dashboarding and tools that are based on SQL for data warehouses, but then you also have folks that want to interact with the data very quickly and will use something like ClickHouse or Druid in order to do that in OLAP. OLAP stands for online analytical processing and is effectively a fast interface that supports fast queries. Then you’ve got more traditional batch processing, which normally folks have thought about Spark. What you’re saying is that you can combine all of these things in the same data lake, including OLAP query loads?

Ali: Yes, I actually think you can get all the way there. The data lakes are a broad source. Big, large, cheap storage, but kind of data swamps.

It turns out there are some recent technological breakthroughs that show you how you can basically turn them into a structured relational storage system. The way you do that is you build transactionality into these data lakes.

Once you have that, you can now start adding things, like schemas, on top of them. Once you add schemas on top of them, you can add quality metrics. And once you have that, you can start reasoning about your data as structured data in tables instead of data that’s just files.

Martin: I get putting structure on top of a blob store, but you still need a query later, right? Building a query engine that’s super-fast that can respond to analytical queries, there’s entire companies that do that.

Ali: Yeah, so it turns out there’s two APIs you need. One is the data frame API. That’ll enable all the data science and machine learning. Then you can build a SQL layer on top of it, and there’s nothing that really gets in the way of making this as performant as the state-of-the-art, fastest MPP engines out there. You can apply the same tricks now because you’re actually dealing with structured data.

The real use case for streaming

Martin: It feels like, especially in data, there’s always kind of the trend du jour that everybody’s excited about, but they’re not ever really sure if the market’s real or not. People have been saying this a lot for real-time and streaming use cases.

It’s very clear that people want to process data at different times and speeds. Batch, we know, is a very large market, where you’ve got a bunch of data, you want to do a whole bunch of processing, and then it’s stored somewhere else and you do some queries.

More and more people are talking about streaming analytics, where as a stream comes in you do the queries before it hits disc.

I sit in pitches basically as a full-time job, and a lot of the things motivating the streaming use case seem a little a contrived.

Ali: There’s the latency and the speed and how fast you can get this stuff. That’s one side of the equation, and that’s what everybody focuses on.

Oftentimes when we ask the business leader, “Hey, so what kind of latency would be okay with you?” They’ll say, “We want it to be superfast like every 5 minutes, every 10 minutes.” And you can accomplish that with batch systems.

Then when you dig into, “wouldn’t you want it to be even faster?” It turns out that streaming systems, the weakest link will dictate the latency. There’ll be some upstream process that has nothing to do with the system that you’re putting in place. And if that upstream link, if that one place where you’re loading the data in or something, if that’s coming in every half an hour, then it doesn’t matter how fast the rest is.

I think the actual latency, this obsession with, “We need it in less than 5 milliseconds.” For most use cases, you don’t have that.

There’s another side of the equation, which people don’t focus on because it’s harder to understand or explain, but it might be the biggest benefit out of these streaming systems, which is, it takes care of all the data operations for you.

If you don’t have a real-time streaming system, you have to deal with things like, okay, so data arrives every day. I’m going to take it in here. I’m going to add it over there. Well, how do I reconcile? What if some of that data is late? I need to join two tables, but that table is not here. So, maybe I’ll wait a little bit, and I’ll rerun it again. And then maybe once a week, I rerun the whole thing from scratch just to make sure everything is consistent.

In some sense, all the ETL that people are doing today and all the data processing that they’re doing today could be simplified if you actually turn it into a streaming case, because the streaming engines take care of the operationalization for you. You don’t have to worry anymore: “did this data arrive late? Are we still waiting on it? Is the thing consistent?” They’ll take care of all of that.

Martin: You think ultimately a large part of this becomes stream processing?

Ali: What I’m saying, provocatively, is that in some sense all of the batch data that’s out there is a potential use case for streaming.

I think that stream processing systems have been too complicated to use, but actually under the hood they take care of a lot of data ops that people are doing manually today.

Ali’s end-to-end architecture

Martin: I would love to talk through what you think a modern data stack looks like. We talked to a whole bunch of folks, and it seems there’s a best practices stack forming, but very, very few people know what it looks like.

Let’s say you get hired, Ali. You have a new job, VP of Data, and you were to build a data infrastructure that does both analytics and AI-ML, what product category — not specific products, but product categories — would you use end to end?

Ali: If I get hired into a big company, I’ll spend the next five years fighting political battles on who owns which part of the stack, and which technology I would need to get rid of. There’s a lot of org chart, and human, and process problems, but let’s say, I get in there and they say, he gets to have it his way.

Martin: He’s got all the juice, that’s right.

Ali: Obviously, trying to do something on-prem makes absolutely no sense at this point. And when you’re building that cloud-native architecture, don’t try to replicate what you had in the past on-prem. Don’t think of it as big clusters that are going to be shared by users.

One big change that happens in the cloud that on-prem vendors don’t think of often is that the networks in the cloud are invisible. Any two machines can communicate at full speed, and it can also communicate to the storage system, to the data lake, at full speed. This was not the case on-prem, and things like Hadoop and so on, they had to optimize where you put the data and the computation had to be close to the data.

So, you move it into the cloud. Typically, you have data flowing in from some of your systems. Depending on what kind of business you’re in, you have IT devices or you have something from your web apps. Sometimes it goes to streaming queuing systems, like Kafka. And from there, it lands into the data lake.

Martin: Into the data lake. So you’re saying the data goes directly into your data lake.

Ali: That’s the first landing place. If you don’t do that, you’re actually going to go back a decade or two in the evolution. Because if you don’t put it into the data lake, then you have to immediately decide what schema you’re going to have. And that’s hard to get rights from the beginning. The good news with data lakes is you don’t have to decide the schema. Just dump it there.

Step number two, you need to build a structural transactional layer on top of it, so that you can actually make sense of it. There’s three or four of those technologies that appeared roughly at the same time, and they all enable you to take your data lake and turn it into a lakehouse.

Step number three. You need some kind of interactive data science environment where you can start interactively working on your data and getting insights from it.

Typically, people have Notebooks-based solutions, where they can iterate with Notebooks. They use things like Spark under the hood, and they’re interactively processing their data and getting insights from it.

And that’s really important because a lot of data science in organizations ends up not being advanced machine learning. It ends up being, okay, so we have this data coming in from our products or from our devices or whatever it is. We have to massage it, get it in a good form, and we need to get some basic insights out of it.

If you want to get into the predictive game, you need a machine learning platform. There are now these machine learning platforms that are emerging, many of them are proprietary, inside the companies. You can read about them, but you can’t get your hands on one.

Martin: And this is for operational ML?

Ali: This actually goes from training the ML model, so actually featurizing it, creating a model that can do the predictions, tracking the results, making sure that you can make them reproducible and reasoning about them to moving it into production, which is the hardest part. Moving it into production where you can actually serve it inside products. That’s the job of the machine learning.

Martin: And the people that use the machine learning platform in your world are the data scientists, the data engineers, or both?

Ali: It’s different organizations, today, unfortunately. The serving part, the production part sometimes is owned by IT, and the creating of the models happens by data scientists that sit in the line of business.

And there is friction in those organizations, because IT operates at a different wavelength from the data scientists, but the machine learning platform needs to span both. If it doesn’t, you’re not going to get the full value out of the machine learning work that you’re doing.

Martin: Can you talk a little bit about where the data pipeline and DAG tools fit in in all this?

Ali: That would be the first step of this. I talked about training immediately. But the hardest part really is to take that data that’s now sitting in the data lake and build the pipelines that featurize it and get it in the right shape and form so that you can start doing machine learning on it. So, that’s step number one. Then, after that, you start training the models.

To orchestrate that automatically and make that workflow just happen, you need software that does that, so that’s definitely the first mile in the ML platform.

Martin: And if I want to take my traditional BI dashboard and attach it to this, where does that attach?

Ali: That’s the last mile. BI itself typically uses something like JDBC/ODBC. To make that really fast and snappy and work on top of the data, you need some capability that makes that possible.

In the past, your only option has been to put it in a data warehouse, and then attach your BI tool to it. I’m claiming that with the lakehouse pattern that we’re seeing, and with some of those technological breakthroughs I mentioned, you could connect your BI tool directly now on that data lake.

Martin: To where? To the transactional layer that’s built on top of it?

Ali: Yep, if you have something like Delta Lake or if you have something like Iceberg or Hive ACID, you could connect it to those directly.

Martin: If you didn’t have any legacy technology, it seems like doing a data lake makes a lot of sense. Is there a simple migration path to this?

Ali: I think it’s harder in the West. In Asia, it’s easier because there’s not lots of legacy. It’s harder in the West because the enterprises have 40 years of technology that they’ve bought and installed app data in and configured. They need to make that work with what ywe’re talking about.

Whereas if you’re building it from a clean slate, you can actually get it right more easily.

Martin: Are you actually seeing more usage of data lakes for companies that aren’t encumbered by legacy?

Ali: The companies that are really succeeding with this stuff… take an Uber. They’re doing predictions, and the predictions are a competitive advantage. You press a button, and within a second, it tells you what the price of the ride is. It basically simulated the ride. It knows what that meter is going to tell you after an hour ride with traffic conditions and everything. It gives you exactly the right price — can’t overprice, can’t underprice. It matches supply and demand of drivers with surge pricing. It can even put people in the same car to lower the cost.

All of these are machine learning use cases, and those stacks, these are all companies that are 10 years old. They didn’t exist. They don’t have lots of legacy data warehouses and legacy systems. They built it custom for this use case, and it’s a huge competitive advantage.

Where to from here?

Martin: Is this the durable stack that lasts for the next decade, or is this converging on something that looks a little bit different than you can articulate from here?

Ali: I can’t predict the future, but I’ll tell you a few ingredients of it that just make sense long-term.

If I’m an enterprise and I’m sitting there as a CIO or someone that’s picking the data strategy, I would make sure that whatever I’m building is multi-cloud. There’s a lot of innovation happening between the different cloud vendors. They have deep pockets, and there’s sort of an arms race there, so make sure that you have something that’s multi-cloud.

The second thing I would do is, as much as possible, try to base it on open standards and open source technology if possible. That gives you the biggest flexibility that, if the space again changes, you can move things. Otherwise, you find yourself locked into a technology stack the way you were locked in to technologies from the ’80s and ’90s and 2000s.

Storing all your data, dumping it first in raw format into a data lake, is something that’s going to remain because there’s so much data that’s being collected. You don’t have time to figure out exact perfect schemas for it and what we’re going to do with it. So, either we dump it somewhere, or we throw it away, and no one wants to be that employee that threw away the data, especially when it’s so cheap to store it.

And the third thing I would do is I would make sure that the stack that you’re building, the way you’re laying it out, has machine learning and data science as first-class citizen. Machine learning platforms didn’t exist 15 years ago, so that probably will change quite a bit. I think the exact shape of the machine learning platform I don’t think will look exactly the way it is today.

But many of the ingredients are right.

Martin: Perfect. Thank you so much. I don’t know if we’re on a race to see who speaks faster, but I think you win.

Ali: Thank you for having me.

• 

  Ali Ghodsi
• 

  Martin Casado is a general partner at a16z where he invests in enterprise companies. Prior, he was cofounder and CTO of Nicira (acquired by VMware) and is the creator of the software defined networking movement.

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Gen Z—those born between 1995 and 2010—now makes up 35 percent of the population and represent $143 billion dollars in spending power. This episode is all about how brands can better understand, collaborate with, and resonate with this hugely influential segment of consumers. 

Our guest, Tiffany Zhong, is the 23-year-old CEO of Zebra IQ, a company that helps brands interpret the wants of Gen Z consumers and helps Gen Z creators turn their content into businesses. In its recent Gen Z Trends Report, her company highlights important cultural trends and Gen Z behaviors based on a trove of proprietary research. In this conversation, Tiffany and a16z general partner Connie Chan discuss the key differences between Gen Z and millennials, the growing power of short-form video on platforms like TikTok and YouTube, our changing perception of luxury, and how Gen Z is shifting the paradigm around money, education, and work.

The pair breaks down how brands can partner with Gen Z influencers in a way that’s compelling, not cringeworthy, and why when it comes to memes and the art of emoji, you’re probably doing it wrong.

Show Notes

• The importance of marketing to Gen Z, their unique characteristics [1:22], and the platforms influencers use most [3:05]
• The rise of TikTok and text vs. video [6:20]
• How Gen Z creates different personas for different platforms [9:22]
• Tips for finding the right influencer [11:31], and a discussion of how many influencers have become company founders [14:16]
• Common mistakes when marketing to Gen Z [17:20] and the importance of corporate branding [20:21]
• Gen Z’s view of money, work, and higher education [22:06], as well as shopping and luxury [25:01]
• How Gen Z finds friendships and tribes [28:44] and the potential of texting [31:04]

Transcript

Gen Z basics

Tiffany Zhong: If you don’t have the youth using your product or talking about your product or sharing your product, I hate to break it to you: you’re irrelevant. And so that’s why every single company that is targeting consumers needs to care about Gen Zs, whether you’re a Fortune 500 company or whether you’re a startup.

Connie Chan: Are there perceptions that Gen Z has around millennials?

Tiffany: Gen Z considers anyone who is not really speaking their language or not understanding their trend, a boomer. It doesn’t matter if you’re a millennial, it doesn’t matter if you’re a Gen X, it doesn’t matter if you’re a boomer, Gen Zs are going to call you boomers anyway.

Connie: Do you think, though, given how personalized Gen Z’s preferences are, that there is a definitive “this is cool,” “this is not cool”?

Tiffany: It changes weekly. So you have to keep up if you want to understand what’s cool or not.

Connie: That’s really hard on the wardrobe, man. [laughs] Would you say that the difference between Gen Z and millennials is a much bigger gap than between millennials and older generations?

Tiffany: I would say so, because Gen Z is the first generation that’s mobile-first and mobile-native.

Connie: I totally agree. Millennials will say we’re mobile first, but there’s a lot of stuff that we still feel much more comfortable going to a computer to do. Big ticket purchases, we still feel like we’re safer on the browser, for some reason.

Tiffany: Whereas Gen Zs do everything from their phone. We’re used to that. We’re used to buying things from our phone, signing documents from our phone. For better or for worse.

Connie: Requiring much more instant gratification, I’d say. Even the YouTube videos now feel too long to me, if the first minute is the person apologizing and trying to be politically correct. They just need to get to the point, or the ROI has to be real.

Tiffany: Yeah, if it’s good content, if it’s entertaining content. If not, then boom, we’re out. And you’ve lost us.

Connie: When people say “Okay, I want to go be an influencer now.” Before, for millennials, you became a YouTube star. Now is it more desirable to be a TikTok influencer versus a YouTube influencer? Certain [platforms] are clearly easier to go viral on.

Tiffany: Twenty-nine percent of youth in America want to become vloggers or YouTubers, versus 23 percent want to become professional athletes. So more people want to become YouTubers than athletes, which is a massive shift.

On the platforms that Gen Z wants to be an influencer on, TikTok seems the easiest for people because we’ve obviously seen Charli D’Amelio becoming one of the biggest influencers in under a year.

Connie: Totally. The conversations around the actual TikToks right now are living on other platforms, but it’s super valuable.

Tiffany: Exactly. TikTok stars are all spending time on YouTube now. It’s a natural growth phase. So when you say: are TikTok stars the new YouTube stars? There’s a whole correlation there, in the sense that if you’re big on TikTok, the way you can really continuously build an audience that is sustainable is on YouTube.

Connie: But many have not been able to be as successful on YouTube. It’s more likely they will not be successful, actually.

Tiffany: Because they haven’t really adapted on how to make long form content. They’re used to making 60-second videos, which doesn’t translate well to YouTube. On YouTube you want to be making 10 minute videos, because that’s how you monetize. There is no easy virality factor. You just have to be really good at distribution and marketing, honestly.

Connie: Especially on TikTok, there’s so much remix meme culture. It’s not necessarily 100 percent original. And maybe that’s part of why it’s so hard to translate [TikTok content] to YouTube: for the most part you have to come up with something completely original.

Tiffany: People who are really, really good storytellers will be able to do so across different mediums, whether it’s TikTok, whether it’s YouTube, whether it’s Instagram, whether it’s Twitch.

Connie: I think for short-form video, especially the stuff on TikTok, it’s about: what’s the punch line? What’s the actual point of the video that makes it interesting? And that’s why it so democratizes video creation. You don’t need a ring light to be a good TikTok creator. You literally just need your phone.

Tiffany: Yeah, on TikTok, you get 30 seconds, you can record it with your phone, you can have whatever quality of video, and as long as you have a good storyline people will watch it and people will share it. Or if you’re adding value to the viewer’s life.

Connie: Yeah, short videos are not just jokes. I mean, I cringe whenever people say TikTok is a bunch of people dancing to music, because I’m like: you clearly have not used this thing. [laughs] There’s educational stuff on it. There’s financial advice on TikTok. There is stuff that teaches you how to cook. So short video is a really powerful format, I think. And it’s basically getting rid of the fluff that you don’t need and delivering maximum value per second—literally, per second because you can lose the person after three or four seconds if it’s not good enough.

TikTok and the preference for video

Tiffany: To your point, people think TikTok is just a fun lip-synching app or dancing app. And it’s not. It’s a place where you can learn anything you ever wanted to learn, whether it’s about cars, whether it’s how to take photos, how to model. I’ve watched so many TikTok videos about videography tips and iPhone tricks, all sorts of stuff. It’s just endless amounts of short-form education.

Connie: I think that phrase has never been used to describe TikTok: short-form education. I’m curious on your thoughts about on the kind of content that historically people would argue works better in text. How does Gen Z react to, you know, that super thoughtful op-ed on the New York Times, or product reviews—things that you can actually read much faster than you can watch?

Tiffany: Gen Zs prefer video over text for like 99 percent of things.

Connie: How do you balance that with efficiency, though, where you can actually read some of these things much faster than you can watch for some of these things?

Tiffany: True. But not only do we want to be able to consume the content in a reasonable amount of time, we also want to be entertained at the same time, which is why video is such a huge format for Gen Z. Text is less relevant because there are less emotions. You can’t see someone talking. Sixty-five percent of Gen Z prefers FaceTime to any other form of communication to keep in touch with friends.

Connie: And people don’t realize that when Gen Z is doing a FaceTime phone call, it’s not like they have to hold the phone the whole time.

Tiffany: Oh, yeah. They might be video chatting their friend or their parents while simultaneously doing like three other things. Multitasking is what we were born into because of smartphones. We’re used to switching between tabs quickly, switching between apps quickly.

Connie: I definitely see different communication behaviors across different generations. One thing I think people don’t realize is just how many young folks have multiple Instagram accounts, for example, or multiple Twitter accounts, because they have to show different aspects of their personality and segment parts of their lives.

Tiffany: Every person has dual personalities. You have a personality that you bring to work, you have a personality that you bring to your friends, you have a personality that you bring to your family.

Connie: I have many more than that, but yes.

Tiffany: And, and so that’s how Gen Zs have started to establish themselves. They want to be able to be super fluid and switch across these different identities. This finsta—fake Insta account—which is really just for personal friends, this one’s for this set of friends, or this one’s for this set of interest-based friends. This one’s for this community. That’s how these finstas start being created.

Connie: But it’s more like on TikTok, they can be a different version of themselves. On Instagram, they might still keep that polished version of themselves. You have different personas on different spectrums of that authenticity scale. And on different [platforms], you’re going to reveal more information or less information about yourself, too. Some you’ll reveal your actual name, where you live. Some it’s all random usernames, on purpose. There’s more control over what people can see and how they would use it.

Platforms and personas

Tiffany: Gen Z is definitely very smart about the perception that they put out there across different social media networks. Gen Zs are brand strategists from age 10. They learn: okay, my Instagram needs to be like this, my YouTube needs to be like this, my TikTok needs to be like this, my Twitter needs to be like this. It’s so different than how millennials and Gen X perceive content.

Connie: I definitely think the way that millennials grew up on social was to put our best foot forward. You always wanted to make sure the photos that you were posting reflected well on you, or you would untag yourself on the Facebook photo so it wouldn’t be linked back to your profile. We used to all do that. And just think about all the filters that we use on our photos, all the photo apps. But I do feel like there is this change swinging back to: don’t put a filter on everything. Or: it doesn’t have to be in the most flattering angle. But it’s not necessarily that they will do that across all social media.

Tiffany: Your main Instagram, you still care about your follower count, you still care about your likes, you still care about your comments. For finstas, it doesn’t matter as much. If you get one “like,” it doesn’t matter. Because it’s really just where you can be your real self.

Connie: Something I’ve noticed on Gen Z and TikTok is there’s less of a fear of being on video; there’s less of a fear around creating in general.

Tiffany: Totally. TikTok has made people really comfortable with being themselves.

Connie: Showing the no filter life.

Tiffany: Yeah. Because the weirder you are, the more chances you will go viral. The YouTuber Emma Chamberlain is one of the fastest growing Gen Z influencers. She has one of the highest engagement rates across young influencers. Now, her content is all very authentic, she’s very much herself. She mixes in that very relatable aspect with the very aspirational. And I think the best influencers are able to be both aspirational and relatable.

That’s why raw photos, raw videos are actually bridging the connection between influencer, creator, and fans. A really polished version of yourself doesn’t seem very attainable. When you’re a fan sitting at home, you want to feel like you could be that influencer too, someday.

All about influencers

Connie: How do you find the right influencer to work with? Historically, people just look like at, okay, how big is your following? What should they be thinking?

Tiffany: You look at the type of content they’re posting. Is that similar to the type of content you post on your social media? Is it on brand? Are they in your niche? Are they already talking about products or your space in general? And then there’s the checking if they have real fans and authentic fans by looking at their engagement rate.

SocialBlade is a really simple website that lets you look at any accounts, any pages on social media across YouTube, Twitch, Twitter, TikTok, Instagram and see how fast someone is growing, see how many followers they got yesterday, how many followers they got 7 days ago, 30 days ago; how many followers they lost, as well. And so that’s a really authentic way to go and track how fast an account is really growing. You can see their relevance through growth and their engagement through that.

And I think you will see that a lot of these smaller influencers actually have really, really high engagement rates because they have more time to spend, so their fans reciprocate. Recently, an influencer called Bella Poarch started becoming super relevant with her head bobbing TikTok videos. She’s really blown up. Now, you could have spotted that a couple months ago if you just looked at SocialBlade and watched how she literally grew exponentially.

Connie: So for Gen Z, how would you balance brands choosing influencers versus traditional celebrities—people from movies, TV shows? You’re laughing—I feel like you have an answer that’s probably contrarian to what a lot of marketers believe today.

Tiffany: Celebrities still give you that legitimacy factor to a certain extent.

But it better be extremely on-brand to be working with this very specific celebrity that you choose. Not because of their fame, but because maybe they’ve talked about your brand already, or they drink your brand, or wear your brand, or use your brand, or eat at your restaurant, whatever it is. There has to be something like that there. Do not pay a celebrity a million dollars to promote a brand that they don’t give a shit about. I’ve seen many brands that have just burned money on celebrities.

Influencers are good for more authentic collaborations that are closer to home for the fan. Celebrities are not relatable. And so I think there’s a good way to mix in both celebrities, massive influencers, and also micro influencers, if you really want to be strategic in how you utilize your money.

Connie: So celebrities and superstars still exist. It’s just the ones that have lasting power are the ones that feel like they’re your friend and have some level of being relatable.

Tiffany: Aspirational and relatable. Gotta be both.

Connie: On the influencer side, do you believe that the lifespan of someone’s popularity has also shortened in length? Where previously you might have a celebrity or an influencer that you love and you follow for like 10, 20 years, do you feel this new generation is going through them quicker? How do you think about the lifespan of content, movies, TV, influencers themselves?

Tiffany: Your shelf life can actually be extremely long if you think about it from a very strategic standpoint of brand building as an influencer. Now, a lot of influencers aren’t really thinking about a 10-year lifespan. They’re thinking “how can I make as much money in the next year as possible?” And I think that is a huge problem because they aren’t treating themselves like they are their own media companies, they aren’t treating themselves like they are a company and they are the CEO.

The people who have had really long shelf lives are people who have adapted with their audience, people who listen to their audience, the people who engage with their audience and make their fans feel like they are being heard. As your audience grows older, your content adapts, as well. You grow older, your content matures a little, and your fans grow older, as well.

It’s more important to have longer term customer retention and lifetime value than customer growth. It is more important to have 1,000 super-fans than 10 million fans who will never buy anything from you.

Connie: As these short video platforms potentially go into commerce, what are your thoughts around creators and influencers making merchandise themselves—and becoming stores, really?

Tiffany: I think creators are starting the new billion-dollar commerce brands and the new billion-dollar media companies. We’re seeing that with people building tech companies, [like] David Dobrik. He built an app, raised venture financing for it. His merch brand is doing incredibly well.

Connie: But you would also have to say he’s one of the top YouTubers. He’s not indicative of most influencers.

Tiffany: He’s not, but he is a really good role model for a lot of creators and what they can do. Any creator that has a really strong fan base can establish their own commerce brands. They are a media brand already because they are creating content. Now how can they parlay that into something that is relevant to their audience?

Connie: And it’s also very dependent on how the platforms allow you to monetize, either through ecommerce capabilities, or more gifting, more memberships, you name it. The platforms so far in the Western world have not done very much.

Tiffany: A SKU might be a phone call, as SKU might be shoes that they have designed, all sorts of different product lines that they’re coming out with. The YouTuber that I mentioned, Emma Chamberlain—really young, really big audience. She started her own coffee brand and it’s doing really well with Gen Zs.

Gen Z marketing mistakes

Connie: What are the big misconceptions or the big mistakes that brands have made when they’re trying to target Gen Z?

Tiffany: I think the biggest mess ups are when brands randomly jump onto bandwagons or trends without fully understanding where the trend has come from, what the trend means.

Connie: And how long it can last.

Tiffany: And how it is relevant to Gen Z. If you don’t speak Gen Z’s language, but you try to without actually spending the time to understand it, you get laughed at and mocked on the internet and turned into a meme, negatively. That is when you become a very cringeworthy brand.

Connie: I find when brands try to use memes, though…

Tiffany: It’s cringe.

Connie: Sometimes they get it wrong; they very often get it wrong. [laughs] So I would say for a brand, if you want to use a meme see what the community comes up with first and then just retweet that kind of stuff. Do not attempt to create your own version of it.

Tiffany: Reproduce it, and it comes out cringe, it comes out awful. You have to understand the origin of it.

Connie: How long does the memory of that cringe reaction last?

Tiffany: Depends on how viral it goes. if it goes really viral and there are press articles about how bad it is, then it may take longer to recover. A recommendation I have for anyone who is trying to understand Gen Z trends per se, is: open TikTok. Don’t just watch the TikToks…

Connie: Make one.

Tiffany: Make a TikTok. Read the comments. That is how you can understand Gen Zs really, really quickly. Put in the work and you’ll actually be able to do an awesome campaign. But reading the comments on Gen Z TikTok pages, reading the comments on TikTok gossip pages like TikTok Room on Instagram. Those are all Gen Zs. That means all the comments you read there are posted by Gen Zs. And that means that is how they’re talking. Whether it’s trends they’re talking about, whether it’s slang terms they’re using, whether it’s emojis they’re using to express themselves.

Connie: So, the art of texting. One of my favorite slides in your deck was actually the emoji dictionary, where it was showing that the traditional happy face is actually not a good thing to send to a Gen Z because it can be [interpreted as] an extremely passive aggressive smile.

Tiffany: It’s part of our personality to be self-deprecating and to be really honest, but masquerade that honesty in a joke.

Connie: This is why sarcasm is now so hard to read through text. It seems very easy to misread a text, and now misread the emoji. I did not know that the cowboy emoji, in your opinion, is actually a negative thing too.

Tiffany: Oh, yeah. It was very surprising for a lot of people. I’ve actually converted a few Gen X friends into Gen Z-style texting. I had to teach a Gen X about how to do text reactions.

Connie: Text reactions, as far as I know, are okay.

Tiffany: Texting the thumbs up emoji is like…

Connie: It’s an acknowledgement.

Tiffany: …passive aggressive.

Connie: Oh my gosh. [laughs] So you talk about needing to experiment and being willing to figure out how to talk to this generation, or else you could be laughed at. Do you believe there is a correlation between Gen Z, cancel culture, and an increased fear of speaking incorrectly to this group?

Tiffany: One hundred percent. I think with this year has come an increased fear of being canceled on social media, especially when many brands acted incorrectly. Brands who had never thought about how they would engage in a political discussion were suddenly forced to do so. And many just didn’t adapt fast enough. Although some did and some were applauded for it by Gen Zs.

Connie: I’d be curious about your thoughts around the need for companies to be transparent on either how they’re making their money or how much money they’re making.

Tiffany: Gen Z is very perceptive of the brands that they buy from and that they shop from, and also the places that they want to work at. And so they’re very value driven around human rights, and the environment, and political reform, and education. With that, brands need to figure out what they stand for and live by it.

Connie: Do you think brands are able to stay out of that discussion and not have a stance?

Tiffany: Not having a stance is taking a stance. Not having a stance means you don’t care about these things. If you really want to appeal to a wide range of Gen Zs, figure out what your values are and live by it. Talk about it, make that part of your brand. Lean into it and make that part of your whole brand marketing strategy. But don’t jump from value to value really quickly just because it is trendy. Gen Zs will see right through that.

How Gen Z views money and work

Connie: One thing that people often talk about is how Gen Z is really good at figuring out how to make money on their own to buy the things they want at a younger age, versus relying on parents. I would love to hear your thoughts on money and work as a category.

Tiffany: I think there’s going to be a future where work is more project-based. The new American Dream for Gen Z is being able to work wherever we want, whenever we want. Now, Gen Z is the side hustle generation because from a young age we realized that we can hustle to make money online.

I really break it down into three categories. Freelancing: so, Fiverr, graphic design, etc. Making strategic investments: building a GOAT and Grailed store, building meme pages, selling ads on the meme pages or flipping the meme pages, buying and selling the right kinds of street wear, getting 10X what you paid for. And then the third category is creating content: becoming a fulltime content creator on TikTok, YouTube, Instagram, or Twitch. Gen Zs are realizing that we can make money in all sorts of different ways. And we also don’t have to be tied to one place. We don’t have to be tied to one 9 to 5.

Connie: And this has to extend to college, too, as people look at college student debt. One big question has been what is higher education going to look like, as people don’t always see the return on investment? There’s no shortage of curriculum.

Tiffany: I do think that a lot of kids benefit from higher education. I’m not sure if these prestigious colleges are exactly where they should be spending their time and their money, both for opportunity cost, but also debt-wise. You’re spending four years getting a degree that you may or may not end up having your lifetime career in. And you have that debt that you have to pay off for the next 10 years, which is crazy to me. So I think gap years are interesting. All these alternatives: apprenticeships, internships.

Connie: Do you think that desire to try different things also extends to post-college, potentially more job hopping?

Tiffany: With Silicon Valley and millennials, specifically. It is common to be in a job for two years and switch to another one.

I think the gig economy is going to become even more relevant for Gen Zs because it gives Gen Zs the freedom to do whatever they want. So, not having to sit inside an office from 9 a.m. to 5 p.m. every single day, having to request for paid time off. If you work for yourself and if you are in the gig economy, or if you’re flipping shoes, or doing these side hustles, or turning these side hustles into real businesses, then you might have more time to travel or become a content creator. Gen Z really wants to be able to explore different categories, different types of work.

Connie: Yeah, and they need the freedom to do it. Let’s also talk about shopping. What does luxury even mean now? Streetwear can be very, very expensive, actually. A black baseball hat can be very expensive. What do you think about the future of luxury?

Tiffany: Yeah. So thrifting’s becoming cool and relevant again. A lot of influencers who are wealthy are thrift shopping and they’re showing their thrift hauls.

Connie: But that value needed for content—that it better be good value per second—is that also extending to actual purchases?

Tiffany: Value is important, but so is convenience and so is staying on trend. To a certain extent, Gen Z is voting with their dollars. But there’s always that convenience factor that also comes into play. And so brands like Boohoo and SHEIN are really relevant with Gen Z and really popping off with Gen Z because of the fast fashion nature of it. I get these ads from SHEIN and it’s $10 for like, a sweater. This is crazy. Where’s this even from?

Connie: But will you ever grow up and then say, okay, I’m now cool paying $600 for that sweater? Not that sweater, but a sweater.

Tiffany: I think that the future of fashion is going to be a mix. It’s going to be a mix, in my opinion, of people being able to put together things that they buy at the thrift store combined with the latest Off-White shoes, the latest Yeezy shoes.

Connie: But that’s not that different. Millennials also spent a lot on a purse or a belt. Same thing.

Tiffany: Right. But you get embarrassed to wear cheap clothes, as a millennial…

Connie: I’m cool doing it. [laughs]

Tiffany: …which I think is becoming less of a thing. You don’t get shamed for wearing cheap clothes anymore.

Connie: I also want to hear your thoughts overall on how shopping behaviors are changing. How do you make shopping fun?

Tiffany: In regards to shopping, there are more stores doing pop-ups and doing these limited-edition or time-based activations that are really cool, really relevant, to lure Gen Zs to come in, try out the products, and take some cool photos. You’re seeing a lot of direct-to-consumer brands making pop-up stores or even just going brick-and-mortar, more as a marketing expense as opposed to a place to drive sales, which is very interesting. There’s obviously a massive paradigm shift there, when you see these consumer brands that are backed by VC firms spending the capital that they otherwise would have spent on ads to do in-person activations. They realize that having people be able to tangibly see something, touch something, is still just as impactful as being able to order stuff online.

Connie: So brick and mortar still matters?

Tiffany: There’s obviously lots of ways to make your brand fun and interactive. For Gen Zs, their “third places” are all digital, which makes sense. It’s Fortnite, it’s Discord, it’s House Party, it’s Twitch, it’s even apps like Squad. Those are the places where Gen Zs are making new friends, hanging out with their old friends. So, gone are the days where all of your best friends have to be within a mile of you. Now you can have a best friend who is 4,000 miles from you and you can still have as intimate a connection as someone who is a block away from you. Only the internet has made this possible for us. Technology has made it a lot easier for us to make friends with people who are also interested in gaming, or fashion, or basketball—really as niche as you want to go.

Finding friends and tribes

Connie: When you’re finding friends, how much of it is people who like the same influencers, the same brands? How much of it is interest-based? Has that changed? How do you even go about finding your tribe online?

Tiffany: Gen Zs are finding their tribe through being able to search in specific hashtags or specific rooms of people who are interested in same things. Going into subreddits, that subreddit leading to a Discord community of people who came from Reddit, to wanting to chat in a room. There are lots of Gen Zs who are tweeting a list of their favorite influencers. And in that tweet it includes, “Hey, if you’re also interested in—insert influencer’s name—DM me and we’ll add you to our group chat.” So, brands, influencers, they all fall under interests now. That is an “interest.” This is the modern-day Facebook pages. You’re co-signing it by buying the merch, by tweeting about it, by making stan pages on Twitter and Instagram.

Connie: I look at trends that are happening in developing countries, specifically China and Southeast Asia, and they’re very, very mobile-first, too. Even older generations are mobile-first there. And I think that has led to the development of more things like the superapp model, where you have one app that does multiple things. Do you think Gen Z will be more receptive to something like that, versus older generations in the Western world that still seem to prefer one app that does one thing, for now?

Tiffany: For this generation, we’re optimizing for convenience. So if things are bundled together, that saves us time. I think that’s really important for Gen Z.

Connie: Fewer taps to do the same thing.

Tiffany: Yeah. I mean, we’re consumed by so many notifications, so many products every day, so many apps every day, so much content to consume that I do think that there is going to be a massive bundling of things. It’s going to be really tricky to get right. But we’re already seeing a lot of bundling happening for Instagram, including Reels, commerce, shopping, etc., that they would not have done five years ago. Even three years ago they wanted separate apps for each kind of function because that’s how we thought about apps.

Connie: I want to talk about texting as a potential new channel. More and more, when I’m shopping on a site, I’m getting a text that gives me a discount code if I purchase it right away. Or it tells me when something’s being shipped. You also hear about Gen Z not opening email. Talk about texting as a channel.

Tiffany: Texting is now becoming a replacement for email. But does that mean that texting is actually becoming less personal of a communication format, now that advertisers and brands are texting us?

Connie: Yeah, so as that increases, do you see the same issues as email where you’re going to want to filter this stuff out, eventually?

Tiffany: One hundred percent. Once we start getting bombarded, we’re going to become more selective, or a new medium will become more relevant for us. SMS shouldn’t be our email inbox.

Connie: Because it’s weird, right? You can do far less on a text than on email. And it’s actually much more invasive.

Tiffany: I barely even give people my number, let alone companies my number.

Connie: But then at the same time, when I receive these texts, they definitely work on me. And I do click in, and I sometimes do complete that purchase.

Tiffany: I guess it’s for the brands that you really, really, really, really love. If they’re texting you, you don’t feel a sense of invasion. Now, you would only feel comfortable with that for very select brands that you’re a huge fan of, where you want to be notified when something has launched. You want to get alerted before it goes out to the public. You can use SMS as a way to facilitate that intimacy between a brand or an influencer and an individual—ideally with two-way communication. And so you’re going to offer some sort of value, whether it’s discounts, whether it’s getting something exclusive that others can’t get or will get later on. So you’re a super-fan of those brands.

Connie: Thank you so much. It’s been so much fun chatting about Gen Z, and I will be much more self aware now the next time I text you with an emoji.

• 

  Tiffany Zhong
• 

  Connie Chan is a general partner at a16z where she invests in consumer tech. She's well-known for her deep knowledge of the Chinese consumer tech landscape and spotting those trends moving from east to west.

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We’ve already talked a lot about podcasting, both evolution of the industry as well as the form, but where are we going with the future of audio, more broadly? Can we borrow from the present and future of video (e.g., TikTok) to see what’s next in audio (more layers, more interactivity)? Can we borrow from the past of audio (i.e., radio) to see what’s next for audio experiences (more blending of music, talk, podcasting)? Where do all these mediums converge and where do they diverge — when it comes to user experience, product design, recommendations, discovery?

Gustav Söderström, chief R&D officer (who oversees the product, design, data, and engineering teams) at Spotify — the world’s most popular audio streaming subscription service — joins this episode of the a16z Podcast for a deep dive on all things audio with a16z general partner Connie Chan and editor in chief Sonal Chokshi. They cover the past, present, and future of audio — going high level into the big trends and also dipping down into the trenches — especially given the increased blending of talk/ podcasting, music, more. What are the challenges to designing for different mediums, on both front end and back end (including machine learning and different graphs), when listeners want everything in one place when and where they want it… yet their contexts shift?

But the conversation more broadly is really more about what happens when we give creators (of all kinds!) tools — not just for expression but for fan engagement and monetization too. We also discuss the themes of super apps and full-stack approaches when it comes to innovating on top of a protocol, as well as how innovation happens in practice: How do mediums — and organizations — evolve, prioritize, “disrupt themselves”? All this and more in this episode.

Show Notes

• How audio and podcasting have changed [2:30] and the merging of music and video through TikTok [5:01]
• The importance of mobile devices [10:21]
• Augmented audio and interacting with creators [13:04]
• How Spotify designs products [16:19], and building “super-apps” [17:58]
• Technical challenges to integrating media types [24:07], how audio is defined as it merges into new forms [26:54], and licensing issues [32:26]
• Recommendations and discovery algorithms [36:54], challenges platforms face [43:11], and the importance of subscriptions [47:13]
• Thoughts about the future of audio [53:12]

Transcript

Sonal: Hi, everyone. Welcome to the “a16z Podcast.” I’m Sonal. And today we are talking about one of my many, but actually probably [my] most favorite topics: the future of audio. Our special guest is Gustav Söderström, the chief R&D officer of Spotify, which is the world’s most popular audio streaming subscription service. As a reminder, none of the following should be taken as investment advice. Please see a16z.com/disclosures for more information.

Also joining this episode is a16z general partner Connie Chan, who covers consumer, writes a lot about tech trends and product in China and beyond, alternative monetization models, and more. And she and I have actually done a couple of podcasts on podcasting. One, a podcast about podcasting with Nick Quah. And the other, on how we, at a16z, podcast. You can find both of those episodes as well as other resources on the topic at a16z.com/podcasting.

Note, also, that Spotify actually got into podcasting in 2015. We were actually included as one of their launch partners for that, among select others, and say we’re huge fans of the pod.

Gustav: We still are, so it’s still true.

Sonal: Thank you. Anyway, in this episode, we actually go beyond podcasting to talk about the broader category of audio — past, present, and future. So we chat about the parallels and differences in audio and video including referencing an episode I recently did with Eugene Wei on TikTok, which you can also catch in this feed. We discuss the trend of interactivity as well as augmented audio, and where we are right now. What’s possible, what are the challenges? We talk about where podcasting and music converge and diverge, both on user experience and design, as well as technically in machine learning. And, finally, we go deep on recommender systems. The idea of “hearing” like an algorithm and where subscription models come into machine learning.

But we also talk throughout this episode about the trade-offs of full-stack approaches, regardless of what kind of company you are, and the topic of super apps as well. And we’re also really talking about how innovation happens in practice. Whether it’s having an opinionated point of view about the future, or listening to users, disrupting oneself — and how to change an organization, and much more.

But we begin, however, with a super quick debate on how much things have or haven’t changed in the podcasting world. At least, since we did our last podcasting episode over a year and a half ago.

The current state of audio

Connie: I actually personally think that audio hasn’t changed that much yet. A lot of things are still — I don’t know if broken is the right word. But just — problems that are not solved yet. Discovery is still difficult, search is still difficult. It’s really like a one-way listening experience. You aren’t interacting with other listeners, you aren’t interacting with the creators. Creators still have to rely on very old business models for monetization that ultimately don’t work for a lot of long-tail creators. A lot of those big problems still exist. But I do have this optimistic feel that we’re on the cusp of change that’s going to come to the broader audio market.

Sonal: You’re right, those things actually haven’t changed very much. I was thinking of the fact that the content landscape in podcasting has super exploded. In the last year, two years alone, Spotify itself has led a number of content acquisitions, which is such an interesting evolution.

Gustav: Yes. It’s both very much the same, but very much more of the same, right? So, like, the forklifting of your time into your AirPods, that just keeps increasing.

Sonal: Right.

Gustav: There’ve certainly been shifts in listening behavior due to COVID. A lot of listening was in the car, that shifted to speakers in the home — so, overall, there’s much more listening. And to your point, certainly, we’ve invested aggressively in content and exclusives. The creator side of this landscape has changed in a direction that we wanted to change.

But I would also agree that we’re on the cusp on the consumer experience. What’s so interesting about audio is, it feels like you have this cheat sheet, which is what happened in video. We just haven’t done monetization in a 21st century way yet. We have no interactivity. You can really just look at the other media industries and see what’s missing, in a sense.

Sonal: So, Edison Research, which publishes a lot of the leading work and studying podcasting behavior — they argued a few things last year. That one of the major inflection points in podcasting, interestingly, came through Spotify because of the streaming. And that brought in, kind of, a new generation of users. Two, the other argument they made. And this is, of course, pre a lot of the content acquisitions — is that for a new generation, the medium of audio is really not that different than video. That, in fact, for a lot of people, their default podcast player is often a video app, or just turning off the visuals and listening. And so, I’m curious, for your guys’ thoughts on where audio and video — which is another big trend — do and don’t intersect? Both from a trend perspective, and a product development perspective, and then we can dig in deeper on other aspects.

Audio vs. visual

Connie: I mean, video is really just the combination of using your ears and your eyes. It’s the audio plus the visual. Which means the stakes are actually higher for audio, because I can’t have, like, a 20-second gap of silence in a podcast and expect you to be okay with it. But in a video, you can go quiet and there might just be some visual distraction, and you don’t have to be “on” as much every second. And so it’s still a different medium. But I do think that the stakes in audio are higher.

Gustav: So I think that when you talk about audio, it’s different things, depending on the type of audio, actually. So you have, kind of, foreground audio, which is more similar to video. It is the main activity you’re doing. You’re really concentrating. It requires most of your attention. Then you have background audio. Like, you’re listening to music, and you’re actually paying attention to something completely different. You’re working out, or you’re studying or something, right? So there are these different modes of audio that don’t really exist in video. Video is mostly all your attention, or you’re doing something else, right? 

This is also the benefit of audio. That’s why it’s so much engagement, because you have both foreground moments and background moments. But even in the foreground moments, when you’re paying full attention, you can still do other things. You can drive, you can do dishes, you can walk around the house, right? So, it is this other mode that video doesn’t cover. That’s why we think it is almost as much engagement as foreground video, but it’s not nearly valued the same yet. And that’s not because it’s less valuable — we think that’s because it’s undervalued.

And you can think about it the other way as well. You have some video that actually works quite well as audio, that you can background, that you watch every now and then. Joe Rogan, for example — it certainly has video, right? And that actually does help the user experience. But it is what we call backgroundable video or foregroundable audio, if you want to call it that.

Sonal: I just wanted to comment, Gustav, on your point about the modes. That’s a phrase that I use when I think about describing people’s behaviors. And I actually describe it less as foreground and background, and more as passive versus active mode. And so, I really believe strongly that audio has different modes. Sometimes you’re just in “hanging out in chill” mode, sometimes I’m in passive mode, which means I just want to listen to other people. Other times I’m in active mode, which means I want to talk, or super active mode which means I want to lead a discussion. So I just think it’s really interesting to think in terms of modes.

I’d love to hear your initial thoughts on just the mediums differences between audio and video. What do you make of the differences and similarities between TikTok, and what we can and can’t learn from TikTok when it comes to product in audio? Do you guys have any thoughts on that? I mean, Connie, you’ve written so many posts about TikTok since very early on.

Connie: Yeah. Like, TikTok is an extreme example. If you don’t look at the screen and you just listen, none of the videos make sense. You’ll miss the punchline, like, the whole video.

Sonal: Yeah.

Gustav: Exactly.

Connie: Value prop is also within the visual for TikTok.

Gustav: So, I think there are at least two similarities. What they do really well is —  they take, to Connie’s point, commodity music — that if you just listen to it in the background, you miss the whole point. But then they let their users uniquify that commodity music, right, by adding uniqueness to it with their video.

Sonal: I think you just made up a word, by the way, uniquify.

Gustav: Yeah.

Sonal: Keep going.

Gustav: And I think that’s a great pattern, right? You have something that is commodity. You can use your user base to turn that into something that is non-commodity. It’s this engine that takes these clips and creates unique content around it. So I think that’s a really interesting pattern that you could probably copy to other businesses that has commodity content. Let your audience do something with it to make it unique.

The other analogy that I see to audio is specifically music. If you think about Eugene Wei’s post on seeing like an algorithm. What he said was that the medium itself is built to be understood by an algorithm. You’re presented with one item at a time, you either consume, or you swipe. So it’s built for the algorithm to understand what you’re paying attention to versus, for example, a scrolling feed, where the algorithm has no idea which item your eyes are actually looking at.

Sonal: Right, isolating the specific variables so that the product developer knows what is working or not working, essentially, for the user.

Gustav: Exactly. And if you think about music, actually, it’s the exact same thing. You present one audio track at a time. You either listen to it or you skip. So, in that sense, you can say it’s a similar sort of UI, but in audio.

Connie: The tricky part is actually just the length of the song versus the length of the TikTok video. Because you get to a very quick decision if you like that TikTok video or not — literally within, like, two, three seconds. For a song, as many of you know. Like, the first couple of seconds of a song doesn’t sound anything like the chorus or the ending, so you just have to go further into the song before you really gauge if someone truly likes it or not. But to me, that’s the only difference.

Gustav: Yeah. And TikTok, you have more evaluations per minute because they’re shorter clips. But it’s also more direct. But it is interesting that you mentioned this, because this is what is happening in the label industry. It is super clear that intro matters more and more, so you do have the TikTok effect in music. You know, songs used to start slow, they don’t anymore because people skip within the first 10 seconds.

Sonal: Oh, that’s so fascinating. So the TikTok effect — where people are now creating different kind of music.

Gustav: I would say one more thing on TikTok. So, while there are some similarities between evaluating audio one track at a time, and evaluating video one track at a time, there’s a big difference which is — TikTok has your full attention. If you’re at full screen and you’re paying full attention, then it’s a pretty good signal. But if you’re washing dishes and listening on a speaker, you get very poor signal. So it depends on the context and you have to take that into account when you look at the signal.

Sonal: I’d love to probe briefly on this part. Which is, you both have talked a lot. Connie, you, in particular, have written so much about how mobile is literally the thing that made a lot of China’s apps work the way they do, because everything was mobile first. And we talked about mobile leapfrogging in our posts from what now, five years ago?

Connie: Right, right.

Sonal: Wow, that’s been a long time. So, where does that come in when you think about innovation in audio? And then, Gustav, I’d love your thoughts on this as well. Because when you said that in the pandemic, a lot of the listening behavior has shifted to home speakers, I’m curious how that changes your views, given [the] initially mobile default interface?

Connie: So, if I just break down what a phone is and the different components of it. Like, you have the touch screen, which means whatever you’re doing on the phone, you can have more interactivity, ideally. But you also have camera and GPS. And, you know, the camera is the unlock for TikTok, and the microphone could be the unlock for a bunch of audio platforms. Because, now it means that I don’t just have to be listening. I’m not just leveraging the speaker on the phone, but I’m leveraging the microphone and I’m giving back. The microphone, in particular, for audio and video, I think is dramatic.

Gustav: Yeah. That is one of the sensors that is super interesting and under-leveraged for audio, I would say. So, one of the benefits of being a streaming service is that we understand the consumption situation. We understand if you’re listening on a speaker but putting on an Apple Watch or a phone — we understand if you’re in your car, for example, because the phone is connected and so forth. So we actually think that’s a very important signal, and we try to think of them as, kind of, different jobs to be done. And what we want to try to understand is the situation that you’re in. And it’s obviously a combination of your play history, your time, and your taste. But a device is actually a really good signal.

So there are two levels. One is the UI and the hardware that you can leverage. And that changes when you go from a phone to a connected speaker, for example. You have much less control. You actually still do have a feedback channel, in terms of a microphone, as Connie mentioned. But you have less UI, right? So we’re thinking about multimodal consumption quite a lot, where you have some devices that are really good for input on your body, but they’re not that good for output — you actually want the sound in your speakers. That’s why we built this remote-control protocol so that you don’t have to interact in the same place that you’re listening —  you can interact on one device and so forth.

The other way to think about it is on the content level. So one of the things that happened during COVID, when a lot of consumption shifted from the car to the home, was that we have this very successful playlist called The Daily Drive, where we mix music and talk — and create, literally, your daily drive. Now people stopped driving, right? So then we tried to pivot and we create [the] same job to be done, but not while driving — it’s different. So these are the two levels — kind of, the content level and the pure UX interactivity level.

Augmented audio possibilities

Sonal: Okay. So we can shift into discovery and recommendations in a bit. But before we close this thread, what do you guys think of this trend and phrase — augmented audio? Which means different things to different people. But the idea that you can actually, to your point, Connie — much like video has many layers, you can actually bring more and more layers into audio as well. Do you guys have any quick thoughts on that?

Connie: Oh, so many. But that really just leads me to the belief that audio today is still this more “sit-back” experience. It’s very much like a one-way consumption experience, the same way that we consume television, or the same way that we consume movies. And, kind of, like — more YouTube, live streaming, that kind of format hasn’t really arrived in mainstream and audio yet. And so even just capturing the comments — the feedback to podcasts — like, that kind of content is not well harnessed today. So there’s so many more layers around the listener feedback, or interacting with other listeners, or interacting with the creator. A lot of fun should be added on and layered on into audio that, right now, at least, doesn’t exist.

Sonal: It doesn’t have to even necessarily be fun. I mean, as a creator, I found the news — when you guys rolled out your polls feature — to be quite interesting. Because we just had the debates here in the United States, and I literally was like, “I wonder if a lot of the political news shows should do, like, their own polling as part of their audio experience?”

Connie: I mean, it’s not just fun, it’s instant feedback.

Gustav: Yeah. I agree. We started with PULSE which is both a safe and constructive way to bring feedback. You mentioned the consumers or the listeners talking to each other. You mentioned the creator talking to the listener. We try to focus on the creator, and what tools does the creator want? And, actually, not just for having fun — but to your point, Sonal, to be a better creator. What information do you want from your fans, and what would make it easier for a creator to produce another episode, for example? And so we started with PULSE, which is one way to get clear answers on questions you have. And we want to continue in this way — focusing, not really on listening to listen to conversations. I mean, you have Instagram, Facebook, Twitter — there’s lots of places to go and talk to other users, but there aren’t a lot of places to have good conversations with the creators.

Connie: And I think if you focus on creators, there’s also a huge opportunity to expand the funnel of creators. If you look at trends in video, lots of the top trending YouTube videos are actually reaction videos, where people are watching a video and showcasing a reaction. And TikTok is all about remixing. There’s a lot of great audio content out there today, that if you talk about augmented audio — you could take a podcast and then have another person share their thoughts directly, just like a sports broadcaster, even — commenting directly on what’s happening in the audio, whether it’s music or even another podcast.

Gustav: Yeah. You have these two extremes like the old-world broadcast, one-way media. And then on the other extreme, I would put gaming, where the interactivity is the experience. You’re not being broadcasted anything, you’re actually creating it. And then you have this thing in between. And I think audio needs to move towards interactivity. And like I said, there is basically a cheat sheet where you can look at other types of media. And as soon as you add a feedback loop, the creator gets a chance to improve. So I think that’s vital.

Sonal: Tell me more about some of your thinking behind polls. When you guys design a product, do you actually have an opinionated philosophy that, “This is how we think people are going to use it?” Or are you just giving them the bare minimum and then unlocking your community to, kind of, let loose? A simplified way of asking that is also, is it a Steve Jobs point of view, or a Bezos point of view?

Gustav: That’s a great question and a great way to put it. And it’s a tough question to answer. It’s definitely not a Steve Jobs point of view, in the sense that we know how people are going to use it. But we try to be slightly more opinionated. We don’t have the complete bottoms-up, or throw stuff at the wall. I think it’s due to our history. So, when we’ve developed products in music, it usually involved — once you came up with the idea, you had a three-year roadmap to go and license that idea from four majors. And if you licensed the wrong thing, you lost four years. So you needed to be right, and you needed to be more sure, because the cost of being wrong used to be so high for us. And I don’t know if it’s good or bad. I think if we had grown up in a world where the cost of being wrong was just the engineering time put into it or something, and you can just pull it back, maybe we would be different. But we have a pretty specific culture where we actually do plan quite a lot more. I wouldn’t say Steve Jobs, for sure. And Daniel himself actually talks all the time about distributing decisions, but it is more opinionated.

And then for PULSE, we’re lucky enough to have Gimlet and all these studios in-house, with lots of fantastic creators. So we get to test this internally, and we use them as an internal inspiration. And sometimes they are the product owners, because they represent the user needs.

Sonal: That’s fantastic. Connie, more thoughts on interactivity? I feel like you live in this world, and you talk so much about China apps and what’s possible when it comes to interactive audio.

Connie: So another interesting thing about creators that comes from looking at what’s working in China, is not just giving them feedback on what the audience wants to hear next, or what the audience is thinking. But also separating your average listener from your super listener — the person who really wants to, even pay you directly for your work. And helping you identify who your real true fans are, right? If you think about the creator economy — very clear trend that’s already been in Asia for a while now.

So, something like the QQMusic, which is the main music app that people are using in China. If you have someone who is hosting a radio show or, kind of, a listen-together type of group chat, there’s the option to, basically, be part of their paid fan club. And then if you’re a part of their paid fan club, you get a different badge on your own profile, you get access to exclusive virtual gifts that you can send that host — so everyone knows that you’re a part of that paid fan club. You can get a different announcement when you enter the room, different kinds of bonus check-in tasks. There’s a bunch of new features that get unlocked if you’re a part of this creators’ fan club. And, ultimately, what that allows the creator to do is monetize better than just a traditional advertising route. Because in addition to receiving normal virtual gifts from their listeners, from anyone who drops in and participates, you also are cultivating your small following of super fans who really, really love you.

Sonal: I love that you’re pointing that out because it’s basically making this link, that these tools and features are not just about getting more information or data— but, actually, they’re paths to monetization as well, which is super interesting.

Connie: Well, it helps you create your own empire in a different way. Like one feature I love is this battle feature, where you can almost battle another radio station at the same time, and almost compare how many gifts each of you are able to aggregate in a certain period of time.

Sonal: It’s like duets with an audio challenge.

Connie: It’s really focused on how to help creators motivate their community and build that core fan base.

Gustav: So, one of the things that I think is really interesting with these things that you mentioned — they’re dependent on actually having a logged-in service, so that the creator can understand their audience. That wasn’t really possible over the previous protocols. You got download numbers, <Yeah.> but you couldn’t really understand your audience and who was your super fan. You know, what they look like, and who they are, and where they live, and so forth. Whereas, that protocol doesn’t actually support feedback to the creator — it’s a one-way broadcast protocol. 

But because we’re now, sort of, full stack, we can start doing these things that have happened in other industries. And the thing that happened in video, and in many of these other things — like, you take text messaging, for example. It used to be [that] standardized and innovating on that text messaging protocol needed a ton of carriers to sit in different forums and agree, right? So the benefit was ubiquity and reach, but innovation was really slow. And then at some point, something like Snapchat happened, that verticalized the whole thing — and, you know, WhatsApp and so forth, and innovation just ran away. One day, you had disappearing messages, the next day you had stories, the third day you had lenses — because it didn’t really have to wait. And so, I’m really excited about that happening to audio.

Connie: Yeah. This is what we mean when we say, like, very early innings of audio.

Gustav: Exactly. But there was, like, a technical foundation that needed to exist. That does exist in China, to your point. They’re all vertical.

Sonal: Yeah. I’ve been very obsessed with — and the student of — the history of innovation. And to me, this is the classic arc from when you go from a utility layer to, like, a value-add layer. And, of course, there’s a lot of debates around what platforms should and shouldn’t have control over. And that’s something that’s playing out a lot with crypto, and a lot of other discussions. That said, I think the point you’re making, Gustav, which makes it less academic and more interesting to users is — it is really — comes down to — you are giving me something I can’t get right now.

Connie: Yeah. If you have one app that can give you a vertical solution — basically, give you everything you want — that app’s true understanding of you is very strong, and its ability to personalize things towards you is higher. Your ability to create a profile, that you then are proud to share with other people, or that you want to build upon — whether it’s earning different levels or different points, that also increases.

I mean, I love what Gustav is saying about how things are more vertical. There’s a lot of benefits when you take, kind of, the super-app mentality. And a super app is basically a product or a platform that focuses on all the different needs a particular customer wants, versus giving a single-feature solution. Recognizing that, “Oh, this person loves listening to these kinds of music, but this person also probably loves listening to all these other things. So why not let’s offer this all-in-one package? We now better understand that listener, and we can solve more of their problems.”

Gustav: So, we were actually quite inspired by the super apps of China when we thought about podcasting. The obvious solution, if you’re going to build a podcasting app — if you come from a pure design angle — is to build a standalone app. But the trade-off, then, is distribution. And so, we looked at it more from a super-app point of view. And we realized that what users actually wanted was all of their audio — you know, which they used to have on radio, music, and talk, and so forth mixed. And we had a zero-user base in podcasting, so we’d be starting from scratch. We had hundreds and millions of music users, and that’s an advantage in itself. But more importantly, we understood these users. They were logged in, and so we could just augment their moments. And one of the interesting things we found was that it turns out that your music listening is actually very predictive of your podcast listening.

Connie: You can probably guess a person’s age range from their music listening alone, right?

Gustav: Yes, you can. For sure.

Sonal: So, you’re saying people’s music listening predicted their podcasts taste?

Gustav: Yeah. When you want to cold start a podcast listener, it turns out that your music listening is actually a really good signal for that — for which podcast you recommend.

Sonal: That is incredible to me. I just think people’s music listening is so much more visceral and less intellectual — that I’m just so shocked by that fact.

Gustav: I would not say it was obvious to me either, but it’s, like, a very clear result. It also supports the idea of the audience — that you should think of them as one person, right? And try to serve them in the different needs they have.

Connie: Yes, think of the customer as one person.

Challenges of integrating media

Sonal: Right. What you’re basically both really saying is — when you think of the super-app mindset, it’s a cohesive identity of a user’s needs. And, in fact, if I were to visualize it, I think of that classic Da Vinci Renaissance man [Vitruvian Man], where you have like this person at the center, and then you have multiple spokes of interests — kind of, radiating around them. And then you think of each of these moments in their day. It could be time, it could be interests, it could be need. It could be whatever job to be done, to use a Clayton Christensen framework — and that you’ve referenced a few times, Gustav. But what you’re both also essentially saying, is that a super-app — once you have one — is built in distribution. And so you’d be silly not to use that base and do the cold start.

Gustav: Yeah. It’s much easier to say, “Let’s put a competing team over there and let evolution take care of [it].” They build their own app and they compete. But it’s at the cost of the user to do it that way. And so the first thing we did was, we figured out that instead of having the apps be as different as possible, you actually wanted to have them be the same thing. And you can say that radio has always done this. People have been mixing these mediums, so it didn’t seem that far fetched. But it wasn’t clear. And if you optimize for ease of implementation, you have small things such as — just the fact that the UI has to change from skipping a whole song, when you’re listening to music — to, all of a sudden, skipping 15 seconds back and forth, and scrubbing within a podcast. That’s a big challenge to solve dynamically in the same UI. It would have been much easier to just maximize the two different hypotheses.

Sonal: Yeah. So, basically, what I’m hearing is, even something as seemingly mundane to the user as the ability to scrub forward 15, 10 seconds — which I do all the time in my podcasts. If you’re in music, you can just skip an entire song forward. And even that kind of trade-off is, like, actually really complex when you’re doing it in the same UI. That’s super fascinating.

Gustav: Exactly. So the UI has to be much more dynamic.

Connie: I mean, even how you show a track versus an album cover, right? Or a podcast episode versus the podcast cover — like, it’s a very different thing. It’s not easy to pull off. And it gets harder and harder the bigger the company is, because it requires real changes that are top-down, that have to come from leadership. It’s a change in your org structure, it’s a change in your release cycle. It’s a massive change, and it’s very hard to pull off.

Gustav: It was painful. We needed to “force.” It’s not like people didn’t want to do it, but you needed to get people to work with each other instead of putting [it on] a different team. And it certainly needed global prioritization, from Daniel down. And we have this system to prioritize things globally, called <inaudible> in Spotify, which was very helpful to get these things through the company. And I don’t think if we’d had that global prioritization tool, we could get this through the company. It’s very hard to do. But this is the benefit of software, right? And this is one of the benefits of being full stack. We can actually try to solve these problems, and actually improve the consumer experience.

Redefining audio as media types merge

Sonal: So, let me ask you guys a quick question — especially you, given Spotify worked within the existing UI to blend from music to podcasting. Where do you stand on the definition of podcast, music, audio? I always talk about how audio is a huge category. Like, I honestly think trying to homogenize audio is like trying to homogenize text. It’s like — a word is the same thing as a book, is the same thing as an article, as a blog post, as a tweet. That’s ridiculous. However, Connie, you made the argument in our podcast about podcasting, with Nick Quah — how podcasting and music— and I agreed with you, as well, then — that there’s a big difference between the spoken word and the sung word. And so I’d love to hear your guys’ thoughts on, where are we today?

Connie: Radio is the integration of both talk and music. They live very symbiotically together. And if you look at most podcasts, they have a music introduction already. There are sound effects in a bunch of them too. So this combination, or this belief that normal talking can be improved with music, or music can be improved with talking breaks, has been here forever.

Sonal: But even then, where does, and doesn’t the blending of music and podcasting actually work, and where does it fall apart?

Gustav: Right. So we had this intuition that people wanted their music and their podcasts in the same app. And that certainly turned out to work. But there was a category where they’re actually related. It is the same session, right? So this is the thing that we just released. So now we are going to let creators do this new type of session, where they can mix talk with licensed music in a seamless session.

So, you see these two user needs. If you take the Clayton Christensen approach, you see podcasters really wanting to use and talk about music, but they can’t — because the creators do not get paid for some burnt-in song in a podcast. And then you see the music creators that would like to talk about the music. So you have both of these sides at the same time. And it’s been really hard to solve it, especially if they were two different apps. But now it feels very natural that you should be able to have this new type of show.

So you’ve seen us play around with things like Daily Driver, for example, for a long time, where we mix talk and music. And we’ve seen a lot of success. People love hearing their news, and then their new music in the same session. Especially when they’re driving — trying to switch to the music session and hear the new releases as well. But so what we were thinking now is, we want to enable anyone to do that.

And on the consumer side, it is neither a podcast nor a playlist. It’s just, <Yep.> the best of podcast and the best of playlisting. But it is neither, because podcasting has the problem that you actually aren’t allowed to feature music in it — and playlisting has the problem that you actually can’t comment between the tracks. So we created this new format where you can do some talk, then you can add a Spotify track in there — then you can do some more talking. And so the user can then listen to the talk part as if it was a podcast. They can listen to the track, they can skip the track — but they can also save the track if they like it. One of the things that radio has missed. So it’s a new format. But, hopefully, it’s not new in the bad sense, that you have to learn anything new — it should be just like listening. Because, then it works the way you, kind of, always wanted it to work.

Sonal: What would you call this new format? I think very broadly of, again — I mentioned how audio is as heterogeneous as text, so it’s ridiculous to use one word for everything. But it is a new kind of audio experience. It’s not a podcast, it’s not music, or a song.

Connie: I think of this as going back to radio. For me, this is the new radio station.

Sonal: Yeah.

Connie: This is the new way you can listen together.

Gustav: In a sense, a very obvious innovation — but also an innovation that requires tons and tons of licensing work over many years, and a big investment in podcasting and creator tools and so forth.

Connie: I’m smiling because it’s going to open the door for a whole batch of brand-new creators. People who don’t want to host a podcast and talk the whole way through, but now can use music as their passion — as their content — as the thing they’re, kind of, anchoring their talk around. And then this also brings about curation, social discovery. I mean, I can even think of several a16z colleagues, myself, that I think would be really good creators on this new platform.

Gustav: That’s what I’m hoping for. I’m hoping for you, Connie.

Sonal: I think she means Anish, because Anish is a side deejay.

Connie: No, my stuff will all be probably Chinese music.

Gustav: We want that too.

Connie: Yeah. But the point is, it really opens the door to new batches of creators. And it brings in social discovery, and it brings in the idea of curation. It’s back to, kind of, the Spotify playlist, but with more color, right? And with more storytelling.

Sonal: Augmenting, I might even argue.

Connie: And the interaction that you can have with the listener, right? In Asia, you can have people order different songs and pay to try and see what’s already on the playlist, and change that playlist — even in real time. So the kind of interaction you can build on top of this is also exciting.

Gustav: And you spoke about augmenting there, and I think that’s a great point. So we spoke about TikTok, and I mentioned this pattern of taking a, sort of, commodity licensed music and letting your users make it unique. So one way to think about this is, it’s a similar pattern. We’ve had tremendous success by letting our users work with the music catalog and playlist it. You know, they create billions and billions of playlists that have helped them, and has helped other users. But it has also helped all our algorithms to learn, right? So you can think of this as a similar pattern, where you take the commodity catalog, but you let any creator, through Anchor, work with it and make it more unique and uniquify it, right?

Sonal: I love it, uniquify again. Well, the other interesting point is when Eugene and I talked about TikTok on this podcast, he did bring up that one of the big unlocks, as minor as it might seem for the remix culture as well — was the ability to quickly license, combined with the creator tools, combined with the distribution — so that you do, then, get this “creativity network effects” flywheel. Which, sort of, then reinforces.

Connie: Yeah. It’s a big way that people are interacting with music on the QQMusic app. When you tap into radio stations or listen together, you see all these different hosts, and you can listen to them live. When you’re listening together with other people, you can choose different topics or categories — like friendship, music, emotions, talk shows. And the interactions that you already see happening on these radio stations are “listen together” — there’s a chat that’s usually going on while people are listening to music. There are different leaderboards for these different creators. You can have different tasks that the creator asks you to do. You can order songs, you can see what’s next on the playlist. You can gift the creator, and thank them for curating this kind of music. And you can even subscribe to their fan club, right? Like, if they always have great music choices, you can make sure that you’re always able to know when they release something new, or when they go on. So it does unlock a brand new batch of creators that today don’t live on YouTube. Today they’re not podcasters. But they have a lot of things to say, and they love music. So a lot more people will be able to participate — be creators themselves, build a following, and eventually monetize.

Gustav: I agree. The increased participation of new types of creators is really interesting, because there are all of these creators who clearly want to talk about music, and there are all of these artists who, you know — they’ve always wanted to be on radio. <Mmhmm.> Like, they want to be featured by someone, but business models [are] often a problem. No one has been able to solve that, [so] both parties actually get paid for that. We solved what I think is a harder part, actually — of licensing all the music in the world and paying royalties to all organizations. We’ve already solved that, so it feels like a very natural product for us to play with.

Connie: Yeah. When I was growing up, I used to listen to radio shows. You know, I used to listen to Delilah, and she would have stories in between. And then she would have audience people call in. And then she’d have nice, soft music to go with that story.

Gustav: Exactly.

Connie: And it was fantastic.

Gustav: And then you probably recorded the tracks, right? Because you really wanted the music?

Connie: And that’s how I discovered music too, right? And that’s how she could also resurface music from the past, rather than having us listen to only stuff that was released in the last 18 months. Let’s resurface some of these oldies, and this is potentially a great way to do that.

Sonal: What’s really fascinating to me about this is, it’s almost like a vector to social. Because there’s nothing more inherently social than music listening, and music sharing. As you’re noting of playlists, music curating — and to your earlier points about it — unlocking creators. One of my favorite podcasts, actually, is “Song Exploder” by Hrishikesh Hirway. And I actually think I heard about this podcast from Eugene, actually, like a year ago. And it’s now they’re going to be a Netflix show. And, you know, he really deconstructs these songs on air. But imagine all the people — like all the kids, all the adults, who just lie around listening to music, talking music with their friends, bonding over music. So, to me, what’s really fascinating here is — there is a social vector, both socially and para-socially with acquaintances and strangers, when you think about them connecting with fellow fans of those playlists and other people. So I think there’s actually a really interesting vector to all that too.

Gustav: Yeah.

Sonal: Because TikTok is not a social network, but this theoretically could be.

Gustav: So this is an interesting point. We think about Spotify more like YouTube and TikTok, than Facebook and Twitter. It’s actually not about following your friends, but I think you’re right. I think there are so many creators out there who would love to tell a story about a specific piece of music, right? Their own story, some story, or something. And we’ll see how it gets used. I’m hoping, obviously, that many artists would like to tell their story of their own album that they released, for example.

Sonal: Yeah.

Connie: Yeah. Amazing.

Gustav: There are many different things that could happen.

Connie: Even in that great example where the artist is telling the story, that artist doesn’t have to sign up and say, “Okay, I’m going to start a brand new podcast.” That is such a big responsibility and commitment to take on.

Gustav: Exactly.

Connie: And now you, kind of, have these, kind of…

Sonal: A Trojan horse is starting a podcast <inaudible> basically.

Connie: This really lowers the bar of commitment for creating a show. And you can try it with no real consequence, and get that distribution, too.

Recommendation algorithms

Sonal: Okay. So, now let’s, then, talk about — how do you solve — this is, like, the big elephant in the room — and, potentially, the big exciting thing in the room — recommendation and discovery. How do you, then, think about that side of this? Both in the context of Spotify shows, and also beyond. We opened this conversation about what has and hasn’t changed. This has been a broken problem “in podcasting.” It might not be as broken in music. We’ve talked about TikTok, we’ve talked about the parallels and differences between video. Let’s bring it all back together around this theme and topic of recommendation and discovery.

Connie: For music, there is a commitment of more than two or three seconds to figure out if you like a song, right? So, the bar for who you trust as your source for who is giving you that recommendation is higher. And so you either have to have a system that builds trust, showing that their algorithm has given you enough hits. Like, TikTok can’t be wrong five times in a row. Stakes are really high. So you either have an algorithm that is so good that it knows enough about you already — that the majority of the time, when they give you something, you like it. Or you have a creator that also has that same kind of hit rate. That you realize, “Hey, most of the stuff that that person likes, I also like.” And that is also a great way to, kind of, get that discovery element. It’s all about giving the user this end trust — that they’re willing to test your recommendation because, say, 80%, 90% of the time, you’re going to be right.

Gustav: So I think you’re completely right. That was a success with user playlists. There are literally many billions of different curations of the Spotify catalog, so you literally have something for everyone. And either they find that playlist, or you can use machine learning to learn from that to be able to serve users. Then you have the UI elements themselves. And I think that’s different between music and podcasts. Music is easier, in a sense, because it is three-minute items and you can skip through. And what we see in music is that it’s, like — the investment of how much time do you spend, versus finding one jam. So it is actually okay if even most of the songs, theoretically, are not that good. If they’re easy to skip through, and, like, the seventh song is, like, your dream song. Because that can make your entire week, or maybe month, right?

So I try to think about it — I think Chris Dixon said this, “a fault tolerant UI.” If your machine learning is perfect, you only need to unshow one item. If your machine learning is 1 out of 10, you probably need to show 10 items, because then there’s always one jam on the screen. You have to adapt your user interface to your, kind, of level of recommendation. And so, these playlist formats — we try to think of it as, kind of, a GTD — get things done. Can you quickly go through and like, “Yeah, that was perfect, save that to my library.” It’s like a productivity flow in the discovery moment, which is very different from the consumption moment, when you may be on a speaker. And then it’s not okay that you have three bad songs in a row, but it’s okay if the fourth one is good. Does that make sense?

Sonal: That goes back to modes, actually. Thinking about the mode the user is in.

Connie: Yeah. I also think, if there are good mechanisms in there for the creators to have potential financial payoff from participating, the creators are actually going to be incented to have discovery. That incentive is actually built in. Because you cannot have thousands of concurrent Spotify shows all showcasing the same music. No one is going to want to listen to that. And so, all these creators are naturally going to be incented to showcase you something brand new, because what they’re really being valued for is their ability to curate, and then match that with the storytelling.

Let me give you a concrete example. When I go to the gym and someone is trying to do a workout, and they’re talking through, and they have music sliced in between. Or just think about a yoga class — they want that variety of music. They don’t want you to be listening to the same thing, time and time again. And now even that gym workout, that yoga class — could exist as a Spotify show, where they’re making you do pushups and counting down, and then there’s music right there in the background. You have to really think [about] what this can unlock.

Gustav: I’m definitely hoping for that yoga and pushup workout to happen. You have to make it happen. <laughter>

Sonal: Okay, Connie. So either you make a yoga show, or you do, like, a Chinese song playlist.

Connie: No. But the point is like, there’s so much context that can now be wrapped around recommendations. Like, even the time of day — what are the right kinds of shows that work for the morning, what are the right kinds of shows that you want to wind down to. Those creators will have the incentive to naturally pick what they think makes sense for you.

Gustav: Exactly. So I think there are two things that are really interesting here. So one is, when we think about machine learning overall, and recommendations from a product point of view — and this is completely borrowed from Andrew Ang, by the way, so it’s nothing that we came up with. That we try to use is — if you think about what algorithms do really well, they tend to scale really well. They tend to be able to personalize, at an okay level, to hundreds of millions of people. Humans don’t do that really well. Humans are incredibly smart and creative, though, but they don’t scale so well. So one way to think about this, that I think Andrew Ang coined — was to let the editor, for example, or the creator, if we’re talking a Spotify show, but an editorial playlist — this algotorial principle that we use.

Sonal: Algorithm plus editorial.

Gustav: Exactly, algorithm plus editorial, that we call algotorial. You literally think of the editor as the product owner. This is the product person that has the idea and the hypothesis. And they come up with what the job to be done is, or what the hypothesis is, or what the use case is. So, for example, you take something like songs to sing in the car. No machine came up with that idea. It was a human who sat and said, like, “I think there’s a user need here. People want to scream their lungs out when they’re driving to work.” So how do you teach a machine this? The algorithm doesn’t understand what “songs to sing in the car” means. Is that, like, a bit of ’80s music, is it a bit of movie music? But for a human, it’s super clear — like, this is a song to sing in the car, this is not. So what the editor does is, they literally create, like, a playlist of a few thousand tracks, and then the algorithm can understand it. And they can personalize it to 300 million people and scale it, right? So the job of a product owner is to create this data example, this data wireframe — I think, is very useful. That loop has been very useful for us.

Sonal: So, basically, bundling the best of human creativity with the best of algorithmic scaling, in order to deliver on the personalization and recommendations to a mass of users.

Gustav: Exactly. Humans have to come up with the ideas. They have to show the ML system what that idea actually looks like for the ML system to understand it. Because the ML systems are great at scaling, but not great at coming up with new ideas.

Sonal: Can you give me a little bit more color on some of the challenges here? I’d love to hear about how you have to think about solving them — what’s hard about algotorial. But then more specifically, about how you had to negotiate that, when you transitioned from music to podcasting — and then now in blending the two. I want to hear a little bit more color about it, basically.

Gustav: So, in music, we have, really, two sources — traditionally — of recommendation information. One big source is the playlists, the other is editors. But then we have the third way, obviously, which is the engagement from the users — listens, and skips, and so forth. Those are the signals in music. But music is different because the items are three minutes long — like we spoke about, it’s more like TikTok. Then you go to podcasts, and it’s like maybe one and a half hours, and then you get one skip. <laughter> It doesn’t fit at all with, like, “Let’s just, you know, feed the machine,” right? It’s very low signal. So we had to think about it completely differently. But, not only is it much further between the skips — we don’t have anything equivalent to a billion playlists. So we had to go back and start working with “old tech,” like knowledge graphs.

You have other advantages in podcasts which is — there’s actually information in the audio. You have other signals. You have show notes, and you have the transcripts on the shows. So we started working with those technologies instead to get some understanding. So, actually, these two stacks are quite different. We certainly could leverage a lot of learnings, but they’re not the same thing because there’s such different objects.

Connie: Especially because podcasts are usually multiple people on a podcast. There’s oftentimes a host and a guest.

Sonal: You actually don’t know who people are following sometimes, who they care for.

Connie: You don’t know. If there’s, like, a Joe Rogan talking to Elon Musk, you don’t know if it’s because I like Elon Musk or if I like Joe Rogan. That’s quite different than music, where there’s a bunch of artists — any song they put out, I’m going to like, I’ll take a listen to.

Sonal: It’s like a cult of personality show, because you’re following the host, in that case. In this case, you’re following the artist. But one thing that I think is really interesting when talking about the knowledge graph is the mood graph. I always talk about — coined the phrase when I assigned an op-ed on it a number of years ago at WIRED. Because I actually think we’re missing a huge opportunity in optimizing things. Frankly, my playlists are all organized by mood and emotion, they’re not organized by any other criteria.

Gustav: That’s a great point. And in music, that is one of our biggest vectors. Like, one of the biggest sections of editorial playlists are the mood playlists. You’re completely right.

Sonal: Ooh, that’s great. It’s interesting you bring up a knowledge graph, Gustav. Because it’s tough to know — is it a book author? They’re just listening to every single podcast they’re on? Is it a content thing? It’s so complex and multi-dimensional.

Gustav: Exactly. And the answer, as far as we can see is — it’s all of the above. There’s “personality cult,” there is, you following a certain guest around all the podcasts that they visit. There’s interest. It’s just going to computing — I don’t care who is talking, right? So you really need this knowledge graph with all of those dimensions, and then you need to be able to let the user, kind of, traverse along these different dimensions. And then you can lead them to some discovery. You remember this debate around music — everyone had a music friend that influenced them. And for a while, early Spotify, we invested heavily in social to try to replicate that. But it turned out that most of your friends on Facebook — they don’t inspire you so much musically. If you average them, it’s just the U.S. billboard. So we take the same approach in podcasting.

I mean, we have a core belief that if Spotify can make you discover something that you wouldn’t otherwise have discovered, it will be more important in your life. So we really try to make sure that we measure and understand how many discoveries we generate for you.

Sonal: It’s almost like a new metric of return on discovery. Instead of return on investment or return on energy, if I think about every app, what is my return on discovery — or ROD — on that particular platform?

Gustav: I’ll borrow that from you. But another difference from these things is that we are, revenue-wise, mostly a subscription service. So in machine learning, in the practical world, there’s been a lot of deep learning and so forth. But in the academic world, for a long time, there’s been a lot of focus and discovery and exciting results around reinforcement learning. But, you know, AlphaGo and all these things.

Sonal: Yeah. We’ve actually talked about it on this podcast quite a bit, too.

Gustav: And not to go through it. But the main idea is just — you look for some long-term reward and you backpropagate it through time — instead of looking at, what is the most likely next click? And so, I think, if you have a service that is free only, and, you know, you have an average engagement — same every day — it’s going to be really hard to, like, backpropagate signal. It’s going to be noisy. But if you have an event four months down the line — that is, you know, I went from just consuming ads to paying $120 per year, you have this massive amount of, sort of, gradient you can backpropagate through time.

Sonal: Oh, I love this.

Gustav: And the thing that is different between, for example, YouTube, or TikTok is — every month, all the paying users, hundreds of millions of them, they go and they evaluate. It’s like, “Should I still pay?” And they vote with their wallet regardless of how much they actually consumed. So we have a different signal that is not just engagement, and consumption, and attention. We can see — do you keep paying? And, obviously, as you know, it’s not really possible to do the real reinforcement learning. You basically need a perfect simulator of the world. But you can approximate it quite well. And so that’s something that is happening in the rest of the industry as well, slowly. You need enough signal for that to really be valuable. So that’s something I’m excited about in the recommendation space.

Sonal: What you’re basically saying — I talk about this quite often on the podcast, about how subscription models change so much. But what you’re saying, which is so fascinating to me, is that it’s also a way to get much better signal into your system.

Gustav: Right.

Sonal: You’re also basically saying — you’re essentially weighting higher people with more skin in the game, which is exactly how you want to design something.

Gustav: Exactly. Everyone has saves and likes. But you can think of, like, paying $10 as a super big like every month.

Sonal: Yes, exactly. You’re weighting it higher. And you have that data because people are logged in and they’re streaming. One of my favorite books is James Carse’s “Finite and Infinite Games.” And he just died, actually.

Gustav: Yes.

Sonal: Rest in peace, James Carse. But the idea — what you’re saying is, you’re playing a repeated game with your users. Which then gives them an even better game board to play on, versus a transactional game only.

Gustav: That’s exactly it. Which is a big problem that is important to solve, I think. You can try to understand what the user actually values long-term versus just in the moment.

Connie: Yeah, subscription fees is a fantastic business model. But also, I can see how that would allow new revenue streams for these creators. And I’m not just talking about the people who create the music, but I’m talking also about the people who are going to create and deliver a brand new experience that lives on top of the music. If those people can find some kind of financial payoff in participating, that’s a brand new revenue stream. And then think about the possibilities — the kind of interaction you have with that listener at that moment — is another area you can charge for.

Sonal: I also love that. While we’ve talked so much about putting the power back for creators, it really does actually most empower the listener. Just one quick question, Gustav. How do you think about the tension between data, and all the data you’re getting, and all the signals, and where it goes too far? Like, is there a risk that sometimes, listening to your users, you’re missing out on what they don’t tell you? And how do you think about that as a head of R&D at a company where you’re not just abstract R&D — you’re actually building product?

Gustav: Yeah. I think that’s a fantastic question and really hard to answer. It is an age-old problem. I think one way to think about it is to simplify it a little bit. Algorithms — they, kind of, look in the rear-view mirror and draw a straight line into the future. And so, that’s great for a while. But product development — usually, good product development is based on some sort of ideally contrarian hypothesis. And your machine learning is not going to come up with a contrarian hypothesis, right? So you need some mechanism for that to happen. And so, we try to think of this in different ways. I mentioned algotorial, where the editor actually has the ability to say, like, “No, I believe in something different.” So we try to build in this mechanism where humans can go in and, you know — they have the steering wheel, they can take a left turn or something, and then the algorithms follow.

And, you know, there are incentives to not do it. It is always going to be safer to keep going straight for a while more. Why take risks? All of these things, right? But back to playing infinite games. If you play the game, you know, many times — think about it as game theory — now you have to end up in a place where the optimal thing is to try new things every now and then, to try to cover as much space as possible. And as I said, we have a culture of being quite specific in the hypothesis we have. And we try to think about it, as do many companies, sort of, a portfolio. I want to have some things that are quite contrarian, and [have] a pretty high chance of failing. Whereas, I want a bunch of things that are obvious. But that balance — I mean, no one has the perfect solution, but everyone at some scale has to start thinking about it.

And so we found a few mechanisms that were useful for product development. One was to take the concept of simple prioritization, and the Kanban board, all the way to the C-suite. You know, everyone thinks they’re good at prioritizing, but they’re not. And I bet that in most companies, the C-suite is the worst at prioritizing. They actually want to do everything. And so, we have something like five to seven things that the company needs to do. And Daniel owns that. But the one rule is — two things cannot have the same priority.

Sonal: It reminds me of the Steve Jobs bio anecdote, where at one of their off-sites, they put a whole list of things, and he literally crossed everything off the list and they only did the first four. What you’re describing, though, is not just siphoning off what to do versus not to do, but what to order the priority, from the top, so that the managers don’t have this friction and they don’t waste in terms of building things.

Gustav: And that’s the trick.

Sonal: Yes, I agree. And the other thing that I think is fascinating about that, is that when you say that Daniel, kind of, owns that too — when you are disrupting yourself, so to speak — like, when you went from music to podcasting. Putting that higher up on the bets board in his office is, like, “Hey, no complaints, guys. This is it.”

Gustav: So that’s exactly what happened. Podcasts was the number one company bet for two years, and everyone in the company knew it. And so, what happens if you don’t have that? You push that decision to managers and you create conflict in your world. The truth is, Daniel can’t have any idea, in a company of thousands of people, what is going to clash with what resources.

Sonal: Of course.

Gustav: The only thing he can do is, like, when you clash, this is the priority.

Sonal: I love that as a management thing.

Gustav: It’s so simple. Everyone thinks it’s so complicated. It’s actually very simple. It’s — the discussion is hard. Actually, prioritizing is very hard.

The future of audio

Sonal: Okay. So we started with talking about where podcasting has been. We’ve gone through what’s shifted — the parallels and differences between video and music. We’ve talked about the trend of interactivity, and augmenting audio in different ways. We’ve talked about recommendations and hearing like an algorithm, even, and an editor. What do you guys think is, sort of, the future of a lot of these? Like, where do you think the future is, kind of, going?

Gustav: My guess is that if we use the cheat sheet of other media, I think audio is going to increase on the creator side just like the other mediums. I think it’s going to increase in numbers of creators.

Sonal: The market for audio is bigger than I think people realize. Or, as Connie said earlier too, we’re still in the very early innings. So my obsession is this two-word phrase that I use all the time — of world-building. And to me, one of the missed opportunities in audio for a long time — and, you know, Gustav, you painted this range from gaming models, all the way to music models, to different things. I actually think we’re starting to increasingly see more game-like behavior in audio. And I’m so excited for that kind of world building. 

But it’s a very different kind of world building, because audio has an immersiveness that’s very different than the visual-based world-building of other worlds. And so I’m super excited for what we can do. I mean, I already think about our expanding podcast network as a form of world-building. And when you’ve mentioned Spotify shows, that, to me, is another form of world-building, because you’re essentially bridging different worlds and creating new experiences. And so, to me, that’s actually the thing that I’m most excited about.

Gustav: So I think that’s a great way to think about it. And you think of the music world, the podcast world — and now you can think of this new world where you can mix them, and then you can have other worlds. The thing that I think is going to happen is, you look at something like audio — and it’s so easy to create, it’s even easier to create than video. So, as we both make it even easier and lower the friction for everyone, we let creators make more money and we add these new formats. What I’m hoping is that that market is going to grow as well, just like we’ve seen the market for creators growing in other media.

Connie: I think audio will be further optimized in the sense that you can almost peel apart the different nuggets of a podcast, right? You can take certain segments now. You can take a commentary around it now. And you’re going to be able to do new things when you break apart a song, when you break apart a podcast, and you can see what that will unlock. TikTok is breaking apart a song — kind of, getting to a specific 5, 10 seconds slice of it, right? A snippet. And then, this idea of now taking something that used to be, you know, one piece of content and chunking it down to different things — now [that] gives you new building blocks to build new kinds of shows, new kinds of interactions. Which means things will get much more participatory. More people can become creators. More people can probably become listeners. More listeners will find each other, listeners will become stronger fans of their creators. So I think there’s a very hopeful, very optimistic future, where now technology actually can help everyone win.

Sonal: That’s fantastic. I love that. Gustav, Connie — thank you so much, you guys. Thank you for joining the “a16z Podcast.”

Gustav: This was super fun.

Sonal: Super fun.

Connie: Thank you.

Sonal: I wish we could all talk for hours. Take care, everyone. Bye.

Connie: I should put in a plug for my Spotify show. <laughter>

Sonal: The China Song Show, Connie?

Gustav: It’s going to be huge.

Sonal: Bye, guys. Have a really good day or evening for you. Take care, everybody.

Gustav: Bye. You, too.

Sonal: Thank you.

• 

  Gustav Söderström
• 

  Connie Chan is a general partner at a16z where she invests in consumer tech. She's well-known for her deep knowledge of the Chinese consumer tech landscape and spotting those trends moving from east to west.

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• 

  Sonal Chokshi is the editor in chief as well as podcast network showrunner. Prior to joining a16z 2014 to build the editorial operation, Sonal was a senior editor at WIRED, and before that in content at Xerox PARC.

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