Pod Insights The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: Scaling to $1BN+ in Revenue with No Funding: Surge AI | The Most Insane Scaling Story in Tech | About this episode Edwin Chen is the Founder and CEO of Surge. Founded in 2020, Surge has scaled to $1BN+ in revenue with zero external funding. At the same time, their competitor, Scale.ai raised over $1.3BN to reach $850M ARR. Today, Surge have the world’s largest model providers as customers and have just 120 employees. Agenda: 00:00 — “Everyone Else Is Just a Body Shop” — Edwin Calls Out the Whole Industry 01:05 — Why 90% of Big Tech Is Wasting Time on Useless Problems 03:45 — “I Don’t Do 1-on-1s” — How Surge Kills Meetings and Still Moves 10x Faster 05:55 — Will a Single Person Build a $1B Company? 08:10 — 100x Engineers Are Real — Here’s How to Spot Them 12:10 — Why Most PhDs Are Useless in AI Training 14:20 — Built to a Billion With Zero VC — Edwin Explains How and Why 17:00 — “No Sales Team, No PR, No BS” — Why Surge Stays in the Shadows 21:15 — The Real Reason AGI Might Take Until 2040 24:45 — Will Synthetic Data Kill Human Labelling? 29:00 — “Academic Benchmarks Are a Scam” 31:05 — Why the Real Bottleneck in AI Isn’t Compute or Models — It’s THIS 33:00 — What Every AI Company Should Be Asking (But Isn’t) 35:15 — “No, I Wouldn’t Sell Surge for $100B” 39:00 — Is the Application Layer Doomed? Edwin Predicts the Future of AI Startups 46:30 — Have the Leading Foundation Models Already Been Founded? 48:10 — AGI Could Be Dangerous — And Most People Are Ignoring Why 20VC: Scaling to $1BN+ in Revenue with No Funding: Surge AI | The Most Insane Scaling Story in Tech |
Transcript 14,582 words · 3 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 I think a lot of the other companies in our space, they're just not technology companies at the end of the day. They are either body shops or they're body shops masquerading as technology companies. One of the things that we simply tell everybody when we first join, quality is the most important thing. I definitely want to sell for thirty billion or even a hundred billion. If you think about us as a company, I already have everything I want. Yeah, we're profitable. I have complete control of our destiny. And so I'm really lucky to have all the resources I want to already do anything that I want.
0:27 Harry Stebbings? This is 20 VC with me, Harry Stebbings, and today I feature probably one of the most impressive companies that I've ever featured on the show. Founded in 2020, Serge now does well north of a billion dollars in revenue. And the crazy thing, they've never raised a dollar of outside funding. Their founder Edwin Chen barely ever does an interview. He never talks publicly and today he agreed to sit down with us to break down the incredible last five years and his biggest lessons. But before we dive into the show today, I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data and the projects that we're working on across dozens of platforms, products and tools.
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4:25 Harry Stebbings? You have now arrived at your destination.
4:28 Harry Stebbings? Edwin, dude, I'm so looking forward to this. I am like the biggest fan of your business from afar, which makes me feel incredibly weird because we haven't met before, which means I'm basically a stalker. But thank you for joining me.
4:38 Speaker 00 Yeah, thanks for having me. It's wonderful being here today.
4:40 Harry Stebbings? Now, I wanted to break the show into two different parts. The first part being kind of the story of this incredible rise. And then the second part really being assessing the future of data, data labeling, and taking a kind of more analytical approach. If we start on the story itself and pre actually the founding of Search, you said to me that 90% of the people while you were working at your Google, your Facebook, your Twitter, 90% of the people there were working on useless problems. I thought that was a very interesting place to start. Why were they working on useless problems and what did it teach you about efficiency seeing that?
5:15 Speaker 00 Yeah, so I think the biggest lesson for me was that you can build a completely different kind of company with 10% of the resources and 10% of the people. But you're still moving 10 times faster and building a 10 times better product. Like, imagine you could just magically remove the 90% of people who aren't working on interesting problems. What would happen then? Well, if you have a company that's one tenth the size, you don't need to hire as many people. So you spend less time interviewing, you spend less time in meetings, you spend less time giving people updates for the sake of updates. And if it's one tenth the size, that means everybody has a better view of what's going on around the company. Because there isn't all this clutter masking important stuff. And because the talent density is higher and teams are smaller, that means the communication is a lot higher and the iteration speed is a lot higher and better ideas just percolate around more quickly.
5:56 Harry Stebbings? Prioritization is slightly ambiguous according to different people. Everyone feels that their project is important and more important than someone else's. How do you determine priorities within a company and determine what matters versus what doesn't?
6:08 Speaker 00 Yeah, I mean, I think a big thing about being small is that when you're smaller, that means that I, other people around the company, we just have a much better view into the customer problems themselves and what everybody's working on. And so it's kind of like at these bigger companies, a lot of your priorities, a lot of the things that you're building, they're simply, you're simply building them to impress someone like. Hey, I need to impress my VP. I need to impress my manager. I need to impress my director so that I can get promoted. And you're not really building things or prioritizing things because they're good for the end customer. They're good for the end product. It's more like, okay, I have this priority to, like, let me think about it. It's like, I have this priority to improve an internal tool. Okay, why are you improving the internal tool?
6:51 Speaker 00 Well, it will make people 5% more productive. Why do I want them to be 5% more productive? Because they're spending 20% of their time interviewing. Why are they interviewing? Because they, like, they're growing for the sake of growing. And it just leads to this perpetual cycle where a lot of your priorities are just divorced from like the end customer, the end product. And they're almost like priorities just for the sake of internal company machinery.
7:14 Harry Stebbings? What do you think no one knows about working within these big, incredibly hailed companies that they should know?
7:20 Speaker 00 I think one of the things that people don't realize, again, from the outside, it's that how much of what you're building, again, is for this internal company machinery? And how much of the internal company machinery is simply because a lot of people within these organizations, their goal, again, their goal isn't to build a product. Their goal is to tell their friends they're a VP of a thousand-person org. And that sounds impressive. And so their goal is to think about, okay, so how do I, how do I grow my org even faster? How do I find more teams that I can hire? How do I have these monthly performance reviews where, again, now that I've built this thousand-person org, I need to prove to my VP, my CEO, that the thousand-person org I'm building is efficient and useful.
8:04 Speaker 00 And so, like, basically, a lot of the work that goes on in these large companies, it is simply to kind of perpetuate and grow even, even further a lot of this, like, very, very big company machinery that exists purely, purely for, like, internal reasons.
8:19 Harry Stebbings? When you're hiring, how do you determine between managers who like to brainstorm and tell their friends that they have thousand-person orgs and they're very powerful and they are very important versus doers, those that execute work and complete tasks? How do you determine the two? And are there very clear differences?
8:36 Speaker 00 I think a big part of it actually just boils down to the kinds of questions they ask me. Like some people, when I interview them, they will ask really interesting questions about our product. They will brainstorm about ideas to make our product even better. They'll be like, okay, yeah, like I went to your webpage. Why don't you improve these things? I tried signing up as a worker. Why did these things happen in a flow? I tried working on this project. Like, what if you guys did this instead? And other people are like, if I join in a year, will I be able to be a manager of a company? If I join, will you be able to hire, like, will I be able to hire 20 more people to support me? And so it kind of just boils down, I think, a lot of times to the kinds of questions that people even have at the forefront of their minds.
9:14 Harry Stebbings? Can I ask you, in terms of meeting cadence? I'm sorry for being granular and I told you we go off schedule. But I've had Toby on the show in the past to, from Shopify, who's obviously advocated for no meetings. Given the ability to spend lifetimes in meetings that are quite pointless, how do you approach meeting policy and what does and doesn't belong in the org?
9:33 Speaker 00 Yeah, so I'm a big fan of that. So like I, for example, personally, I actually have no one-on-one meetings. And it's kind of funny because oftentimes people will ask me, well, how often do you meet with your reports? How often do you set aside for like for these meetings? And I just don't have them at all. Like oftentimes, like I will just give people my calendar, my Calendly, and they're just surprised at how blank it is because I tried to avoid filling my meetings all day. And so I will actually go out and I like, sometimes when people join, they'll be like, okay, I need to go and have one-on-one meetings with these 10 other people that I'm going to be operating with on a weekly basis. That's just because that's so used to when you come from Google or Facebook. And I tell them, why are you having these standing one-on-one weekly meetings?
10:17 Speaker 00 Like, did you not talk to them every day during Slack? Are you just like unaware of what they're doing? Like, it's almost like a negative sign if you're having a one-on-weekly meeting because it means that you just don't know what's going on with these people. You're not, you're like almost waiting for your weekly meeting to raise, raise interesting questions and raise interesting problems. And so I think we're pretty ruthless internally about killing meetings when they're unnecessary.
10:38 Harry Stebbings? We mentioned like the efficiency of teams, small teams. Before we dive into surge, one of the kind of hot topics of the day is the future, where billion dollar companies will be built by single people. Do you agree with that vision of the future? Or do you think it's slightly overdramatized?
10:54 Speaker 00 Yeah, I mean, I absolutely believe that that company was this one day. You think about it like I've always believed in 10x engineers, even 100x engineers. And already you have a lot of these single person startups that are already doing 10 million in revenue. And so if AI is adding all this efficiency, then yeah, I can definitely see this multiplying 100x to get to this one billion dollar single person company.
11:12 Harry Stebbings? You can't drop 100x engineer without me diving on it. We've been so focused for so many years on 10x engineers. What have been your biggest lessons on 100x engineers? Do they exist actually in reality? What are the signs? Talk to me about that.
11:27 Speaker 00 I mean, like even today, you see how we are like honestly so much more efficient than some of our peer companies. Right? And so even for that reason alone, you can, you can already see the fact that a 10x engineer or 100x engineers exist, like if you even just break it down. Some people are simply two to three times better, two to three times faster than anybody else. Right? They just code faster. There are some people who simply have two to three times more better ideas. There are people who simply work two to three times as hard. There are people who have two to three times fewer meetings. There are people who simply have ideas that something other people can't think of. And so if you just multiply all these things together, right? And like two to three X is often actually an underestimate. Like I, I know people who yeah, literally are five times more productive coders than anybody else.
12:12 Speaker 00 And now add in, you know, all the AI efficiencies that you get, like you can, you just like multiply all those things out. And yeah, you, you get to 100.
12:18 Harry Stebbings? Do you think AI turns 10x engineers into 100x engineers? Or average onex engineers into 10x engineers?
12:26 Speaker 00 I would say that maybe both today, but definitely even more so in the future. It's like good people have so many ideas that they just don't have time to implement. And if you think of AI today as something that isn't necessarily coming up with the greatest ideas, although it can, but it often just removes a lot of the drudgery of like your day to day work, a lot of your day to day coding. And so if you don't have to spend that time on the drudgery, but you just have like these endless ideas that are just bouncing around your head and AI just helps you put them to paper. Then I, I do think it kind of disproportionately favors people who are already like the, the 10x engineers.
13:01 Harry Stebbings? You mentioned the comparative efficiency in the landscape. Without naming names, a lot of people around you, it's not naming names, but a lot have raised a lot of money to get to a smaller stage than you are. If I were to push you into a camp, is that a result of you being phenomenally efficient where you deserve credit? Or where they've bluntly been incredibly mismanaged and resource allocation has not been done well?
13:25 Speaker 00 I mean, I think it's both. I mean, I think a lot of the other companies in our space, they're just not technology companies at the end of the day. They are either body shops or they're body shops masquerading as technology companies.
13:36 Harry Stebbings? What do you mean by body shops and body shops masquerading as technology companies? I get it, but a lot of people criticize the space with this and say, oh, it's just labor camps or it's... So what do you mean by body shops or body shops masquerading?
13:47 Speaker 00 I think the way to think about it is a lot of companies in this space, so they don't have any technology. And when I think about technology, it's like they don't have any way of measuring quality of the data that they're producing. And they don't have any way of improving the quality of data that they're producing. They are literally just body shops in a sense that they sometimes literally have no technology at all. They don't have a platform where workers are doing work. And so what they're doing is they're simply finding people, like they're recruiting warm bodies. They're looking at resumes, like anybody with a PhD, they'll just instantly hire them. And then they're just passing them along to the AI companies, to the frontier labs. And so, again, they have no technology, they have no way of measuring what any of these workers are doing.
14:27 Speaker 00 They have no way of knowing if they're doing a good job or not. So they have no way of doing things like, hey, what if I A/B tested this algorithm for improving quality? What if I changed this method of allowing workers through? What if I tweaked our tools in order to change these questions around? Would it make the workers more efficient? Would it improve their quality? Or would it actually make it worse? They just have no way of doing these things because again, at the end of the day, what you're passing to like their customers is just the body itself, the person as opposed to the data. And so what that means is they just, like again, they just have no technology to measure or improve anything.
14:58 Harry Stebbings? Do you think you have a fundamentally different business then? Because you're all lumped in the same category. But if they're passing along a warm body and you're passing along data, it's a phenomenally different product and it's monetized differently, no?
15:09 Speaker 00 Yeah, again, like if I think about the way we think about it, it's maybe the following. So we have always started out with quality of the data as our number one principle. And as a result, we need to build a lot of technology in order to to measure that and improve that. If I think about like what what goes wrong, it's that people often just don't realize how difficult quality control is. And people often think that humans are smart. And so if you just throw a bunch of humans at a problem, you'll get good data. And what we found is that that is completely untrue. For example, I went to MIT, but yeah, I think half of the people who graduate with a CS degree, they they can't even code. So it's a really challenging problem to detect high quality. And second, if you actually take the folks from MIT who can code, they're actually just going to try to cheat you. They're going to sell their accounts to somebody in a third world country.
15:52 Speaker 00 They're going to try to use L M's to generate the data for you. They're going to come up with all these crazy methods to cheat a system. So it's also this really, really challenging problem to detect low quality. It's actually really adversarial. And so what we found is that when you want to get the highest quality data to train L M's that are already, you know, super intelligent, you actually need to build a ton of really sophisticated algorithms. You can't just take warm bodies or, you know, try to improve your methods for resume filtering and then throw people at a problem and get good data and results out of it. Like the teams I know who tried this, they actually end up moving 10 times slower than anybody else without realizing it.
16:22 Harry Stebbings? Okay, so we mentioned before the background you had pre obviously being the hailed companies, Google, Facebook, Twitter's. And then you said there about the focus on data quality. Can you take me to the founding moment for you? Leaving the last company and deciding that you were going to go all in on search?
16:39 Speaker 00 Yeah, so I I used to work as an M O engineer at a bunch of the big companies. And the problem I just kept running into was that it it just kept on being impossible to get the data that we needed to train our models. For example, I used to work on our ad, search and ad systems at Twitter. And one of the first things I wanted to do was build a sentiment classifier. Yeah, it's a super simple problem. All you need is 10,000 tweets labeled as positive or negative to train your models. But our human data system at the time was literally just two people we'd hired off of Craigslist working nine to five. Even just in order to get started, we had to wait a month. Then we had to wait another month for them to label the tweets inside a spreadsheet because the tools we had were just terrible. And when we finally got the data back, it was actually just completely junk. They didn't understand slang like, she's such a bad bitch. They were actually labeling this negative when it's actually really positive.
17:23 Speaker 00 And they didn't understand hashtags and all these other aspects of the tweets. And so I actually ended up just spending a week labeling tweets myself because that was so much faster and better. And at the same time, this was actually really simple stuff. But the bigger problem we wanted to solve was how do we optimize our ML systems for the right objectives? And how do we build feeds that are engaging in a positive way for users? Thinking about, again, about Twitter. This was the old days when it was a purely chronological timeline. And so one of the things we wanted to do was just make it easier for users to discover the tweets that they really cared about. And so the question was, how do we train our recommendation arrows? And the obvious choice was clicks and retweets. Like you just train your arrows to produce as many clicks and retweets as possible. But the problem is we tried doing these things and it turns out to be this incredibly negative feedback loop. Like, once you optimize for clicks, the most clickbaity content starts rising up to the top.
18:05 Speaker 00 You get lots of racy content, lots of girls in bikinis, a lot of listicles, about 10 horrifying skin diseases, and so on. And so we wanted to train all of our models on all these deeper principles instead, where we'd ask our human graders to label tweets and recommendations with product principles, like whether this is a top of voice connecting somebody with their interests, or if somebody just had this really interesting insight into a particular topic. If we couldn't even get simple sentiment analysis right, like again, labeling whether a tweet was positive or negative, we definitely couldn't get this more complex data, the quality of scale that we needed. Like, we basically started a surge in 2020, right after the launch of GP3. And I think it really is because there was just so much more that you could see the industry moving towards. And if we really wanted to progress it in all these really, really big ways,
18:49 Speaker 00 we just needed a different kind of data, data association to help the industry.
18:52 Harry Stebbings? Okay, so you realized this data problem in 2020. You leave Twitter. What happens then? You go heads down into product build for several months. You go about recruiting the first team members. Can you just take me to the build? I mean, 2020, dude, it's not that long ago. Like, a billion in revenue and you started in 2020.
19:12 Speaker 00 Yes, so the way it worked was, so I've always been a really big fan of MVPs. And so I've already just built myself or V1 in a couple of weeks. But like, I think the really nice thing was, so again, I had worked in this space for a really long time. So I already had a very clear vision of what I wanted to build. So as opposed to feeling like I needed to go out and hire 10 engineers in order to build a product, instead of feeling like I needed to go out and front raise, you know, 10, 20, 30 million dollars in order to hire, you know, more people. I just wanted to build it myself and I wanted to talk to customers myself. And so that's what I did. I've already built the V1 in a couple of weeks. I posted about it on my blog. I told people about it that I met. And yeah, there actually was this giant demand for the data already.
19:57 Speaker 00 So I think we were very lucky early on.
19:59 Harry Stebbings? So you post on a blog, you get some demand. You said there about the MVP and deciding, you know, you'd build that first and not raise money. The traditional thinking in the valley is, I need money because I need money to build. Why do you think that's maybe wrong and how would you change your advice founders differently?
20:17 Speaker 00 So I think one of the things that's always driven me crazy about Silicon Valley is that it really is just a status game for most people. People are just raising for the sake of raising. Their goal isn't to build some great product that solves an idea that they fundamentally believe in. Their goal really is to tell all their friends that they raised 10 million dollars and to get ahead by a nanosecond. I have a lot of friends who've worked at Google for 10 years. When you think about starting a company, they actually often tell me they don't even have a problem that they want to solve. They're kind of just bored and they want to try something new. And at the same time, they can actually definitely pay their own salaries for a couple of months. But the first thing they tell me is, yeah, they're, they're going to go out and raise some money. And so they might try talking to some users and they might try building an MVP. But the only reason they do that is just to check off some checkbox on a YC application.
20:59 Speaker 00 And then what happens is they will just constantly pivot around random ideas until they get something that happens to get a little bit of traction and that sounds impressive to VCs. And so they spend all their time tweeting and tweeting hot takes and networking and going to all these VC dinners. And it's all just so they can get this headline about raising $10 million. I really think that people's first instinct should instead be to find some big idea that they fundamentally believe in that could change the world. And I don't really care why they believe in it. It could be because they have a lot of experience in the space. It could be because they talked to a bunch of users, but it really has to be something that they believe in, that they'd double down on for the next few years. Like if you think about startups, startups are all about big risks, right? You have to believe in something enough that you're going to take a risk building it.
21:41 Speaker 00 If all you're doing is jumping around from idea to idea every week until you land on something that gets you a thousand retweets, you're not taking any risks. You're just somebody looking to make a quick buck.
21:49 Harry Stebbings? I have so many questions off the back of that. You mentioned that kind of the loving the MVP and kind of the ease of doing so. Given the tooling that we have today, the ease of MVP has never been greater. Do you think there's any excuse for going out to raise now without an MVP, given the lovable's, the raplets of the world, meaning it's just so much easier?
22:07 Speaker 00 Yeah, for 90% of companies now. Like there are, sure, there are some companies where you actually do need a lot of capital in order to build hardware or like whatever it is for a couple of years. Like you really need a lot of us before you, before you, uh, before you're gonna get to your, your like actual MVP. Probably 90, 95% of products that are out there for 90 to 95% of startups that people are building. No, just go out and build your MVP and see, yeah, see if it gets any traction.
22:31 Harry Stebbings? You said about the inherent risks that you take on when you start a company, obviously. Do you believe in the advice that you should only pursue ideas that only you can do? In other words, the idea is specifically tailored to you and not everyone could solve that problem? Or do you think that's bullshit and it's actually about execution?
22:47 Speaker 00 I actually do believe in it. Again, if you think about the idea of a startup as something that, a place where you can take big risks, where you can build something that nobody else can. And you're willing to just go all out to, again, create something that literally nobody else could. It does have to be something unique to you because again, like otherwise you're like, sure, you can get to like a decent and medium sized company with a commodity idea. But if you really want to go big, if you really want to build a generational foundational company, I think it really should be about an idea that is. Almost like unique to you.
23:20 Harry Stebbings? You said about people maybe gaining value or self-worth in raising big amounts, going to conferences. That is how most people do gain self-worth. When you think about where you derive your own self-worth from, sorry to be personal, but given yours is clearly not that. How do you think about where you get self-worth, self-value from?
23:39 Speaker 00 I think it's kind of funny. So if I think about the things that have made me happiest in the past few years, I can think of like two things off the top of my head. So one is sometimes our customers, whenever they launch their next big model, one of their first things that they'll do is they'll reach out to me and they'll be like, "Hey, just want to send you a note that we couldn't have done this without you." And I think that's just so amazing to hear. Like, again, if you think about like, how often do you get to play a role in building some of the most important technology of our time? And then right after their launch, like these very, very top people who are very, very busy, one of their first thoughts is to thank you because of how critical you were to the operation. I just think that's so cool. So that is one of the things I often think about.
24:19 Speaker 00 And then I think the other thing that I often think about is, again, in many ways, Surge is embodiment of me and my interests. And what I've always loved doing is analyzing data and figuring out how to use that data to make models better or to make products better. And so every now and then, when I just get the chance to write an analysis myself of the latest frontier model, or I get to read some of the analyses our internal employees are creating based off of the data that we're providing. I just think it's so cool that a lot of the data we're providing, it's just so insightful and it like helps people build models in ways that they just wouldn't know how to otherwise.
24:53 Harry Stebbings? Going back to that story then, so you build out the MVP, you post it, and then you said luckily, you said it very nonchalantly, Edwin, which is very sweet, but like, people came and people liked it. What did that look like? How did the initial demand come to you?
25:07 Speaker 00 Sorry, I think I say it nonchalantly because it felt very nonchalant. I think what would end up happening is, so I would find all these people who really were desperate for a lot of really high-quality data. I mean, the way it worked is they would just email me with their request, or we would just jump on a live meeting and we would just get started. And it might take a week or a couple of weeks to negotiate some sort of. So W or contract, just because you know, a lot of this does have to live within the confines of their company. But yeah, I mean, I think we're really lucky and again, I had a lot of experience in this space. And so a hell of experience working with ML engineers and research scientists in the ways that they wanted to get data and the way they wanted to look at it. And so I think things just moved very, very quickly.
25:51 Harry Stebbings? In the early days, everyone else is acquiring supply side of talent, correct? All the other people that compete in the space and you're not acquiring that talent supply, you're building product, correct?
26:01 Speaker 00 It was both because I mean, obviously we need a talent supply in order to make our product work. So there are some companies in this space who will simply think of it as a pure supply problem. And they don't give any consideration to the technology, like both the technology, the underlying technology, like, how do you identify these people? How do you make sure that they're doing good work? How do you remove the bad quality work? Like they're just literally not thinking about any of the technology aspects at all. And they're also not thinking about the product at all. Like, how do you present the data to the customers? Like one of our principles, like one of the principles that I've always had, even prior to surge, when I was just an ML engineer or data scientist. One of the things that I've always tried to encourage is what we call this visceral understanding of the data.
26:45 Speaker 00 Like, I really just want you to go in and get your hands dirty and look at the data. Like historically, a lot of ML engineers, they kind of just don't take the time to look at the data. And maybe that's because the data just isn't all that interesting. Like when all you're doing is drawing bounty boxes around cars, sure, I don't need to look at a thousand bounty boxes. But when you're doing is yeah, creating poetry, creating mathematical equations, creating new research. Like you want to get your hands dirty with the data to see what it is that you're producing, what you're teaching your models. And so I, I think it actually really is important to this aspect of viscerally understanding the data that you're getting.
27:17 Harry Stebbings? And so we're there doing both building product and acquiring the talent supply in unison. What did we end the first year at? Like, did we have immediate product market fit?
27:26 Speaker 00 I mean, I, I think it was very, very obvious that there was just huge demand for this product. And there were just so much more that we, that we could be doing.
27:32 Harry Stebbings? So Edwin, when there's huge demand for your product, this is even more so the time when everyone goes, now raise money, hire CS teams, hire sales teams. Why did you not raise money then? I get it at the start when, hey, you didn't want to do what everyone else did. Why not raise money when it was a hair on fire problem and you had so many people calling you?
27:52 Speaker 00 I mean, I would say there was nothing that raising would help us with. Like, we were very lucky to be profitable from month one. And so we didn't need the money. We didn't need a sales team. Like, I didn't, I actually didn't want a sales team going out and selling our product. Like, I wanted people to buy us precisely because they understood the value of high quality data. They saw all the gains that our data was producing. I didn't want them to buy us simply because they heard about us in some tech chronicle. Because that would almost put them at odds with the kind of product that we were building. Like, one of the things that I think is actually really important, especially early on, you want customers who believe in your product and not people who are simply giving you a little bit of money. Because your early customers will shape the kind of product that you're building. Because, yeah, you're building for them, you're, you're building for their needs.
28:36 Speaker 00 Like, they're giving a lot of really, really great feedback. And so you almost want customers who share the shame overall vision. And so that was actually very important for us. Like, I, I didn't want sales teams who would email 10,000 people and be like, "Hey, any, any thoughts on, on getting good data?" It was just very counter to the kind of, kind of product that we wanted to build.
28:54 Harry Stebbings? How do you think about what you just said there in terms of building with your customers, being so close to them, letting you shape your product? But then also not doing the Henry Ford of building a faster horse? And then also not building a product that bluntly isn't relevant for a wider audience base? And you really just kind of tie yourself into a few small, what, few clients?
29:12 Speaker 00 So I think this is where we actually have a really, really strong vision of what our products should be. So again, like going back to what I said earlier about how most companies within our space, but maybe also at large, they don't have product principles that they try to adhere to. Again, like, we had very strong product principles from the start. We wanted to focus on quality above all else. Like, if whoever thought that we couldn't give the quality that we wanted, we would just say no. As opposed to these other companies where they're almost like desperate and racing around, just trying to get any traction that it can, they're trying to prove to the reason. That their numbers are always going up. They're almost like focused on getting $10, $100, $1,000 wherever they can. And so as soon as some customer comes to them, even if that customer is counter to the kind of product that they're building,
29:56 Speaker 00 if they're offering money, they'll just say, sure, I'll do it just because they'll give me another logo from a website. They'll give me another case study to show another customer. They'll give me another talking point with my VCs. I think we're very lucky to not have to worry about that because we could build for the long-term vision we had. As opposed to again, like as opposed to pivoting every few months, like we just wanted to double down on the idea that we actually believed in.
30:20 Harry Stebbings? Is there a time when you let quality slip in any area of the company? And with hindsight, what did you learn from that?
30:26 Speaker 00 Um, no, I think we've, we've never let quality slip. It's such a principle ingrained into everybody at the company. Like one of the things that we simply tell everybody when we first join. Quality is the most important thing. It's more important than anything else. If you have to make a deadline slip because for whatever reason, you don't think the quality is there. If we have to say no to a project because we just can't handle it right now. We can generally handle a lot of things, but we just want to engrain this principle that is okay to say no. It is okay to kind of like let other things maybe slip just because we care about quality at the end of the day.
30:58 Harry Stebbings? Most founders have a challenge where they need to hire now, but they haven't found the perfect person. And so they hire a seven out of 10. They let the quality bar slip because they need someone in the role. How do you think about that and what would you advise them?
31:11 Speaker 00 Yeah, I think the funny thing is like, again, I've been at all of these other companies. Oftentimes when people are saying like, yeah, I, my hair is on fire and I really need this engineer. So I know they don't meet the bar, I'm going to lower the bar to hire them. Like actually the, the engineer, like what are they doing? They're building probably a feature that nobody cares about. They're building an internal tool to improve the productivity of everybody around the company by 2%. While at the same time, having so many meetings with them that they like take up 5% or 10% of the time just talking about the feature. Like a lot of the things that people hire for just actually aren't all that important. And so again, like when you don't feel like you have to hire for the sake of hiring, like when you have the mentality.
31:52 Speaker 00 That, okay, if your company only grows by 10% or even 0%, that's actually positive. Like, I think people right now, they have this view that if someone were to tell you, oh, yeah, my, my engineering org only grew by 2% this year. Your initial reaction is going to be, okay, you guys must not be doing well. And so there's like this negative incentive where people feel like they need to hire just in order to prove to other people that.
32:14 Harry Stebbings? Do you think now we're in an opposite world to that, though? Where you see the reduction in force from, say, a Microsoft and you see better performance than ever from them on a revenue per head. Do you think now we're seeing the counterbalance of that, which is the desire to be the smallest team, the fastest team to X error and the smallest team to it? And now revenue per head is the most important metric.
32:34 Speaker 00 I honestly don't pay enough attention to, to like, uh, these kinds of Silicon Valley Twitter discussions for me to have a sense of whether this mentality is, is becoming more pervasive. I can believe in it. I can hope for it. I don't know if it's true right now.
32:46 Harry Stebbings? Do you worry that by not being so ingrained in social, you miss out on certain elements that it is important to be in? Or do you think that purity of mind you get is really so valuable?
32:57 Speaker 00 It's kind of funny because, again, I used to work at Twitter and I, I loved Twitter back in heyday. But I actually really am glad that I'm not surrounded by the default ways of Silicon Valley thinking. So every now and then, if something is important enough, like maybe there is some big new product is actually really cool. Or there's some really, really interesting new research paper. It'll be like big enough that even though I'm not monitoring Twitter every day, it will just reach me in some other way. Yeah, one of our employees will tweet or like post it in our stock channel or somebody will email it to me. So like the really important stuff will manage to percolate itself to me in other ways. But I actually am really glad that I'm not worrying about what people are saying about us on Twitter.
33:35 Harry Stebbings? I, I love that, especially given the irony of being at Twitter for, for a number of years. I do have to ask, so first year ends, what do you end revenue at on the first year?
33:44 Speaker 00 Yeah, let's just say we've been, we've been doing really, really well from the start.
33:48 Harry Stebbings? You, you said publicly about being at a billion in revenue now. Did it look like relatively even growth? Were there elements where it was much more accelerated than others? I'm just intrigued and say whatever you feel comfortable to in terms of that.
34:01 Speaker 00 Yeah, so we've always been very, very successful from, from literally month one. Things definitely hit an inflection point with ChatGPT because I think people just saw how incredibly valuable human data in RHF was. So definitely ChatGPT was an inflection point for us, but even before that, we have very, very strong growth.
34:20 Harry Stebbings? So post ChatGPT, you really see the inflection point. Another one that I guess is probably quite an important one is scale of selling and the movement of customers away. How did the world change for you with the scale acquisition?
34:33 Speaker 00 So it's interesting because I think it was an open secret where a lot of top researchers already knew who we were. Like they already knew that we were the biggest and the best in the space, even though we've been pretty under the radar. And so most people were already working with us. There are a lot of teams who are using scale for legacy reasons, or they just didn't happen to know about us. So we've been getting a lot of new interest for them too. I think the more interesting thing has been, it's kind of been really fun seeing how we've opened their eyes to what really amazing, really high quality data can actually look like. Like a lot of them have tried getting human data from other teams and they tell us it's been this slog. They'll spend months trying to improve the data quality for really basic stuff. And it'll look like it's better for a month, but then it will just quickly regress. We have this concept where we just want to get started immediately. We want to show them really, really high quality data immediately.
35:16 Speaker 00 Like one, one of the big concepts for us as a company is we always want to be producing data that you something couldn't get anywhere else. Like there's so much richness and complexity and types of things that we do that we just want to open up new avenues of research and open up new avenues of new types of products. I, I think a lot of these new companies or these new teams who, who've been coming to us, it did help. It's just been a breath of fresh air for them.
35:39 Harry Stebbings? I spoke to Garrett at Handshake right after the acquisition. He said, like, I'm just staying up all night. There is just a tidal wave of scale customers moving to us. Did you have the same though, in terms of that tidal shift in customer demand shifting to you as well as the realization that you mentioned there?
35:55 Speaker 00 Yep. I mean, so I would say I'm pretty sure that a lot of these other companies, like at the end of the day, people want high quality data and they don't want to be working with body shops. And so I think we've seen like a massive wave of interest because like, yeah, like the space is really large. And there are a lot of teams who are still using scale for legacy reasons. But it's like, at the end of the day, we were already the biggest to invest in this space. And so even when there were teams at some of these larger companies who weren't working with us already, they kind of like knew who to turn to.
36:26 Harry Stebbings? Do you think everything has a price, Edwin?
36:28 Speaker 00 I mean, I think for some people, then like they have a price, but I think we don't.
36:33 Harry Stebbings? You said you wouldn't sell to Zuck for $30 billion. Would you sell for $50 billion?
36:38 Speaker 00 No, I mean, I definitely want to sell for $30 billion or even $100 billion. If you think about us as a company, I already have everything I want. Yeah, we're profitable. I have complete control of our destiny. And so I'm really lucky to have all the resources I want to already do anything that I want. And there aren't many companies who can say that.
36:55 Harry Stebbings? What are you doing this for? I've interviewed a thousand founders. In the nicest way, I've almost never met a founder like you. In a nice way, it's really special. But with a pure mindset like you have, what are you doing it for then? To build a business that you can pass on to the next generations? To build a legacy? What is it for you?
37:13 Speaker 00 I mean, I think it really is to help achieve AGI. Like, if you think about every, every, like, what do kids dream of? Like, yeah, when you're a kid, you literally dream of building AI that can do all these amazing things. And now we have the chance to do it. Like, I really do think we are such a critical aspect of what all these companies are building. Like, again, a lot of our customers at these frontier labs, they would just often tell me, they wouldn't be able to build what they're building without us. And you're just amazed at what we do. And so being able to be this critical part of what is literally the greatest technology of both our time now, but also maybe one of the most important things we can ever build, that's amazing. And so why would you get acquired and stop doing that? Because yeah, getting acquired would be really limiting.
37:55 Speaker 00 It would be this admission of failure and jumping ship because you can't make it on your own anymore. When we're the opposite, we're incredibly successful and there's literally nothing else that I'd want to do instead.
38:04 Harry Stebbings? It is 2040 and we still do not have AGI. What is the primary reason why that would be the case?
38:10 Speaker 00 So I think there are two reasons. One is that there will always need to be more breakthroughs, whether it's breakthroughs in yeah, how you leverage all this data or breakthroughs in different types of algorithms that you're building. And then another one is just how you gather that data. It's like in order to cure cancer, how will you gather the data that's needed to make those breakthroughs? Maybe you're going to have to run real world experiments, real world studies. And those studies will simply take time. Will there be a way to speed up those experiments through various kinds of simulations or just other forms of gathering data? I don't know, but there's some of the question, how do you get the data even faster? Which I think will be very, very important.
38:46 Harry Stebbings? Speaking of kind of evolutions with AGI there, I do just want to ask on like the changing nature of data. How would data needed evolve as AI gets smarter and smarter and smarter with each evolution?
38:57 Speaker 00 So a lot of people talk about this shift to PhD level data. And yeah, I think it's important. Like, we basically have the biggest group of the smartest people in the world working on our platform. We actually have Harvard professors and Stanford PhD students and Princeton computer science theorists working on all these really interesting problems with us. Like, it's kind of crazy. If you think of all the PhDs, even at Google or Meta or Microsoft, we have way more than all of them combined doing work for us in a single day. And it's also true, they're not just writing random JavaScript code to improve ads. They're actually pushing the frontiers of science when they're collaborating with these models. But I think what people underestimate is that having a PhD isn't enough. Like, a lot of PhDs, they just aren't good at this type of work. There are a lot of body shops and recruiting shops in our space that basically just look whether you wrote down that you have a PhD on your resume and they'll just instantly give you work if so.
39:42 Speaker 00 But a lot of PhDs just aren't very good. Like, I think 80% of the computer science PhDs I know, they write shitty code because they're only good at math and algorithms. And then you think about people like Ernest Hemingway, he didn't have a PhD. I don't think he even went to college. And so I think there are two things that are important. There is this underestimated aspect. Perspective for a space where you actually need a lot of technology in order to make sure that you're delivering really high quality data. Like, I think it's like a lot how like Vimeo has a lot of so-called high-quality videos. But yeah, they don't have any algorithms. And so YouTube's videos are way higher quality and more engaging in the end. And then the second is it's just that a PhD isn't enough. Just because you have a PhD doesn't mean that you can make some breakthrough in physics. What you also need is street smarts. Like, you need that creativity and the mental fortitude to think of really interesting problems.
40:26 Speaker 00 And find these problems and probe elements and see whether they can solve them today. And then teach them in really interesting ways. Because otherwise, if all you're kind of doing is throwing PhDs out as a problem, all you're doing is teaching models how to hack silly benchmarks and get good at basically the equivalent of SAT problems.
40:41 Harry Stebbings? If that's the landscape mode today, which is PhDs aren't enough, because a lot of PhDs aren't great quality. How does that change over time? Like, will you have a dramatically larger supply side? How will the tooling of the supply side change? How will our ability to turn around work change?
40:57 Speaker 00 Again, I think this boils down to the technology that we build. Over time, it's simply true that people are going to be trying to solve more and more problems. Like, we have hundreds of thousands, millions of people working on our platform. And when you do that and you have a thousand projects, like 10,000 projects that are literally running in any given week. How do you make sure that you are building technology to identify who are the the top 1%, top 2% of people who can really push the boundaries of physics problems with these models? Or how do you identify the top 2% or 3% of people who are writing the most amazing poetry? How do you find those people? And then also, how do you remove the worst of the worst?
41:37 Speaker 00 The people who inevitably try to cheat you and spam you, and they will basically regress the models if you allow their data through. Like, it actually is a really, really prevalent problem, and you just need a lot of technology to build this. And then at the same time, like, these are researchers who want to move really fast. Like, researchers at all these frontier labs. Again, like, all the algorithms are changing every day, and so they want to try out new projects every single week. And so if you're not moving fast enough, like, if you're unable to create a new template, or you're unable to find the expertise that you needed, like, literally within the next day or the next week. It's just going to be too slow for these researchers. And again, if you don't have any technology to manage these 10,000 projects and automatically create them and automatically identify the really high-quality data, it's just going to be too slow for them.
42:20 Harry Stebbings? Speaking of slowness of data and quality of data, I would love to push you on this. When you think about like bottlenecks to progress today, if I were to rank them one through three, one being the most pressing bottleneck and three being the least pressing, you've got access to compute, you've got algorithms, and you've got data quality. If you were to rank them one through three, how would you rank them?
42:41 Speaker 00 I would definitely rank data quality first, followed by compute, followed by the algorithms.
42:45 Harry Stebbings? If compute continues to prove to be the unlock, where throwing more compute at it unlocks more and more performance, does that denigrate data quality in the prioritization stack?
42:58 Speaker 00 I mean, I actually just fundamentally don't believe that you can throw more compute at a problem. Because if you're not getting the data that the computer is essentially trained on, or if you don't have the right objectives and evaluation metrics that again, your computer is optimizing towards, you're just going to fall into this trap of seeing progress that actually isn't there. I can give you some examples. So let me talk about why I think data quality is such a problem. So I think data quality issues have already been a huge setback for a lot of frontier labs. Like, one of the things that we often hear from teams over and over is that before they used us, they tried getting data in other ways. And so they'd train their models, they'd evaluate their models, and their metrics kept going up. But after six months or even a year, they realized that their training data was shit, their evaluation data was shit. And so all the progress that they thought they were seeing was actually completely misleading.
43:43 Speaker 00 And they either made no progress, or their models after six months were even worse than when they started. Like, for example, we see this a lot with LM Arena. So LM Arena is this popular leaderboard of LM models. And it's basically the equivalent of clickbait. What happens is that you have people going on to what's called a chatbot arena. They'll enter a prompt, they'll see two model responses, and then they'll vote on which one's better. But they're not taking the time to really read or evaluate the model responses at all. Like, one of the models could have made, completely made everything up. And these participants, they'll vote on it because it has emojis and nice formatting. We've literally seen this in the data ourselves. Like, one response will just be a complete hallucination. But because it has an emoji and because there's a couple of words voted, people were just like, okay, yeah, like, that looks good, that looks much better than this other thing than that I didn't take the time to fact check at all.
44:27 Speaker 00 And so one of the things that we've learned is that the easiest way to improve in this arena Is simply to make your model responses a lot longer. Like, one of the funny things is that you actually take the top model on this leaderboard, the number one model, and you ask it, "When did the Pope die?" It will give you a really long response that seems impressive, but it gets the answer completely wrong. It tells you that Pope Francis is still alive. It will even tell you that there are search results that indicate that Pope Francis died in April, but actually, these were just rumors and misinformation, and he's still alive. It's wild that this model will say this. So again, like, what happens is that there are a lot of companies who are trying to improve their leaderboard rank. And so they'll see progress for six months because all they're doing is unwittingly making their model responses longer. They're adding more and more emojis, they're adding more and more formatting. And so they see their models climbing on this leaderboard, and so they think they're making progress,
45:10 Speaker 00 when all they're doing is training their models to produce better clickbait. And they may finally realize six months or a year later, but it means they basically spent the past six months making zero progress. This is what happens when you kind of like throw compute at a problem without understanding the underlying training data that you're like, again, like throwing to compute towards. It actually just sets your models back.
45:29 Harry Stebbings? When you look at like Grok, obviously announcing their recent developments and how they performed in the latest benchmarks and came out as number one, are those benchmarks bullshit then? How much weight should be placed on the importance of those benchmarks? And how reflective are they truly of model quality?
45:43 Speaker 00 If you watched a Grok for a launch, the Grok for a live stream, I think you would even have heard Elon himself saying, like, "Yeah, these models are really good at," I forget the word he used, but like, "They're really good at homework problems, they're really good at these academic, very narrowly scoped problems." It's basically the equivalent of making them really good on SAT problems, and I'm making them good at problems that people are actually facing.
46:05 Harry Stebbings? Were you surprised by how far Elon has been able to get with Grok, as fast as he has done, or not?
46:11 Speaker 00 I am not. It's kind of funny, so before we worked with the team, I didn't really have a conception of what an Elon company was like. But yeah, I mean, we worked really closely with the XAI team, and it's actually just incredibly refreshing to see how they operate. They are all very, very mission-oriented, they're all incredibly smart, and they work incredibly hard. Like, it will be 11:00 PM at night, and I'll DM them, and someone will want to jump on a meeting, and yeah, I jump on a meeting with them, I see them. They're in the office, and there's a ton of people behind them, so like, they're all... There was like crazy hacking together on all these problems. And so I actually think it's incredible. And it's this kind of embodiment of what a startup can do when you really believe in something and are kind of like willing to do whatever it takes to achieve it as opposed
46:54 Speaker 00 to living within the confines of this giant bureaucracy. So I think it's actually really impressive.
47:00 Harry Stebbings? Is there anything that you think Elon does specifically to inspire his team to have that form of culture when they're not a small company?
47:07 Speaker 00 I think it's almost that you know what you're getting into when you work at Grok, or when you work at XAI, or any of these other companies. You know, when you interview that these people are incredibly mission oriented. You know, when you interview that everybody works super hard. You know that if you want to work there, you're going to have to be the kind of person who has the same values. Otherwise, you just shouldn't you just shouldn't join because you'll be miserable. It's this fact that it has such a strong culture and such a strong belief in what they're doing. It just attracts people of similar talent.
47:37 Harry Stebbings? I do want to touch on kind of the working hours that you mentioned there. But everyone poses synthetic data as a big threat. And what happens to your business when we have synthetic data that is obviously created automatically and labeled automatically? How do you think about the role of human labeled data in a world of predominantly synthetic data? What's your thoughts there?
47:57 Speaker 00 So I think synthetic data is actually really useful in some places, but I think people overestimate what it can do. I'll give a couple of examples. So right now, there are a bunch of models that have been trained really heavily on synthetic data. But like I mentioned earlier, it means that they're only good at very academic homework style, benchmark style problems. They're actually terrible our real world use cases. So yeah, synthetic data, it's made models good at synthetic problems, not our real ones. And we actually hear from a lot of companies who tell us they spent the past year training their models on synthetic data, but if only now just realize all the problems that's caused. And so they've spent actually months throwing a lot of it out. Like a lot of them tell us that even a thousand or a couple thousand pieces of really high quality human data that we generated for them, it's actually been worth more than 10 million pieces of synthetic data. And so a lot of the work that we do is simply cleaning up all
48:41 Speaker 00 this synthetic data. And if you think about like why this happens, it essentially is because the models collapse on this very, very narrow scope of like similarity that the synthetic That creates and so it just doesn't give the models the kind of diversity and generalizability that they need. And then one other point is that there's also this interesting phenomenon where models simply make a lot of mistakes and have certain misunderstandings that humans never will. Like, I was actually just playing with one of the frontier models recently, and it kept on just randomly outputting Russian characters and Hindi characters in the middle of its responses. This is a mistake that would be obvious to any human, to any second grader, but the model just didn't know. And it's like shocking that a model in 2025, a frontier model in 2025 would do this. And so it's almost like you always need this external value system as a kind of safeguard to make sure that the models are working properly,
49:26 Speaker 00 just because the models themselves have such a different set of way of thinking.
49:29 Harry Stebbings? I'm an ambassador in poolside, which, if you don't know, obviously is kind of in the same space as, say, a cursor or a windsurf. But that bluntly, they seemingly are much more behind because they've built their own models. And they believe very much in the power of verticalization of models and specific models, or specialized models, so to speak. How do you think about the future in terms of monolithic, generalized, very large-scale models versus the requirement to have very narrow, very specialized models for things like code creation and development?
49:57 Speaker 00 I think there's an opportunity for both. And the reason I think that is, it's because on the one hand, you have these giant, all-powerful models. And sure, they can be really, really good and really, really powerful. And in like a raw capability sense, I think they'll be able to encompass all of these different use cases. But in the same way that a company, so take a company like Google or Facebook, there are simply some products that they can't build because building those products would be counter to like culture or the business goals of like the overall parent company. And so in the same way, sometimes you need to be able to move faster and to take big bets on certain kinds of products. And all-powerful model just can't kind of let that happen because if you let it happen within this like one small domain, it will kind of almost like pervade the entire model.
50:42 Speaker 00 So sometimes you do need to like the smaller models to break through if they have like a really unique view on how they're operating.
50:47 Harry Stebbings? You are very composed as a leader, as a CEO. Where are you not meeting the bar? Where are you not great and you are aware of it?
50:57 Speaker 00 So I think one area where I'm not great, which is kind of funny, but one area where I'm not great is I'm really bad at understanding financials. So sometimes people around a company will try to tell me like, hey, have you been paying attention to our revenue numbers? Have you been paying attention to our costs? Have you been paying attention to our margins? Do you even know what they are? And I don't. There are just like these financial metrics that like I could not tell you what EBIT does. I mean, I know what the acronym stands for, but the difference between that and revenue and profit and net margin and I actually just don't know any of these terms. And it's just like this blind spot, like no matter how much I tried to try to understand these things, I just can never remember.
51:36 Harry Stebbings? What single metric defines the health of the business to you? What metric, if I showed it to you every morning, you'd be like, okay, I know the state of my business.
51:44 Speaker 00 Like if I could paint my perfect North Star, and this is something that I think we want to work towards. Like it's something that we actually want to build for the industry. It's like, are models progressing in fundamental ways? Like, are they actually getting more intelligent? Like, are their capabilities improving? Again, as opposed to simply climbing up a meaningless clickbait leaderboard. So are these models progressing? And then how much of that is kind of like due to us? Whether it's due to our training data or whether it's due to the evaluations we provide? Or whether it's due to the insights that we provide all these researchers for ways that they can improve their models? Like, if there are way to measure that, I would love it. I think the closest proxy we have for it today is just like the variety of projects that we're creating.
52:29 Speaker 00 One of the things I really, really believe in is we want to make it easy for all of these researchers to come up with new ideas and to not be blocked by data. So the more complex, the more diverse, the more creative projects that we can provide, that is like almost a proxy for that overall North Star.
52:47 Harry Stebbings? Final one, and then we'll do a quick fire. But you mentioned Elon and X and the hard work in that culture being so ingrained. I recently said that bluntly Silicon Valley and China have increased the intensity required to win in terms of work ethic. You must work seven days a week if you want to build a $10 billion plus company. And the ability to put your phone on the side and not check an email does not exist anymore. If you want to build a $10 billion plus company, you've built a $10 billion plus company. Do you agree with me?
53:18 Speaker 00 So I would say, I think you have to be willing to work hard. Like, you have to be willing to jump on a call at 2:00 AM. And yeah, customer, like, I think one of the things that I love is that. Sometimes customers won't call me, although they call me at 2 AM, 3 AM and they'll be like, hey, our models are freaking out. I need a bunch of data to fix it by 6 AM. Can you do it? And maybe going back to the question of things that make me happy, like, nothing makes me happier than knowing that, yeah, we can, we can deliver it us, like, yeah, we can deliver 10,000 data points to you in the next few hours, even if you call us at 3 AM to fix some critical bug, critical fire that you're facing. That is actually something that makes me incredibly happy. And so I think you have to be willing to work hard. I think a lot of people do confuse working hard with creating value.
54:00 Speaker 00 Like, again, it's maybe a trope to say, but you have to work smart and not just hard. Like, if I think about a lot of what I'm doing, oftentimes the best ideas come to me when I'm just walking around, not necessarily when I'm at my computer. I mean, I think we all work really hard, but I wouldn't confuse the number of hours we spend with actual progress.
54:18 Harry Stebbings? What trait of yourself do you love most or is your favorite trait, Edwin?
54:24 Speaker 00 Good question. So at least the thing that I really enjoy is, like, I've always really enjoyed writing down insights in written form, and I think I'm pretty good at it. And so this ability to deliver some novel insight about a model, or deliver some novel insight about an algorithm, or deliver some novel insight out of data set, and communicating that to our customers, I think I'm pretty good at it, and it's something I really, really enjoy.
54:49 Harry Stebbings? Dude, I want to do a quick fire. So I say a short statement, you give me your immediate thoughts. Does that sound okay?
54:53 Speaker 00 Yeah, that sounds great.
54:54 Harry Stebbings? So what one widely held belief about AI do you think is completely wrong?
54:59 Speaker 00 So I think a lot of people think AI safety is overblown, but I think they ignore the paperclip maximizer problem, where you have AI models that are accidentally trained towards the wrong objectives, even though this is a big problem that all the models face today with all the issues around Alan Marina and benchmark hacking. So I actually think it's a really important problem that people should be thinking more about.
55:17 Harry Stebbings? So you think AI is much more dangerous than we let on?
55:20 Speaker 00 Both dangerous, but that it can be accidentally maximized towards the wrong objectives that like today. Okay, sure. Like if you optimize towards these LM arena objectives or benchmark hacking, the worst that will happen is that your models will regress in progress a little bit. But the more fun of a problem is that people don't realize this. And so in the future, when the models are more powerful and yeah, you're basically accidentally maximizing AI models towards the wrong objectives and you just have no idea what will happen. It's almost like a similar phenomenon to what's happening today. But because the AI models are so much more powerful, like yeah, they're literally building the code for an insurance company or they're literally building the code for some trillion dollar company. Just the consequences can be much worse.
56:01 Harry Stebbings? You mentioned about gaining true passion, love for building towards AGI. I hate myself for asking this question. It's a shit question. I hate it. I'm so embarrassed. But if you had to put a number 2028 or 2038, which bracket would it be in and why?
56:16 Speaker 00 So I think it would be 2028 if you're talking about automating a job at the average engineer. And then 2038 if you're talking about curing cancer.
56:23 Harry Stebbings? Sorry, 2028 automating the job of the average engineer. I had Vlad on the show from Robinhood and he said 50% of code created by Robinhood is now by AI. Benioff said the same on the show, 50%. Are we not at that stage already? How much code from Serge is created with AI?
56:39 Speaker 00 I don't think we're at that stage yet, at least if you're, again, if you're, if you're working on deeper problems that aren't just random features. Like again, if you're concentrating your company on the 10% of problems that are most important, I don't think models today can write 50% of the code and you know, come up with 50% of the ideas that are actually going to be meaningful to your company. Sure, if 90% of your company is writing little features that nobody cares about or improving the efficiency of your code base by 1%, then yeah. But I don't think we're at a point if you're, if you're really working on meaningful problems.
57:10 Harry Stebbings? What is question should every AI company be asking themselves but isn't?
57:15 Speaker 00 So if you're Frontier Lab, the question is, are you actually improving your models into raw intelligence, or are you just hacking benchmarks? If you're a product company, yeah, the question is why do Frontier Labs won't be able to instantly replace you?
57:26 Harry Stebbings? Do you think they will? I don't ever worry about that in terms of application layer being absorbed by model layer, just because I think there is infinite product breadth that they could go after. They can't go after everything.
57:37 Speaker 00 I think they can't go after everything, but there are so many things where, yeah, you literally just want to chat with the model in its very simplistic, universal interface that, again, like I think about Google Search, like I actually do feel, I mean, I have felt that maybe 50% of the things I use to Google Google Search, they are replaced by ChatGPT, or they're even better with ChatGPT. There's a very pleasing aspect of a universal, all-intelligent interface that I think people will just gravitate to.
58:04 Harry Stebbings? What would you do if you were Sundar today? Would you kill your golden goose with the ads engine?
58:08 Speaker 00 So the difficult problem, I think, for Google is they have to be willing to take a short-term hit to all their advertising revenue in order to build something better. That's just really hard.
58:17 Harry Stebbings? Plantament one, what did you believe about the future of AI that you now no longer believe or have changed your mind on?
58:23 Speaker 00 So I think the thing that I've changed my mind on is how I see a world where there actually will be multiple, multiple frontier AI companies, frontier AGIs, just because every one of them will be able to go in a different direction. Like you see it already, you see it today playing out already with the differences and the strengths and weaknesses of OpenAI and Anthropic, and I think that that trend will continue.
58:46 Harry Stebbings? What does that mean? I'm sorry, if you just play that out, what does that landscape look like then? Because OpenAI and Anthropic are so unique in their properties and characteristics. It means there'll be 10 more of them? What does that look like?
58:57 Speaker 00 I don't know if there'll be 10 more of them, but I can certainly see even like three more of them. And I just think each one will have different trade-offs that they're willing to make, different focuses that they'll have. And like even today, like Claude is really, really good at coding. Claude is really, really good, I think, at enterprise and like instruction following. Whereas ChatGPT is, yeah, it's like more optimized for consumer use cases. Like I think it actually has a really, really great and fun personality right now. And then Grok, like, yeah, Grok is willing To maybe answer certain questions that maybe it should, maybe it shouldn't. But it's willing to be a little bit transgressive in ways I actually think are very, very interesting. And so I actually think that just like this willingness to have different personalities and different
59:39 Speaker 00 boundaries and different focuses on your models that leads the models to be good at different use cases. Just in the same way that like, yeah, there's like, I think analogy is there isn't a single poet. There isn't a single mathematician that is like the greatest mathematician of all time. They all have different focuses. They all have different ways of approaching these problems. And I think that richness, like what we often call like richness of human intelligence that will apply to models as well.
1:00:01 Harry Stebbings? Have the biggest model providers been founded today?
1:00:05 Speaker 00 It's a good question. I don't think so yet. I can actually see big, new, even more powerful model developers appearing in the next few years.
1:00:13 Harry Stebbings? How does that look? Because like when you think about like funding them, the capital intensity or capital requirements are so large. I don't know anyone who's willing to all the big players in the financing world bluntly have already got their horses in terms of this race. How does that even work?
1:00:27 Speaker 00 I think it's because it depends on what you view the long-term vision for AGI to be. Like despite all of the immense progress that we've made, if you believe that there's only, we're only like, I don't know, 1%, 5% of the way towards AGI. Because yeah, we literally want AGI systems that can in the future cure cancer and send rocket ships to Mars. And like design entirely new philosophical systems, like these are big, massive problems. As opposed to simply automating away the job of the average L3 or L4 software engineer. It's if you believe that we're only 2% or 5% of the way there, there's so much more headroom. It's like, almost like asking, do you believe 10 years ago that Google is going to be the final search engine in the world?
1:01:09 Speaker 00 Like, sure, if you, if you're like only looking forward to the like the next five years. If you just think of the immensity of what AGI could do, there's so much more ahead of us than behind us. That there could be these serendipitous, very, very creative breakthroughs that just nobody's expecting. In part because like, maybe it's going to be created by, by some of the AIs themselves or AIs in concert with humans. There's just so much opportunity ahead of us that it would be almost a miss to think that we've already solved it.
1:01:34 Harry Stebbings? Do you believe AI will be able to create 10% increases in GDP gain or in productivity increases in the next 10 years? That's often touted as a number which would create $10 trillion of value.
1:01:46 Speaker 00 I absolutely believe it.
1:01:48 Harry Stebbings? Love this. This is a good round. Final one, Edwin. You can give yourself one piece of advice going back to day one, starting the company, going back to starting the MVP. What do you know now that you could tell yourself then?
1:02:01 Speaker 00 So I think it would be to focus always on the 10x improvements that you can make as opposed to worrying about 10% trivialities.
1:02:07 Harry Stebbings? Edwin, listen, I so appreciate the time. As I said at the beginning, I've been such a fan of the incredible journey. You've been fantastic. It's been very atypical in most ways, bluntly, having this discussion, which has been so great for me. So thank you so much for joining me.
1:02:21 Speaker 00 Thank you. It's been great chatting.
1:02:23 Harry Stebbings? I have to say, that show is a real symbol of why I love what I do so much. Over a billion in revenue, no funding, he doesn't do interviews. The fact that he sat down with me and opened up as he did simply meant the world to me. You can find the full show on YouTube by searching for 20VC, that's 2-0-V-C on YouTube. But before we leave you today, I love seeing the team come together to make this show happen. What I don't love is trying to keep track of all the information, the data and the projects that we're working on across dozens of platforms, products and tools. That's why we use Coda, the all-in-one collaborative workspace that's helped 50,000 teams all over the world get on the same page.
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Previous 20VC: Cognition CEO Scott Wu on Acquiring Windsurf: The Process, The Deal, The Rationale | Did Google Overlook a Goldmine in the Core Asset and Did Founders Leave a Sinking Ship | How Cursor and Cognition Deal with Ever Increasing Reliance on Anthropic Next 20VC: Lovable Raises at $2BN & Hits $100M ARR | Is Cursor Worth $28BN at $1BN in ARR | How Do All Providers Deal with Anthropic Dependency Risk | Are Seed Funds F******: Have Mega Funds Won | Figma IPO Breakdown: Where Does it Price? Page updated September 29, 2026 · transcript and summary are machine-generated
