Pod Insights The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch 20VC: Scale's Alex Wang on Why Data Not Compute is the Bottleneck to Foundation Model Performance, Why AI is the Greatest Military Asset Ever, Is China Really Two Years Behind the US in AI and Why the CCPs Industrial Approach is Better than Anyone Else's About this episode Alex Wang is the Founder and CEO @ Scale.ai, the company that allows you to make the best models with the best data. To date, Alex has raised $1.6BN for the company with a last reported valuation of $14BN earlier this year. Scale tripled their ARR in 2023 and is expected to hit $1.4BN in ARR by the end of 2024. Their investors include Accel, Index, Thrive, Founders Fund, Meta and Nvidia to name a few. In Today's Show with Alex Wang We Discuss: 1. Foundation Models: Diminishing Returns: What are the three core pillars that can meaningfully improve foundation models performance? Why is data the single largest bottleneck to the performance of models today? What data do we need to capture that we do not currently, that will have the biggest impact on model performance moving forward? Will we see the largest companies in the world revert back to on-prem with the increasing security challenges of migrating all customer data to foundation models? 2. AI: A Military Asset in Global Conflict: China + Russia Why does Alex believe that AI has the potential to be an even more powerful military asset than nuclear weapons? If this is the case, should we have open systems? Do we not have to have closed systems? Why does Alex believe that the CCP's approach to industrial policy is better than anyone else's? How does Alex evaluate the rise of Chinese EV car manufacturers in the last few years? Does Alex really believe that China is two years behind the US in the AI race? 3. "I Get Fairer Treatment in Congress than in the Press": Why does Alex believe that the best PR is no PR? Why does Alex believe that he got fairer treatment in congress than he does in the media? Why does Alex believe that all founders should look to own their own distribution channels today? 4. Alex Wang: AMA: What are some of Alex's biggest lessons from Patrick Collison on the impact that a hot company brand has on the ability for that company to hire the best? Does Alex think Trump is going to win? What would be the impact if he were to? Why does Alex believe that enterprise software will be changed forever in the next few years? What question is Alex never asked that he thinks he should be asked?
Transcript 12,508 words · 4 speakers
Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.
0:00 Speaker 00 At its core, this AI technology has the potential to be one of the greatest military assets that humanity has ever seen. Potentially even more of a military asset than nukes. Let's say China or Russia had AGI today and the United States didn't. I would imagine they would use that to conquer. The CCP's system is incredibly good at taking very aggressive centralized action and centralized industrial policy to drive forward critical industries. They have a clear shot at racing forward.
0:31 Harry Stebbings? This is 20 VC with me, Harry Stebbings, and my word, what a show we have in store for you today. This was such a special one to do live in the studio as we welcome Alex Wang, founder and CEO at Scale AI, the company that trebled revenue in 2023 and is expected to finish 2024 with 1.4 billion in ARR. Earlier this year, they raised $1 billion at a reported $14 billion valuation. And this show was immense, really one of my favorite shows to record in recent times. And so let me know what you think and you can watch the full show from the studio on YouTube by searching for 20 VC, that's 20 VC. But before we dive in, let's face it, your employees probably hate your procurement process.
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3:27 Speaker 02 You have now arrived at your destination.
3:30 Harry Stebbings? Alex, I am thrilled that we could do this in person. Thank you so much for joining me today.
3:34 Speaker 00 Yeah, great to be here.
3:36 Harry Stebbings? Now, listen, it's funny. I told you I tweeted before, like, we should skip the founding stories because there are many, many great times you've told it before. But I want to dive straight in. And I want to ask you the question of when we look at model performance today, let's just start high level. Do you think we're seeing a case of diminishing returns where more compute doesn't lead to better performance?
3:56 Speaker 00 Yeah, I think it's pretty fascinating. I mean, I think there's been, this is especially coming up now, where OpenAI has had GPT-4 since fall of 2022. And since that timeframe, we haven't yet seen a new base model or a new, a new model that's jaw-droppingly better than GPT-4. You know, we haven't seen the GPT-4.5 or the GPT-5 or the other labs haven't yet come out with models that are leagues- Leagues better than GPT four despite way, way more compute expenditure. Since when chat GPT came out, you know, you can look at the graph of Nvidia's revenue and it just inflects. It's just like it just goes straight up after GPT four came out and it goes from, I think the Nvidia's data center revenue. They were doing roughly about five billion a quarter.
4:39 Speaker 00 And then it shoots up to now it's, you know, north of $20 billion a quarter. So there's been tens of billions going to 100, more than 100 billion of spend on high end Nvidia GPUs. You know, all in the same timeframe, we haven't yet seen the big breakthrough since GPT four, which actually that model was, came out before this huge inflection in Nvidia expenditure. So overall, it's this interesting thing where we're seeing investment into compute go up dramatically, go up exponentially right now, but we're still, I think as a community, as an industry, kind of waiting for the next great model.
5:15 Harry Stebbings? So do you think we've reached this kind of asymptotic of performance? Where actually we'll see this kind of plateauing in performance while we wait for that. And do we think that's like a monthly thing? Or do we think that's kind of like self-driving? Do you remember with self-driving, we saw kind of the plateau in performance actually for kind of several years? And actually, it was only recently where we see that inflect again.
5:32 Speaker 00 It's kind of this interesting thing. So there's three ingredients that go into these AI models or three pillars. So there's compute, of course, there's data and there's the algorithms. The history of AI is that progress comes from sort of all three of these pillars sort of being built altogether. You certainly need a lot of computational capability, but you need the algorithmic advances, like the transformer originally or RLHF or, you know, whatever future algorithmic advances come. And then you need the data pillar to support it as well. And I think a lot of the plateau that we've recently seen can almost be explained at a very high level from hitting kind of a data wall. The GPT four was a model basically trained on nearly all of the internet and using a huge amount of computational capabilities.
6:14 Speaker 00 And a lot of the, a lot of, I think what the industry has been doing over the past few years is scaling the computation dramatically, but not necessarily by building up the other two pillars in tandem. So there needs to be, I think, a combination of more algorithmic improvement. But in particular, we need to ensure that there's more data to support it.
6:32 Harry Stebbings? When you say data wall, what is the data wall? And what can we do to enable our overcoming of it?
6:37 Speaker 00 Yeah, so at a super high level, we've used up all the easy data. We've used up all of the internet data. The Common Crawl and newer versions of the Common Crawl.
6:46 Harry Stebbings? Just so we understand, easy data is stuff on social media, anything not behind paywalls, anything that's easy and free to crawl?
6:54 Speaker 00 Anything that's easy and free to crawl, or stuff that can be torrented. There's a lot of reports that there's a lot of torrented data in some of these models, or basically anything that is sort of like already written down and easy to get from the open internet. And then the first stage of a lot of this AI improvement has been these advances in pre-training, which is basically training these models to be really, really good at emulating the internet. And right now, we're at a point where these models are exceptionally good at emulating the internet. They're better than any human at emulating the internet. But the problem is, when we think of AGI, when we think of powerful AI systems, we want much more than just emulating the internet. You want AI systems that can do tasks. You want AI systems that can solve difficult problems. You want AI systems that humans can collaborate with to solve all their daily problems.
7:39 Speaker 00 This process of building agents and AI models that are capable of all these things, we're not going to get there from internet data. And we've already used up all the internet data.
7:49 Harry Stebbings? Why are we not going to get there from internet data? When we think about effective agents, and when we think about software doing the work, not just selling the tools, as I think Sarah Tavell said quite well before, why is existing data not equipped to do that transition from tools to work?
8:02 Speaker 00 The simple answer is like, a lot of the thought process and a lot of the thinking that humans go through when they are doing more complex tasks, that doesn't get written down on the internet. So for example, if I'm a fraud analyst inside a large bank, my job is like understanding, based on a set of transactions that seem suspicious, whether or not it's a fraudulent transaction. And I need to analyze all sorts of different pieces of data, and use my deduction, and use all my human intelligence to make that decision. That process that I go through, it's not like I'm writing down step by step, like, oh, I looked at this piece of data, and then I looked at this piece of data, and then based on that, I deduce this. And I'm not writing all that down on the internet to later be crawled by these models. One way to think about it is like, all of the reasoning and thinking-
8:47 Speaker 00 That is powering the economy today. None of that gets written down on the internet. And so if you just train on the internet, the model has no ability to learn from all of that.
8:56 Harry Stebbings? So how do we codify and capture the data that's not codified already? As you said there with the fraud analyst, the thought process, the analysis, the discussion that goes on in internal meetings that's not codified in data sets. How do we capture that to enable us to do the work?
9:10 Speaker 00 What I really believe is what we need from now forward is frontier data. We need to basically have data abundance of frontier data. Where right now we're in a sort of data scarcity mindset or we're hitting a data wall. And this frontier data is exactly what we're talking about. Frontier data in my mind is, you know, complex reasoning chains, complex discussion, agent chains of models going and looking up a piece of data, doing some reasoning, looking up another piece of data, maybe correcting if it has an error, tool use, all of the key components that we would think of an agent being able to do, that all needs to be encapsulated into the frontier data to power the forward capabilities of these models.
9:46 Harry Stebbings? How do we capture that data?
9:47 Speaker 00 There's basically three pillars. So first is there's a lot of this data that's locked up in the world's enterprises today. And none of that gets on the internet for very good reasons. But just to give a sense of scale, you know, JP Morgan's proprietary internal data set is 150 petabytes. GPT-4 was trained on an internet data set that was less than one petabyte. So the amount of data that exists inside large enterprises is just absolutely astronomical. So there's one process of just sort of mining all this existing enterprise data for all the goodness that exists within it.
10:20 Harry Stebbings? But you would never get that open sourced, would you? So this is all proprietary and then delivered custom to that customer?
10:25 Speaker 00 Exactly. This is to be a process of like every enterprise, like I have a set of very important problems for my enterprise. Then I need to go through the process of like basically mining all my existing data and refining all that existing data for use for AI systems to solve my own problems.
10:40 Harry Stebbings? So when we think about kind of breakthroughs, we said about diminishing returns at the beginning. You know, I spoke to one of the most powerful CTOs in the world the other day. And they said the real breakthrough in kind of this question of are we reaching diminishing returns is like whether we can really solve reasoning. How do you think about our ability to solve reasoning and that impact of data that you mentioned there in helping us navigate that?
10:59 Speaker 00 Yeah, I actually think that if you look at what these models can do, they're very good at reasoning in situations where they've seen a lot of data before. You know, I think we like to think about these AI as if they're like little human intelligences, but they're very different. Human intelligence and machine intelligence are very different. Humans have a very general form of intelligence. If a kid is raised in a very small neighborhood, they can live their whole lives in that small neighborhood, and they can go to an entirely different part of the world, and they can navigate, understand what's going on. No AI system today would be able to do that level of sort of drag and drop in one situation to another situation and figure out what's going on. I think we have to be cognizant that that's a limitation. But what that means is that for any situation that we want these models to perform well in, we need to have data of that situation or that scenario,
11:44 Speaker 00 and actually, the model will perform really well. So there's kind of two ways to think about resolving the reasoning gap that exists in these current models. One is, obviously, you build some sort of general reasoning capability, which would definitely be a big breakthrough. The other one is just it's a data problem. It's like you need data for every scenario where you want these models to reason well in. You just need to overwhelm them with data in all those scenarios, and you're going to get models that can reason really well.
12:10 Harry Stebbings? How do we move from an environment of data scarcity to data abundance when we appreciate the immense amounts of data that, say, JP Morgan or Goldman Sachs or any large enterprise has, but also the proprietary nature of that, which won't actually go to generalized models, which will help the world or humanity or any of these kind of breakthroughs actually occur to everyone else? How do we move from that data scarcity to data abundance? Is it synthetic data that we create? How do we think about that?
12:32 Speaker 00 Yeah, so I think the second part is, to your point, new data that has to be produced. We need the means of production of new frontier data to get us from, you know, GPT-4 to GPT-10. I think when we think about chips, this is very natural, which is, oh yeah, we need to build more and more fabs. We need to build bigger fabs. We need to like increase the resolution and get lower and lower nanometer fabs. Like for compute, it's very natural for us to think about increasing the means of production. But I think we don't think about this with data. And I think we need to do something very similar. And this process of producing data, it's sort of a hybrid, human synthetic process. And that's really how we think about it, which is you need algorithms that can do a lot of the heavy lifting in producing synthetic data, but you need human experts who are going
13:15 Speaker 00 to be able to guide the AI systems and basically help provide input as to, you know, when the AI system gets stuck, or when they have a factuality issue, or when it's in a situation where it hasn't encountered before. A lot of autonomous vehicle scale up has been through these safety drivers. You know, you have safety drivers inside the car. And when the car starts screwing up, you have the safety driver disengage and sort of take over. And you need that kind of setup for these AI systems. You need, you need AI models to be generating large amounts of data and then humans who can kind of take over and nudge the models when necessary to make sure that you get really high quality data.
13:49 Harry Stebbings? What does that look like in the structure of organizations today? Like, do we create new roles for these AI savers?
13:55 Speaker 00 Yeah, yeah. Trainers is one term, AI trainers or contributors is another term. For what it's worth, I think this process of, of contributing data to AI is actually one of the highest leverage jobs that humans can have. And the reason for that is, let's say I'm a mathematician. I can either go into my hole and, you know, do pure math and try to do pure math research. That's one trajectory for my life. The other trajectory is I use all my skills and talents and intelligence to help make these AI models smarter. Let's say I make G P T four just like a little bit smarter on math. If I take that little bit of improvement of the model and I sum that up across all the times that G P T four is going to be called and used across every math student
14:40 Speaker 00 who's going to use G P T four, every company that's going to use G P T four, every developer that's going to use G P T four, that's a huge amount of impact. And so as a human expert, you have the ability to have society wide impact by producing data to help improve these models. What we see is for scientists, mathematicians, doctors, human experts in the world, it's an incredibly exciting proposition to be able to, I can transmit my capabilities, intelligence, training, all of that into a model that's going to be able to have society-wide impact. I mean, it's an incredibly exciting proposition.
15:14 Harry Stebbings? How do we think about the structure of data? Often people talk about the biggest challenge in data governance is actually just like the structure and cleanliness of it. When we look at the 150 petabytes of JP Morgan data, I have no idea. But I presume it's not structured perfectly for a lot of models to ingest efficiently. How do we think about the structuring of this huge data set that I'm sure all large enterprises have and the challenge that that poses?
15:37 Speaker 00 So again, I think this is a case where there's two parallel efforts. One is mining existing data, which by all means is going to be a one-time hit. There's going to be a one-time benefit that you get from mining all your existing data, and it could be really meaningful.
15:50 Harry Stebbings? Do you think in five years' time, everyone will have mined their largest data sources internally?
15:55 Speaker 00 I don't think everyone will, but certainly the most sophisticated companies will. And then we'll be at a point where we still need to make the models better. At the end of the day, it'll all boil down to data production. What are the means of forward production in the same way that you need the means of forward production for chips and all the other things that you care about?
16:11 Harry Stebbings? Okay, so we have that in terms of mining existing. You said there was another form.
16:15 Speaker 00 So there's data mining, and then there's forward data production. These are the two core directions for where we need this data to come from. And I think kind of taking a broader step back, I think that a lot of AI progress at this point is fundamentally more data bottleneck. If we were able to produce compute and data in lockstep with one another. So as Nvidia continued to manufacture hundreds of billions of dollars worth more of chips. If we were able to produce a proportional amount of data as we got more and more chips and we were able to produce these two together. Then we would get astronomically more capable.
16:51 Harry Stebbings? But just so I understand, so when we think about increasing the supply side of data, what is the literal ways that we can do that? What comes to my mind is actually Dan Sirok at Limitless. But he basically has this new hardware device which records every single thing that you say and do. And it produces your own personal AI because it has everything that you've ever said in the day. That is a new form of data creation in my mind. How do we increase the supply side of data?
17:15 Speaker 00 So there's probably two main pieces. Is like this effort from Limitless or other efforts, which is basically much more longitudinal data collection, collecting more of what's naturally happening in the world. There's a bunch of forms of this. So one is like in a workplace, I think you're going to want, you know, as creepy as it sounds, some kind of constant data collection of what apps are using, what order apps are using, you know, where do you copy paste one thing to another?
17:39 Harry Stebbings? You have a lot of this with RPA and a lot of UI pass flows.
17:42 Speaker 00 Yeah, exactly.
17:42 Harry Stebbings? Quite used to that.
17:43 Speaker 00 Yeah, yeah, yeah. So process mining, which is one of the terms in SAS, but basically like the continued collection of existing enterprise processes. Then there's the consumer version of that, which looks kind of what you're referencing, or maybe it's with the meta Ray-Ban collaboration, or whatever device ultimately does it, but sort of something that collects, you know, the longitudinal view of your own life. And then there has to be a real investment towards human experts collaborating with models to produce frontier data. So both of the things I referred to before, both enterprise process mining and, for lack of a better term, consumer data collection, those are all going to produce valuable data sets, but they're not going to produce the data that's actually going to push the models forward. Because to push the models forward, you need really highly complex data
18:26 Speaker 00 that's going to be able to push the frontiers of what the models can do. So this is where you need the agentic behavior, this is where you need the complex reasoning chains, this is where you need advanced code data, or maybe advanced physics, or biology, or chemistry data. These are the things that are really needed to push the the boundaries of the models. I think this is a global kind of infrastructure level effort that needs to happen. Like, I think we need to think about it as how do we get the world's experts to collaborate with the models to help produce AI systems that are going to be the world's best scientists, or the world's best coders, or mathematicians?
18:57 Harry Stebbings? When we think about the commoditization of the models, as everyone says that we have, how do we think about like proprietary access to these data sources? People have said to me before that I don't mean to throw shade, but like OpenAI's models are not necessarily better, they've just had better access to data, they've bought more data, whatever, whatever. But data being the central kind of superiority element of why they had better performance in the past. Will we see one model get access that others don't? How do we think about like fair and equitable access to data from the model side?
19:24 Speaker 00 Yeah, well, I actually think to your point, if you think about the competitive playing field of these different model providers against one another, there's three pillars, right? There's algorithms, compute, and data. And data, I think, is actually the primary pillar that you can imagine a real durable competitive advantage emerging. So if you think about where are their moats in this LLM race, or where are their moats in this foundation model game, I think data is one of the few areas where you can produce a sustainable moat. Because the issue is, algorithms, that's IP that at some point the rest of the industry will learn about. You know, you can have more compute than other people, but other people can just spend more money and buy that compute. And data is one of the few areas where you can actually produce a long-term, sustainable competitive advantage.
20:07 Harry Stebbings? I agree. When you look at some of OpenAI's agreements, they obviously partnered with the FT to get access to all of the FT's historical library, and they've done quite a few actually with Axel Springer, I think. That is access that a lot of other models do not have, which will make their content superior in whatever queries they have in that respect.
20:21 Speaker 00 Yeah, exactly. And I think this is the, it's the start of this form of thinking of sort of data as a moat. You know, there's the FT, there's Axel Springer, these are the first indications of this. But in the future, these labs are going to be thinking a lot about, okay, what's the data that I'm going to use to differentiate relative to my competitors, and how am I going to produce that data, and what is the long-term durable advantage created by that? I actually expect that, you know, everything we've been talking around data, around model commoditization, we're going to see companies start building data strategies that drive more differentiation in the market over time. I mean, another way to think about this is, you know, right now, in San Francisco, you know, researchers and the big CEOs brag about how many GPUs they have. The biggest indicator of how serious
21:03 Speaker 00 they are about AI is, is how many GPUs they have. But I think in the future, they're going to brag about, you know, what data they have access to, how much data they're producing, and what are their sort of unique rights to, to different data sources. And I think that's actually going to be the primary plane of competition in the future, versus just, okay, Jensen's giving me however many hundreds of thousands of GPUs.
21:23 Harry Stebbings? Given data strategy being a potential element that one could win on and compete on in different ways, do you think we will not see the commoditization of these models over time?
21:34 Speaker 00 There's two futures. One is that even data strategy becomes something that very quickly commoditizes and and different labs sort of copy one another, or they all they all end up converging to the same direction.
21:47 Harry Stebbings? A hundred percent, because especially with a lot of the content producers, they're not going to do exclusive agreements with one model and not other models.
21:53 Speaker 00 Yeah, different labs need to have strategies to produce their unique data sets. Let's say Anthropic, for example, has focused a lot on enterprise use cases. And maybe they need to develop a data strategy that enables them to have a very differentiated access to new data to support those enterprise use cases. Or maybe OpenAI with ChatGPT needs to develop, you know, a unique data strategy that lets them leverage the fact that they have all these users and all this reach. The various labs are going to need to lean into where they're going to be able to get proprietary differentiated data going into the future.
22:27 Harry Stebbings? Do you think we're going to see a reversion back to on-prem? I'm jumping around so much, but I'm loving this conversation, sorry for that. But when we think about the like 150 petabytes of JP Moore data, I don't know if they're going to be like, yeah, I'll throw it all in the cloud, my most sensitive data. Will we see the reversion back to on-prem and models that work on-prem for these large enterprises?
22:45 Speaker 00 This is a super interesting question. I think when we talk to
22:48 Harry Stebbings? Thank you, Alex.
22:50 Speaker 00 I think when we talk to these large enterprises and the leaders within these enterprises, they are very quickly realizing this fact that you stumbled on, which is that their enterprise data might be their only competitive differentiator in an AI world. They're extremely, extremely cautious about, you know, if they do a deal where all their data somehow a model developer gets access to it, or they share it in some way, then, you know, they could be mortgaging away their entire future. And so I think they're they're very, very cautious about that. And this is actually why I think there's a very big sort of opportunity for whether it's open source models or the Llama models or the Mistral models or whatnot, basically these models that that can go on-prem and that enterprises can take and then customize
23:34 Speaker 00 on top of their own data and then never has to go back to a model developer or cloud or anything like that. I think that there's a huge unmet need there. And I think that's actually where most Serious enterprises are going to go towards, which is I need really, really strong guarantees that my data is not going to be used in any way to improve my competitors.
23:52 Harry Stebbings? I think AI services will actually create more revenue over the next five years than AI models. We saw actually Accenture come out with, I think it was $2.4 billion in revenue from generative AI and OpenAI was obviously at $2 billion. How do you think about, I'm just intrigued with Scale AI today and working with some of the largest enterprises, a services component, that learning and adoption curve is challenging for large enterprises. Do you see that as a core part of your business in the next few years as we scale the education curve?
24:18 Speaker 00 First of all, I think you're right. There's so much value to be generated from AI, for sure. But then there's this natural question of where's the value capture going to be, right? You know, there's this fascinating thing, you know, if you go back and read High Output Management by Andy Grove, there's like, there's these chapters around like, oh, you know, for Intel, like, first we thought that like, this is where the value capture is going to be. But then we realized it was going to be in this other part of the stack. And so we had to migrate to that part of the stack. And then we had to migrate again. And it's this incredible case study. You know, I remember reading that, and I read it maybe a decade ago in a different era of tech. And I was like, this is weird. This doesn't feel very relevant. And then now in AI, you're seeing it once again, where it's so new and so nascent, where exactly where in the stack value will accrue feels like it's constantly
25:01 Speaker 00 moving. And I agree with you. I think that the models themselves, there's so much competition there. I don't know how much value accrues at literally the model itself, but everything above the model and everything below the model, I feel very confident there will be value accruing. So for the infrastructure, I mean, Nvidia is the biggest company built on AI today. Like, they're the third most valuable company in the world. Nvidia is more valuable, you know, their market cap is higher than Meta and Google and Amazon and Saudi Aramco. I mean, it's really staggering. Like Nvidia is an incredible, incredible company. And that's below the model. And then above the model, you have all these apps and these services that are going to be built on top of it.
25:39 Harry Stebbings? So I was arguing with someone this morning, actually, on the way here, though, and I was saying like, yes, okay, we have Notion AI and we have, you know, Box, the storage company who are going to implement AI solutions into their existing storage products so you can extract information better. Yeah, have you seen the numbers? Salesforce are now growing at single digits. Like, Mongo are now growing at single digits. Point being, actually, the commoditization of these features means it'll be better products for us. But I don't know if you'll get value extraction from that in the form of increased pricing. How do you feel about that?
26:09 Speaker 00 Yeah, so our thesis on this is there was this article that flew around, the end of software, right? I saw this, Chris Pike. From Chris Pike, yeah. It was an intentionally provocative point of view, I think. So for those that haven't read it, what was the core premise, just so they understand it? I thought it was like a brilliant comparison, but he basically drew this comparison of software companies today to media companies, pre-social media. The rough comparison was, in the older days of media, you had all these incredible media companies, there were these high-end shops where there were like all these experts producing this very differentiated content, but then it got disrupted by social media and the internet broadly, because all of a sudden you just had, the content distribution cost came down dramatically.
26:52 Speaker 00 The world of media consumption turned into this like very broad constellation, where you would consume whatever media was produced by anybody that was interesting to you, and it was sort of like much more on-demand versus being this sort of walled garden of large media producers. And basically this comparison to, that's what's about to happen to software, which is now the enterprises live with this walled garden of some small number of software providers. And what's going to happen now with generative AI and all these other trends is, they're going to have this constellation of all of these different apps and point solutions, and this sort of portal to that constellation of various software providers. And this sort of like, we're going to move from this current world of like smaller number
27:33 Speaker 00 of walled garden SaaS apps to this sort of much more decentralized universe. Do you agree with that? It's intentionally provocative, right? But I think one thing that is true is, I do think that enterprises and the world writ large are going to demand greater levels of customization. They're going to demand greater levels of personalization and stuff that is really purpose-built for their business, like a glove. The first tech company that ever did something in this direction was Palantir. You know, they got a bad rap for a long time, because everyone thought that Palantir, oh, they're just a consulting company. But Palantir's point of view, which was provocative as well, was like, no, what we're going to do is we're going to go into enterprises, we're going to understand exactly what their
28:17 Speaker 00 problems are, and we're going to help them build the Perfect application for them that's built on top that connects all their data and all that stuff. And if we can do that, then we're going to build something that's that's far more valuable for them than what any other software provider is going to be able to produce. And they did this, obviously, before generative AI, before all these tools that are going to make this this motion a lot more feasible. But I do think there's an element that this is like the way the world is moving, which is now especially that the software production costs and software creation costs are going down so dramatically. We're going to end up moving towards a world where more and more of the software that enterprises consume are going to be customized and custom built and purpose built for exactly their problems.
28:59 Harry Stebbings? What does that mean in terms of the makeup of engineering teams of large enterprises? Do they shrink? Do they focus on different things? Do we just have teams of the world's best prompters? What does that mean in terms of the changing structure of engineering teams?
29:10 Speaker 00 Yeah, well, I think software engineering in general is going to change dramatically. A lot of what developers spend a lot of time on today, they will not need to spend time on going into the future as the models get better and better at coding. But there's certainly big parts of what they do which are irreplaceable. And over time, I think that the part in particular that's very, very valuable is this sort of like general process of going from what are my customer problems? Or what are the sort of like problems I need to solve? And like translating those into engineering problems and scoped sort of like tickets almost that can be solved by an AI engineer.
29:46 Harry Stebbings? Everyone says that we're going to see the end of per-seat pricing, like you said, Chris had that provocative article. But everyone talks about the end of per-seat pricing. To what extent do you think we will see the end of per-seat pricing in this next wave of software? And especially with the data lens where you could see a more consumption-based pricing model aligned? Do you think that truly takes over?
30:05 Speaker 00 The reason that per-seat pricing doesn't make sense going to the future is that at an enterprise today, certainly, most of the productive work is done by their employees, done by people. But in a future where you imagine more and more of the work is done by AI agents or AI models, then per-seat pricing doesn't really make sense. As a provider of software, a provider of solutions, you want to make sure that you're capturing the Value that you're providing to the people, but also the value that your agents or your AI systems are producing. That shifts a lot of the world towards consumption-based pricing versus per seat.
30:39 Harry Stebbings? One of my biggest worries is, obviously, we're in London. We specialize in many things here, long lunch breaks and regulation. And so diminishing on London. But my question to you is, I really worry that we're going to see regulatory provisions which stifle innovation because of Consumer Data Protection Acts and just unnecessary regulation around data access. Do you think I am justified? And how do you navigate the regulatory access to data question?
31:08 Speaker 00 It's a really important point. And I think that certainly what we've seen in the EU is a very restrictive approach to data. My personal belief, I don't think that more permissive regulations around data are at odds with being a liberal democracy. More sort of liberal data access provisions are in fact very compatible with being a liberal democracy. And I think that we as a society need to figure out what the right balance there is and how we sort of square the circle. But I think this is a very important question because it's almost like, I think in the United States, there's been a huge amount of effort and real regulatory effort on terms of how do we ensure we do not slow down chip production?
31:50 Speaker 00 How do we make sure that we can keep manufacturing huge amounts of chips and the US won't be disadvantaged from that perspective? We need to take a similar lens to data. So how do we, from a policy standpoint, both in the US and in the UK, frankly, how do we think about ensuring that as countries, we're not tying one hand behind our backs for future data production for these models?
32:10 Harry Stebbings? Do you think the US is currently tying one hand behind its back in terms of that?
32:14 Speaker 00 I'll put it this way, we're definitely not taking a pro-data regulatory.
32:17 Harry Stebbings? What would a pro-data regulatory stance look like?
32:20 Speaker 00 I think there's a few things. I think first, there's large data sets that do not lend proprietary advantages to specific players that need to be sort of centralized and made accessible to whole industries. So, simple examples, safety data in, let's say, aerospace, which is a hot topic, obviously. But safety data in aerospace should be collectively pooled for the purpose of advancing the entire industry forward. Or the data I mentioned before, or the example I mentioned before, fraud and compliance in financial services should be pooled together and should build forward capabilities. So I think there's like entire industrial sectors where there should be some degree of data pooling to just push forward the overall industry.
33:03 Speaker 00 And I think what you need is, in a lot of consumer-facing areas, you need to, we need to work through a lot of the existing restrictions to make sure that those don't prevent AI progress. So one great example here is actually HIPAA in healthcare and all the PII and other limitations. Right now HIPAA and all the PII regulations will more or less prevent patient data from being used to train AI models. But I think we can agree as a civilization, as a human race, we really want to learn from all of the existing medical data on how do we cure human diseases going forward. And so we need to figure out like, how are we going to make it so that like, there's very clear anonymization provisions, or there's a very clear and obvious way in which you
33:47 Speaker 00 can use existing patient data to improve future health outcomes.
33:50 Harry Stebbings? I heard actually that China apparently, I can't remember who said this on the show, but they said they're like two years behind the US in terms of AI progress. I heard that and I thought that is absolute shit. And I think when you look at like data provisions and what the Chinese government will be willing to do in terms of data access and data provisions and regulation, I think if they are two years behind, that will very quickly catch up. How do you see China being two years behind and do you agree with that?
34:14 Speaker 00 Two years ago, they were probably more than two years behind. When OpenAI first produced GPT-4 in the lab, China were nowhere near that. But just even the past few months, there was a Chinese company, 01.ai, that produced a model Yi Large, YI-Large, that is one of the best models in the world. I think it's just behind, so it's behind GPT-4O and Gemini and Claude 3 Opus. And it's the next model right behind that in the leaderboards. So it's one of the best models in the world. So we've already seen them meaningfully catch up. They are like Chinese. LLM and AI capabilities are, I would say, right now, pretty close to neck and neck with US capabilities.
34:55 Speaker 00 And I think if you plot the path ahead, based on everything we've talked about with data, they have a clear shot at racing forward and racing ahead of us. It comes down to, at its core, the CCP's system is incredibly good at taking very aggressive centralized action and centralized industrial policy to drive forward critical industries. And what we've seen even in the past few years on, or the past few decades frankly, on solar, how the CCP has been able to make, take industrial policy to the point of like being by and large the world's leader in solar. And then most recently EVs and how the CCP system and approach has been able to create very, very cheap EVs.
35:36 Speaker 00 You're seeing this pattern play out over and over again where the CCP approach to industrial policy is not the most innovative. But once an industry has been established and it's about, you know, turning the crank, they are better at turning the crank than any other economy in the world.
35:51 Harry Stebbings? I totally agree with you. I saw actually a chart, I can't remember who tweeted it yesterday, but I think it was, I think it was either Elon or Bill Ackman. And it was basically showed countries, different kind of creation of EV providers. And it showed like the US, and it was like the US, I mean, without Tesla, it would have been in the dumps because it would have only had me in General Motors. But China was like up and to the right. Does that worry you?
36:10 Speaker 00 It worries me a lot. You know, one of the elephant in the room topics, which I think, you know, as an AI community, we rarely discuss, is that at its core, this AI technology has the potential to be one of the greatest military assets that humanity has ever seen. Let's say you had AGI and you have one country with AGI and another country without AGI. Which one will win in a war? Well, probably the one with AGI is going to like figure out all, how to produce all the weapons or will figure out a brilliant military strategy or will be able to hack the other country's systems. And it is potentially one of the greatest military assets that the world's ever seen, potentially even more of a military asset than nukes. And so if you think about this, you know, we're in a geopolitical environment that is increasingly tense.
36:53 Speaker 00 You know, the amount of conflict in the world is, has been monotonous increasing the past few decades. You're seeing these multiple Wars being fought in the world and some of them without very clear paths to resolution. And there are totalitarian leaders right now in the world, many of them, for whom like, let's say China or Russia had AGI today and the United States didn't, I would imagine they would use that to conquer. That's a really scary outcome for the world writ large. And I think it's one that the Western world needs to spend a lot of our thought and effort towards preventing that outcome.
37:26 Harry Stebbings? Given that concern, should we not have closed systems? Obviously, open systems have a lot of benefits, but the challenge of open systems is anyone can use them. And that means that Russia can use them, China can use them, and everyone has same levels of access, or supposedly so. Should we not have closed systems with what you just said?
37:42 Speaker 00 I think there's a bit of a dichotomy that must emerge. I think we need to think about the most cutting edge and the most advanced systems. Those we will want to ensure are closed for geopolitical reasons, for military reasons, for whatever reasons. Like, as we develop systems that are genuinely so, so powerful, we will want to keep those closed. That doesn't preclude us from making open, less advanced versions of the technology that frankly just have the ability to produce a lot of economic value. And I think that's where we are with Llama right now. Like, Llama 3 in and of itself is not a military asset, yet. And I think that there's clearly a line underneath which I think it's totally fine to have open models. So that's what we need to be thoughtful about, is where that line is and when are we getting close to it?
38:24 Harry Stebbings? Before we discuss some kind of company building principles, which I do want to touch on, 10 years' time, what does that foundation model layer look like? Who's independent, who's been acquired, what does it look like?
38:33 Speaker 00 I think at its core, what we've seen about the foundation model race is that it is incredibly expensive. And it is expensive to the level of, you know, these malls have gone from costing hundreds of millions to a billion dollars, to maybe multiple billions. I think in 10 years' time, maybe they'll cost tens or hundreds of billions. There's just not very many entities that have that much discretionary capital to invest into these AI models. So naturally, what will happen over time is AI effort, the foundation model efforts will coalesce around nations or the large tech companies over time. Basically, you will see the most, all these hyper profitable business models, whether that's a nation state or one of the hyperscalers,
39:17 Speaker 00 those will be the only entities that could possibly subsidize or underwrite these massive AI programs. In the future, already it looks like a battle of giants, but at that point, it's even more a battle of giants.
39:28 Harry Stebbings? So do you agree with me in saying that you'll see all of the smaller players acquired by the large cloud providers, your Google, your Amazon, your Nvidia, you name your large incumbents, but especially the large cloud providers, and have them integrated into their existing solutions?
39:39 Speaker 00 Yes, with maybe an asterisk that there's some of these partnerships that I think it'll be interesting to see how they play out. You know, the OpenAI-Microsoft partnership or the Anthropic-Amazon partnership. One of the most interesting questions of this technology era is, how do these partnerships actually end up playing out long term?
39:56 Harry Stebbings? Listen, I do want to touch on some company building principles. Let's start with, I can't remember the exact, you said on PR, it was a brilliant statement, this was it, which is the best PR is no PR. What did you mean, Alex?
40:07 Speaker 00 At its core, the traditional press industry is not particularly conducive to great companies being built. And let me be more specific around that. A lot of traditional press is oriented around generating clicks. And so the traditional press engine will, it'll build you up and it'll generate clicks on the way up and it'll tear you down and generate clicks on the way down. This is in contrast to, I think, 20VC and other direct outlets, so to speak, where founders and companies have a direct channel to get their message out and explain what they're working on.
40:36 Harry Stebbings? I think the other thing, and I actually think it's a little bit unfair, I feel for traditional media, I don't care about clicks. Like, yeah, we have sponsors, respectfully, if we didn't have them, we'd still be doing the show. I don't do sensationalist headlines. I'm not going to put some glossy thing, "Scale AI predicts military devastation" with this episode because I'm not there to just optimize for clicks.
40:58 Speaker 00 Exactly. Yeah, you're there to genuinely educate and explain what's going on to your audience.
41:06 Harry Stebbings? It's almost unfair, though. Can you imagine if someone said, "Hey, I'm going to do Scale AI, but I don't care if we lose money." You'd be like, oh, fuck, how do I compete with that?
41:15 Speaker 00 Yeah, it's pretty stark. You know, I feel like I've received more fair treatment testifying in front of Congress than I have from various media outlets over the years. It feels like this totally ridiculous statement, but I think we're in this perverse state of of a lot of traditional media where the system itself, you know, because of this sort of like very click oriented approach versus a genuine educational approach, it almost has no way of being fully fair to the companies. And so I think it's imperative is on the companies themselves to properly tell their story through direct channels and through podcasts and through avenues where their their message won't be altered.
41:53 Harry Stebbings? I completely, I think this is why founder brand today is more important than ever because if you don't own your means of distribution, it will be contorted.
42:00 Speaker 00 Exactly. It's kind of a shocking state of the world, but I
42:04 Harry Stebbings? Has that changed your strategy then?
42:06 Speaker 00 Yeah, I think for us, we think a lot about how do we get the direct message out there? And how do we develop to your point, like, what are, what are the purest ways that we can transmit and explain what we're doing? And this is, this is a great example. You know, you'll ask me a question, I will answer exactly how I believe that, I think. And this will go out to your, to your listeners and your viewers. One of the purest forms of getting the message out there.
42:26 Harry Stebbings? I think one thing that people make a big mistake on though is they then try and build the direct channels for the companies. And respectfully, people don't follow scale. People follow Alex. It's much easier to build followings with personalities than it is companies.
42:37 Speaker 00 I think this is true. I think there's like so few companies that can, OpenAI is one where like, I think OpenAI is an entity has like a lot of, as a lot of meaning as a brand, but
42:47 Harry Stebbings? It does, but if you look at the amount of times that Sam Altman trends versus the amount of time that OpenAI trends, it is disproportionately higher to Sam Altman. People still now more than ever love the cult of personality.
42:59 Speaker 00 Yeah, that's a fascinating thing. I mean, that definitely should be a
43:02 Harry Stebbings? And that transcends actually, when you look at Lionel Messi at Miami, when you look at Margot Robbie with Barbie, the celebrityization of individuals in organizations or in movements drives everything.
43:12 Speaker 00 That's fascinating. I mean, it probably speaks to a deep, a deep human need. I think we as people, we have a lot of circuitry to understand individuals. We have this ability to understand individuals. It's very hard to understand what an organization means. There's no intuitive.
43:26 Harry Stebbings? So should founders give a shit about traditional appeal? Should they care about getting in the traditional press?
43:30 Speaker 00 I would argue no. I would, I would argue we're in an era now where they, they shouldn't. They should think about, what is an interesting point of view they can have? What is the most pure way to get that point of view across?
43:40 Harry Stebbings? When do you feel the press tried to tear you down unfairly?
43:43 Speaker 00 We've had, I would say that like, almost precisely, we've had this, this story where we had an incredible rise up and incredible come up. Maybe we initially became a unicorn back in 2019 and for the few years after that, you know, it felt like smooth sailing. And then, starting in about 2022, right, when it was, or the entire media narrative, let's say, was tearing down tech companies. Because, I mean, in some ways, it was very fair. Many, many tech companies received very high valuations. There was an incredible amount of excitement in tech and then the markets all crashed. That, and starting in 2022, was when I noticed, for us specifically, the tone entirely shifted.
44:22 Speaker 00 Where it was the media engine pointed itself towards, you know, pointing out all the missteps from companies like us or a lot of our peers versus trying to point, you know, trying to take a balanced perspective. And there were even, you know, another example of this is, so, starting in about 2020, we began working with the US military and the US DOD. This is obviously long before the current defense tech hype wave and, and long before all that. But it was, it was driven by an intrinsic belief that we had as, that I had and we had as a company, that it's important for the United States DOD to have access to incredible AI technology. That was like a fundamentally important thing for the future of the world. And in the years after that, by and large, the traditional media engine actually tore us down
45:05 Speaker 00 for supporting the US government and supporting the military versus taking a broader view that maybe this is a positive thing actually to, to support, um, support the US military. This almost goes to what I was saying about the dichotomy in treatment and, you know, testifying in Congress versus with the media. You know, I testified before. Congress about AI's use in the military. I would say my, the treatment I got there, I think, was a properly broad one. It was, hey, obviously, this is powerful technology that we need to be thoughtful of. But it is so important that America leads in this. And, you know, thank you for, for everything that you're doing. That, that I felt was the response. Whereas in the media, it's this incredibly scornful perspective around, is this a good thing? Do we trust this company? Like, what does this mean? I mean, it's, it's this shocking.
45:50 Harry Stebbings? But I think it goes back to the incentives drive outcomes. And what is the incentives of media versus the incentives of Congress? Congress are not there to sell you or clicks. They're there to hopefully get to an informed decision on the best outcome.
46:01 Speaker 00 Exactly. Yeah.
46:03 Harry Stebbings? On the incentives drive outcomes, I loved something you also said. You said, why hiring people who give a shit is harder than it sounds. What do you mean? And how do you think about that when hiring?
46:13 Speaker 00 If you hire people who we say give a shit internally, but who really, really care. You know, they really, really care about their work product. They really, really care about the quality of their work. They really, really care about the organization. They care about making sure that the company has an impact. They just really care. And what that means, how that manifests is they're willing to sweat every single detail. And if they get roadblocked or there's like something in their way, they'll spend the extra, they'll go the extra mile to make sure that they get through those things. That's how startups work. These small teams of people who each care 10 times more than the average employee, 10 or 100 times more than the average employee inside a big company. You end up just solving so many more problems than, than the big company.
46:51 Harry Stebbings? How many people do you have in scale today?
46:53 Speaker 00 We are about 800 people.
46:55 Harry Stebbings? 800 people. You are now getting to the kind of bigger company size. It is harder, you know, the kind of only hire A plus players or A players. A players by definition are rarer. Can you have 800 A players?
47:07 Speaker 00 I think the answer is yes. You know, what we say a lot internally is how do we hire the Navy SEALs? Not, not the Navy. Not that there's anything wrong with the Navy, but how do you have, you know, a really small elite group where you're really hiring the cream of the crop? And this goes down to process. You know, for us, at this point in the company, still, I approve every hire. I will either indirectly interview or look at the interview feedback and look at the, understand every single person who we hire to ensure that we're keeping an exceptionally high bar. And that way, if there's-
47:35 Harry Stebbings? What percent of the time will you go against the recommendation of the team on a new hire?
47:40 Speaker 00 Maybe on average, twenty-five to thirty percent, like a lot, like a lot. And I think usually it's due to, you know, maybe there's a new hiring manager who, you know, needs to get calibrated, or it's like an edge case of various forms. But to me, the way I think about this is like, I, as the founder of the company, I have seen everybody who's come in and I've seen who succeeds and who fails. I have as an algorithm, almost like, developed the most fine-grained dataset of understanding what it looks like for people to be successful at scale and what it looks like to have the Navy SEALs versus the Navy. And it's my job as a founder to help ensure that we as an organization are actually utilizing all this knowledge and utilizing all this learning that's been happening over the past eight years in the organization and carrying that forward.
48:24 Harry Stebbings? Final one, what was your biggest like management or leadership fuck-up? So like for an example for mine, I'm like, people act out of fear or freedom. You know, when you bring someone in, some people act out of like, you have to perform, you have to perform. And other people act out of, hey, I trust you, I respect you, do your best work. And you just have to identify which camp someone's in and then hopefully if their skills are there, they should operate to their best. I wish I'd known that when I started and I didn't and I just tried to act out of fear for everyone there. What do you know now that you wish you'd known and why did you fuck up?
48:56 Speaker 00 The biggest one was actually, you know, in the same era of like twenty twenty, twenty twenty-one, was thinking that hypergrowth as a company meant that you had to hypergrow your team. In those few years, we did what a lot of tech companies did. We like doubled, tripled the team year on year. In twenty twenty, we were about a hundred fifty people. By the end of twenty twenty-two, uh, we were over seven hundred. It was this insane amount of hiring and this incredible amount of of hypergrowth as a team. What I found out is when you hire that quickly, it is impossible to to do what we've just been talking about, which is maintaining this high bar, maintaining this feeling of excellence within the team.
49:37 Harry Stebbings? Did you see the reduction of that bar in real time?
49:39 Speaker 00 It was kind of subtle. It was something where you would hire all these people in and you'd notice it like the next year or the next six, or six months later. You would notice it slowly and that the organization, you know, there were challenges that the organization. Usation used to be able to to deal with and solve that slowly just calcified and we weren't able to we weren't able to get around. And so you'll notice, you know, from the end of twenty two, where I said we were seven hundred people to now we're eight hundred people. Team has mostly kept the same size, but the company, the revenue of the company has grown dramatically.
50:07 Harry Stebbings? It's funny, companies have like brand inflection points, they go hot, they go cold, they go hot again. Do you know what I mean?
50:13 Speaker 00 Yeah.
50:14 Harry Stebbings? It feels like from the outside, scales hot again.
50:17 Speaker 00 Well, that's a.
50:19 Harry Stebbings? I don't mean that like to be super nice or or not nice. I didn't mean it when saying you're cold. But it's just weird how brands have moments of heat and not heat.
50:27 Speaker 00 This is a fascinating thing, actually. I actually asked Patrick Carlson this question as well. And Stripe, obviously, is an incredible company that has, for a lot of their lifetime, I think, has been one of the iconic Silicon Valley companies. And I asked him whether or not he thought that the fact that they were such an iconic company was beneficial in all the hiring they did. And he made an interesting point, which was that the best people they hired, he thinks would have been people who would have joined, whether or not they were the hottest company in Silicon Valley. It was the sort of like off the beaten path people who are actually the the best hires they could have gotten. And a lot of the people who joined because they were the hottest company in Silicon Valley, for one reason or another, weren't necessarily the most valuable employees.
51:11 Speaker 00 And so there's this, there's this element where I think the common belief and the common narrative is like, you want to be the hottest company so you can attract the best talent, so you can hypergrow, so you can then go keep growing. And I think that's often so, so difficult. And it's much more about like, how do you develop an ecosystem of talent that is like very self preserving, keeps a very high bar and always seeks out and searches for the best people? And then, independent of whether the company is hot or not hot, because you will have, to your point, you'll have moments where you're hot, moments where you're not hot, moments where you're hot, not hot. And you need that talent ecosystem to be self preserving, independent of that, to drive the best out.
51:43 Harry Stebbings? I also think it depends on function. Like, when you look at a lot of go to market functions, traditionally for sales, they do like concentrate towards hotter brands. And actually, if you can get a concentration of incredible salespeople, especially as you at Brandography, I'm thinking of like OpenAI's kind of go to marketing in London. Unbelievably good, one of the best in London, and it's. Because they have an amazing brand. Do you see what I mean? So it depends like how close you are to the nucleus and what function you're in.
52:06 Speaker 00 Yeah, I think that's right. Yeah, yeah. But then if you look at core technical development at OpenAI, a lot of that is still driven by people who have been at OpenAI since before they became the hottest company ever. You know, another company that I think experienced this or went through this is Airbnb, Brian Chesky, right? And I think that, you know, he's talked about this publicly after the pandemic. He all of a sudden realized like, hey, I have to, he had to kind of like rebuild the entire company. And he massively shrunk the team. He invested a lot more into talent density. And then he built the team to remain small. And I think that they're even now the most or one of the most profitable companies per head in all of tech. And that's because of this sort of this realization that he had that he didn't need to keep growing that team to see the financial gains or the financial output.
52:51 Harry Stebbings? Listen, I want to do a quick fire. So I'm going to say a short statement. You give me your immediate thoughts. So that sound okay?
52:55 Speaker 00 Yes, let's do it.
52:56 Harry Stebbings? So what have you changed your mind on most in the last 12 months?
53:00 Speaker 00 I think it's actually everything about this hypergrowth stuff that we've been talking about. And it's really around divorcing team hypergrowth from company hypergrowth and extra investing into quality and excellence.
53:11 Harry Stebbings? What's the biggest misconception you hear most often about AI?
53:15 Speaker 00 I think the biggest one today is all that's between us and AGI is compute. And I think we need data to get there, too.
53:23 Harry Stebbings? Tell me, you can have any board member in the world who you don't currently have. You have an amazing board, but who you don't currently have, who would you choose as your next board member?
53:31 Speaker 00 This is a great question. You know, I think, obviously, I don't think this is practical, but I do think Satya Nadella has been one of the most brilliant business strategists of the modern era. What he has accomplished at Microsoft is just staggering. And I think any board would be very lucky to have him.
53:49 Harry Stebbings? Unfair one for me to ask, but I actually like it, which is what question are you not asked? Or are you never asked that you feel you should be?
53:58 Speaker 00 That's an interesting one. The interesting one is like how my perspective on AI has changed in the successive eras. And I mention this because I started the company in 2016. The first three years of the company were just full focus on autonomous driving and autonomous vehicles. And then in 2019, we actually started working on generative AI. We started working on OpenAI on GPT-2. And so we are one of the few AI companies that I think has seen multiple eras of the technology and has seen the sort of the first boom and bust cycle with autonomous vehicles. I think it's an interesting one, which is like, what's the same in these successive eras? And what's different? That's an interesting question. How's your view changed? Are you most excited now? I'm quite excited, but I think there's also reasons to be cautious.
54:42 Speaker 00 In autonomous vehicles, one of the things that happened in the autonomous vehicle craze, there were a lot of promises that were being made that were divorced from the technical reality. And so a lot of the prominent autonomous vehicle companies, a lot of the prominent organizations were making bolder and bolder promises to be able to raise money. And those were, at first, they weren't super divorced, but over time, they became more and more divorced from the technical realities. And that resulted in this very painful trough where it's sort of the promises weren't met. And so it felt like the entire industry was falling apart. And actually, at the end of the day, you know, now we have Waymos driving around San Francisco, perfectly proper L4 autonomous vehicles driving around, Tesla Autopilot has gotten really good.
55:26 Speaker 00 If we had more measured promises along the way, I think now we would feel amazing about autonomous vehicles. Whereas instead, we went through this huge up, this big down, and maybe it's sort of like on the upswing again. I think this is one of the big concerns I have about generative AI, which is, I hope not, but the same thing might happen again, which is that we have these really big promises that are starting to get made about the technology that get divorced from technical reality. And then that creates this sort of gap that is bound to cause a hangover. Will Trump win penultimate one? I still think it's a toss up, actually. US elections are so strange to think about because it always gets decided by the swing states.
56:08 Speaker 00 I don't trust anyone on the coasts to have any fine-grained understanding of how the swing states will play out. I have no idea. I don't think anybody should listen to anybody who lives on the coast to understand what's going to happen. It always boils down to the swing states.
56:21 Harry Stebbings? Final one for you, my friend. What scale in 10 years' time?
56:24 Speaker 00 You know, hopefully doing something very similar to what we're doing now, which is continuing to be the data foundry for AI and serve the data pillar for AI progress.
56:33 Harry Stebbings? Would you like to go public?
56:34 Speaker 00 For sure. Yeah. Well, one thing I think a lot about is how do you solve problems that will never go out of style?
56:39 Harry Stebbings? But would you like to be the CEO of a public company? Do you know what I mean? Like, when you look at the Colsons, you're like, I don't know why you would if you were Stripe.
56:45 Speaker 00 There's clear benefits to being a public company, for sure. But I think Stripe is an incredible company in that they can be incredibly profitable and they can accomplish all their core financial goals without needing to go public.
56:57 Harry Stebbings? Listen, Alex, I loved having you on the show. Thank you so much for joining me. As I said, it's so much nicer to do this in person. I'm sorry for the many kind of meandering pivots and turns, but this was fantastic.
57:06 Speaker 00 Yeah, this was a lot of fun.
57:09 Harry Stebbings? I have to say, I absolutely love doing that conversation with Alex. I want to say a huge thank you, Tim, for being so open and honest with quite a few of those revealing questions. If you want to watch a full episode live in the studio, then you can check it out on YouTube by searching for 20VC. That's 20VC on YouTube. But before we leave you today, let's face it. Your employees probably hate your procurement process. It's hard to follow. It's cobbled together across systems and it's a waste of valuable time and resources. And as a result, you probably are facing difficulties getting full visibility, managing compliance and controlling spend. It's time for a better way. Meet Zip, the first modern intake-to-pay solution that can handle procurement and all of its complexities. From intake and sourcing to contracting purchase orders and payments.
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Previous 20VC: Reid Hoffman on Foundation Models: Who Wins & How Do Incumbents Respond | The Inflection AI Deal: How it Went Down | Why Trump is a Threat to Democracy | The Future of TikTok | Lessons from Sam Altman, Brian Chesky and the OpenAI Board Next 20Product: Loom CPO Janie Lee on Three Core Skills that Make the Best PMs, How to Find, Pick and Train the Best PM Talent and Lessons from OpenDoor and Rippling on Product Breadth, Pricing and Talent Density Page updated September 29, 2026 · transcript and summary are machine-generated
