20VC: Is Anthropic's Coding Business Worth $2 Trillion? | Should American Enterprises Work With Open-Source Chinese Models? | Why 80–90% of Neo-Labs Die in the Next 18 Months? with Eno Reyes, Co-Founder @ Factory

Eno Reyes is the co-founder and CTO of Factory, the agent-native software development platform building autonomous "Droids" for enterprise engineering teams. Factory has raised $220 million, most recently a $150 million Series C at a $1.5 billion valuation, from investors including Khosla Ventures, Sequoia Capital, 20VC, NEA, Blackstone, Insight Partners and Nvidia. Before founding Factory, Eno worked as a machine-learning engineer at Hugging Face, training, optimizing and deploying large language models for enterprise customers. AGENDA: 00:00 Are We Underestimating AI by an Order of Magnitude? 06:35 Why Can the Smartest AI Model Be the Cheapest? 18:51 Is Anthropic's Coding Business Really Worth $2 Trillion? 33:41 Will Continuous-Learning Models Help or Hurt Factory? 40:43 Will 80–90% of Neo-Labs Die in the Next 18 Months? 44:33 Should American Enterprises Work With Open-Source Chinese Models? 55:42 Must AI Founders Radically Rethink What a Great Outcome Looks Like? 1:04:30 Do Pedigree and Credentials Still Matter in AI Hiring? 1:19:17 Which Is the Biggest Threat: Claude Code, Codex, Cognition or Cursor? 1:24:20 What Seems Crazy Today but Will Be Obvious in Five Years?

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  I see a world where the smartest model is actually the cheapest. People are thinking about outcomes in AI and they're looking at 20, 30, 50 and they're saying that's ludicrous. That's crazy. That is underestimating by an order of magnitude how massive a transformation this is going to be. The TAM of frontier models is frankly overweighted right now. Eight and 10 billion is the new 1 billion. Two of the largest companies that provide models today have explicitly said, we are going to go after every single one of these industries and businesses that we provide intelligence for. I think it could be 80 to 90% of Neo Labs die in the next 18 months. Calling open source models Chinese models is a psy op by the frontier labs

• 0:41Speaker 00

  to basically trick people into thinking that they're scary and otherize them. In three years, 99% of workflows are going to be done on open models.

• 0:51Harry Stebbings?

  This is 20 VC with me, Harry Stebbings. Now, I am fed up of speaking to visionary, insightful leaders who actually aren't building today in the trenches at the cutting edge of infrastructure and AI. Today, we have CTO and co-founder of Factory, Eno Reyes. He is one of the most articulate and insightful thinkers about the value stack of AI that I've interviewed. Factory is one of the leading companies that specialize in autonomous software development. I co-invested in their round with Sequoia and Eno is incredible in this show today. Get your pen and paper out. There's going to be a lot of notes taken in this discussion. But before we dive into the show today, what do Uber, Curse and Harvey have in common? Well, they made the really wise decision to build on Fireworks.

• 1:34Harry Stebbings?

  Fireworks is the specialized intelligence platform behind many of the world's leading AI products. Companies use Fireworks to deploy the latest open models, specialize them with their own data and run them in production with the speed and reliability modern AI applications demand. But getting models into production is only the beginning. As AI usage scales, the best model for one request isn't always the best model for the next. Well, that's where Fireworks Nexus comes in. Nexus connects to the AI coding tools and harnesses your engineers already use and intelligently roots each request to the best model for the job,

• 2:13Harry Stebbings?

  helping you optimize for quality, latency and cost without changing how your engineers work. To see intelligent model routing in practice, visit fireworks.ai/20vc to create a free account and use the promo code 20vc50 to claim $50 in credits for 20vc50. While Fireworks AI powers product intelligence, Asana keeps the work moving. Most companies have tried AI. Most aren't seeing results. Not because AI doesn't work, it's because AI hasn't reached the workflows yet. That's the gap Asana is built to close. Asana is the operating system for human agent teams. Your easy button for AI productivity across every team.

• 2:56Harry Stebbings?

  Ready to go AI teammates pre-built for marketing, ops and IT. No prompt engineering, no setup. They show up where the work is happening, already on boarded in your workflows, ready to deliver. With Asana, your whole company can work on the same plan towards the same goal, whether you're a team of 10 or a team of 10,000. Asana, where humans and agents workflow together. Try it at asana.com. That's A-S-A-N-A.com. While Asana organizes the work, Framer scales the story. If your team wants a website that looks and feels handcrafted, but is still fast to ship, Framer is built for exactly that. Here's what I love.

• 3:35Harry Stebbings?

  Framer is the pro AI website builder for creators, teams and businesses that really care enough to get every detail right. The agents close the gap between AI-generated ideas and production-ready website work because it all happens where the site actually lives. It lands on the canvas, it stays editable and it can be published when the team is ready. Build custom code components, create and manage CMS content, optimize SEO settings and ship everything all in one place. The agents bring speed and scale. You bring taste, judgment and control. It's an enterprise-level solution, premium hosting, enterprise-grade security and 99.99% uptime SLAs, which is why the world's leading brands like Perplexity and Miro build in Framer. Learn how you can get more out of your site from a Framer specialist

• 4:21Harry Stebbings?

  or get started building for free today at framer.com/20vc for 30% off a Framer Pro annual plan. That's framer.com/20vc for 30% off. Framer.com/20vc. Rules and restrictions may apply. You have now arrived at your destination. Eno, dude, it is so good to have you in the studio. I obviously had Mattan in the studio. I was chatting to Keith, your boy, over the weekend about you. So thank you so much for joining me, dude.

• 4:49Speaker 00

  No, thank you for having me. I'm super pumped to be here.

• 4:52Harry Stebbings?

  Now, I hate background stories. I'm sure you listen to podcasts, it's like, how did you get into this? And you're just like, I'm bored of this. But downstairs, I asked you, how did you get into technology and fall in love with it? And your story was like heart wrenching and compelling. So I have to ask it, how did you first fall in love with computers, do you think?

• 5:11Speaker 00

  Yeah, I think that the beginning was my parents. So both my parents went to art school. They were always in love with technology and how that sort of intersected with creativity and media. And I think that where that started, especially with my dad, was he was born in San Francisco in the late 60s and he was six years old and he got actually hit by a bus. That changed the trajectory of his life in a pretty crazy way where, you know, he wasn't playing sports. He wasn't able to do as much of that traditional 1960s kid stuff. Instead, he went to technology and computers, like the early days of when, you know, you barely had screens and were instead sort of tinkering.

• 5:54Speaker 00

  And he spent most of his life basically embracing technology as a way to extend his own reach beyond like what I'd argue his physical body could do.

• 6:05Harry Stebbings?

  It's so interesting how life delivers you a hand, so to speak, and how consequential that hand is to kind of how you are today.

• 6:12Speaker 00

  A hundred percent.

• 6:13Harry Stebbings?

  It's very hard to transition from a father being hit by a bus to margins. Only a venture capitalist could do that in such a swift transition.

• 6:22Speaker 00

  Well, some margins in AI might make you feel like you've been hit by a bus, so.

• 6:28Harry Stebbings?

  Yes, absolutely. And we chatted before. You said the cheapest model isn't necessarily the cheapest system. And I read this when I was doing the work over the weekend and I was like, huh. Can we just unpack that? The cheapest model isn't necessarily the cheapest system. What does that mean?

• 6:44Speaker 00

  Yeah, you know, it really comes down to this idea that when you're thinking about price, you should not be thinking about the inputs to the price, you should be thinking about the outputs. So I think about the price of the outcome. So let's take software as an example. How much does a code review cost is far more interesting than how much do the tokens inside of that code review? And so you take a very sophisticated model. If it's able to do that code review immediately without making any mistakes, getting the right outcome right away, searching the right phrases and you use, you know, a thousand tokens or whatever, it will be significantly more than that, a million, versus you use a cheap model and it spends time, it's running, it uses 50 million tokens.

• 7:27Speaker 00

  And ultimately, that price difference of the full outcome makes the higher quality model cheaper. Now, that's not how all tasks go, but for many of the most intelligent demanding tasks, I see a world where the smartest model is actually the cheapest.

• 7:44Harry Stebbings?

  I totally hear you there and it kind of goes against the a token is a token theory. But will we then have millions of specialized models with every company having specialized models operating on their own data? Because to your point there, it'll be able to work much more efficiently.

• 8:00Speaker 00

  Yeah, I think that there's a real world where the speciation of models increases very rapidly. This is sort of the world where the fireworks and the people who help make models possible, I think, win because the alternative is you only have a very few specialized providers that have models. Now, in our view, there is going to be probably a difference between the commodity task executors. So this is just like your everything model and that we think will be dominated by open models. And then you have businesses that will say, well, we do a lot of commodity tasks, but there's a couple of very high volume specialized tasks that only we do. And for those, your commodity model won't be good enough. Your frontier model will be too expensive.

• 8:45Speaker 00

  And so they'll want something in between where they can take a commodity model and make it good enough via post-training. And then ultimately they'll run that and they probably will be the only consumer of it. So they won't even give it to the rest of the world. They'll just keep it entirely internal. So I think that that will lead to a lot of models, not like a, you know, it won't be millions, but it'll definitely be quite a lot.

• 9:06Harry Stebbings?

  When you look at the post-training required, when you look at the implementation required, I look at that and I think the company structures and teams today are simply not equipped to do that. How will we solve for that? Is this just a moving into an incredibly services and implementation heavy world where we have these insane AI teams coming into every, how do we solve that?

• 9:26Speaker 00

  Yeah, and I think that this is one of those things where right now the recipe for post-training and building models does live primarily in the heads of a specialized few group of people. But that's also how software development was like 20 years ago. So in my mind, the same tools that are currently democratizing access to software are actually the tools that will be used to democratize access to intelligence in general. So I see a world where you're a company, an enterprise, and you say today, well, we don't have the, the knowledge or the skillset to build our own specialized models. Well, very shortly, already to a certain extent, you can open up a platform, go into their, you know, webpage, click a couple of buttons, describe the task you care about, point it towards those workflows that happen in your business today and out comes a model. And that model will be really good.

• 10:11Speaker 00

  And so I think that right now there's like a couple of companies that claim that like recursive self-improvement and model training will be only their domain. And I think in reality, many businesses will have access to that technology via software services that other companies sell.

• 10:26Harry Stebbings?

  You said about kind of focusing on the outcomes and not the inputs. That is a great idea when it's a very verifiable output, code review. When there is ambiguity to something, which could be a marketing conclusion, did it come through X channel or Y channel? My girlfriend's a lawyer, different legal notes, it's ambiguous. Some people like it one way, some people like. How do you think about the importance of verifiability in determining outcome quality?

• 10:54Speaker 00

  Yeah, I mean, verifiability is ultimately the single most important property of success with current AI systems. And I think that the way that we'll sort of progressively address this is by building new ways to verify the work that we do. And so, you know, I'll be really concrete about that. What a bunch of the eval creators and model trainers have done in some of these domains like healthcare and legal, where you don't really have a concrete set of verification strategies, they'll take experts, they'll bring them in, they'll have them create effectively, you know, their own new forms of verification. Maybe they show two examples side by side and they say, which one, based on your judgment, is better? And being able to then take intelligent models that have a lot of the ground reasoning and

• 11:39Speaker 00

  the knowledge that these domains have, combine them and say, now you, as the model, go and build this similar form of verification. I actually would argue the frontier right now of AI is AI systems that can build verification where there is none, and thus they can progress into tasks that today humans consider to be too difficult for AI to resolve.

• 11:59Speaker 02

  AI systems that progress into verification, what does that actually mean?

• 12:04Speaker 00

  To make it as concrete as possible, you know, imagine that you are walked into a room at a law firm and they say to you, hey, you're going to start basically judging how to determine whether or not these new hires are good. What does like a very novice manager do? Well, they kind of go by their gut, they're looking, and what they're doing is if they are actually intuitive, they'll go by their gut and make good calls. They'll say, that, you know, new grad is going to be big at this firm. I like them, I'm going to continue to promote them. But when you have a giant firm or you sort of systemize, you realize that that approach to management is very rare. In reality, you need to go to the firm and you need to start writing stuff down. Here's the things that we like about great new hires. Here's the things we don't like.

• 12:48Speaker 00

  Any company starts to learn managing at scale looks like this. You have to write down the things that you care about and build a framework or a system to analyze how the job to be done. I think that great AI systems are basically relearning this like management strategy of saying, you can go by your gut. And honestly, a great AI system can be right very often without this sort of structure or framework. But in reality, what you'll need to do is you'll need to write down, this is what good looks like, this is what bad looks like, and here's how we judge. And what's interesting is, yes, that makes the system better at determining what good looks like, but it also changes the incentives. Because when you write down, this is what good looks like, people read that and then they start to act more like what good looks like.

• 13:33Speaker 00

  And so you have to be careful, because if you say, good looks like A, B, and C, you're going to get a lot of A, B, and C. But if you basically built the wrong incentives, then that system will end up following that pattern regardless of if it's actually good or not.

• 13:46Speaker 02

  That is brilliant statement. Show me the incentive and I'll show you the outcome. And it's the hardest thing about actually venture firms, when you run them as a business, because if I set you, you know, the goal of three deals per year, you'll give me three deals per year.

• 13:59Speaker 00

  Yep.

• 14:00Speaker 02

  I don't want three deals per year, I want a great deal, I want a factory. I don't care if it's three or six or one.

• 14:06Speaker 00

  A hundred percent.

• 14:07Speaker 02

  And so it fundamentally, to your point, changes the output that you get. I think I made a big mistake, because I'm in Merckor. I think we did it at like two or three billion, my memory should be better, but I'm older than you, and I didn't do the latest round at, you know, whatever, $20 billion, because I thought, how much bigger can it be? Like, maybe 100 billion, but that's a 5X, it's like, not that exciting. A 5X, and that's with no dilution. And I'm now thinking that I'm completely fucking wrong, and that there is a pathway to 200, 300 billion in the data requirements that will be needed. How do you think about what I just said?

• 14:43Speaker 00

  No, I think that's, that's totally true. I mean, people are thinking about outcomes in AI, and they're looking at 20, 30, 50, and they're saying, that's ludicrous, that's crazy. That is underestimating by an order of magnitude how massive a transformation this is going to be. However, I think that what people underestimate is that the types of businesses that are going to become massive do not look like businesses 20, 30, 40 years ago, where they had a technology mode, or they had some sort of key capability that no one else could replicate. Instead, it's basically collections of people that understand what the future looks like, a little bit more clear-eyed than the other people. And so these data companies, like you mentioned Merckor, yes, they sell data, but every person

• 15:27Speaker 00

  at that company gets how AI is going to look much clearer than the average human, and that makes them worth significantly more than, you know, even what investors will say.

• 15:37Speaker 02

  Going back to what we said about lots of specialized models and companies working on their own data, which is obviously proprietary, I'm confused. How does this not reduce the TAM for frontier models?

• 15:48Speaker 00

  I think it might. I think that the TAM of frontier models is frankly overweighted right now. The world basically assumes that there's going to be one to three companies that have total domination over the intelligence era. And not only is that, I think, is just a silly proposition, because generally people don't like that sort of strong dominance of a couple of small companies or large companies, but the real question is, how do you defend your margins if you're a model lab when there are so many options? And I think that margin profile shrinking is going to change the expected value of these businesses. Baked into two, three, four trillion dollar valuations is an assumption that you can

• 16:33Speaker 00

  basically 2X the price of those tokens and people will buy them.

• 16:36Speaker 02

  Is the margin profile looking shit? And what I mean by that is Anthropic just produced their first quarter, I believe, of profitability, margins seeming to be ripping, and they're throwing off cash now. Is that not going in counter to what you said?

• 16:49Speaker 00

  No, I think, I think also that one of the biggest drivers of that is actually the applications on top of that. And so one of the things that I think is quite clear to all of these model businesses is that the model itself may be a fairly rough trade-off, or rather, the margin profile of the models is definitely worse than the applications. In my mind, if you are a model provider, you're basically looking, A, to dominate the platform era, in which case you want to be one of N companies that sort of get really good at selling inference, or B, you just want to move up to become an application layer company that has really good models. Anthropic seems to be following the application path, while OpenAI seems to be dipping its

• 17:31Speaker 00

  toes in both, but the platform commitment from them seems much stronger.

• 17:35Speaker 02

  Which strategy do you think is right, if you were to bet on one?

• 17:38Speaker 00

  I think that the application layer is going to be a much harder battle, because being model-locked is actually a huge disadvantage if you're trying to sell outcomes.

• 17:48Speaker 02

  Why is that?

• 17:49Speaker 00

  Well, it's bad incentive alignment. If you are a model-locked provider, then you are inherently selling those tokens in order to make sure that your business gets the margin it needs. If you are going to a company and saying, we can give you, you know, the best outcome, you have to do that with only your models. So they can really give you, you know, their best model. Meanwhile, someone who's not model-locked can give you the best model, right? That difference between their best model versus the best model can be massive in the pricing. I mean, we see this right now in real-time encoding. Anthropic can basically only deliver their models' outcomes.

• 18:25Speaker 02

  And the best model also is highly subjective, depending on the consumer?

• 18:29Speaker 00

  Oh yeah, it's totally different based on the task, the profile, the risk-taking that people want to have, tons of different options.

• 18:35Speaker 02

  Can you help me understand, again, I'm very thick, but I don't like cynical questions, I like to be optimistic. I think it's fantastic, seeing Anthropic potentially go out at $2 trillion, but you're essentially placing a $2 trillion price on Claude code.

• 18:48Speaker 00

  Mm-hmm.

• 18:49Speaker 02

  Which is not that difficult to switch off of.

• 18:51Speaker 00

  Yeah.

• 18:52Speaker 02

  What am I missing is that, what should I know that I'm not getting? How should I think about that?

• 18:57Speaker 00

  No, I mean, I think that is fundamentally the risk for an investor, is that you're making a $2 trillion bet on one of the most competitive application markets in one of the most finicky segments of the market, which is, you know, dev tools. And so I do think that part of what needs to happen in order to make these companies like Anthropic and OpenAI realize the value, is they either, A, which they're pursuing, have to go through regulatory capture, in which case they go and they sort of scare politicians into thinking that they must own the means of intelligence, and thus they become the only providers of the most frontier capabilities. Or B, they have to figure out a way to build applications and outcomes that match the true Pareto frontier of cost and quality.

• 19:42Speaker 00

  And I think that means opening up to more models. So it's actually like, sort of at odds with the two strategies. You either capture it and keep the model, or open up to everybody. This is a very hard decision, and one that I think you can start to see OpenAI actually grappling with, as they've let more models into their harness. They're not making it official, but they're clearly supporting an open model ecosystem in a more direct way.

• 20:06Speaker 02

  Do you think Dario's marketing message has been mistaken?

• 20:09Speaker 00

  I think that the marketing of AI in general was probably one of the worst marketing jobs done by, you know, contemporary capitalists. It basically did the opposite of what you want. Scare every single person, tell them it's very unreliable, basically threaten their well-being and livelihood with the technology while you roll it out at scale. I think that the challenge is that the things that Dario brings up are not only well-intentioned, but there are very real threats from unregulated and dangerous AI. I think that there's a way, though, to communicate about this without maybe embellishing the economic ends. And because I think that the carrot and stick here can just be, one, the technology will be incredibly transformative.

• 20:54Speaker 00

  Two, and humans will have a huge role in that transformation and you will have a huge role in that transformation. And three, if we don't do this, you know, well, then like all other technologies, there's going to be risks. But the moment you start talking about the singularity and AGI and create this godlike mythology out of AI, you're going to scare a lot of people.

• 21:13Speaker 02

  We're going to be the last remaining private company. Truly, say, ah, you flew here on United. I don't know how that's going to work.

• 21:23Speaker 00

  Yeah, I mean, you know, there's going to be a lot of change if people think that there's going to be one company. And I think Sam Altman just did an interview where, yeah, and kudos to him, it's hard to go back and say, I was wrong. He basically says, I totally underestimated the momentum of the economy, the momentum of existing businesses. And so I predicted this future that actually has not come true. And I think that that's a great reckoning where you go and you say, I didn't think that this was going to happen and it's clearly not going that way. So I'm revising my prediction.

• 21:54Speaker 02

  What did you not think was going to happen where you had to revise your prediction?

• 21:57Speaker 00

  Oh, I love that question. I think the biggest thing that surprised me where I've had to go back and seriously correct my priors is building a business is much more reactive than planning. And what I mean by that is almost every one of our best decisions as a company has been in reaction to some information and a split second decision, sort of, rather than a master plan that we forecasted six months ahead. And I think that knowing that, you start to look at the rest of the world, you hear from other business leaders, how that's also how they make decisions. And you start to realize that the world is just this like constantly reactive feedback loop where people are just talking to each other and making decisions and no one actually

• 22:41Speaker 00

  has an answer to what the future is going to look like. And so you basically just start defining it in real time in group chats and in the actions that you take as a business. So I think that like learning that in real time has made me totally, one, question the existing world and structure, like basically nothing's guaranteed and anything could change. And two, it really gives, it's quite empowering. It makes you realize you're basically a couple of decisions away from even greater outcomes and you know, and even bigger business than you had prior.

• 23:11Speaker 02

  Totally get that on that anything could change. One thing that I hope changes is margins and margin profiles. Help me understand, when we look across the wave of incredible businesses in AI, the margin profiles are still lower than they were previously, 30 to 35% say, as a barometer compared to 70, 80% with SaaS.

• 23:30Speaker 00

  Right.

• 23:31Speaker 02

  Is that a momentary period in time where in a build out phase and they expand over time, or is that just a net new, but the revenues are going to be much larger?

• 23:39Speaker 00

  Well, not all businesses in AI have bad margin profiles. I mean, I know we've got good margins.

• 23:44Speaker 02

  Okay.

• 23:44Speaker 00

  And that actually comes in a way that I think is also customer aligned in that we are so focused on sort of thinking about these outcomes themselves as a, as the thing to be valued. And so we've kind of gone away from a lot of common paths that we see other AI companies doing. We don't subsidize consumers in like the dev tool space, you know, that hurts us in a lot of ways. We don't have the mind share from self-service users.

• 24:08Speaker 02

  So you have no PLG?

• 24:10Speaker 00

  I would separate self-service from PLG. We have a lot of focus on PLG within companies that we've deployed to, to allow the adoption to increase. But what we don't have is like a consumer, public facing plan that is, I would say, super rational for a current consumer, unless you're optimizing for quality.

• 24:28Speaker 02

  Should you, in a land grab environment like today, should you?

• 24:31Speaker 00

  I think it's a really good question. In my mind, the biggest reason for this is there are two players with effectively infinite money who are trying to flood the market and their intent is if we flood the market, we keep you. But remember what we talked about just a second ago, you don't keep them after you sort of pull back the subsidies, as we've learned. I think that probably the consumer will continue to follow the sort of like most cost effective solution. And so what does that look like in one, two, three years? I think it's open models. I think the most cost effective solution for a model is going to be the cheap one that you can run locally on your computer. And so we want to make sure that we are the product that best fits that type of experience. So that's why we're so optimized on local and on open models today for on-prem, but

• 25:16Speaker 00

  in the future it's for all consumers. And so we won't have to subsidize as much as we'll have to create an amazing experience for self-service. So I think of this as like a long game where eventually the self-service will come to us, but not because we subsidize, but because we have the best product in market.

• 25:32Speaker 02

  So if you're advising me as an investor today on how to think about margin and how that plays into my decision to invest or not in a business, what would you say?

• 25:40Speaker 00

  I think that for us, this is a part of our strategy. It doesn't mean though, that it's the only way to win. I do think that there's probably going to be businesses where they're able to lock you in because of a workflow or a system of record that they produce. And the margins or the subsidies temporarily reducing margin can be a route towards gaining, this is a classic strategy, right? It's not, this isn't even new to AI, you know, gaining customer base. What I would say though, is that if there's no path to increasing the margin profile, that is very risky. And I think that a lot of investments are being made in businesses where the promise is simply that they will like raise prices, but you won't see a consummate increase in the value of the platform. It is so competitive in AI right now.

• 26:23Speaker 00

  If you are not also raising the outcomes and the value that you get out of the product while you raise that price, people will churn and move to another thing. So I do think it's, it's quite tricky and the margin profile actually matters a lot, but it's not end all be all.

• 26:36Speaker 02

  Well, you have that churn in enterprise sales. You work with some of the biggest companies in the world. I'm sure you sign year-long, minimum multi-year-long.

• 26:45Speaker 00

  Yep, yep.

• 26:45Speaker 02

  You have pretty sticky client bases there, no?

• 26:48Speaker 00

  I think so. I think that people also see these year or multi-year partnerships as just that, like a partnership. Part of what makes it interesting to build right now is that a lot of what you're selling is not only the technology, but your knowledge about how to best use that technology, which I would carefully differentiate from consulting or professional services in that you don't actually have to go in and do all of the implementation or, and I honestly think if you have a product that requires 100 FDEs to get it deployed, you just have a bad product. But instead, I think that if you have the advice and the knowledge of the direction that you think the world should go on, you're selling that with the product. And so people are willing to go and buy that.

• 27:30Speaker 00

  And I think that if they see it from you today, they sort of know that in a year, you'll also still have that same knowledge and sort of forward thinkingness. Now, obviously lots of people can give that, but if you combine that with a product that then acts a little bit more as a platform or a system rather than a tool that people use, you can also get stickier by just being something that you build on top of.

• 27:52Speaker 02

  We spoke about the different frontier model providers, essentially having this really challenging dynamic of being locked into their own models.

• 27:58Speaker 00

  Yep.

• 27:59Speaker 02

  When serving, say, core code or any application that they choose to serve. One then thinks that the value becomes in the routing of models, like the tasks to the model, what's optimized for each use case, cost, latency, function, whatever it is. An open router gets bought for $8 billion. All the value's in the routing, great. And then everyone is doing routing. Ramp has a routing provider. One of my companies, Merge Dev, has one. We're in another startup, Requesty. And I'm like, well, the routing is completely commoditized.

• 28:27Speaker 00

  Right.

• 28:28Speaker 02

  Help me understand, what world do we live in?

• 28:31Speaker 00

  Yeah, well, I think that routing is a really interesting technology in that I don't think the technology itself is necessarily that differentiated. And so if somebody comes up and says, look, Stripe bought Open Router for $8 billion because of the technology, then I'd say either A, if they have insider knowledge, then that was a bad decision. If B, they're just sort of assigning that to it, I think they're missing what I read when I read the letter to shareholders, which was that they see this as a bet on where the direction of capital allocation is going. Think about it like this, what are tokens other than intelligence? And how do you get tokens? Well, you pay for them. How does the infrastructure layer get it? Energy.

• 29:13Speaker 00

  It's literally like translating energy into intelligence and you're just trading dollars along the way. All of this is basically converging towards one thing, whether you call it allocating energy, allocating intelligence, allocating money. Businesses need to allocate whatever this is in order to grow and expand how they operate and grow and expand their bottom line. If you're Stripe, you already control the flow of one out of the three of these. With Open Router, rather than the routing technology, you actually just gain the information of where these models are going, right? You start to understand what are people doing with intelligence? How are they allocating it? To what models are they using? So now you start to control the second of these three things.

• 29:56Speaker 00

  Maybe they'll make a play into, you know, energy infrastructure or data centers at some point, but just ownership over those two is a massive bet on how to think about where companies allocate resources. And so for them, I think this is very reasonable. But what's interesting is you wouldn't pay $8 billion for the same company that had no users with better technology. And so that difference, I think, is really important. And it gets towards that broader idea of the technology is just no longer the moat.

• 30:24Speaker 02

  Do you think it was a good buy?

• 30:25Speaker 00

  At $8 billion, it would have to be really foundational to the team that becomes whatever this next bet on allocating, you know, capital is for Stripe. Or rather, helping the businesses that Stripe has as customers allocate capital. I would say that if they think that data gives them insight into how to run Stripe better as well, that could also potentially make it worth it. $8 billion is quite steep though, so stranger things have happened. It feels like eight, eight and 10 billion is the new 1 billion, so.

• 30:57Speaker 02

  I'm intrigued, you know, you obviously have a routing product within Factory. What do you see? What insight do you get from that that maybe the world doesn't see?

• 31:06Speaker 00

  Well, one of the most interesting things, and I've been talking about model routing for these products that you've mentioned, which is gateway routing, and that's sort of how it's referred to because ultimately the routing effectively happens outside of where the task is being completed. And so a lot of companies will do this. They'll look at Ramp and Stripe and Open Router and they'll put a model gateway. And that gateway is just how all of the different tools and products of the company route to LLMs. What's interesting is that we've seen that you can definitely get some nice cost savings doing this, like 10, 20% from these types of products. But you really need something fundamentally different when you have agentic workflows because to actually take the most advantage out of models,

• 31:51Speaker 00

  you need to do something that's a little different from just routing. You need your agent or your system to dynamically understand the task it's working on and understand how to allocate intelligence in a much more stateful way. And when I say stateful, all I mean is like you need to know not only what's going on today, you need to know what just happened and what's going to happen in the future. And that can't happen outside of where the task is being completed. You have to be in there in the task.

• 32:18Speaker 02

  Is that related to the importance of context window expansion that everyone talks about?

• 32:23Speaker 00

  I'd say that in a way that taking the context window and expanding it was solved not at the model layer, like outside at the endpoint, but instead inside of the agent with something called compaction. It's exactly similar. People really want the problems to be solved sort of somewhere else, like in the model or in the gateway, but more and more we see it's the harness that solves these problems. The thing that is sort of underestimated about why the harness continues to solve this is that the harness is effectively the new sort of application. It is just where all of the logic happens. It's where the state is maintained. It's the easiest and the best place to basically do work with AI.

• 33:03Speaker 00

  And so as that starts to accumulate, I'd say like more advantage or technology benefits, I think we're starting to see people ask questions like, should I be building my own harness? Should I get a really great harness? What is a harness? And that's something that I think at Factory, we're trying to spend as much time as possible educating people about sort of what does well in the harness versus what can be done outside and how to think about like building versus buying a harness.

• 33:31Speaker 02

  The context window expansion is one, and then continuous learning and the kind of the rise of the first truly continuous learning model. Does a continuous learning model help or hurt Factory's business?

• 33:41Speaker 00

  Well, it's interesting because there there are sort of two directions for this. I would say that there was an idea of continuous learning from over the last couple of years that said that you would have a like a literal LLM-like model where all of the learning happens internal to this closed-loop system. That technology has not been developed. It doesn't exist. There's sort of attempts and early looks at it. But in general, anybody who wanted to try and hold all of the learnings behind an API would be able to potentially accumulate advantage that would make it harder for others to use that model in their product because ultimately they would be accumulating all of the learning. But in reality, what has happened is quite the opposite.

• 34:24Speaker 00

  Basically, model providers have even acknowledged that all of the continual learning happens at the harness layer. That learning is basically something that businesses are going to find very critical that they own, that they are the sovereign of. And I think that that is ultimately the question of the next five years of AI. Who is the sovereign of your intelligence? Is it you or is it some other company?

• 34:50Speaker 02

  Sorry, can you unpack that for me? What does that actually mean? Who owns the data outcomes that are generated from the task that you do?

• 34:58Speaker 00

  Basically, who owns the learnings and the workflow that successfully achieved the outcomes for your business? A great example for this would be if you were a law firm and all you did was just outsource every single one of your cases to some other company. And in fact, maybe it's like 10 different companies. Then come five years later, those other companies can just turn around and screw you over because they know exactly how to do your entire business. What's interesting is pretty much every company in the world is thinking to themselves right now, what's going to happen in a couple of years if I outsource all of my intelligence to someone else? And what's interesting is that at least, you know, two of the largest companies that provide models today

• 35:43Speaker 00

  have explicitly said, we are going to go after every single one of these industries and businesses that we provide intelligence for.

• 35:50Speaker 02

  Do you see with large enterprises, the biggest companies in the world are scared of OpenAI and Anthropic coming after their business?

• 35:57Speaker 00

  Not all of them necessarily think they are going to come after the business, but the largest companies in the world are very wary of the model labs coming in and promising them intelligence and sort of luring them into a trap. That's something that I know, there are many businesses that are starting to become aware of this, like Palantir has been quite loud about this idea of owning your intelligence, Microsoft as well. Satya wrote a great piece about this. I think that all of that opining is very spot on. At the end of the day, if it's not your intelligence, then there is just a real risk that either A, they come after your business, or B, if they disagree with what your business is doing, they have a little bit more leverage and control than I think the typical business owner would like.

• 36:40Speaker 02

  And when we talk about sovereign intelligence and owning that outcome, is that why on-premise is so important?

• 36:46Speaker 00

  I think that's a huge part of it. To most businesses, on-premise isn't even about the technology. It's just about the idea, which is that if I need to, I can take control and ownership over every dimension of this software and we get to stay in control. And that element is interesting because for us, we offer an on-prem offering. It's in fact one of our most popular offerings, Factory Private. And even though we offer this, a lot of the businesses that we talk to actually go with our SaaS model because they just know and have the peace of mind if they need to switch. Not only do we have it available, but they understand exactly how it would work. And so I think that a part of this story is just being able to share with people that we are incentive aligned.

• 37:30Speaker 00

  If you need this, we have it and you won't lose anything. And so on-prem and owning your intelligence are very similar stories.

• 37:38Speaker 02

  How much does it help you or hurt you that Cursor was bought by SpaceX? It gives some amazing scale benefits in terms of access to compute, but it does make them model bias.

• 37:50Speaker 00

  Yeah, that outcome for the folks at Cursor is obviously amazing. So yeah, needless to say. And I think that where it helps us is it's going to be a very hard story to become model independent or rather stay model independent when you're attached to a model lab. So they're going to want to push Grok. The products are going to become increasingly oriented around Grok. That I think will become a challenge. There's also some, to be honest, trust and enterprise-related concerns that they're going to have to deal with with their new brand. But ultimately, the team there is obviously incredibly competent. And so I don't discount them as a player in this market. However, I do think that most of the enterprises are going to have a second look

• 38:34Speaker 00

  at the idea of sort of seeding their software development lifecycle to a provider who is one, likely to be model-locked and two, has an existing sort of history or pattern of maybe struggling to operate in these larger and more secure environments.

• 38:50Speaker 02

  Can I ask you, when we look at the cadence of model development today, it's just so fast. And like I actually use arena.ai as like a discovery mechanism to new models.

• 38:59Speaker 00

  Right.

• 39:00Speaker 02

  I'm using suddenly these weird models that I've never heard of and would never have used before.

• 39:05Speaker 00

  Right.

• 39:05Speaker 02

  And I'm loving the output. My question to you is, will we see the cadence of model creation sustain in the way that we are today? In other words, the rate of new models keeping on coming? Or is this a momentary period at the start of a new cycle?

• 39:19Speaker 00

  It will likely sustain for quite a long time. And this actually gets to another interesting property of like the open routers. A lot of people treat model routers as effectively a information or news stream about which model is next. It's kind of a free advertisement every single time a model drops. You know, now Stripe can tweet new model on our router and you see Stripe's name with this news cycle. So I think that actually it's very common for people to basically use new model drops and all this news as a way of keeping up with AI in general. New models will likely continue to drop as one, it gets easier. It's just going to become fundamentally easy to build models. And two, this idea of sovereign intelligence, also like humans,

• 40:02Speaker 00

  we're going to have models with tons of different opinions, tons of different perspectives. That I think is going to play nicely into how people sort of operate in today's world. A lot of the times you don't go with a business because it's purely the best performance. You go because you like the person who started it or you want to buy from someone where you saw them on the news or on TV and you agreed with their statements. Models are going to be like that as well, where they emit opinions and they have takes that are different from the ones that are most popular and people will gravitate towards those. So I see this actually just getting much faster and even broader before it shrinks.

• 40:36Speaker 02

  A lot of guests on the show before have made kind of bold statements that like 70, 80% of the Neo Labs that we have today will die in a given time period, three to five years, whatever you want to choose. Do you think that's true? And how would you advise me and other investors on the Neo Labs that will thrive versus die? This next wave.

• 40:55Speaker 00

  I think it's plausible it's even more. I think it could be eighty to ninety percent of Neolabs die in the next eighteen months. And die is going to be a funny word to use because it'll probably be for a lot of them incredible outcomes. So I don't know if it's necessarily doom and gloom as much as it's these businesses may not make sense as independent businesses. A lot of what I think matters for a Neolab is you should ask questions like, one, is this business attached to a durable workflow? Two, is that workflow going to change if new frontier models get better? And three, if this workflow were to be introduced to a new business, then would that new business figure out something even better?

• 41:40Speaker 00

  And so basically, is it durable to effectively an entirely new way of thinking or new way of working? If all three of those are true, legal is a great place where I think, one, new models won't necessarily get better without access to the data. Two, it's obviously a very proprietary workflow. And three, we're still going to have legal system in five, ten, twenty years. So probably all the Neolabs focused on legal are going to have great outcomes. Versus, I would argue that there's some places like a lot of knowledge work that's related to intermediate tasks, like people operating in Excel and Jira, that's just not going to be differentiated. The workflows are very common. I think that we may not use a lot of tools like that in five to ten years. So this like general computer use, all this other stuff just may not be as valuable as an independent business.

• 42:25Speaker 02

  You know, the thing that strikes me is just the misalignment in capability progression, which sounds like a real word bank. But like when you look at like coding and customer service, amazing, undeniable. Legal, good, but not in the same level as coding and customer service. And then other things, honestly, marketing copy, visuals, a lot of, it's so not there. If I wanted to use AI to clip this show, it misses both of our faces because it goes to the middle.

• 42:51Speaker 00

  Yep.

• 42:51Speaker 02

  It has no understanding of how to align clippings between an audio edit and a video edit. It's so far off. Will we see a real multi-year time lag between different sectoral capabilities progressing in the same way that coding has done?

• 43:07Speaker 00

  Definitely. The biggest reason why, for example, clipping a podcast is still such a hard problem for models is likely because the maybe handful of businesses that deal with media haven't devoted a hundred percent of their time to just taking that knowledge that lives inside the heads of people and bringing it into AI. And the moment that we start to see businesses capitalize on that delta, I think the progression will happen extremely quickly. So it's purely a matter of time before most workflows become something where a business capitalizes on that first bit, which is a workflow that currently has proprietary data or proprietary knowledge.

• 43:47Speaker 00

  You don't normally think of clipping a podcast as proprietary, but I think for the most part, it's a real skill. And a lot of the people couldn't even describe how they know when to do the right clip. And that intuition, writing it down is hard.

• 44:02Speaker 02

  Oh, I totally think, knowing like the hook. If you started ten seconds earlier and the hook was ten seconds in, your chance of virality goes down significantly.

• 44:12Speaker 00

  Yep.

• 44:12Speaker 02

  The skill of knowing what is, it kind of goes to taste.

• 44:14Speaker 00

  A hundred percent.

• 44:15Speaker 02

  But it's actually not. I completely get you there. We always hear about the Chinese open source ecosystem and the questions around security and everything that is in between. Do you think those are justified? Or do you think actually we should leverage it and thank them for their capabilities?

• 44:31Speaker 00

  Calling open source models Chinese models is a psyop by the frontier labs to basically trick people into thinking that they're scary and otherize them. In reality, in my mind, the open models that come from a bunch of different places are not any different from a existing frontier model from a lab. They just happen to be have been created by people, you know, a couple thousand miles away. Now, there are some very real challenges with taking in models from really any business. What's interesting is it's actually the same challenges with models from like OpenAI and Anthropic. You should ask questions for all of your models, like, one, what is potentially being censored by the creators of these models?

• 45:12Speaker 00

  Two, are these models going to be able to solve the problems that I care about? And three, if this model goes away in like six months, twelve months, will I be able to switch to something else? And if the answer to all these things is like, yes, yes, yes, then I do think that there will be concerns about using those models. For me, the Chinese models specifically have demonstrated no examples where they have some sort of security risk or backdoor compared to American models. Instead, they are just biased in a way that American models are for their own creators and preferences. These are things you have to be aware of, but typically don't change the day to day.

• 45:52Speaker 02

  So you don't think that American companies should be concerned about using open source Chinese models?

• 45:57Speaker 00

  As of today, the current Chinese frontier models should be analyzed by American companies for the tasks that they care about. And if you're, for example, working on national security in the United States, you definitely should not be using Chinese models. But I do think that we have to be sort of clear eyed and say, for a code review, it's very likely that a Chinese model and American model will give you the same. I'll give you a great example of this. If you are writing a 10K that expresses your business's current state and some of the examples in preparation for, you know, sharing finances with investors. Let's say a part of your strategy is about introducing recursive self-improvement to models. And you care about AI and your business is leaning heavily into it.

• 46:42Speaker 00

  If you use a model from this provider, it will block you. The answer is anthropic. And so if you are writing a model about American defense or preparation, I highly recommend against using a Chinese model. But all of these are basically contextual based off of the preferences of the creator of the model. So it's just like any other technology. You have to be aware of who created it. And you have to be careful because if the person who created it doesn't want you doing the things that you're going to do with that model, it is going to be harder. That's something that I think people need to be aware of for all models, though.

• 47:13Speaker 02

  Guillermo from Vercel tweeted last night or yesterday about the weighting or usage of open models significantly increasing at a much faster rate to tokens used on closed models or frontier models. What percent of workflows will be completed with open models in three years' time?

• 47:31Speaker 00

  In three years, ninety-nine percent of workflows are going to be done on open models. But one percent of those tasks is probably going to be thirty, forty percent of the economic value of the future of intelligence.

• 47:44Speaker 02

  Wow, but you still think that sixty percent will flow to open models?

• 47:48Speaker 00

  I think almost all usage of models in three years are going to be primarily open. But that difference between frontier and open is going to become actually larger than it is today. And I think that this is actually pretty aligned with how maybe that percentage is overweighted. But like you've listened to how Sam and Dario talk, the use cases that they're talking about their frontier models being used for are incredibly niche. They're talking about bio research at the frontier. They're talking about super advanced LLM and AI development. They're talking about security and defense. These are use cases that really only fit a very specific profile of effectively the frontier of science and technology. This is where I think labs like OpenAI and Anthropic actually can be incredibly differentiated

• 48:32Speaker 00

  because they already have the muscle to work on those very frontier problems. But if you go into any business in the global 2000 today, you ask any random person, what are you doing today? It's not something that needs the true frontier of intelligence like ninety-nine percent of the time. And so cost will dominate. OpenAI and Anthropic could release an open model that they then inference on. And I think that that could actually be a great business for them.

• 48:55Speaker 02

  Given the commoditization of the model layer, like we've spoken about, people seemingly chastise Microsoft. Given that commoditization, do you think Microsoft have actually played a great hand in having a little bit of a bet through OpenAI, but not being tied down with an extensive model investment layer?

• 49:13Speaker 00

  I think Microsoft might be one of the best positioned hyperscalers, honestly, with respect to AI because of this independence. Right now, Satya has played a masterful game of getting huge upside from the OpenAI investment and work. Kevin Scott as well, who I know is like sourcing a lot of that deal. That team found a lot of the potential of what AI was going to be. But they currently realize that one provider is just simply not sufficient to cover what intelligence is needed in the enterprise. And so now they've moved towards more concretely expressing, one, that we support all AI developers and Azure should be a place for inference on Anthropic and OpenAI and open models, most importantly.

• 49:57Speaker 00

  But two, even if you do want that frontier intelligence, you can come here. And I think that that duality, that model independence is going to be massively valuable for a business that wants to accelerate really like work for all other businesses. I think it's a very clever positioning because they captured the upside with a bet. And now they're capitalizing on the market as a whole.

• 50:19Speaker 02

  Is Zach wrong then to be putting as much money as he is into Spark and building out that program?

• 50:25Speaker 00

  I think he's right for humanity in that we need more open models like that, especially like American made open models. I think that a lot of people, even with respect to what I said earlier on Chinese models, people are gonna buy us. And so, open models are great because it just increases adoption in America and abroad. But two, I think that as a business, they're going to have to build on top of that and take what they did with Meta, the monstrous consumer business that it is. They need to power all of their operations with these new models and the investment will be well worth it.

• 50:58Speaker 02

  So would you buy Meta or Microsoft today if you could only buy one?

• 51:01Speaker 00

  If I could only buy one, Microsoft for sure.

• 51:03Speaker 02

  Wow.

• 51:04Speaker 00

  The biggest thing that Microsoft has going for it is that infra. They own so many of these data centers, they're spending so much on build out. No matter what model runs on top of that, Microsoft is gonna win.

• 51:14Speaker 02

  Do you worry about the debt cycle? And what I mean by that is there is so much cash being put out into the data center build out. And we've just never seen levels of debt like this before. And you're seeing that in bond pricing for Meta and other things. Do you worry about that sustenance or do you just think, fuck it, we're still so early?

• 51:33Speaker 00

  I think that it actually should start to concern investors in that if you do not have a huge amount of free cash flow, then taking on massive amounts of debt is bad, it's dangerous. And so, for the Microsofts and the Googles of the world, they have access to businesses that are durable, are existing with huge barriers to entry. And I think that as a result, those businesses may take a hit from a future, for example, collapse in value or even if it's not a collapse, just like a minor hit in the projected future cash flow from AI. Those businesses are still gonna be around and so I think it's a risk. But to be completely honest, if you're OpenAI or you're Anthropic,

• 52:16Speaker 00

  the hundreds of billions in free cash flow that you need in order to pay back the debt that you're taking on in order to accommodate these data center build outs, in order to get the next big training run, it's totally existential for them. And so, they need to become the single greatest free cash flowing businesses in the history of technology in order for them to just live. And so, it's a pretty massive bar, yeah.

• 52:38Speaker 02

  Do you think they should be moving into the chip layer as they are? I mean, we've got the wonderfully named Jalapeno and then we have Anthropic, you know, now reportedly working on their own chips. Do you think that is the right move?

• 52:48Speaker 00

  I think so. I think verticalization is clearly the strongest way to unscrew yourself from taking on a massive amount of debt and burden. In the future, if they become multi-trillion dollar companies, they will simply have to enter in this market and own more of that infrastructure layer. So, it seems like quite obvious that they wanna play there. I think the biggest question is how this changes their relationship in the midterm with their current vendors. Because ultimately, their explicit goal is to replace their dependency on you. So I think that that's going to be an interesting challenge to navigate.

• 53:21Speaker 02

  I think it's a little bit like competition in VC, which is like, ah, no one really has any loyalty anymore. I'm so sorry to say that.

• 53:27Speaker 00

  No, it's true.

• 53:28Speaker 02

  Yeah, Jensen's like, I'm working on Nematron, I'm buying Poolside, Sam knows that fully.

• 53:33Speaker 00

  It's co-opetition with everyone.

• 53:35Speaker 02

  Yeah, listen, our job is to survive.

• 53:37Speaker 00

  Yep.

• 53:37Speaker 02

  Do you worry about where we are in the market today? I have like, you know, older, wiser friends being like, Harry, this is peak froth. And then I'm also like, peak froth, but also Curse had just sold for $60 billion after four years. That is cash that's coming back to hospitals, foundations, that ain't froth or IRR, that's cash back.

• 53:57Speaker 00

  Yeah. No, I mean, I think that what I am less concerned about is a like 2008 style financial crisis or massive bubble or asset crash. I think that that seems disconnected from the true reality of where this technology is and is going. We've already started to see outcomes in science and in sort of like real, serious, human prosperity style changes. Now, that it's very early, but the technology, pretty clearly to almost all experts in the fields of science and technology, is on a trajectory towards accelerating human prosperity. That is hard to deny from a outcome perspective.

• 54:39Speaker 00

  Now, what's more interesting is who are the businesses that are going to be the sort of backbones of that transformation? We are probably in like the Yahoo era where we don't actually have, or at least widely recognize, the Googles of the world or like the sort of the thing that comes after. And so, today, when I look at OpenAI and Anthropic, there are, I think, more analogies to Netscape and sort of these technology companies that were first. But ultimately, it's hard to navigate being first. I think Steve Jobs always had like a really great strategy at Apple of not being first, but of being the best at almost everything they did. And that's something that we also like a lot, you know, being first or best, but we heavily lean towards being best.

• 55:20Speaker 02

  I'm continuously changing my mind on outcome sizes. We're an investor also in a lovable of the world. Something that's a $13.5 billion business at 600, 700 million of ARR. Fuck dude, the trajectory of company growth is just unparalleled. Do you think investors need to change their mindset on outcome expectations and company growth expectations?

• 55:43Speaker 00

  Yeah, I think that it is hard because there's a current space where people are experimenting and they're buying a lot of technology in a fairly speculative way. And so, it's hard to index on AI for every other possible industry. But you start to look at some of the other industries that are being influenced by this wave and even like CPG, right? It feels like every other day you hear about a massive brand that got bought for billions of dollars that started two or three years ago. So I think that maybe it is just true that the world gets faster, it grows bigger, better than ever before. And we're starting to see the early days. And I think that a lot of people say this is what the singularity will feel like.

• 56:26Speaker 00

  Things will just move faster, things will grow bigger, there will be more. And we'll start to normalize it and build models and say, oh yeah, that's just the way it is today. But I think it might just be us expanding as an economy.

• 56:40Speaker 02

  Before we move to like internals, which I do wanna touch on because you've got really interesting takes on hiring. We saw our table go for two and a half billion, give or take. Listen, it's a fantastic outcome, incredible, but it's just a reduction from the $11 billion price before. Will we see a generation of SaaS companies sell, exit before we see this wave of cannibalization that could occur?

• 57:01Speaker 00

  Yeah, I mean, I think that certainly we will. And I heard this from a fellow founder who told me this and I totally resonate. Basically, contemporary SaaS businesses are more like movie studios now, where you have to hit a blockbuster and you have to keep hitting blockbusters in order to keep the attention of the world. And if you are an airtable, you made that like one movie that was a hit and people loved it. So no doubt, it's a great business, great outcome. But if you sort of rest on that, then yeah, bending spoons will come and eat you. And I think that the new world is much more about continuously getting bigger and growing larger. And so, I won't be surprised to see a huge wave of M&A of these businesses

• 57:46Speaker 00

  because they're still good businesses fundamentally, or you can make them good businesses. They just aren't going to be Stripe or these massive things that capture fundamental pieces of the economy.

• 57:57Speaker 02

  I think you have very good parallels also like gaming companies. Whereas like you have a banger of a game and you will have a hardcore user base that will sustain for five, seven years and that lifecycle is great, but you need another big, big hit.

• 58:09Speaker 00

  A hundred percent.

• 58:09Speaker 02

  I totally get you there. Listen, your hiring is candidly different. When we were chatting before, you said we expect 100% of our future hires to come through acquiring companies and bringing their founders and teams into Factory. I read this and I was like, honestly, I was like, wow, that's hard. Because often founders make bad employees. I would suck as an employee. How do you determine, ooh, a team will thrive internally within Factory? Or, oof, that's an opinionated, pretty arrogant, egotistical founder, that's not a team player?

• 58:41Speaker 00

  I think that's a great question. To me, the most interesting thing about this is that the profile of an organization has changed so rapidly that acquiring an organization is no longer what it was 10 years ago. And what I mean by that is if you are someone who just created an open source project, then you are quitting your job and you're spending five, eight months just building that one thing. And that shows so much more conviction than you can track in an interview. And so, it's much easier actually to spot these talented people who are sometimes companies of one, who are just, they're ready to execute. They wanna be a part of a mission or they're already operating towards a mission. And basically, what you're offering them is more resources to do that.

• 59:25Speaker 00

  And so, I think that a lot of what we're looking for when we try to bring a team in is, are you mission aligned? Are you someone who's going to operate independently and be able to take on a huge amount of responsibility? And what's interesting is all of these people are also aware of this lack of technology mode. And so, they're pretty willing and ready to basically either integrate everything they did in a couple of days or scrap what they've been working on in order to build something even bigger. And so, for us, this profile is just such a match made in heaven for a team that already operates with a ton of former founders, a ton of people who were previously working at startups.

• 1:00:01Speaker 02

  Can I be a dick?

• 1:00:02Speaker 00

  Please.

• 1:00:03Speaker 02

  Mission aligned.

• 1:00:05Speaker 02

  Of course, no one wants someone who's not mission aligned.

• 1:00:08Speaker 00

  Right, right.

• 1:00:08Speaker 02

  And then also like willing to take on responsibility. It's like groundbreaking. Do you know what I mean?

• 1:00:13Speaker 00

  No, I know, I totally get what you're saying. In my mind, mission aligned for us means that you're literally working on the exact problem that we're working on and doing it very well. So I think that when people come in and they say, well, I loved that we were working on payments. And in a way, payments is just like AI for software development. You're kind of like, all right, I'm sure that you absolutely could work on this. And in fact, maybe we'll actually hire that person. Right? So I'm not suggesting if you worked on payments, you can't join Factory. But the people that we're looking at are already deep in the weeds of building harnesses for software development, where they've already built something that tens of thousands of people are using on a daily basis. And maybe they even say, that's better than the stuff we're getting from Factory.

• 1:00:54Speaker 00

  Or they've thought so deeply about the problem of outcomes in AI and measuring that. And they already have sat down with business leaders to say, I want to solve how to translate these AI inputs into AI outcomes. And so mission aligned to me isn't like a property that you can suggest or say. It's actually extremely evident in the work of the founder. And so that has made it very easy to stress test. I think, are you going to do well at a company? Because you're basically doing the same thing, except backed by us.

• 1:01:24Speaker 02

  Mission aligned goes contra what Chamath has got a lot of heat over the weekend for saying.

• 1:01:29Speaker 00

  Yep.

• 1:01:30Speaker 02

  Which was like Silicon Valley has become too money centric and that's now a problem. Do you think he's right?

• 1:01:36Speaker 00

  That is something that it sounds like Tremoth would say, because frankly, that's how he's made his money. I think about what we're doing and how we got started. When we created the concept of the software factory, something that he, you know, loves to use that term as well. We said to ourselves, we're looking at this very hard, fuzzy, ambiguous problem. We're eating so much glass. We're going up to people saying, this is going to exist, this technology, here's how it works. And people would say, leave, like go away, like it has nothing to do with the pursuit of an outcome. Because at that point, you're literally trying to almost be right about a technology. And so it's like, sort of very producty, it's very engineering heavy, it's kind of contrary to what the world is saying.

• 1:02:19Speaker 00

  Or at least people are saying, maybe at some point in the future, they're kind of dusting you away. In my mind, that sort of mindset of I'm trying to build a thing, I see the way the future might look, I'm going to get super in the weeds, I'm going to have people tell me I'm wrong every single day for three years straight, until they eventually agree with you. I think that that's actually filled in San Francisco. Like everywhere you go in Silicon Valley, there's tons of people who just care about technology, about doing something that might change the world. And I think that what you sort of get around that, though, are people who do want to profit on top of that. And their only like way of participating, because their background might not be in building, is to try and like sort of build financial instruments around it.

• 1:03:04Speaker 00

  That's actually healthy and important part of the ecosystem. But it's not the only thing that happens in San Francisco.

• 1:03:09Speaker 02

  I actually think it's the paradox of what Chamath says, which I think the influx of money has led to a 1% realizing, I've got bluntly a lot of money, whatever I do, I can always go back to a big company, to a great company and get paid a lot.

• 1:03:23Speaker 00

  A hundred percent.

• 1:03:23Speaker 02

  So I'm going to choose to work on something that's really interesting. Do you see what I mean?

• 1:03:27Speaker 00

  Yeah. And I mean, there's just no shortage of people who are just purely trying to realize a dream in San Francisco. It's, it is amazing to see. And I think that it's actually quite harmful to that culture, that people really want to create a narrative that it's purely financial seeking. Because in today's world, the words that you say and how you portray something, that becomes a part of the story that the intelligent systems that we're building ingest. And they, they're like world model, LLMs, and the tools that we're going to use to do work on a daily basis for the next decade. That technology is built on the stories that we tell. So I'm always trying to share a little bit more of the optimistic side of how I perceive the world to be.

• 1:04:12Speaker 00

  Because I think that that actually helps make that world occur with a higher probability.

• 1:04:17Speaker 02

  We've talked about team additions. Cognition placed a lot of emphasis on like the chess champion and like the math prodigy. Do you think we are overweighting the importance of traditional certification? Or do you think that is the right thing to focus on in a more verifiable, engineering heavy hiring process?

• 1:04:37Speaker 00

  Yeah, I think that it is conventional hiring wisdom to look at pedigree and look at achievements and say, that's like the right way to pick people who are going to be smart. I think though, that in many ways, the sort of like least agentic path that you could take is to only try and hit the goals that other people set in front of you. And that tends to look like you go to the right school, you do the right competitions, you like follow the rules well enough that you then get recognized for how well you follow rules or like operate within the system. Now, actually, there's plenty of smart people who are going to do that. Because that's also how you almost guarantee a great outcome for your life. To be clear, you can still find many smart people who follow that path.

• 1:05:19Speaker 00

  But in our mind, the most important trait in an individual to hire for is how capable you are of operating outside the bounds of what today the system calls the rules. That is something that is very hard to measure for. And so ultimately, I think you need to look outside of that traditional pedigree and start to look for people who, A, that system might have overlooked. I know that I'm a big proponent of this, you know, I mean, I went to an Ivy League school. I learned firsthand that that is barely a signal for competence. There are plenty of idiots who went to Ivy League schools. And I think that the clearest signal for me that someone's done something great is that they have built something that they care about, that they want to tell the world about. And I think that you can see that all over.

• 1:06:04Speaker 00

  And that's why I say it's not just companies that we think will sort of make up the people we quote unquote acquire. But it's, you know, one person shows who just built something in their spare time that demonstrates that they are going to go outside of the boundaries of what traditional systems would reward.

• 1:06:19Speaker 02

  How do you think about placing a value on those one player, small player teams? You know, we're seeing Poolside being bought and a lot of the employees moving over to Nvidia, $12 billion rumored price. How the fuck do we put a price tag on heads?

• 1:06:33Speaker 00

  No, it's a great question. I mean, and this is ultimately probably one of the biggest challenges of capitalism in general, is this like desire to place a value on humans and talent, which is sort of challenging. It's like, obviously, basically, some of the best outcomes in history have come out of effectively like one person making a gut check or the right call. People say this about like Jeff Bezos, is he worth 100, you know, $200 billion or is it the company that built it? In my mind, there is something magical that happens in the connections between the people. So it's not that any one node is worth $100 million. But when you put all of these nodes together, the graph they make, that can be worth tens of billions of dollars. And so I think that that's actually one of these interesting parts about a talent strategy,

• 1:07:16Speaker 00

  is that you have to constantly be thinking about the graph you're building. And those nodes can come in and they're almost a little bit more, you know, there's traits and properties that you want them to have. But the best people are people that make that graph much stronger than it was before. And that, I think, can easily be worth 10, 20, 30 plus billion dollars, especially to companies who are operating in a model where their talent was built pre-AI. Any business that was decided on their graph before this insanely, you know, game changing technology was created, has to update their graph very rapidly. And by bringing people in, that can be the difference between a $2 trillion company being a $4 trillion company. So almost anything's worth it.

• 1:07:58Speaker 02

  Penultimate one, before we do a quick fire. What do you see other founders make in terms of mistakes on hiring that makes you go, oh no, you know, Sarah or Simon, I wish you hadn't done that?

• 1:08:09Speaker 00

  I think the biggest thing is performative work culture. Like people who look for people who are saying, I'm going to grind 24/7 and I'm going to be unstoppable and I'm going to work myself to a bone. I think a lot of founders see that and they sort of are catching a signaling that this person is going to be really productive. But what we have found is that this sort of performative work culture, this like 996 sort of attitude, is almost always correlated with making up for some other detractor or trait that, you know, basically means that this person might not be a great hire. And I think that that actually applies at the company level as well. All the companies that sort of, for the most part, say, you know, we work people to the bone Saturdays and Sundays and we've, you know, have to have people, you know, in all

• 1:08:54Speaker 00

  the time. At different points in your company's life, you will work on weekends, you will work 15 hours in a row, like that's going to happen. I think trying to make that your culture points out that your business doesn't make a ton of sense without it.

• 1:09:08Speaker 02

  I get in a lot of trouble in the UK and in Europe for being kind of the 996 guy. And I think that people take 996 from me too literally. I definitely do not mean 9am to 9pm, six days a week.

• 1:09:19Speaker 00

  Yeah.

• 1:09:19Speaker 02

  I mean, a culture of, if I ping you on Sunday morning saying, hey, a big client's got a problem, we need to jump on a call with the buyer there, you jump on on Sunday morning.

• 1:09:29Speaker 00

  Yep.

• 1:09:29Speaker 02

  It probably won't happen, but it's not, I'm sorry, it's my weekend and I will resume on Monday.

• 1:09:34Speaker 00

  A hundred percent. I mean, and that's just the reality of, of building a startup.

• 1:09:37Speaker 02

  Yeah.

• 1:09:37Speaker 00

  I mean, you know, I am constantly talking to people on weekends and we're doing stuff that indicates that the business operates outside of Monday through Friday. I think though, that there's clearly a difference. And that's why I say performative work culture. Anytime you create an incentive to show people that you're working rather than to actually do work, you're basically incentivizing the wrong thing. And so I think really focusing the business on outcomes, like it's interesting, you just And you jump on a call with a client in order to achieve something. Well, that's pretty easy to see that you're not talking about the act itself. You're talking about the outcome you want to achieve. And that difference is pretty massive when you're hiring.

• 1:10:18Speaker 00

  And I just see a lot of people think that they're making the right call by only selecting for people who have that trait. And I think you miss out on a lot of great talent who knows that that's kind of bullshit.

• 1:10:27Speaker 02

  I think the other thing is senior engineering talent, especially with families, say, is instantly put off by the performative, often young hustle culture. And actually, I've learned that the leverage that you get from like, especially when it comes to infrastructure engineering or architectural engineering is very real.

• 1:10:43Speaker 00

  I mean, right now, basically, being able to point a set of agents in the right direction and knowing from the beginning what direction to go on is worth not only more because it gets the job done faster, but it now translates into real dollars, right? Like, if you spend 100 times more tokens trying to get an outcome because you just don't know as much, it doesn't matter that you worked harder, it just basically means that you missed the ball the first time.

• 1:11:08Speaker 02

  I have Brandon on the show from McColl, and he said that they spend more on tokens than they do on engineering headcount. My dear friend Jason Lampkin from Sasta, who we do a weekly show with on news with Rory, said that we'll give $100,000 of tokens to our best engineers.

• 1:11:21Speaker 00

  Yep.

• 1:11:22Speaker 02

  Where do you sit on that today? And how do you think that changes?

• 1:11:26Speaker 00

  We actually don't even think about allocating tokens or credits towards people like that. Like, I think that that's actually, in fact, a very weird way to think about it. And I think that's at a measure that ultimately people are looking at inputs. What we think about is how many tokens or basically how much spend that we allocate towards projects and outcomes. So I'll give you a great example. There's an evaluation that we have been hill climbing against in order to try and see if we can build a system that beats it. It's incredibly difficult. It's called program bench. And one of the things that we've done is effectively allocated almost seven figures of like credits in one day on this benchmark. That was currently being done by like one person.

• 1:12:07Speaker 00

  So I guess you could say that we allocated seven figures of credits to that person. But in reality, what we're trying to do is we're trying to see, does our research pan out on this project? And so of course, we're willing to spend that much in order to see if that outcome comes true. Similarly, for a lot of our engineers, like what we do is we try to say, basically scope out these projects. And once you know the scale and scope of the project, all you have to do is basically share the bid of what you think it's going to cost. And then we go and we send it off. And we in fact have products called agent effectiveness that let you allocate and look at how many credits were spent on a given project in order to measure, are you achieving the outcomes that you'd like in your business, given the spend that you're putting towards those areas?

• 1:12:52Speaker 00

  And I think that in this new world, the like relationship of one to one mapping, like agents to humans or like saying like an agent has a name is sort of a weird way to think about it, when really you have an agent system and you allocate capital towards projects. So I see that number for some businesses approaching eight and nine figures easily.

• 1:13:11Speaker 02

  Final one before we do a quick fire. What really good idea did you say no to that was very hard to?

• 1:13:16Speaker 00

  I think self-service is by far for us, the hardest thing to continuously say no to. It is actually quite painful as a builder of products. I want more people to use our product. And there's these certain types of adjustments that we could make. And I think many of them would unfortunately come at odds with, one, making our business more successful, or two, the experience for the enterprise. And that's something that I don't see as being permanent. Like, I do think we're going to hit a certain scale where we're allowed to pursue many things at once. But it constantly sort of nags at me that I can't just like hit a button and then 10 million people are using the product. Because when we look in the market at comparable solutions that have, you know, millions of users,

• 1:13:58Speaker 00

  basically the biggest difference is economics and that's it. And so we know from a product perspective, we have a lot that's there. It's just that we have to make a hard decision to not subsidize.

• 1:14:09Speaker 02

  If you own the outcomes of those consumers and you get that data back, could you not make an argument that the improvements that that data would provide to the core product would outweigh the cost to serve those free self-serve users?

• 1:14:23Speaker 00

  So that's actually how we use self-service today is basically we have a product that, you know, obviously you can download it off the internet and you can try it. We have a, to be clear, a pretty steady stream of, you know, tens of thousands of people that use the product every day from that segment. And I think that for those self-service users, they are giving us feedback and they're helping shape, I would say more of like the experience from a user at the individual level. But I think at this point, a lot of that can be achieved with less than, I'd say, 250,000 people. And so, once you start to hit critical mass, you get that feedback loop, you have everything you need. You don't need like five, 10 million people to get those bug fixes in.

• 1:15:04Speaker 00

  However, there is something really special about seeing the community build like sort of media and content and storytelling around your product. And I think that that's a part of the experience that we have to work really hard to create a comparable for.

• 1:15:19Speaker 02

  Yeah, a grassroots community, that grassroots brand is harder if you don't have that.

• 1:15:23Speaker 00

  Yeah, 100%.

• 1:15:25Speaker 02

  We're going to do a quick fire round.

• 1:15:26Speaker 00

  Let's do it.

• 1:15:28Speaker 02

  In the UK, we have a game, and I'm probably getting in trouble for this, and it's called Shag, Marry, Kill.

• 1:15:33Speaker 00

  Right.

• 1:15:34Speaker 02

  Brutal, but you get the theory, which is, you know, buy for the short term, buy for the long term, and sell hard.

• 1:15:40Speaker 00

  Yep.

• 1:15:41Speaker 02

  Meta, Microsoft, and Nvidia.

• 1:15:44Speaker 00

  Oh, this is a fun one. I think that I would have to say marry Microsoft, shag Nvidia, and kill Meta. And I'll tell you why. Microsoft, to me, represents a software company that has become an everything company that really does sit in the lifeblood of almost every Fortune 500. There is not a single business that doesn't have Microsoft something. And I think that that means that that company is going to be here for a very long time. I say this also as a former Microsoft employee for a year and a half. Nvidia is the current kingmaker of technology. They get to decide who is currently even sitting at the table.

• 1:16:25Speaker 00

  And so I have no doubt that that's going to continue to grow massively. And how durable that is, I think that it's just a matter of if they accumulate power at the right rate. If they do it at the current rate, and then at some point, people might ask questions about, are we going to let one company control the whole supply chain? But today, I think it probably, the answer is what other choice do we have? So they're going to keep growing. And Meta, you know, I say this with love to the company. I think that ultimately, from a technology perspective, they're really actually quite accurate often. Like, I think Zuck's push into VR was like technologically correct. That felt like the right move. I think the push now into open models technologically is correct.

• 1:17:10Speaker 00

  I think that they have one cash cow, which is their ads business. And I think that that means that they are going to have to work really hard to figure out if there's anything other than that that can sustain the business. So it just makes it the weakest of the three.

• 1:17:22Speaker 02

  Will Nvidia be a $10 trillion business in three years?

• 1:17:25Speaker 00

  In three years?

• 1:17:26Speaker 02

  Yeah.

• 1:17:26Speaker 00

  I think that there's a real serious chance that if we let SpaceX be worth two or three trillion, then Nvidia probably is worth 10. And so I think that the answer is likely yes.

• 1:17:39Speaker 02

  It depends on the buoyancy of multiples.

• 1:17:41Speaker 00

  Yeah.

• 1:17:42Speaker 02

  To use it, you said about the entrenchment of Microsoft in businesses. Would you be a buyer or a seller on Salesforce given that?

• 1:17:48Speaker 00

  Oh, I'm a buy on Salesforce.

• 1:17:49Speaker 02

  Really?

• 1:17:50Speaker 00

  I believe that the businesses that are going to be most durable are the ones that have a workflow and a system of record that they've defined that everyone agrees is consensus. So you look at like the Salesforce of the world, you look at today at least Atlassian. And I think that the biggest thing is when people say, I hate that software and everyone buys it, that's probably a pretty good business because they're not buying the software, they're buying what's underneath of it. And that, to me, is actually much more durable than the technology itself.

• 1:18:20Speaker 02

  I'm an investor in linear. Linear are absolutely crushing though.

• 1:18:23Speaker 00

  Yeah.

• 1:18:23Speaker 02

  And what's interesting is actually, and it's a really interesting one, is Atlassian are crushing, and so are linear. And it just goes to, again, the market size being so much bigger than anyone comprehends. And I think we think too much in zero sum. I take from you, and so you lose.

• 1:18:37Speaker 00

  Yep.

• 1:18:37Speaker 02

  We're both just crushing, actually.

• 1:18:39Speaker 00

  I think that's really true. And one thing that's important is Linear and Atlassian are selling the same thing. It's the same workflow agile and a system of record that represents agile. I will say, though, that one of the things that is pretty clear to me is that the way we build software is fundamentally changing. And I think that agile might be one of the things that gets hit with this new way of developing. And that makes me think that there's a huge opening for what the next system of record and the next workflow looks like. And I do think it's going to have to be much more radical. And it will be very challenging for both Linear and Atlassian to transform into that new way of building.

• 1:19:16Speaker 02

  Codex, Cursor, Cognition, Claude Code, rank one through four in terms of threat level you feel from them.

• 1:19:24Speaker 00

  Oh, threat level is interesting. I think in my mind, the current ranking would be, because I actually feel that I have to condition this by saying, I don't feel a impending threat from the rise of these players. Because I think that they're actually all going in a different direction from what we're building. And that's really important. And I'll touch on that in a second. But in terms of, I'd say, relevance to the conversations when I'm talking to enterprise buyers. Number one is Claude Code. I think that it's just brought up in every single conversation. And so we always have to sort of share with people why we see ourselves as largely complementary to the Anthropic platform and suite. The second is now Codex has increasingly been referenced in deals and conversations.

• 1:20:09Speaker 00

  Where people are saying, look, like we were on Claude Code and now we're switching to Codex. That's actually the greatest news for us because it shows how basically unsticky this is and it gives uncertainty. However, they're coming up way more frequently now. And I think it's because their work platform is better than Anthropic's. So it's not Codex for coding, but rather Codex for work that's coming up more frequently, which is very fascinating. Then I would say cognition, because they're basically the only other model independent vendor in the enterprise. And so I'd say that one of the big, like, sort of consistent feedback points we hear from them is that, you know, they're building this cloud offering. It's very sort of futuristic on the idea of imitating a software engineer as a human.

• 1:20:52Speaker 00

  And so I think that that is something that makes people ask, is that different or the same as your strategy? And then Cursor is sort of present, actually, in a lot of these businesses. I don't think that anyone really perceives Cursor to be their primary enterprise software development strategy. As much as an IDE, which is, I think, still a great business because they're still going to get a lot of usage. But that sort of is how I see them. And this is most informed by this idea of almost all four of these businesses are sort of coming to enterprises and saying, we're going to build you a eventually human level AI replacement for labor. And then we're going to sort of Indiana Jones swap this for the people in your business. And I think that that is just so different from what we're going into.

• 1:21:36Speaker 00

  And sort of sharing with them, which is that you're not going to replace human with AI. As much as you're going to build a new system for developing software. And humans are going to build that new system alongside AI. And that new system is going to look very unfamiliar. And so it's not so much a one-to-one labor mapping as it is a entirely new development methodology. And that's something they're really only hearing from us right now. And I think it sort of contextualizes the many tools in the space compared to factory.

• 1:22:06Speaker 02

  Will Chamath be successful with his, I can't remember, 1809 or whatever it is?

• 1:22:12Speaker 00

  Yeah, I think that my answer would be it depends on how real the software is. Like, I haven't seen any examples of it sort of like working in an enterprise environment. I think that if they're very focused on building software that works and delivers outcomes. I believe that he has just as good a chance as anyone and is very well connected. But ultimately, I do think that part of this is about like building with the enterprise. And so I think that that's probably going to be the biggest question is like, can they get enterprise traction in the markets that matter? With people who take them seriously as a like full-time software development opportunity.

• 1:22:50Speaker 02

  Single biggest advice on selling to large enterprises in today's world?

• 1:22:55Speaker 00

  I think the biggest thing that I've learned about selling to enterprises is to stop treating it like persuasion. Where you're trying to convince them that you're right. And instead treat it as a discovery opportunity to learn about what's currently the biggest problem they care about. And that approach difference is, I think, unique to new markets. So if we're selling something where the market's established, it's finite, zero sum. And everyone knows it's a commodity, like databases where there's a million options. I do think persuasion is the strategy. You're trying to sort of convince them, you know, all else being equal, you buy from your friends. And I think that in this market, it's much more about trying to understand just how big of an opportunity it is. And learning with the customer.

• 1:23:40Speaker 00

  And I think that people actually put a huge amount of value in enterprise and especially in software. On people who they perceive to be trying to problem solve with them. And so if you're on the same team and you're both trying to problem solve. Then you're going to land on a real problem that that business has not yet solved. And that means almost 10 times out of 10, you're going to bring value if you can figure out a solution to their problem. So I think that that's like something that people underrate. You're not trying to trick them or like persuade them. You're just trying to help solve a problem for them.

• 1:24:10Speaker 02

  Totally agree. Age-old enterprise sales doesn't change that much.

• 1:24:14Speaker 00

  No, definitely not.

• 1:24:15Speaker 02

  Final one, I like to ask the question, which is what seems ludicrous or strange today? That will be incredibly commonplace in five years' time. And I can give examples of, you know, finding your husband or wife online, bizarre.

• 1:24:28Speaker 00

  Yeah, it's a great one.

• 1:24:29Speaker 02

  Putting your credit card details into your phone. Of course, I'm not doing that. That's so dangerous. What today do we think is crazy that would just be obvious in five years' time?

• 1:24:39Speaker 00

  I think that the biggest thing that we're going to be surprised by is the fact that we let a sort of like priestly class of maybe 2 million people. Decide the fate of all software for all of humanity. In three to five years, it'll be actually like unthinkable that you couldn't just generate the thing that solved your problem with software. On the fly, in the moment, for nearly any problem that you have in front of you that can be solved by information manipulation. Like today, we sort of see a little bit of that with lovable and bolts and these tools that let you sort of build personal applications. But I think that this will look quite interesting when you think about what problems, not like maybe an individual consumer has, but really like a general society we have.

• 1:25:24Speaker 00

  Like, you'll be walking on a vacation in Belize and the boat operator that gets you from point A to point B. Will have a software and interface fully custom to them that looks better than like your HR IT software back at home. And I think like this sort of total dispersment and distribution of amazing software to the entire world. Is going to make everything just feel way more futuristic. And that, I think, is going to happen very, very quickly on the order of like three to five years from now.

• 1:25:52Speaker 02

  I love doing what I do because I genuinely just get to pursue my own curiosity in a very natural way. So thank you so much for entertaining my curiosity and you've been an amazing guest.

• 1:26:02Speaker 00

  Thanks for having me. This was a fantastic conversation.

• 1:26:06Harry Stebbings?

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  Your easy button for AI productivity across every team. Ready-to-go AI teammates pre-built for marketing, ops and IT. No prompt engineering, no setup. They show up where the work is happening, already on boarded in your workflows, ready to deliver. With Asana, your whole company can work on the same plan towards the same goal. Whether you're a team of 10 or a team of 10,000. Asana, where humans and agents workflow together. Try it at asana.com. That's A-S-A-N-A.com. While Asana organizes the work, Framer scales the story. If your team wants a website that looks and feels handcrafted, but is still fast to ship, Framer is built for exactly that.

• 1:28:14Harry Stebbings?

  Here's what I love. Framer is the pro AI website builder for creators, teams and businesses that really care enough to get every detail right. The agents close the gap between AI-generated ideas and production-ready website work. Because it all happens where the site actually lives. It lands on the canvas, it stays editable, and it can be published when the team is ready. Build custom code components, create and manage CMS content, optimize SEO settings and ship everything all in one place. The agents bring speed and scale. You bring taste, judgment and control. It's an enterprise-level solution, premium hosting, enterprise-grade security and 99.99% uptime SLAs. Which is why the world's leading brands like Perplexity and Miro build in Framer.

• 1:28:57Harry Stebbings?

  Learn how you can get more out of your site from a Framer specialist. Or get started building for free today at framer.com/20vc for 30% off a Framer Pro annual plan. That's framer.com/20vc for 30% off. Framer.com/20vc. Rules and restrictions may apply.