Only 10% of Neo-labs Will Survive | Factory CTO, Eno Reyes

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.

8 in 10 billion is the new 1 billion.

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, and Eno is incredible in this show today.

There's going to be a lot of notes taken in this discussion.

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." 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 three years, 99% of workflows are going to be done on open models.

Ready to go?

Eno, dude, it is so good to have you in the studio. I obviously had Mattan in the studio. I was chatting to Keith Raboy over the weekend about you, so thank you so much for joining me, dude.

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

Now, I hate background stories. I'm sure you listen to podcasts and 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. And so I have to ask it: How did you first fall in love with computers, do you think?

Yeah, I think that the beginning was my parents. So both my parents went to art school, and 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.

And 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, like, 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.

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.

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.

100%.

It's very hard to transition from a father being hit by a bus to margins.

Right.

Like, only a venture capitalist could do that in such a swift transition.

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

I mean, 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?

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 theright outcomeright away, searching theright phrases, and you use, you know, 1,000 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.

And ultimately, you know, 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.

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.

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, you know, 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. 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.

When you look at the post-training required, when you look at the implementation required, I look at that and I think that company structures and teams today are simply not equipped to do that. How will we solve for that?

Is this just moving into an incredibly services and implementation-heavy world where we have these insane AI teams coming into every company? How do we solve that?

Yeah, and I think that this is one of those things whereright 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 knowledge or the skill set 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, web page, click a couple 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. And so I think thatright 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.

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.

Yep.

When there is ambiguity to something, which could be marketing conclusion, did it come through X channel or Y channel? My girlfriend's a lawyer. Different legal notes.

Right.

It's ambiguous. Some people like it one way, some people like it. How do you think about the importance of verifiability in determining outcome quality?

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 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 frontierright 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.

AI systems that progress into verification. What does that actually mean?

Yeah, 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, you know, basically judging how do you determine whether or not these new hires are good?"

What does, like, a very novice manager do? Will 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, you know, new hires. Here's the things we don't like. You know, 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 it goes well, you know, 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 beright 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.

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.

It's a brilliant statement. Show me the incentive and I'll show you the outcome.

100%.

The smartest thing about actually venture firms, when you run them as a business, because if I set you, you know, the kind of goal of three deals per year, you'll give me three deals per year.

Yep.

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

100%.

And so it fundamentally, to your point, changes the output that you get. I think I made a big mistake because I'm in macaw. And 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. 5X. 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?

No, I think 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 moat 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, Mercor, yes, they sell data, but every person 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.

Going back to what we said about kind of 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?

I think it might. I think that the TAM of frontier models is, frankly, overweightedright 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, 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 $2, $3, $4 trillion valuations is an assumption that you can basically 2X the price of those tokens and people will buy them.

So.

Is the margin profile looking shit? And what I mean by that is Anthropic just produced their first quarter, I believe, of profitability. Margins are seeming to be ripping.

Yeah.

And they're throwing off cash now. Is that not going in counter to what you said?

No, 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, you know, N companies that sort of sell, 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 toes in both, but the platform commitment from them seems much stronger.

Which strategy do you think isright? If you were to bet on one?

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.

Why is that?

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, you know, 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 thisright now in real-time and in coding.

Anthropic can basically only deliver, you know, their model's outcomes.

And the best model also is highly subjective, dependent on the consumer.

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

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 to see Anthropic potentially go out at $2 trillion, but you're essentially placing a $2 trillion price on Claude Code, which is not that difficult to switch off of.

Yeah.

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

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 tell, 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.

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, you know, 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.

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

I think that the marketing of AI in general was probably one of the worst marketing jobs done by, you know, contemporary capitalists in that it basically did the opposite of what you want. Scare every single person, tell them it's very unreliable, and basically threaten their well-being and livelihood with the technology while you roll it out at scale.

And so 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, you know, the technology will be incredibly transformative. 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 god-like mythology out of AI, you're going to scare a lot of people.

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

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."

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

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, you know, 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 has an answer to what the future is going to look like.

And so you basically just are 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, an even bigger business than you had prior.

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.

Right.

Is that a momentary period in time where we're 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?

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

Okay, great.

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 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. That's, you know, that hurts us in a lot of ways. We don't have the mindshare from self-service users.

So you have no PLG?

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.

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

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. And so 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 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 con, you know, 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.

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

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 can be a route, or temporarily reducing margin can be a route towards gaining, you know, this is a classic strategy,right?

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 AIright now. 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 quite tricky, and the margin profile actually matters a lot, but it's not end-all-be-all.

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.

Yep.

You have pretty sticky client bases there, no?

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 buildright 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, you know, 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.

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. And so 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 know, you build on top of.

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

Yeah.

When serving, say, Claude 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.

Right.

An open router gets bought for $8 billion.

All the value is in the routing. Great. And then everyone is doing routing. And like 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.

Right.

Help me understand, what world do we live in?

Yeah. Well, I think that, you know, 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, you know, 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.

I mean, think about it like this. What are tokens other than intelligence? And how do you get tokens? Well, you pay for them. And how does the infrastructure layer get energy? 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. Maybe they'll make a play into 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.

Do you think it was a good buy?

At $8 billion, it would have to be really, 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, I mean, stranger things have happened.

It feels like $8 and $10 billion is the new $1 billion, so.

I'm intrigued. 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?

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 at 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, 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.

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

I'd say that in a way that taking the context window and expanding it was solved not at the model layer, like outside of the endpoint, but instead inside of the agent with something called compaction. It's exactly similar. People really want the problems to be solved, like, 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.

And I think that 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 the logic happens. It's where the state is maintained.

It's the easiest and the best place to basically do work with AI. 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.

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

I think that, well, it's interesting because there's 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. Basically, model providers have even acknowledged that all of the continual learning happens at the harness layer. And that learning is basically something that I would argue 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?

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

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, you know, 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. And what's interesting is I think that pretty much every company in the world is thinking to themselvesright 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 have explicitly said, we are going to go after every single one of these industries and businesses that we provide, like, intelligence for.

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

I think that not all of them necessarily think they're 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.

And that's something that I know, you 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.

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

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 the 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.

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.

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 biased.

Yeah, I mean, that outcome for the folks at Cursor is obviously amazing. So, yeah, needless to say. And I think that where it helps us is, one, you know, 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, and that I think will become a challenge. There's also some, you know, 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 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.

Totally get that. And I mean, unbelievable outcome for team, man. Amazing team. Can I ask you, when we look at the cadence of model development today, it's just so fast.

Yeah.

And like, I actually use Arena.ai as like a discovery mechanism to new models.

Right.

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

Right.

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?

I think that 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 an 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 that 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 people, one, it gets easier. It's just going to become fundamentally easy to build models. And two, this idea of sovereign intelligence also is, like humans, we're going to have models with tons of different opinions, tons of different perspectives.

And 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.

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 model or the Neo labs that will thrive versus die in this next wave?

I think it's plausible. It's even more. I think it could be 80 to 90% of Neo labs die in the next 18 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. And so a lot of what I think matters for a Neo lab 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? And so basically, is it durable to, like, effectively an entirely new way of thinking or new way of working?

If all three of those are true, legal's 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 a legal system in five, ten, twenty years.

So probably all the Neo labs 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, and 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.

You know, the thing that strikes me is just the misalignment and capability progression, which sounds like a real word bank. But like, when you look at, like, coding and customer service.

Yep.

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. Like, if I wanted to use AI to clip this show, it clips this.

It misses both of our faces because it goes to the middle.

Yep.

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?

Definitely. And 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 100% 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.

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 theright clip. And that intuition, writing it down, is hard.

Oh, I totally think it. And knowing, like, the hook, if you started 10 seconds earlier and the hook was 10 seconds in, your chance of virality goes down significantly.

Yep.

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

100%.

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?

I think 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 an existing frontier model from a lab.

They just happen to 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. And I think that 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? Two, are these models going to be able to solve the problems that I care about? And three, if this model becomes, if this model goes away in, like, six months, 12 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.

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

I think that 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 an 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 that 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.

If you use a model from this provider, it will block you. The answer is Anthropic,right? 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.

Totally get that. 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?

In three years, 99% of workflows are going to be done on open models. But 1% of those tasks is probably going to be 30, 40% of the economic value of the future of intelligence.

Wow. But you still think that 60% will flow to open models?

I think, I think close to, 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, how if you listen 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 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 and you look at, you ask any random person, what are you doing today?

It's not something that needs the true frontier of intelligence, like 99% 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.

Given the commoditization of the model there, 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?

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.

You know, 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.

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.

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

I think he'sright for humanity in that we need more open models like that, especially, like, American-made open models. I think that a lot of people, you know, even with respect to what I said earlier on Chinese models, people are going to 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 consumer, 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.

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

If I could only buy one, Microsoft for sure.

Wow.

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 going to win.

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.

Yep.

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?

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, like, 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 going to be around.

And so I think it's a risk. But to be completely honest, if you're OpenAI or you're Anthropic, 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. Do you think they should be moving into the chip layer as they are? I mean, we've got the wonderfully named Jalapeno.

Yep.

And then we have Anthropic, you know, now reportedly working on their own chips. Do you think that is theright move?

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 want to play there. I think the biggest question is how this changes their relationship in the midterm because if you are with their current vendors, because ultimately, you know, their explicit goal is to replace their dependency on you.

So I think that that's going to be an interesting challenge to navigate.

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

Yeah.

I'm so sorry to say that, but you know what I mean.

That was true.

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

It's coopetition with everyone.

Yeah. Listen, our job is to survive. 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 it 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.

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, like, prosperity-style changes. Now, 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 an outcome perspective. 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, you know, 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.

Totally get that. I'm continuously changing my mind on outcome sizes. You know, we're an investor also in a lot of the world. Suddenly, it'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?

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, you know, 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.

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 it's, I think it might just be us expanding as an economy.

Before we move to, like, internals, which I do want to touch on because you've got really interesting takes on hiring. Final one. We saw Airtable go for $2.5 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?

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

I think a very good parallel is also, like, gaming companies, where it's like you have a banger of a game, and you will have a hardcore user base that will sustain for five, seven years in that lifecycle. It's great, but you need another big, big hit.

100%.

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. It's really 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.

Yep.

That's not a team player.

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 know, 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 want to 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. 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.

Can I be a dig?

Please.

Mission-aligned, of course. No one wants someone who's not mission-aligned.

Right,right.

And then also, like, willing to take on responsibility. It's not, like, groundbreaking. Do you know what I mean?

No, no, 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, allright, 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 that they're 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, you know, 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. 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.

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

Yep.

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

I think that that is something that it sounds like Shamanth would say because, frankly, that's how he's made his money. Like, 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, you know, 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, you know, 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 beright 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, 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. That's actually a healthy and important part of the ecosystem, but it's not the only thing that happens in San Francisco.

I actually think it's the paradox of what Shamanth says, which is, I think, that 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.

100%.

So I'm going to choose to work on something that's really interesting.

Yeah.

Do you see what I mean?

Yeah. And, I mean, there's just no shortage of people who are just purely trying to realize a dream in San Francisco. 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'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. You know, 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 because I think that that actually helps make that world occur with a higher probability.

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

Yeah. I think that it is conventional hiring wisdom to look at pedigree and look at achievements and say, that's, like, theright 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 theright school, you do theright 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.

So to be clear, you can still find many smart people who follow that path. 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.

And 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. So, like, 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. 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.

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?

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 theright 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, you know, $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 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, 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.

Penultimate one, Puedo, 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?

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. 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, 9-9-6 sort of attitude is almost always correlated with making up for some other, you know, 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, you know, have to have people, you know, in all the time.

I think that at different points in your company's life, you will work on weekends. You will work, you know, 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.

I get in a lot of trouble in the UK and in Europe for being kind of the 9-9-6 guy.

Yeah.

And I think that people take 9-9-6 from me too literally. I definitely do not mean 9 a.m. to 9 p.m. six days a week.

Yeah.

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.

Yep.

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

100%. I mean, and that's just the reality of building a startup. 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.

Sure.

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 mentioned, you know, 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.

Sure.

And that difference is pretty massive when you're hiring. And I just see a lot of people think that they're making theright 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.

I think the other thing is senior engineering talent, especially with founders, 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, like, infrastructure engineering or architectural engineering.

Yep.

Is very real.

Yeah. I mean,right now, basically, being able to point a set of agents in theright 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.

I have Brandon on the show from McCool, 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 the news with Rory, said that we'll give $100,000 of tokens to our best engineers.

Yep.

Where do you sit on that today, and how do you think that changes?

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 it gets 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 a given in one day on this benchmark. And that was currently being done by, like, one person.

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?

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.

What really good idea did you say no to that was very hard to?

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, you know, 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, 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.

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

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 in the, 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, you know, 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, 5, 10 million people to get those bug fixes in. 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.

Yeah, a grassroots community that.

Yes.

Kind of grassroots brand is harder if you don't have that identity.

Yeah, 100%.

We're going to do a quick-fire round.

Let's do it.

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

Right.

Brutal, but you get the theory.

Of course.

Which is short, like, you know, buy for the short term, buy for the long term, and sell hard.

Yep.

Meta, Microsoft, and Nvidia.

Ooh, this is a fun one. You know, 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. 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 theright rate.

If they do it at the current rate, 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 the Zucks push into VR was, like, technologically correct.

That felt like theright move. I think the push now into open models technologically is correct. I think that they have one cash cow, which is their ads business. And I think that that means that they're 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.

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

I think that there's a real serious chance that if we let SpaceX be worth 2 or 3 trillion, then Nvidia probably is worth 10. And so I think that the answer is likely yes.

It depends on the buoyancy of multiples.

Yeah.

To you, you said about the entrenchment of Microsoft in businesses. Would you be a buy or a sell on Salesforce given that?

Oh, I'm a buy on Salesforce.

Really?

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.

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

Yeah.

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.

Yep.

We're both just crushing, actually.

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.

Codice, Cursor, Cognition, Claw Code.

Sure.

Rank one through four in terms of threat level you feel from them.

Oh, threat level's 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 an 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 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. 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 an 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 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 an entirely new development methodology. And that's something they're really only hearing from usright now.

And I think it sort of contextualizes the many tools in the space compared to Factory.

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

Yeah, I think that

my answer would be it depends on how real the software is. 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 it 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?

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

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'reright 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.

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, you know, persuade them.

You're just trying to help solve a problem for them.

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

No, definitely not.

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.

Yeah, that's great one.

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

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.

And 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.

And so, like, today, we sort of see a little bit of that with, you know, Lovable and Bolt 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.

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, you know, your HR IT software back at home.

And I think, like, this sort of total disbursement 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.

You know, 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.

Thanks for having me. This was a fantastic conversation.