Who Wins the Model War: OpenAI, Anthropic or Open-Source

So I think, yes, absolutely, we will see tremendous growth from these tools. I think it takes time to permeate through. Because you can tell like on an individual basis, like almost like on a problem by problem basis, we can solve problems faster with these tools. Now, companies generally organize around solving problems. If you’re organized around solving problems and you have some set of personnel, you might say, this is the number of problems we can solve at a given time based on how many people that we have.

Everyone is now gonna be able to solve more problems with the same number of people, solve the same number of problems with fewer people, but it takes time for the resource allocation to adjust. A lot of businesses will have to ask, do we want to solve more problems now because of the increased leverage that we get, Or do we want to solve the same problem, but now we can do it in a more efficient manner? That’s I think a question that a lot of businesses will be grappling with.

It’s really not obvious because there are dynamics that it’s hard for me to predict. But what I will say is, again, bringing it back to problems, all of these companies are now going to have to think, okay, we have all this new leverage. Do we want to solve the same problem? Do we want to increase our ambition and solve a bigger problem? Or do we want to solve more problems, you know, our users maybe have?

directionally, yes. Because what is a 10x or 100x engineer? I I don’t necessarily agree with the language around it, but like Why don’t? I think, like, it just implies as if like, 10 x 10 x of what? Is it pure output? Like, if you when you say 10 x, it means, like, how much code they’re writing. Yeah. Now I can write a billion lines of code with these tools. It might be shit lines of code, though. So the way that I like to think about it is, like, load bearing individuals in an org.

It’s kind of like if you remove this person, things fall where there there in some orgs, there might be people where if you remove them, nothing happens. And they’re, you know, not load bearing in that case. And so, basically, these people who have very high leverage are now being handed a tool that gives them even more leverage. And so using the language of 10x or 100x, yes, they’re levered up. Can They have even more impact. With that leverage language, those who know how to use leverage will be able to have even more impact, and those who don’t will kind of on a comparative basis be that much less valuable to a business.

Yes. This is a a great point. This resource allocation problem of token it’s not just tokens. It’s like dollars, it’s tokens, it’s people. This is, I think, going to be the thing that over the next twenty four months, every c suite is going to be thinking about. And I think the right way to go about it is what is the core competency for our business? What actually matters for the business that we are doing? And then how do we allocate resources accordingly? In other words, if you’re a logistics company, your core competency is probably not software development.

Now you might have had a lot of software engineers as a means to an end to deliver on your logistics goals, let’s say, but that might not be your core competency. And so what you should be thinking about is not do we get more engineers to make more features because that’s what engineers have in the past been judged by, like how many features do they ship in a quarter. Instead, it’s like, what are the actual output metrics that matter for our business? And how do we now allocate resources, whether it’s dollars, whether it’s tokens, whether it’s headcount, to more dramatically move the needle on that business outcome.

And I think this is this is great for the world because I think part of the reason why so many organizations got so bloated is because we were in a period of time where everyone was focusing on intermediate metrics. For an engineering team, we wanted to ship three features this quarter. Did you ship three features? We shipped four. What a great quarter. Like, that doesn’t necessarily matter for the business at all. And so now it’s like finally coming back to what matters in the first place.

What are the business metrics that we wanna, you know, move the needle on? Is it customer satisfaction? Is it revenue? Is it market share? And you can kind of tie back every individual’s work to that, whether

mean, it’s fun. You know, talking about core competencies. Kirkland spending $500,000,000 to build their own AI tools. My understanding is that building AI technology is not a core competency of that firm, So I was surprised to see it. Now I actually think this is good for Harvey because it’s nothing like trying to do something yourself to make you realize, oh, shit. This is actually really difficult. This doesn’t actually matter for us to have the in house ability to build this ourselves. Let’s go and have someone who is an expert in this to go and build this for us.

That is my sense.

you agree with that? I would disagree. I’d pretty strongly disagree for a couple of things. One, actually, sticking with the Kirkland thing. I think as an example, we’re so used to a world where moat in software was, I know how to do this and you don’t, and so you’re gonna pay me because I have the engineers who know how to build this and you simply cannot. The world going forward, there is going to be nothing that no one can build. Every single piece of software, anyone will in theory be able to build.

Now back to the resource allocation, though, is it worth your time and your energy to go and build it, or should you go to someone else who has already built it or can do it faster? To me, it kind of an example of this is, like, suppose we had a a very busy day at work. I could probably go and pick up lunch for everyone on the team. I know how to do it. I know how to walk out the door, place an order, hold the bags, bring them in.

Now just because I know how to do it, is that an efficient use of my time? Probably not. I’m probably gonna say, you know what? For my resource allocation, I’m gonna pay someone to go and do that for us because at Factory, our core competency is not that the CEO goes and gets lunch for everyone. And I think it’s somewhat similar here, which is like just because you can build a lot of these things does not mean you should. And in fact, oftentimes, you wanna be really ruthless about what are the few things that you and your team own and do end to end.

And then if it’s not relevant to your, like, core business and your core competencies, outsource it.

I haven’t done it in, like, three years. I love like, there’s nothing like it’s just it’s it’s not time efficient. It just doesn’t make sense to spend my time doing that. Oh, I know. Okay. But, like, I I it is, you know, it’s something that I do enjoy. But to your point on model applications infrastructure, I’m not sure if you’ve seen the the meme of this. There’s a meme of the microsoftorg chart, and it shows, like, you know, different segments, and they all have guns pointed at each other.

Just to show, like, in micros you know, there’s a there’s a lot of bureaucracy, and everyone’s kind of fighting for who gets to do what. I think that image is pretty accurate to what’s happening right now with the models, the application companies, and the infrastructure companies, where everyone is trying to commoditize the other. Everyone is trying to say, oh, no. This one is irrelevant. This all the value is gonna be here. All the value is gonna be there. The reality is value accrual is a time dependent phenomenon.

It’s not like there is one person whose steady state gets all of the value. That’s not how it works. It’s maybe for this next year, this person is who has the pricing power, who gets the value. This next period of time, these people get it. We are all, whether overtly or not, and maybe I’m saying the quiet part out loud, like, everyone is trying to commoditize the people that are not them. So for example, we’re model agnostic. We want to give our customers the best pricing, the best performance, the best speed for whatever task they wanna do in their software development.

And we wanna make sure that OpenAI, Anthropic, Google, Microsoft are all under pressure to make sure they give the best models for as cheap, as quick as they can and don’t feel like they can just, you know, charge whatever they want. Now, similarly, you know, the model companies want to make it such that the applications are all trivially easy to build and really the the product is the model. And then the infrared companies have their own spin on this. But the reality is, like, everyone’s trying to commoditize the one that’s not them.

And so from Mercos perspective, it’s very much in their interest that models that have access to proprietary data get differentiated value and capture a huge amount of value because that validates their business model. It’s a big push and pull of who can who can get the leverage. What is the belief that would invalidate yours? The bear case against Factory is if one model provider gets significantly better than all of the others. So, basically, I think a key thing for us is that all the models are gonna be roughly as good as each other.

They’ll be good at well, one is a little bit better at review. One is a little bit better at testing. One’s better at Python, this and that. It all kind of fluctuates every week. Even already people have a have a hard time keeping track what model is number one. What’s the latest thing that came out? If one model ends up going way above all the others, that’s a case where it’s like, okay, we wanna companies might wanna just completely go in with them, but then that’s a monopoly for the entire economy

Yeah.

we’ll stop seeing them as model releases, and they’ll feel more continuous. Like just how before it was, you know, GPT two, then GPT three, then GPT 3.5, then four, four point one, but like and then you get more and more desk, and like, you know, 4.523, like, eventually they’re just gonna not announce it, and it’s just like, hey, look, here’s our model that’s continuously getting better, because it already people have fatigue. Like, engineers at the enterprises that we work with can’t keep up with every single model that comes out, nor should they.

And I think that’s the whole, you know, the case for the application layer, whether it’s us or like a Harvey or whoever else is we’re gonna figure out what model is best for what use case, where the trade off is between cost, quality, speed, and we’ll just deliver that to you based on the task that you have. Because it’s hard to focus on what matters for your business and then also keep track of all these models that keep coming up.

counterbalance. It basically allows you to make the trade offs of what tasks do you want to put what level of intelligence on. And I think it’s a really important counterbalance because a lot of enterprises will realize so many of the tasks that we’re doing, we don’t need the very frontier to do it. Like and we can do it much faster, much cheaper with these open models. Again, it’s part of the resource allocation. And to do good resource allocation, you wanna be able to be anywhere in that cost, quality, speed trade off.

No. Because it’s such overkill, but also there’s a funny dynamic that emerges, which is there’s kind of an ego thing where, oh, no. No. The work that I’m doing, only a frontier model could handle. Oh, this mere open model can’t deal with the work that I’m dealing with. And this is, like, even admittedly, when I first started switching over, I’d be like, I don’t think an open model can handle this. And it’s like, no, it probably can. And it’s kind of a funny thing to to mentally deal with of deciding manually or then having the router do it for you.

So a couple things. One, it’s easy when there’s only one of them. But again, as we said, a new model comes out every week. And if you have to go through the full enterprise process to get every new model in, it’s not very easy. Two is it’s also really expensive. If you’re seeing your costs go up like crazy and not having an ROI case, it doesn’t make as much sense. And think I something that’s interesting is there’s kind of like three phases that we’re seeing happen in these enterprises.

So phase one, this was a couple months ago, was board yells at CEO. Hey, mister CEO. What’s your AI strategy? CEO’s like, shit. I don’t know. CTO, hey, what’s our AI strategy? Let’s make sure we adopt AI. And so then phase two was AI at all costs, token maxing. Part of your performance reviews, we’re going to measure how much you guys use AI. Everyone, you have to adopt. That was phase two. Right? Get as as many people to adopt as possible. Phase two happened a lot faster than people might have expected.

And so now we’re ending phase three. Like, phase two was kind of like the the debauchery, the long night, you know, taking shots, having a great time, using all the AI. Phase three is the hangover where you go and look at the bill, and it’s like, oh my god. We are spending so much. I have no idea what the ROI is. Does this is this helping our business? That’s where a lot of these companies are at now. And I think this is why routing is so important because they’re realizing and this is a true story.

One of the CIOs I was speaking with realized we’ve been spending hundreds of thousands of dollars per month on people asking OPUS 4.8 questions like, hey. How’s it going? Like, what what are my macros from the food I ate today? Like, what’s the weather like? And it’s like, guys, like, we don’t we don’t need the the frontier of human intelligence to be doing this stuff for us. Let alone it’s not even work related in some cases. But Will

We might see a short term contraction of usage of the very frontier models, but I think it’s healthy. I think it’s healthier to do that than to, like, be blind to it and then have a real sudden change there.

We I’ve literally seen this with dozens of our customers where initial and this is a lesson on our post sales team where initially we came in. We were like, oh, by the way, we have these user limits, but here, these are the models go crazy. This is before we had routing. It happened a couple times with customers where the usage would go crazy. They hadn’t spent the time to actually determine what parts of the code base do we wanna dedicate these tokens to versus not. And then they were like, oh my god, we’re spending so much.

This is crazy. We need to put in token limits. And at first, the first time this happened, we were like, oh my god, their usage went down. What’s going on? But spending time with them, we realized, wait, we need to make sure with every customer, we are having a very clear conversation with them of, you know, it looks like you guys are spending a lot of tokens on some of these things. Have you thought about consciously, yes, we want to do this. Sometimes we’ll proactively set in those user limits.

It’s better to be aware as you’re going up as opposed to just going crazy and then kind of realizing. And so what’s happened with Uber publicly has happened privately with a lot of customers of ours. And, yeah, there’s a little bit of shock where it’s like, okay, wait, let’s put in these user limits. But then you come into a question of, well, wait, this team is really important. They should have a different user limit than that team. And we’re just getting towards this world where you have very nuanced resource allocation throughout your org.

I think it’s actually a more nuanced question than we might think. I actually think it can be as low as 0% for some individuals, it and can be as high as, like, thousands, tens of thousands of percent for some individuals.

It depends on what the unique skills of those individuals are. And I’m saying individuals and not devs in particular, because I think the way we even organize roles is gonna be very different, where I’m not sure, like, dev as a word makes sense. Traditionally, it’s like custodians of code. Right? The people who do anything relating to code are engineers or developers. Everyone is gonna be loosely interacting with code in your whether they’re sales or marketing. But I think the difference is there are gonna be certain people where, again, we’re coming back to resource allocation, they get more leverage by using more tokens.

And then there are gonna be certain people where actually they don’t really need tokens at all, and that’s not what how they deliver value to the business. Like, for example, maybe our best salesperson. The way we use them best is not by having them use tokens, but by going and meeting people face to face. That’s an obvious example because they don’t write code in the first place. But I think similarly, maybe there’s some engineers who they actually do their best work by spending time with users, spending time with their customers.

Maybe doing some data analysis, that’s not very token expensive. But then there are gonna be others who are delegating to dozens of droids in parallel working on a ton of different crazy features and refactors and migrations, but I don’t think it’s gonna be a consistent number across the board. In fact, I would argue that if your org has a standard number where it’s like we want every engineer to be at this percent of their salary and token use, you’re probably painting with way too wide a brush.

80 to 90%. It’s typically the planning

it

because those that 10 to 20% could be the most important tokens. 10 to 20% of the tokens, but those are really, really important because it’s kind of decision making tokens perhaps. Sure. But it’s it’s very similar to how we structure human orgs. Oftentimes, leadership makes very key decisions that determine the fate of the company, and that they don’t spend the most hours. Like, if look at the human hours of a company, most human hours are not spent on making the decisions. They’re on gathering data or implementing things, but then there’s a select few hours where it’s like, here is where we’re gonna, you know, make this irreversible decision on the strategy.

And those people that make those decisions are

And that’s what’s happening already. It’s like the frontier models are sometimes they’re getting more expensive or you’re using the ultra high reasoning or, you know, this type this type of thing. And so it’s like, okay. If this planning thing is the very thing, we’ll spend on it. But it doesn’t necessarily mean that most of your tokens are going there. It’s just for certain key steps, maybe you want to spend a lot, and it’s worth that allocating the the budget there. But then once it comes to, okay, we have the plan now.

It’s implement. The open models are typically really good.

We are a very opinionated organization. And so the people who like the opinionated stances that we take are willing to not necessarily go and try to, you know, maximize the dollars that they can get out of in the market. That said, it is still, you know, pretty competitive there.

think is the strongest opinion

that most people disagree with? I would say the opinion that we have that I think in the space that we are in is the most controversial. The way that we treat what product is at factory. I think there’s some very commonly held beliefs at the labs or at some of our competitors who are also doing kind of software development. This is honestly growing up in the Bay Area. There’s a very common Silicon Valley fallacy, which is there’s, like, research is, like, the pinnacle, and then there’s engineers who implement the research.

You know, they’re not quite there, but, you know, they’re still great. And then there’s sales and marketing and all that dirty stuff. Oh, if only we could build a better product and it would sell itself, and we wouldn’t need to deal with, you know, sales and marketing. And it’s just completely delusional. The product at Factory is the entire journey from the very first time they hear our name till their tenth renewal after a decade of being a happy customer. The software is a big part of that journey, but so too is the the marketing that we do and the people that we have running that.

Same with the sales process. The people that present themselves in discovery calls or in demos or in solution engineering, like, entire thing is the product. Everyone is first class. It’s not like we have engineers who are, like, wholly at the office, and you’re not allowed to speak to them unless you’re an edge. It’s like, no. No. No. We have engineers and salespeople sitting next to each other. There are no, like, engineer corner, sales corner, any of that stuff. Everyone is completely intermixed. When salespeople close a deal, engineers say, we closed a deal.

When engineers ship a feature, salespeople say, we shipped a feature. It is entirely one team, entirely cohesive. Everyone there’s no, like, first class or second class, and this is shockingly controversial in the Bay Area and in particular in coding or in AI. It’s messed up. It’s so messed up. And I think that the reality is it will come to haunt some of these companies one day. Because I think right now where there’s a gold rush and everyone’s, like, desperate to sign, you know, and get more tokens from these people, it’s easy.

In my mind, it’s kind of like they’re astronauts in space where there’s no gravity. Your muscles will atrophy. Gravity will come back. And if you don’t have a good sales and marketing team because you don’t give it respect, the second gravity returns, all of your muscles will be atrophied, and you won’t be able to compete. And I would say this, name a legendary company that has a shit sales or marketing team. You can’t.

It very seriously changes, and this is actually why we’re selecting for, very intentionally, like this culture that we just mentioned is really important, because the best engineers are gonna be the ones that don’t see sales and marketing as dirty work, but as, again, an important part of the product. Because as an engineer, you’re no longer, you know, just your job is ship feature. There’s no you are owning full end to end outcomes of here’s the way the customer’s behaving, here’s how maybe we can change that behavior that makes them a better user long term, it makes them more agent native, they get more out of our product.

We can then follow them through that journey, enable the salespeople so they know how to talk about it or they know how to demo it. This, like this is like a full stack engineer that goes way beyond just engineering, but into sales, into marketing, into enablement, and all that. And those are the parts of engineering that really, really matter. Those are the parts that have made engineers typically good founders is when they have that. And the parts of engineering that become less important are funny enough, the things that the Silicon Valley has really bragged about a lot, which is, like, competition winning or, like, Olympiad type.

Are you, like, as fast as possible at coding? Are you do you memorize all the different nuances of these different languages? Those are the parts that don’t matter. If you memorized some coding or some syntax of a coding language that someone else didn’t, it doesn’t matter.

crutch. It’s a crutch. It’s helpful. It’s a good indicator. Like, generally, you can’t win competitions if you’re dumb. Dumb. Right? Like, it’s pretty it’s pretty rare. However, for these types of engineers that we’re looking for, that’s cool, but that’s kind of irrelevant. Like, what have you built? How have you taken ownership and agency of things end to end? This is not like we have people on our team that have won Olympiads, and I think they’re great, and it’s fantastic, and a lot of my friends have.

But there’s also a certain, like especially there’s some high schools that, like, really focus on, like, you must do the math Olympic like, you must do this, the AIMY to then go to the IMO, and, like, this is the path to success. We’re actually that’s kind of anti signal. Because there, it’s like you’re not owning your fate or choosing your agency. You’re kind of going through the funnel. But then there are people on our team who are, like, from the middle of nowhere where no one else in their high school ever did this stuff, and they kind of took the agency of, like, think I this is really fun.

I’m really competitive. I wanna compete at this. And they kind of go and do those competitions on their own. Those are when the signal is still positive for this kind of engineer of the future.

to exist more and more, but I think it’s kind of this like GM or general manager like role for someone who used to be an engineer, where basically you own end to end an outcome that is not just a shipped feature, but like a business outcome. So even at Factory, we have this now where there are people who will own the marketing copy if they’re gonna be releasing something. They’ll own the outcomes in the product metrics. They’ll own enabling the salespeople. So it’s way beyond what a typical engineer does, and it kind of feels like, again, owning more of a business outcome, more entrepreneurial, higher agency, just like spreading their reach know, I

The age of the polymath is back. Like, up, I was so like, I was obsessed with math and physics, and I was so jealous that hundreds of years ago, people like da Vinci, or Euler, or Newton could be polymaths, and it was because their fields were relatively shallow. Chemistry wasn’t that built out. Mathematics wasn’t that built out. Physics wasn’t that built out in da Vinci’s case, art and engineering and sculpture. And so you could get to the frontier of these disciplines in multiple disciplines within your lifetime.

And then growing up in, you know, the early two thousands and two thousand tens, pre AI, fields were so deep. In my case, theoretical physics and string theory was so deep that you could spend literally fifty years catching up on all of the literature and academia that’s existed before you contribute anything new. And so it was like this was infuriating to me because it was so frustrating. With AI, we’re now completely the opposite. These tools can get you up to speed to the frontier. Obviously, with a lot of uncertainty about certain details, you won’t have the depth of other people, but it’ll get you to the frontier way faster than ever before.

And so now if you’re someone that’s good at thinking around constraints, thinking about systems, holding uncertainty in your head and being okay with that, like know knowing there are unknowns and knowing that you can still push the frontier forward despite that, you can be a polymath. You can push forward and create innovations on how to do developer marketing, while at the same time pushing forward the frontier of token caching for software development agents at the same time as, like, you know, being an incredible solution engineer.

Like, these are things that you can now do all at once. And so this is something that’s very top of mind for me. And in our hiring process, we want to find the people that can be those polymaths. The era is totally back. Polymaths are back.

I think to some degree, everyone should be able to do that on their own. But I could imagine a world where there’s kind of someone whose job it is is to, like, find places that aren’t as efficient and similar to operations now, like in organizations, but now it’s just agentified. So they’re using agents to make the organization more efficient wherever possible. But I think in general, you have people that in certain functions that aren’t proactively doing that, probably a bad sign. What do we do today that we’ll look back on and go, oh my god, I can’t believe we did that?

For an engineering team, writing release notes. That’s crazy that people used to spend hours of time writing release notes or, like, writing documentation. So not everyone knows what release notes is. What is release So it’s like, you know, basically cataloging the changes that you’ve made in the last whatever month or so, you know, and sending it out to either internal or external to your users. And generally, like, this and, like, documentation, like Stripe has a really great reputation. They had incredible documentation. Like, so many APIs had horrid documentation.

Stripe was, like, the pinnacle. They were so good at this. Spent a lot of time doing it. Five years from now, it’s gonna be like, oh my god. I cannot imagine, cannot believe that these people that get paid so much money spent hours of their time doing this. I think that’s something that, you know, we definitely won’t do. Does that reduce the impact of Stripe’s great documentation if everyone is equalized? Yes. But I think Stripe has plenty of places that they can differentiate. And I think it’s a better world where everyone has documentation as good as Stripe’s.

How does the product review and especially, like, code review process change in the last few years? What’s cool about this agent native software development is review has been a big problem. Because basically, first phase of rolling out AI coding tools was, oh my god, look how much code we can generate. You know, it’s incredible. I’m generating a ton of stuff. Phase two was some poor staff engineer has to review hundreds of these slop PRs that are like, don’t adhere to your standards, are like completely misformatted and all this stuff.

But what’s great about having this kind of like full end to end software factory as it were is it’s now very clear the ROI of investing in things that make your agents more kind of ready for production. So examples of this are making sure your agents have access to up to date documentation, making sure agents can spin up a remote machine so that they’re not just generating the code, but they can actually run it and see what the outputs are and iterate based on that to make sure that it’s actually good.

Setting up things like CICD or good linters or good pre commit hooks. These are all things that the best organizations at, like, developer experience would invest a lot of resources in, but they would do it because it makes it easier for engineers to work, easier for them to onboard. But the impact of doing that well is just, like, one to one kind of correlated to how many engineers you have. With agents, though, the impact of that is now like 10x or 100x depending on how many agents you’re using.

Because the better your DevX, the better your agent ends up adhering to your standards, which means there’s less time that that poor staff engineer has to go through reviewing your PR, which means you’re kind of faster throughput in your software development.

That value is increasing, especially because the thing that makes it easier for agents tends to be the same as the things that make it easier for humans. At some point in theory, that could change, where, you know, if you’re actually training models or training agents to be as efficient as possible communicating to each other, but then the downside there is it’s not as human readable.

know what I mean? Yeah. Yeah. Yeah. So I think one thing that if you don’t have careful standards in place, it can get bloated pretty quickly. But I think the best organizations who are the most agent native actually put in a lot of guidance on, like, here’s, like, the UI side of things and how things need to be, being very aggressive about, like, pruning anything that’s unnecessary, making sure there’s not, like, bloated, like, I don’t know, comments in all of your code that’s, like, kind of gratuitous or there are ways around it, but that’s kind of where the human’s job changes a little bit, where their job goes from part of our name.

Our name is Factory. Part of why it’s called Factory is because the future of software development is where these organizations, instead of having engineers that build the software, they’re gonna have engineers that build the factories that build their software. Visually, whenever I say this, I always think of Tesla’s factories. I don’t know if you’ve ever seen videos of the inside of Tesla’s factory. Yeah. It’s all these, like, robotic arms going and you have the assembly line going through, and there might not be as many humans in that assembly line.

But you know damn well that humans designed this process to optimize the throughput to, you know, produce more Teslas in this case. So And in this new world of software development, human engineers are not gonna be involved as much in, like, writing the actual code. They’re the ones that are gonna be involved in how do we make sure it’s not just creating all this bloat or it’s technically getting the job done and passing tests, but doing it in a way that is really dramatically increasing debt.

So there’s they’re kind of like building the scaffolding around this factory that produces their software.

Long term, no. Short term, yes, because it’s just a shock to the system where there are all these big layoffs that are happening that are pretty aggressive and thousands, tens of thousands of people that had a job that no longer do. And so I think that does worry me. Long term, though, I am very not worried because the reality is there is a huge number of problems in the world, ridiculous number of problems in the world, and a large percent of them can be solved or can be helped with software.

Very few of those problems that can be solved with software are we currently solving with software. And so if we’re going to be flooding the job market with tons of engineers, that means that we can now allocate them on the broader economy to solve more of these problems in the world. And if we have more engineers who are going and solving more problems in the world, that is a net good.

Yeah. Well, and maybe part of that is because all like, the Googles have been hiring all these engineers, so distributing great engineering talent to more problems, think, is gonna be a good thing. The economy has to match, though, and properly incentivize them. And that’s something that I think will take a little bit of time, which is like the intermediate period. But, like, so many health problems, like so much of pharmaceutical research can be advanced with better engineering. The thing that really upsets me with some of the people who are talking about, you know, pausing AI development or it’s a bad thing and it’s gonna, you know, harm society.

Dementia is kind of a go to example where everyone understands how big of a deal that is. That is something that can be solved with better AI and better software. Like, it’s a matter of time. Like, we will solve it, and we can solve it. And by saying you wanna slow down AI, that’s saying, like, these people who have relationships with loved ones who have dementia, you’re like, no. No. No. Sorry. You guys you gotta maintain that relationship for a little bit longer. We’re scared. We don’t know about AI.

I think it’s like it’s pretty it’s pretty harmful, and it’s pretty selfish to say that it’s something that, to me, it doesn’t make sense.

useful in some It’s like, I don’t think anyone would argue that the government should never intervene ever in the economy because there are some things, especially as it relates to, like, military uses or safety or things like like weapons, like, you’re definitely gonna need, you know, some involvement there. I think there’s some incentivization that can be helpful just because there might be some problems for a society that maybe capitalism doesn’t see the immediate feedback loop of, and so you might wanna juice the incentives a little bit to get an outcome that you’re looking for.

Generally, I’m pretty reluctant. I think you need to have a very good case for why you need to do that. Even like the example of climate change, you know, talking about that one. It’s obviously a very sensitive subject or a very important subject for a lot of people. You know, you could make the case that the faster we develop AI, the sooner we solve climate change because AI, you know, can help us solve a ton of these problems. But to develop AI faster, you might need to consume fossil fuels and emit them and, you know, that emits CO2 into the atmosphere.

And so the question is like, you know, short term, it might be slightly worse, but it ends up getting us to solve the problem way sooner instead of dragging it out over fifty years or a hundred years. There’s some of these cases where kind of free market will incentivize it the right way, there’s some cases where it won’t. But I think you need to be very, very careful about the cases where you do want the government to say, hey. We wanna step in here.

Maybe there’s, like, some short term blips, but, like, long term, absolutely not. Like, not even close. There might be similar corrections to, like, this thing at Uber where, oh, we were going a little haywire. We weren’t allocating it appropriately. And it’s like, okay. Let’s lower consumption a little bit. But, like, on the net, absolutely not.

the biggest bottleneck by far working with all these organizations is the human side of it. It’s just like behavior change.

like, is selling into large

Yeah. Or even on an individual level. Like, if you’re an engineer who’s been an engineer for thirty years, it’s hard to change those pattern. Like, you’re stuck in your ways to a certain degree. But there’s also a funny thing where some of these engineers who’ve been engineers for a very long time or who have been engineering managers, they might be more reluctant to use these tools, but sometimes they’re better because they know how to delegate. They know how to deal with some of the the junior engineers where if you tell them the wrong thing, they’re off in the cave doing the wrong thing for seven days.

They come back with something completely useless. And then on the other end of the spectrum, there are people earlier in career who don’t have as much of a standardized workflow that they’re used to. So they’re more eager to adopt these new workflows, but they don’t know how to manage people. They don’t know how to delegate as well. So it’s kind of an interesting balance there.

So this is the first job I’ve ever had, which I think is always a funny thing to say. Because prior to this, I was a theoretical physicist. Literally never never, like, coffee shop, like, any of that. Literally never have had a job. Like, never have been paid to do anything aside from physics until this, which is a whole separate thing. But I’m not Damon. But I will say the thing that has been the craziest learning and this is obvious to anyone who’s in sales or, like, Chad and Chris.

To them, it’s obvious. To me, the thing that was the most visceral altering thing was meeting people face to face makes such a big difference if you’re trying to sell them something, but also you should never try to sell something. You should always try to understand their problems and see if the solution that you might have can actually help them solve that problem. Like, if you go in a conversation trying to sell something, especially to engineers, don’t waste your time. If you go in trying to have genuine curiosity about it’s really easy because these organizations do their engineering so differently.

And I find it fascinating how, like, all of these different banks, you know, consulting firms, pharmaceutical companies, they have the most different ways of building software. And it’s really interesting to go talk to them and to understand it. The best way of talking about it with them is face to face. People love talking about their problems, and they love talking about all of the bureaucratic nightmares that they have to deal with. And then by understanding all of that, you can actually get a sense, you know, is our software a good fit for them?

Will it help solve their problems? It’s also just so fun to then like meet up with them a year later and be like, remember when you had to deal with that bullshit and now you don’t have to? And that’s just such a rewarding feeling of making

Yeah. So I was obsessed with physics basically since I was 12 because I was a bad student, and my geometry teacher told me that I had to retake geometry in high school. And, like, I never tried in school, but I always prided myself on being good at math. And she told me that I was like, are you kidding me? She thinks I need to retake geometry? Like, I’ll show her. And so my first order on Amazon ever was textbooks for algebra two, trigonometry, precalc, calculus one, two, and three, differential equations, and maybe a linear algebra textbook.

So I bought those textbooks. And then the summer between middle school and high school, I studied all of those, like, all the problems in all of them. And then in high school, took exams to place out of all of those classes. And then I asked my dad what the hardest math was. He said string theory, which is technically physics, not math. But I was like, okay. I’m gonna be a string theorist. And that was literally all I cared about for basically the next twelve years of my life.

All I cared about was math and physics. Ended up going to Princeton because I had a great physics professor I wanted to work with. He’s, this famous professor named Juan Maldacena. And I was, like, the first undergrad to work with him and write a paper with him. And I ended up coming to Berkeley to do my PhD and, you know, work with a great adviser there. And then only at Berkeley, I realized, like, it kind of all comes crashing, like, holy shit. I’ve just been doing this because it’s hard and because someone said I couldn’t do it.

What the hell do I do with the rest of my life? Like, this is crazy. Like, everything came crashing.

Twelve years? Twelve years. You’re slow. I have I have tunnel vision. When I get obsessed with a problem, it is all I think I was at law

Quick quick realization. See, some people are faster. You know? I wasn’t as I wasn’t quite as quick. Honestly, Honestly, part of it was being a as part of a grad student at Berkeley, you have to teach classes. And I was teaching a class to, like, whatever 18 year olds who didn’t give a shit about physics. And I was like, oh my god. This would literally be the rest of my life. It’s like sitting and doing lectures and doing these classes. On rate my professor, I think I had a one out of five.

I was, like, horrible. Yeah. It wasn’t it wasn’t a good fit, but it was the kind of this existential crisis. Like, what what do I do? And so, you know, kind of realized it was probably gonna be either quant finance, which is what a lot of math and physics people do, big tech or startups. I ended up doing the quant finance interviews like every good physicist does and almost took it, almost went to New York to do it. And then last second, I had a adviser that I spoke to who was like, you know what?

Stay at Berkeley for a bit. Don’t do it. You’re always gonna be good at math. You could always go and do quant finance. Stay at Berkeley, explore, learn some stuff, whatever. So I was like, okay. Fine. You know, I’ll I’ll do that. You know? Ended up taking my first CS classes at Berkeley. I learned, like, to code for physics, for, like, simulations and all this stuff, but never in a formal class. And I’m very competitive. And I found that in these classes, was doing better than some of the CS students, which was very competitively satisfying.

Was like, oh, okay. I’m gonna take more of these. And then it wasn’t until I took a seminar in what was called program synthesis at the time. Now we call it cogeneration, and it just completely nerd sniped me. Because the idea here is not machine learning for video or audio or images, but it’s code with the explicit purpose of creating itself. And there’s something just so fundamental about that. And a decade of physics, like physicists and mathematicians, they’re never interested in the case of, like, n equals three or, like, n equals four, four dimension.

It’s always like, what is the n dimensional solution? What is the arbitrary, the fundamental, you know, solution to things? And there was something so fundamental about this idea of, like, code generating itself, and it just got me obsessed. So I stayed at Berkeley. And for the next year, that was kind of what I spent my time on. My adviser was very chill and just allowed me to just, you know, take AI courses. And eventually, realized that the way to actually solve this problem was not in academia, but in the industry.

And to properly solve it in the industry, you’d have to start a company. But I knew nothing about starting companies. Because, again, all I cared about was math and physics, didn’t know anything about this. So what does someone who wants to learn about starting companies do? Well, they order on Amazon Peter Teals zero to one, and they look up on YouTube how to start a company. And so, you know, read zero to one. Incredible book. I know it’s so cliche, but, like, to someone who didn’t growing up in the Bay Area, shockingly, I just, like, did not care about any of that.

And reading this, it was, like, so concise, beautifully written, all that. You know, loved that. And then, you know, I was watching these videos, a lot of them, like Y Combinator, you know, videos and all this stuff. And then I stumbled upon this I think it was, like, a Stanford VC club podcast with this guy whose name I recognized because at Princeton, when I wrote that paper with Juan Maldacena, I had cited one of his papers. So it was a theoretical physicist. Like, I remember this guy’s name.

He was on this podcast talking about how he sold a company for a billion dollars and was a investor at this place called Sequoia. And he also, in this video, seemed like pretty sociable and normal, which I don’t know if you’ve interacted with I’m not sure, dude. Theoretical physicist. Know, compared to theoretical physicists, though, look, he can maintain eye contact. You know, he was somewhat normal. Very

He can he can hold himself in a social setting. Yeah. And so I was like, okay. Who is this guy? You know, I gotta talk to him. So I ended up writing him an email being like, hey. I’m Matan. I also used to be a physicist. I wrote a paper with Juan. Didn’t say the last name because it’s like, if you know, you know. I would love to get your advice. And, you know, he responded that day and invited me down to Sand Hill. And it was supposed to be a thirty minute meeting, but we end up going on this walk and ends up being a three hour walk.

And on this walk, it turns out we had very similar reasons for getting interested in physics, very similar reasons for leaving physics. At the end of it, he was basically like, it was great to meet you, Matan. You absolutely need to drop out of your PhD. And you should either join Twitter right now because Elon just took over, and it’s hardcore, like, for your resume if you, like, voluntarily go there or or you should start a company. And I was like, okay. Thank you so much.

I appreciate you taking the time. Like, I’m gonna, you know, I’m gonna go think about it. But in the meantime, I, like, had already known about Factory. It was just I didn’t wanna ruin the meeting with, a pitch. You didn’t wanna transactionalize this. Yeah. Yeah. Yeah. Because it was so, like, it was in like, we had the exact same reasons for getting interested. I did the idea. Yeah. Like, didn’t wanna didn’t wanna dirty it with No. It’s

like an

Yeah. Exactly. Exactly. And and then the crazy thing, the next day, I go to a hackathon in San Francisco and see across the room this guy who also went to Princeton, who I, like, recognized, but I didn’t, like, know super well. End up talking to him. He’s also interested in this problem. We, like we joke that it was, like, intellectual love at first sight. This is my cofounder, Eno. And basically, that day forward, we spend, like, every day talking nonstop. I had some shitty demo that I built.

Eno is thousand x better of an engineer than I ever will be. And so he and I, for the next, like, seventy two hours, like, put together this better demo. And then I call up this investor, and I’m like, hey. I have something cool I wanna show you. So we hop on a call, I show him this demo. And I’m like, what do you think? He’s like, it’s okay. I’m like, are you fucking kidding me? This is gonna change the world. What are you talking about?

He’s like, okay. Well, would you work on it full time? I was like, yeah. Absolutely. He’s like, okay. Drop out of your PhD and send me a screenshot. And keep in mind, like, my parents immigrated from The Soviet Union to The United States with basically nothing. The fact that I was doing a PhD to them was like their pride and joy. Like, it was the thing they were the most proud of. There was so much momentum. So much momentum. I’m you know, I’m he answered my email.

We got along well. I met the cofounder the next day, and I was like, you know what? Fuck it. Dropped out. Sent him a screenshot, and he was like, alright. You have a meeting with the Sequoia partnership tomorrow morning. Be ready to present. You’ve never presented to a

Yeah. Put some slides together. Keep in mind, I didn’t even know who the hell they were. Like, didn’t it was just like, oh, these random people. Like, okay. Whatever. Yeah. I’ll go talk to them. Wish it was recorded because I’m sure I came across as so arrogant. How did it go? I thought it went fine. They asked some questions. I think I was pretty again, I didn’t know anything about VC land or start up or any of that stuff. Retrospectively, know, like, Alfred and Pat and Roloff, they were all, like, in there.

They’re all asking questions, and I was probably dismissing some of them. Oh, yeah. We’d solve that easily. We do this. We do that. Keep in mind, this was in April 2023. So this was, like, way before anyone was thinking about agents, way before people were even using Copilot. We were talking about fully autonomous software development agents. And it was kind of a blur. You know, the next day Sean calls me, and he’s like, hey. We wanna give you a check. How big was the check? A million dollars.

A million dollars. And you know what he gives me shit for? You know what the terms? Five post. Five post.

when you manage it, it’s seven

It was a different time. Early twenty twenty three was a different time. Things got crazy.

And it was one of those things, a lot of my a lot of people I spoke to were like, you should go shop that around. You can get better terms because it’s Sequoia. And it’s just like when you have a connection like that, there’s a certain thing to me where, like, obviously, you want to maximize the position for the business, but, like, no one else would have believed in me except him. No one else would have understood. Like, I literally had never had a job before.

Like, no other partner I would have met. Retrospectively, it’s like, oh, yeah. What no one else would have done it. And it’s one of those things where, like, trust and loyalty and, like, belief, to me, that matters so much more than, like, the price tag you get or whatever. I wanna make sure that the people that I have in my corner because we’re building a legendary company. It’s not just gonna be ten years. This is like a lifetime. Would you tell founders to take a discount for Sequoia?

So, generally, yes. I mean, they’re the best firm, in particular, if there’s, like, a special connection with you and the partner or there’s a special reason why them in particular. But I think what really matters is you wanna have people that are there for you when the days are tough and when it’s not obvious. Because when you’re a hot company raising a hot round, everyone’s your best friend. It is their job to make you feel special, and they are really good at it. What’s the best way someone’s tried to wooey?

I don’t wanna name names, but there’s this one investor in particular who’s, like, still in the game, but more of the old guard. I’ll say that much. And I remember beforehand, people were like, people told me, like, by the way, he’s really good at making you feel good about yourself. And I was like, yeah. Whatever. I’m I can deal with that. That’s fine. And then I remember leaving the meeting being like, I’m the fucking man. This is my destiny. I’m gonna build a legendary company. Like, I got this.

And then, like, thirty minutes after when it wore off, I was like, oh my god. He got me. Like, he did it. Like, he did the thing. He made me feel special. And, like, a lot of investors, when a company is hot, are gonna do that, and they’re really good at it. That’s why they’re great investors. Think I for me, what’s really important as we’ve built out our board in particular is people who have, like, deep conviction when it’s not obvious. Like, that’s what really, really matters.

Because when a company’s hot, everyone’s gonna be excited. It matters when it’s not, and there are gonna be tough times. How how do they behave then? How did you get Ivanka Trump as an ambassador? That was one of the best hires that I’ve ever made at Factory was this woman, Franchesca. And so the way that Franchesca and I met was at a random conference, I was seated next to her and Alex Paul, who’s one half of the Chainsmokers. And, obviously, people know them as the Chainsmokers.

They’re also incredibly good investors, incredibly good investors, which sometimes people are surprised by. And we got along quite well. Weirdly enough, Francesca and I also grew up in the same hometown, which is a whole and had a ton of that was another kind of weird coincidence. But she’s like, just in the process of, like, them wanting to put a check-in and the way she did diligence and just the way that she could have carried herself, so clear, like, she was a killer. And they wanted some allocation.

I was like, no. No. No. Sorry. Like, you know, it’s gonna be this. And she was fucking relentless, like, came to our office, like, was like, hey. Like, we need to get to this much. How can we do it? I’m gonna make these. If I do this and this and this, the business value that we provide to you is gonna make it worth this more so than giving that allocation to someone. She was, like, kind of hounding. You know, we were having a conversation. I was like, look, Francesca.

Like, if you want more ownership of Factory, you could just join us. And it was, like, kind of as a joke. I was like, oh, you could just join us if you want more. Like, this is the highest we can do. But then we kind of both were like, oh, interesting. And, you know, we talked about it a bit more and then realized, wait, this is an incredibly strong fit. And so we ended up bringing franchise on board. Alex was you know, it was kind of tough because she was incredible, and they were very close.

He’s since been happy because she’s helped us deliver a lot of, you know, returns for them, and we’re the biggest fans of theirs and, you know, kind of we still have a very deep relationship. And she was very close with their firm affinity from her investing days. Then, you know, we were introduced. We got along really well. And so then that was how the connection was made there.

She is first of all, she’s one of the kindest and smartest people that I’ve met. There are people that you meet that, you know, are famous that are kind of like a letdown or like, oh, they’re different than I expect. She is genuinely so kind, so intelligent, and, like, people throughout tech, throughout the world really love her and for good reason. And she has an incredible network. She’s so generous with her time. There is kind of dirty work investor help that she helps out with that some other investors who are more known as investors do not do.

She and the firm more broadly really earned that right on the cap table.

Yeah. So I think what is necessary for the best outcome for the consumers is going to be models that are separate from the applications. You as a consumer do not want to use applications that are provided for you by the same people that are giving you the model because the incentives are misaligned. The incentives are misaligned. Why? Because if let’s say, the example of coding. Like, if I’m a model provider and I’m working with a large enterprise and I’m giving you a coding tool, I want you to use as many tokens as possible because I’m an API business, and I get more money the more tokens you use.

And I don’t have a huge incentive to be more token efficient other than, yeah, I wanna give a good product experience, but not strong incentive. Versus if you have model providers and you have an application layer that allows that enterprise to decide between the different providers. If you’re a model provider, you better damn well be the best or the cheapest or the fastest, or else you’ll never get tokens through to you. So it puts the best incentives on the model providers. There’s that independent agent in our case there, and then that gives the best prices to the enterprise.

It also gives them the best in terms of, like, if one model is really good at this language or that language. It allows them to kind of adjust between them. And in the world where they’re you’re, like, vendor locked in, then you can slowly get, like, laziness and slower shipping. And and as a, you consumer, you end up getting a worse experience.

This is gonna be different from cloud. I think cloud, a lot of people suffered because, you know, the cloud providers came and said, hey. Look. Sign this three year deal. We’re gonna give you a big discount. We’ll get everything good for you. It’ll be alright. Come on in. And then they would do that, and then they would jack up the prices. And once you’re standardized on one, it’s gonna take you two years to switch to something else. So good luck. You’re stuck with us, we’re gonna charge you more.

Everyone has scars from that. So now every CIO I speak to is really keenly aware of we cannot, you know, throw our lot in with just one model provider. We’re gonna need to be agnostic. And so, you know, you could be agnostic by saying, hey. Every engineer, we’re gonna give you Claude code and Codex and Gemini CLI and all these other tools. But then the problem is now you’re asking your engineers to use 10 different tools, or you can use someone like Factory where you can use one tool and you can kind of decide kind of like in an auction on a task by task basis, which model provider do we wanna use?

Do we wanna use an open model? Do we wanna use Frontier? You know, which one of those?

Well, so I haven’t seen them or that use case as much in the enterprise. I could see for maybe consumer use cases where you’re not as cost sensitive because you’re not doing things crazy scale, it’s kind of fun to see, oh, I wonder what Gemini does versus OpenAI versus Anthropic. And, you know, for some enterprises, if there are things that are, like, very sensitive or very secure, you might wanna do that. But for a lot of like, if you’re a nontechnical person building an internal dashboard, you probably don’t need 10 different models to generate different iterations of it.

me understand it? It’s not obvious to me, and part of it is because not too many people that are close to me use those tools frequently. Like, most of the people that I know either don’t use, like, AI tools or they’re, like, technical and using Factory. Also, like, I’m not gonna be no. None of my friends don’t use Factory. Like, what do we come on. We wouldn’t be friends. So I need to understand a little bit more about that user. My sense is they’re probably and we’re still in the early innings, so I’m sure they’re quite agile to figure out what is the exact niche that they wanna occupy, but it’s not super obvious to me what the kind of focus is.

Because my understanding is some of them have been

I mean, in that case, I think that niche does make sense a little bit more. I think it would be ill advised if they were to try and go to the niche of nontechnical people writing code for code’s sake, because I think that is gonna be run by like, if you’re gonna need enterprise controls over who has access to what databases and what code and all that stuff, that’s gonna be run by the engineers. That’s gonna be where Factory goes. If it’s things like if a salesperson wants to build a customized demo app or customized website for something, I could see in some cases that that having some value there.

be crazy.

Like, code generated is growing exponentially. The security efforts aren’t growing in kind. And I so I think there’s kind of a lag there. I think they’re probably going to be in the next couple years some pretty big incidents that occur because of AI generate there honestly probably have been. Whatever incidents that have occurred, no one’s gonna admit. Or typically, they’ll be reluctant to admit if it was like AI involved or not. But also, I think we haven’t even seen the most adversarial behavior yet. Like, I think people can use these tools to be quite adversarial.

Think I security, like the higher the stakes, it’s gonna grow in importance. So I think the security part of the market is really important.

Yes. Using an open model is fine. Like, there are kind of two concerns. There’s one concern is if you’re sending your data externally to, like, a different nation, which is one concern. And I think that the concerns there about, like, we don’t wanna send our data to China or generally, I mean, you should probably wanna keep your data to yourself regardless or, like, within country regardless. But I think the the separate concern is, like, oh, the model itself. Like, even if we host it in The US, is there a concern with the model itself?

And to explain some of the concern there, I think the idea that some people have is, like I don’t know if you’ve seen in, like, those spy movies where there’s, like, a code word where suddenly someone starts acting like, you say the right word, and then they’re, like, in robot mode where they’re gonna go act adversarially. I think the concern is that some of these models might secretly have that ingrained within where you say a trigger word and then suddenly, even if it’s hosted in the somewhere else, or it’s gonna start, you know, trying to intentionally kind of break whatever it is that you’re doing.

Suppose any nation were to try and do that. Suppose they wanted to make a model that had one of these trigger words that’s gonna go and act adversarially. Theoretically, you would want to do that as late as possible. Because if you do that in an early model and someone discovers that they’re literally never gonna use your models ever again. So I don’t see that as a big concern. And also if you’re deploying correctly, like not as a consumer, but in the enterprise, if you’re deploying correctly, data exfiltration or, like, kind of some of this adversarial stuff, generally, you can fight against.

But I do think just from a I’m quite patriotic. I think it’s pretty embarrassing that we don’t have frontier open models in The United States. So I do hope to see us, you know, reclaim superiority there.

Probably on the frontier model lab side. There’s so much to do on the, like, infra build out and energy side of things. But, again, the thing that’s very difficult in the different parts of the world is you have democratic countries where things generally are slower. Suppose you say we need to do this thing. You need to get a lot of support. You need to convince certain people to do things. You need to pass legislation. It takes a long time. But the benefit, though, is, theoretically, we get this balancing act where we don’t go too crazy in any which direction.

Other parts of the world where it’s more authoritarian is, this is the thing we’re doing. We are doing it. We’re acting now. You get to move quickly. Now there’s less kind of correction because what if you’re going on the wrong course? But in cases like AI where it’s pretty clear, like, for build out, you need to build data centers, you need energy, and energy requires a lot of build out as well that has a huge amount of lead time. You can act faster. In the West, things are slower.

So that’s one thing that kind of goes against us. It’s a little bit slower to get this stuff done, especially when there’s all the politics that you have to deal with it.

Yes. But I think that’s, at least in The United States, the beauty of having states, is we get some selection where we can have different experiments of, like, what’s it like for a state that says no to all data centers? Well, okay. There won’t be as many jobs that get created there. Whereas the states that do allow for data centers to be created, you know, people will prosper. They’re gonna have great jobs. They’ll, you know, see the downstream benefits of it. But it’s nice. It’s like we have little petri dishes to test out and see how things work.

That is the beauty of The United States. And I think in Europe, I mean, it’s tough. Think there was some good positioning that Europe had, you know, a few years ago, a few decades ago with nuclear that I think hasn’t been, you know, delivered on as much as of late, but that would have been a world in which Europe would have a way to bounce back a lot in AI on the on the energy side.

is speaking from strongly biased as an application person, like, I’ll take the grab bag. It doesn’t matter. I actually hope for a world in which our users don’t even know which one is under the hood.

Okay. Businesses need to think about their core competencies. If people are trying to expand beyond their core competencies, Kirkland and Ellis, great. Look, have fun. It’s not your core competency. I don’t think it makes

Will we see more of Yeah.

It’s bad if you’re an investor in one of those companies because it’s a little riskier. But I think you can find a steady state. It’s just scary. You just know there’s kind of a sword of Damocles above your head of like it’s just risky.

Yes. Have a good product. The thing about the FTE thing blows my mind is like, for us, when we do FTE, the way I think about it is their goal should be acceleration. Basically, if there’s a customer where we just give them our product, they’ll scale to, like, a million in six months, I’ll throw in FDEs if they’re gonna scale them to a million dollars worth in three months. Great. They accelerated that. If I’m sending in FDEs as services, like, I’m not Accenture here. Like, I’m not trying to be like or Infosys or Cognizant or whatever.

We are not a services company. If we need FTEs to make the product work, we have a shit product. Like, the point of FTEs should be accelerate and get them consuming faster. If you’re putting in FTEs, that’s the only way you’ll get a deal done, I’m sorry, my friend. You have a shit product.

Harry, you’re ruffling feathers as always.

What do you think of the grind slop? I feel like a lot of the things we’ve talked about actually is like, something everyone needs to be wary of is intermediate metrics. And grind slop comes from intermediate metrics. Like, oh, generally, to do things, you need to spend time on it. So let’s focus on how much time do we spend instead of, like, are we doing the thing? The analogies I use is imagine trying to measure who won a basketball game by who sweat the most. Like, you could sweat a ton, but look at the scoreboard.

Like, are you doing what actually needs to be done or not? And I think for us, we wanna focus on, like, getting the best players. I don’t care if you sweat a ton or if you sweat very little. If you’re scoring a lot, great. We want you on our team. Now, generally, for most people, you have to sweat if you wanna, you know, get things done. But I think you are doing a bad job on hiring if you need to, like, mandate certain crazy hours or you need a bed in the office.

It’s like, dude, get a good night’s sleep. Like, you don’t need a bed in the office. Like, just go get get an apartment nearby that’s nice and cozy. Get eight hours of sleep. If you, as an important member of your team at your company, can get your job done on two hours of sleep, you’re not doing very high leverage work.

what like, that’s literally like, when we were 30 people, we had, like, what we call a surge, like a pretty aggressive, like, two week sprint. And as part of it, I got everyone on the team eight sleeps, like, fully free, whatever, $3,000 per person, like, the decadence of startups. Right? But I think the the idea there is, like, we are optimizing for output, and the people that we are bringing on to the team, it’s like seal team six, like the NBA all stars. Like, it is worth every dollar to make them more productive to deliver on these ambitious goals that we have.

And so we can do that and, you know, this at least for me, I think eight sleep helps with my sleep. Engineer, like, great. Let’s do it. Let’s They’re gonna be better. They’re gonna have more of their wits about them. They’ll be sharper. And the type of engineering work that we do is not just like grunt work, how can we spend as many hours to do it? We have droids for that. The work that we do is like might require, like, really deep thought, really kind of, like, every ounce of brainpower that you have, in which case, if you didn’t sleep well, like, you’re not gonna make as good of a decision.

I think generally, we will see the best companies treat teams more and more like whatever, seal team six or NBA, like professional athletes. Not in the way that Google did it with, like, oh, you get, like, a bounce castle and all this, like, weird shit. But, like, we’re, like, athletes. It is kind of like, seems like they’re getting pampered, but it’s kind of a burden. Your diet is monitored. You get, like you have to do your, like, hour long massage after a game to make sure your muscles are recovered for the next game.

You have to do, an ice bath and all this stuff. Like, it seems glamorous, but sometimes it’s not. I think spending on that type of stuff, but obviously in the more, like, intellectual domain, I think that’s what more and more companies will do. If I could spend an incremental dollar to make every person sleep that much better, recover that much better, be that much better at making decisions, it’s probably worth it.

Okay. Honestly, I get that. To be fair, the first year at Factory, I would drink a whiskey every night, and my argument was, and you’d probably agree with this, was, like, for robustness, like, you wanna be a robust human, you can’t have, like, one drink ruins the next five days of your life. Like, the wind blows and then you’re, like, you’re ruined. Right? To some degree, I get it. Like, you want to have some of this stuff. But I also think maybe, you know, again, looking to athletes, what they do is they have in season and out of season.

Maybe it’s like when you’re in season, you’re fucking locked in, you’re not drinking, you’re like optimizing all this stuff with your eight sleep, and then take a week off, go on the beach, drink some mojitos or whatever the hell people drink on the beach. Well, in out of season, you’re Charlie Sheen.

Yeah, mate.

In my mind, the answer here is I think they’re approximately equivalent. Like, to me, it doesn’t really matter. The biggest reason that affects, like, the EV is, like, volatility of the company. That’s the only like, because I think from the business perspective, they’re both very well suited and, like, well positioned there. So you were saying Anthropic? Probably. Past is an indicator of the future, and, like, there’s just been more, like, random, chaotic, turbulent events at OpenAI. But from a business perspective, to me, that’s both great choices.

Yes. It actually this, like, really upsets me. So on one hand, I maybe just implied anthropic there, but on the other hand, I think that has been not only, like, disingenuine and wrong, but it’s, like, really hurt the psychology of a lot of developers, like just people in the world. Does AI a disservice? Does the world a disservice? Because this is again, talking about the use cases that are gonna and the problems that will be solved for society. This feeds fuel of like, we should slow down AI, we should stop doing it.

And honestly, it’s for selfish reasons that they did that. Because if you’re trying to raise unprecedented amounts of money, you know, hundreds of billions of dollars, whatever, the best way to convince people to do that is to say, all of capitalism is gone. The only company that’s left will be me. So you better give us your dollars. And then suddenly when it comes to IPO, when now suddenly all the humans and the people that you might be replacing now have money that you want them to put in your IPO, then suddenly it’s woah, woah, woah.

Oh, no. Humans are pretty important. There are gonna be jobs again. You know, we like you guys. That pisses me off.

It’s

just it’s a shame because, like, for all the philosophizing about AI and intelligence and all this stuff, it’s like incentive is driving the outcome and the incentive is I wanna raise a lot of money.

accounting firms, is one of our largest customer. I know. That’s you said shocking. They are so agent native. It’s crazy. They’re one of our largest customers. How

They’re just like basically, they saw what happened with the cloud. They saw scars of being, like, late and kind of not jumping onto it aggressively. They have some great engineering leaders there who are like, look. This is gonna be scary. Some people are gonna get upset. It’s not gonna be the easiest thing, but we are going to make our org agent native if it’s the last thing we do. And they were honestly pretty early to it as well. To me, I think that’s one of the most interesting things seeing is, like, they’re more agent native than some, like,

mind on the most in the last twelve months is there was a brief period of time where I thought it might be just one or two companies that run away with being kind of the frontier and the best. What seems pretty clear to me is it’s probably gonna be at least four that are gonna probably be approximately as good. And that is a win that is the win for humanity. The bad case for humanity is when there’s one that’s really, really good. I think there’s probably gonna be at least four, if not many others.

And that’s something that it seems there’s, like, kind of growing evidence of, which is kind of my sense is it’s a hot take because I think right now people are a little bit enamored with maybe one or two. But