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Matan Grinberg

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2026-06-13
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2026-06-13
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  1. Yeah. Well, and maybe part of that is because the Googles have been hiring all these engineers. So distributing great engineering talent to more problems, I think is going to 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 going to 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 want to slow down AI, that's saying like these people who have relationships with loved ones who have dementia, you're like, no, no, sorry, you guys, you got to 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 harmful and it's pretty selfish to say that it's something that to me it doesn't make sense.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  2. Short term, yes, 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 are 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  3. The throughput to produce more Teslas in this case. And so in this new world of software development, human engineers are not going to be involved as much in writing the actual code, but they're the ones that are going to 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 they're kind of like building the scaffolding around this factory that produces their software.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  4. Do you 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. 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 going to 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 factories. It's all these 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 opt.

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  5. 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 if you're actually training models or trading agents to be as efficient as possible communicating to each other, but then the downside there is it's not as human readable.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  6. 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.

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  7. 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. It's incredible. I'm generating a ton of stuff. Phase two was some poor staff engineer who has to review hundreds of these slop PRs that are like, don't adhere to your standards or 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.

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  8. Yes, but I think Sharpe has plenty of places that they can differentiate. And I think it's a better world where everyone has documentation as good as stripes.

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  9. So it's like basically cataloging the changes that you've made in the last whatever month or so 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 going to 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 we definitely won't do.

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  10. For an engineering team like writing release notes, that's crazy that people used to spend hours of time writing release notes or like writing documentation.

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  11. 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 identified, so they're using agents to make the organization more efficient wherever possible. But I think in general, if you have people that insert certain functions that aren't proactively doing that, probably a bad sign.

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  12. 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 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  13. Age of the polymath is back. Like, growing up, I was so 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 like 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 the early 2000s and 2010s, pre-AI, fields were so deep, in my case, theoretical physics and strength, it was so deep that you could spend literally 50 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.

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  14. So it's starting to exist more and more, but I think it's kind of this 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 going to 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 beyond.

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  15. But it's a crotch. It's a crotch. It's helpful. It's a good indicator. Like, generally, you can't win competitions if you're dumb, right? 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?

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  16. Is like competition winning or like Olympiad type. Are you like as fast as possible at coding? 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 did, it doesn't matter.

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  17. Yes, it very seriously changed. 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 going to 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 just your job is shrip feature. It's no, you are owning full end-to-end outcomes of here's the way that customers 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.

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  18. 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.

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  19. Demos or in solution engineering, like that 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 engineer. It's like, no, no. We have engineers and salespeople sitting next to each other. They're no like engineer corner, sales corner, and 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. 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 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

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  20. 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 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 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 10th renewal after a decade of being a happy customer. The software is a big part of that journey, but so too is 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

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  21. 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 maximize the dollars that they can get out of in the market. That said, it is still pretty competitive

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  22. 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 this type of thing. And so it's like, okay, if this planning thing is the very key 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 budget there. But then once it comes to, okay, we have the plan now let's implement the open models are typically really good.

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  23. That's nonsense Well, it depends because 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 very similar to how we structure human orgs. Oftentimes, leadership makes very key decisions that determine the fate of the company and they don't spend the most hours. Like if you 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 going to make this irreversible decision on the strategy. And those people that make those decisions are also typically paid a lot.

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  24. 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 going to be very different. 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 going to be loosely interacting with code in your org, whether they're sales or marketing. But I think the difference is there are going to be certain people where, again, we're coming back to resource allocation. They get more leverage by using more tokens. And then there are going to be certain people where actually they don't really need tokens at all. And that's not 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

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  25. 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 and it can be as high as like thousands, tens of thousands of percent for some individuals.

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  26. 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.

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  27. I've literally seen this with dozens of our customers where initially, 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 was 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 want to 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.

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  28. Might see a short term contraction of usage of the very frontier models, but I think it's healthy. Healthier to do that than to be blind to it and then have a real sudden change there.

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  29. Taking shots, having a great time, using all the AI. Phase three is a 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. 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 are my macros from the food I ate today? Like, what's the weather like? And it's like, guys, like, we don't need the frontier of human intelligence to be doing this stuff for us, let alone it's not even work related in some cases, but.

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  30. 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 I think 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, Mr. 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 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 debauchery, the long night.

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  31. No, because it's such overkill. But 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 mentally deal with of deciding manually or then having the router do it for you.

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  32. I think it's a really important counterbalance. It basically allows you to make the tradeoffs 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 want to be able to be anywhere in that cost quality speed trade-off.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  33. I think eventually we'll stop seeing them as model releases and they'll feel more continuous. Like just how before it was, you know, GPT2, then GPT-3, then GPT 3.5, then 4, 4.1. And then you get more and more. And like, you know, 4.523. Eventually they're just going to not announce it. And it's just like, hey, look, here's our model that's continuously getting better because 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 going to 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  34. 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 going to be roughly as good as each other. They'll be good at 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 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 want to, companies might want to just completely go in with them, but then that's a monopoly for the entire economy to be worried about.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  35. Whether overtly or not. And maybe I'm saying the quiet part out loud, like everyone is trying to commoditize the people that they're 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 want to do in their software development. And we want to 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 charge whatever they want. Now, similarly, the model companies want to make it such that the applications are all trivially easy to build and really the product is the model. And then the infra companies have their own spin on this. But the reality is everyone's trying to commoditize the one that's not them. And so from Mercor's 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  36. Man, I enjoy making breakfast. I haven't done it in like three years. There's nothing like it's just, it's not time efficient. It just doesn't make sense to spend my time doing that. Oh, I mean, okay. But like it is, you know, it's something I do enjoy. But to your point on model applications infrastructure, I'm not sure if you've seen the meme of this. There's a meme of the Microsoft Org chart, and it shows different segments and they all have guns pointed at each other just to show like in Microsoft, you know, 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. All the value is going to be here. All the value is going to 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  37. 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 want to 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 core business and your core competencies, outsource it.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  38. I would disagree. I'd pretty strongly disagree. For a couple 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 going to 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 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 going to say, you know what? For my resource allocation, I'm going to pay someone to go and do that for us because at factory, our core...

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  39. I mean, it's fun talking about core competencies. Kirkland's spending half a billion dollars 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  40. To more dramatically move the needle on that business outcome. And I think 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. If you're an engineering team, we wanted to ship three features this quarter. Did you ship three features? We shipped four. What a great quarter. 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 want to 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 it's marketing, sales, engineering, all of it.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  41. Yes, this is 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 24 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 how 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 headcounts.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  42. I think directionally, yes, because what is a 10x or 100x engineer? I don't necessarily agree with the language around it, but like I think it just implies as if like 10x of what? Is it pure output? Like if you, when you say 10x, 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 they're in some orgs there might be people or if you remove them, nothing happens and they're 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. They can 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  43. 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  44. 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 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 going to 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.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source

  45. The world going forward, there is going to be nothing that no one can build. Everyone is trying to commoditize the other. Value accrual is a time-dependent phenomenon. So many of the tasks that we're doing, we don't need the very frontier to do it. We might see a short-term contraction of usage of the very frontier models. I think it's pretty embarrassing that we don't have frontier open models in the United States. Name a legendary company that has a shit sales or marketing team. You can't. The age of the polymath is back. All right. You have a meeting with the Sequoia Partnership tomorrow morning. Be ready to present. No one else would have believed in me except him. We will see the best companies treat teams more and more like whatever SEAL Team 6 or NBA, like professional athletes.

    2026-06-13 · The Twenty Minute VC · 20VC: Who Wins the Model War: OpenAI, Anthropic or Open-Source | Token Maxing, AI Hangovers & The Coming ROI Reckoning | Labour Displacement Fears are BS & Overblown | From Physicist to Sequoia Founder with Matan Grinberg, Founder @ Factory · IDENTIFIED FROM THE TRANSCRIPT · source