Dylan Patel: AI in War, Jobs are Cooked, Chinese Hacking, Microsoft Cope, and Super Intelligence
In this interview Dylan Patel discusses a wide range of AI‑related topics. He warns that anti‑AI sentiment could become a political force within both Democratic and Republican parties, potentially slowing regulation and the U.S. AI lead. He evaluates the upcoming DeepSeek V4 open‑weight model, correcting misconceptions about its training hardware and arguing that model weights are less decisive than the surrounding harness, skills, and agent frameworks that differentiate products like Claude Code, Opus, and KIMI. Patel explains how internal "skills" and agent orchestration provide competitive advantage beyond raw parameters. The conversation shifts to the open‑source versus closed‑source landscape, noting that while open‑source tooling is thriving, closed‑source models dominate usage and cloud compute. He examines the economics of hardware allocation, describing how memory shortages and wafer constraints push resources toward data‑center class GPUs, making hobbyist local inference increasingly uneconomical. Patel critiques Microsoft’s compute strategy, suggesting it has ceded market share to Amazon and Google, and highlights the rapid, orders‑of‑magnitude cost reductions in AI training driven by scaling laws, hardware‑software co‑design, and RL improvements. Finally, he revisits the question of which organization will achieve artificial superintelligence first, concluding that while consensus points to Anthropic, the race remains uncertain. Throughout, Patel interweaves anecdotes, industry observations, and speculative commentary on future AI trajectories.
Chapters
Now with like Claude Code, I think we move 10 times the speed of any of our competitors. All the executives, all these major companies watch podcasts like yours. They listen to these things because it's like, well, this is how I listen to the people that like matter. Hey, if a nuclear missile's heading from China to America, we can use AI to stop it. And Dario was like, well, you can call us. We'll— I'm sure we can figure something out. And it's like, this is just the dumbest response you could ever come up with. I don't know how principled Sam is, but he's certainly one to take advantage of a good crisis. Wait, I actually think like UBI is like perfect. Which I think is crazy. Nowadays, people in the 90th percentile think they're middle class and people in the 50th percentile don't think they're middle class. A buffet of dopamine is less happiness.
I actually didn't know that.
Open Claw is pretty freaking insane. Even without AI going bad and rogue, we know people are bad.
Really?
Cooked. Cooked.
Dylan, round 2. Really appreciate it.
Yeah, I'm excited for this.
Very nice office you got here.
Yeah, thank you so much. Just moved in? We've got a fantastic office that we just got moved into.
So I want to think back to the last time we talked, which is about 18 months— 18— 8 months ago. It really does seem like a long time.
Bro, 8 months ago is 3 years in AI land.
Might as— yeah, yeah, might as well be. All right, so you made a bunch of predictions. I want to go over those first. Yeah, yeah, yeah, yeah.
You got a scorecard for me?
Uh, okay, you said GPT-4.5 too slow, too expensive. We'll come back to these in a sec. I'll go through them one by one. Scale AI cooked. Junior dev market nuked. OpenAI is your pick to reach superintelligence first. Now we're 8 months later, and I want to hear—I want to hear the updates. Let's go through them one by one. So first, GPT 4.5—you may have just absolutely nailed that one. What went wrong with GPT 4.5?
Well, 4.5 failed because it didn't have enough data. Also, it was just very complicated and difficult on a scaling perspective, infrastructure-wise, and there were tons of problems and challenges there.
Yeah, yeah, yeah.
How are you feeling about that now?
I mean, I guess there's nothing—
You can't even use it. You can't even access it. No API.
It was a good model, though. Next, Scale AI cooked. What does the Scale AI acquisition actually give Meta? First, let's start there.
Yeah, so I think, um, you know, for one, Scale AI is like— it's kind of cooked right now.
As a company.
As a company. Right, 'cause everybody's canceling their contracts. Yeah, Google's backing out.
How you feeling about that? Obviously, Alexander Wang running the Meta superintelligence department. division. Still feeling like Scale AI is the orphan child?
Yeah. They've had a good number of departures. They've had a good number of folks churning off of them or turning to other providers. Scale AI has not really picked up a lot on the environment space. When you look at the data labeling companies, there were a lot less than there are the environment companies. In some sense, it's the same end market, although environments will be bigger and so on and so forth. there's like 30 environment, RL environment companies. There's, you know, so Scale has not really picked the ball on that. The leaders in that space are different folks. So I think they've really missed that ball. But I mean, their business is doing fine. It's just like—
That wasn't the point of the acquisition.
Yeah. I mean, who cares? It's Alexander Wang. We got to see how Meta's Avocado and all that stuff comes out. That's the more relevant thing.
That's why they acquired Yeah, I mean, it's, uh, lucky for them they're sitting out of the frontier race at this very moment, but we'll get to that in a minute. Um, junior dev market nuked. That's, uh, it was a strong prediction 8 months ago.
You already see the junior software engineering market is nuked.
A lot has changed. What do you, what do you think about that prediction at the time?
Um, I mean, now it's, it's true, right? Um, you know, you, you look around and this, the, the fresh grads are it's even harder for them to get jobs. You look at Claude code spend, it's freaking nuts. $19 billion of revenue for Anthropic now. All of that is at some multiple is code.
Yeah.
And at some multiple is like, okay, if you're spending $20 billion industry is on code, some multiple of that is the productivity gains and value you're getting. So is it 4x more productivity? versus how much you spend? Because there has to be some premium over value over the premium that you pay for switching.
Yeah.
And I think it is. I think for us, our spend late last year was something like $50K a month, whatever. And it was in the hundreds of thousands of dollars run rate annually. Then 4.6 came out, then 4.6 Fast came out, 4.6 fast with 1 million contacts, that's like 12x the cost of 4.6 period.
So expensive.
I have people who are not software developers. Literally, one day my head of ops was like, guys, our run rate of spend is $6 million. This is not sustained. And this was Super Bowl Sunday, by the way. This is not sustainable. And it was because one of my engineers spent like $8,000, he's Canadian, so whatever, on Claude Code. And it's like, oh shit. And then I asked him what he built and He explained everything he built and I'm like, oh, this is totally fine. I don't mind.
Yeah.
The spend is fine. And then now you've got adoption from more and more people. I've got Jeremy who does our data center modeling. He works on the data center team. He leads the data center team. He tracks all the data centers around the world. He's not been a programmer. We've had other people doing a lot of the backend and all these sorts of things like the vision model or contractors and stuff, or going through and scraping all the permits and filings. He just started using Claude Code and he's building his own tools and he's like, I'm not coding. I'm just telling it what to do and it's doing these things. And his daily spend now on 4.6 fast, 1 million context is $5,000 a day.
Jesus.
And I look at the productivity, it's fine.
Yeah.
So I think it's beyond just the junior developers. It's like all— Cloud Code is not a coding thing. It's for all people.
Yeah. I mean, ever since OpenCLAW became popular just 7 weeks ago, I think I've spent close to 7 billion tokens on it. Luckily, friends at Cursor supply me with the good tokens.
What have you made?
A ton of workflows. Open Claude now is a first-class citizen in my company. It has its own workspace, email address, Drive. It is reading all of my emails, and yes, I have lots of prompt injection defense there. It's doing outbound sales, it's triaging inbound sales. It's really cool. But of course, it took so much iteration to actually get there. That's where those 7 billion tokens comes from.
I joke internally that like my company will not exist in 2 to 3 years because, because AI will make it impossible. And, and that's like the driving force. So like we have to adopt AI faster, faster, faster, faster than anyone else. And so we keep like taking market share from other people in like various areas. But I just got like a shiver. I'm like, fuck, we're behind the 8-ball.
Can I tell you what just happened last night? So I was putting my son to sleep. He wasn't going to sleep. I was like, hey, you got to go to sleep. I'm interviewing somebody cool tomorrow. I got to get a good night's sleep. He's like, "Oh, what? Who are you interviewing?" I was like, "Dylan Patel runs this company. They know a lot about AI chips, and other people pay them to tell them about AI chips." And he goes, thinks for a second. Why don't they just ask AI?
Cooked, cooked. The child knows that my business is done for.
No, I wasn't sure if I was going to bring that up, but I thought it was hilarious.
No, I mean, if you don't have existential threat, then you're not moving fast. Yeah, yeah, totally, totally. I mean, like you look at like. OpenAI and Anthropic, they're like the furthest ahead. But if you talk to researchers, they're like, a lot of them are like freaking out, like, oh my God, you know, our model's behind in this one capability, that means it's gonna be compounding growth. That means we're gonna lose the race to AGI, right? It's like, okay, yeah.
Let's go, let's go straight to it. So obviously, a lot of discussion, a lot happened this, this last week, Department of War blacklist Anthropic. Want to get your thoughts on that. There's kind of a lot of nuance there. So the US government just labeled Anthropic a supply chain risk. What is your read? And then literally later that day, OpenAI closes the deal with the Department of War. What's your read on that situation? And then I got a bunch of follow-ups.
Like, really understand the 2 companies. And by the way, OpenAI reneged on that deal that they did on Friday and signed a new one on Monday, right? With a bit more tighter restriction. But like, you know, the 2 companies, like, there's a lot of interesting stuff, right? If you could take one view from Anthropic, which is like, oh, it's because Anthropic and Dario won't bend the knee to Trump and won't donate to him. That's why they're getting attacked. Because if you look at the OpenAI deal, it's like, oh yeah, it doesn't allow mass surveillance, which is like the main thing, right?
And autonomous weapons.
Yeah. And autonomous weapons. Yeah. Although fully autonomous. Fully autonomous. I was going to say reportedly both allow autonomous weapons to some extent. You could take one view that's like it's just like Anthropic is being dumb. The other view is like Anthropic is just being like dogmatic, right? Like there was a situation where I think it was put out by some press But it was like Dario was asked, or someone at Anthropic was asked, hey, if a nuclear missile is heading from China to America and we can use AI to stop it, but it requires surveillance and autonomous weapons, what do we do? And Dario was like, well, you can call us. I'm sure we can figure something out. And it's like, this is just the dumbest response you could ever come up with, right?
Yeah.
I don't know if this was even fully accurate, but this is what was in the media. At least this is what everyone's perception is, right? So there's 2 It's like, okay, the government and Trump admin are out to get Anthropic. The other view is Anthropic is actually just super dogmatic. I think the truth is it's kind of in the middle.
Yeah.
Then you look at OpenAI, it's like, look, I don't know how principled Sam is, but he's certainly one to take advantage of a good crisis. That's maybe his greatest skill, is to take advantage of any good crisis. He goes out there and he signs deals with the US government. Now the US government is like, even though Anthropic was the first to sign something with the US government in terms of an exclusive, or not exclusive, but like—
The only one with access or implementation in a classified environment as well.
Yeah, exactly. But despite that, Anthropic has lost the plot now, and OpenAI just saw it and they swooped in, and now they're going to be the one there, right?
Yeah, I mean, it's funny you say bend the knee. I mean, just— I think it was today it was reported Dario said the reason they're getting so much shit from the Department of War is because he did not give dictator-style praise to the administration. And, you know, obviously David Sacks has tweeted a ton about Anthropic and Dario and their politics. So how much of this do you think is, is politics at play? And yeah, I'm just curious.
I mean, you also have to recognize that Anthropic has a lot of their policy people are former Biden admin people, right? And so it's like, whereas, whereas OpenAI has hired people from both parties, right, which is the pragmatic thing, right? You go look at like Microsoft, right, who is like known for navigating the government well, they have people from both sides in their policy teams and such, right? You go look at like any major company like Amazon, who has government contracts, or Lockheed, they have people from both sides, right? And it's like, Anthropically, maybe just like to get to your end mission, your end goal, unfortunately requires being a slithering snake and playing both sides and the political. You can't just be a dogmatic person or company. But that is their DNA. That's their strength. That's why they're winning at the same time, is them being so dogmatic about the things they believe and care about. That's why they're winning, right? So I think it's a tough one. The more interesting question to think about is like what happens from here, right?
Especially leading up to the IPO.
Yeah.
Being designated a supply chain risk. Like what? Yeah. Like what do you think? How does that affect their business?
So, you know, OpenAI did sign the deal Friday. All weekend, people internally were like rioting. And Monday they like pulled back and signed a different deal. That Anthropic is basically like, well, we, we exactly asked for this and you guys said no. But it's sort of like, you, you know, this shows, right? If you tell the government you can have everything and slide a little bit back versus you can't have anything except for these things, it's a different argument. And so when you look at like, hey, what is the end state? The US government wants to be viewed as the, as the winner, right? As the, as the king, right? They, they don't want to be the one that's like, oh, thank you, benevolent corporation, for giving me this.
Yeah.
At the same time, the AI labs are like, well, no, we made this technology. We have complete rights over it and what you can and cannot do. It's like, if the US government wants to go full dictatorial, they can just use the Defense Prioritization Act and get whatever they want from you. They can just go get 4.6's weights if they really want it. They could do a number of things to have access to everything Anthropic has. So it's not like Anthropic is even stopping the US government in that case, right? Like from having access to things. And then, and then the question is like, okay, how do you go forward from here, right? The US government can press harder. Anthropic can stand up, but Anthropic can't just back down and kiss the ring immediately, right? They are, they are so dem-coded. They are so, not even dem-coded, right? Just like so, so dogmatic in their beliefs.
Yeah.
If Dario and leadership were to say, okay, US government, you get this, people below that would leave, right? Or people below that would riot, right? OpenAI doesn't get that, right? You've got a lot more, let's say, zealots for the cause at Anthropic than any other company in the space. The only way to make these people understand and back down is to be like, look, the US government's forcing us. I think there's a bit of chess that even if Dario wanted to accept the US government's proposals, he can't because then a lot of his best researchers will be pissed off and leave. Maybe that's the risk you have to take. But also the other one is you could just be like, okay, let's see the US government push us harder and harder and harder. And then when we back down, you can tell them, well, we had an ultimatum and it was like either they just take the weights and we do nothing or we work with them. And yes, we make the killer robots and we do mass surveillance, but we do it with Claude alignment rather than them having the open weights and doing whatever the hell they want. So this is the pragmatic thing to do to prevent AI from killing us all because You know, our killer bots will be better than, you know, OpenAI's killer bots. And then everybody will be like, yeah, you're right. Yeah. Thank you, Dario. Yeah. So sort of like, I feel like that's what has to happen. And at some point, Dario does just have to be like, I love Trump. I love everything Trump says. You know, I love, I love the Department of War.
You know, I mean, they're really like the only tech company that hasn't bent the knee, right? Every, every other tech company has bent the knee, has donated. So it's, it's kind of an interesting position and it does—
It's kind of scary.
Yeah. Yeah. A little bit. Okay. So I want to— let's, let's one more, one more thread on this. So Claude is currently the only model, uh, currently deployed in classified military networks. They obviously were just announced trying to rip Anthropic out. How do they do that within 6 months? Is that even possible?
I don't see why not. I mean, I think, I think the version of the model that they have is like, it's like 3.5 Sonnet or something like that. It's like really 3.6 Sonnet. Yeah. It's like, it's like an older model that they have deployed because it's the weights, right? It's like they have access to those weights. I don't think it's like, They have Opus 4.6 deployed in classified networks, right?
Yeah.
It is. It is fast. Yeah. No, no. I mean, I believe it's like an on-prem thing.
Okay. Okay. Yeah, I guess it would have to be, right?
Yeah. So in that sense, it's like, sure, get OpenAI to give you GPT-5 weights. That's a better model, right? Or, hey, Grok, give me Grok 4.1, Grok 4.2 weights, right? There's a lot more I mean, like, shit, Chinese models are better than Claude Sonic 3.5, right?
You see the government using those?
No, absolutely not. But the point being that there's plenty of sources for that capability tier. And so I think it's fine. I don't think it's that hard to rip it. I mean, I imagine it won't be that hard to rip it out, right? But who knows?
It's kind of wild to think that they're using such an old model for such important work. I actually didn't know that. And now I'm thinking about it and remembering the level of hallucination coming out of those early models, and it's deployed.
I also remember 3.5 Sonic coming out and being like, holy shit, this is so good. I see the sparks of AGI.
Yeah.
Um, no, I mean, that's fair. That's fair. I just think it's kind of, um— there's a lag, right? Like, you know, government is just slow. And this is, like, I think, the biggest risk, uh, that also, like, will help Anthropic people understand and be convinced that fine is China's not using, you know, the oldest model. They're using, you know, the newest Qwen. They're using the newest Kimi. They're using the newest DeepSeek, right? Like, they are not in their military applications. There is no, you know, waiting around. There's no slow timeline to deploy. It's shipping, right?
Yeah.
And so, like, maybe the US has a 6-month advantage, but if the US government has a 6-month time lag between the new model coming out and deploying it, Then there is no advantage.
Yeah.
Right. For the US government and military. And then if China is able to, like, have all these drones that they can manufacture at high volumes and control these autonomous drone swarms with, you know, very advanced AI that is on par with what the American military has, except the American military doesn't have drone swarms because they can't— we can't manufacture drones in high volumes. It's like, wait, our military capabilities are actually just like worse because they have lower cost, more drones with similar intelligence. It's like, where is the advantage for us here now?
Where is the advantage? I mean, we have companies like Anduril, but they're still coming up. It's still nascent. So what is the advantage then?
Exactly right. I mean, there isn't one. And so that's the biggest risk and challenge in that type of— like, if you think like drone swarms are the end-all be-all of warfare, right? There's other things, right? That potentially, you know, the US has. I don't, I don't know what the US military has, to be honest, but like, it could be.
All right. Yeah, you got me thinking on that one a little bit, Dylan.
What you thinking about?
Yeah, no, just it's— I think, I think the craziest part is I realized that, yeah, the US military has these old models and that makes it equivalent to what the Chinese military is able to have currently.
And it doesn't have to be, right? Like, no, of course we can figure out how to do faster, right?
Yeah.
I think flip side is the US government buys data from Google, from Twitter, from Facebook, Meta, sorry, on their users, and then they have their own classified sources of information. It's like right now, building all the data pipelines to mass surveil everyone is actually just hard, right? We haven't done it as a country. I don't think because of the law. I think because it's hard. I think the NSA would absolutely do it if they could, but it's a monumental task. Whereas China has, right? Whereas other countries have, right? Or some other countries have tried and gotten along the ways, but China especially is the closest to that. But if I look at how easy it is to use Opus 4.6 to build data pipelines and transform data and scrapers and all these things, I feel like doing mass surveillance, building a mass surveillance system with Opus 4.6, would not be terribly difficult.
Yeah.
With Opus 5 or whatever, or OpenAI's new model that they're releasing, maybe it's even faster and faster. These autonomous— this mass surveillance, which is one of the red lines in the sand, is a true moral quandary because I don't want the US government to have China-like mass surveillance or better. But at the same time, if you give them full unfettered access to Opus 4.6, they can totally build this.
So you think Dario's line in the sand is coming from a true place of belief?
Look, if you think AI will kill— the x-risk of AI killing us all is pretty high, and it's because AI turns around and uses weapons to kill us all. Autonomous weapon systems obviously make sense to limit, but also because if there's autonomous weapon systems, Who gets to command these autonomous weapons systems? How are they aligned? All these questions just come up. Fine, that's one. The other one is, even without AI going bad and rogue, we know people are bad, generally, in positions of power. If you create mass surveillance systems for the American public, what does that now do to free speech and all these other things?
Yeah.
You and I can come here and we can say whatever we want, We could say fuck Biden or fuck Trump, but whatever, right? Probably nothing gets flagged. But now if you have these scrapers that are pulling every record of people on videos and it's like, oh, somebody's account on YouTube, which is linked to this email or based on these comments is based out of this area and based on this other information is based here and is this person. Now all of a sudden this person said fuck Trump after I said fuck Trump on some random podcast, right? Or fuck Biden after I said fuck Biden on some random podcast. Now all of a sudden they're being surveilled and now can that be turned around. So I think there's real risk to mass surveillance. It's not that it's trivial. I do think the beauty of America is that we can do whatever we want, generally. AI systems make it very, very likely, even pre-AGI, that we can't do that anymore. Bad actors can now manipulate systems to control people, especially as like social unrest grows and grows, right? AI is causing social unrest to grow. The power of capital versus labor.
Which part of AI is causing social unrest? Is that the misinformation? Is it just the politics around it with energy? And I know you talk about water a lot. What part is it?
I think it's like all of the above, right? Like, I think like generally over the last, what, 50 years, we've had capital take more and more share of value versus labor. We have seen social media amplify what other people have versus don't. And so like, you know, even though 50 years ago, you know, rich people lived— or middle-class people lived worse lives than middle-class people do today, I think on an objective basis, at least in terms of like, what is the square footage of their house and how much meat do they eat and what is their access to medicine, right?
Yes.
Because the perception is not based on that. It's based on historical thought process of, oh, you know, we own our own home and we have a car and we can send our kids to college and blah, blah, blah with one wage. even though that was actually very rich 50 years ago and still possible today, but you still have to be very rich. Or flip side, hey, I'm a middle-class person, or I'm even in the 25th percentile. There used to be a belief that people in the 20th percentile thought they were middle class, and people in the 80th percentile thought they were middle class or didn't think they were middle class. They knew they were well-to-do. Nowadays, people in the 90th percentile think they're middle class, And people in the 50th percentile don't think they're middle class.
Yeah.
Because it's the perception game of like, oh, well, like, look at these people, they're going on vacations. Like, yeah, because they're putting a fake life on social media, their peers, right? That's not what their actual life is, right? And then on the flip side, it's like people, you know, see influencers, see like, oh, that is the norm, right? And it's like, I don't have that because I'm poor. Well, no, you're middle class. And like what the amount of vacations and experiences you go on is actually more, you know, I think, I think like obviously it's like It's very privileged for me to say lives are better, at least especially for me, but at the end of the day, on many objective economic measures, lives are better, but people think their lives are worse. Now more and more over 50 years, because it's continued to be a slide of both capital taking more value, income inequality growing, this perception game of social media, and these things keep amplifying, especially in the last decade, social media has really amplified it. Now you have job loss potentially happening. You look at economically, usually people have been fine when GDP is like 2%, which is fine what it is now, except now of that 2%, 1.7%, 1.8% is AI growth.
Yeah.
It's like, okay, who's getting the benefits of AI growth? It's electricians, construction contractors, people in the semiconductor industry, Capital allocators. Yeah, capital allocators. That's it, right? That's less than 10% of the economy, guys. So it's like most people are not experiencing economic growth and they see this inequality and they look for boogeymen. And the boogeyman is the richest companies in the world, the stock market soaring, and AI, right? And all the risk of AI taking jobs, which is about to happen in droves, right? Waymo's about to start deploying in all these cities. Robotaxi's about to start deploying in all these cities. The boogeyman of few million people losing their jobs because of self-driving is going to happen. In addition to that, white-collar work, the amount of work my company can do with how few people it has, the amount of work that you can do with how few people you have would not have been possible. We're able to build an enterprise, but we're enterprising individuals. What about all the people who aren't enterprising? They just work a job and then they see this tidal wave coming. You know, it's very scary, I think, right? And so, yeah.
Do you feel a lot of anxiety in the city right now, even with engineers? It's interesting you say that. I tend to be more optimistic. I think maybe there's going to be more smaller companies, but you're right, there needs to be enterprising individuals to go out there and actually start these companies to build value, because we're not going to have these massive 100,000-200,000-person companies anymore. As you know, Block just laid off half their workforce. So are you, are you generally pretty pessimistic with the, you know, what Dario called the white-collar bloodbath?
I think generally there will, you know, even though there's more surplus in the economy, the allocation of that surplus is harder to get to the people. And so there will be— and markets take time to reorganize. So there will be a hard dislocation. Right. Yes, there will be new jobs, more things for people to do, more service than ever. Right. Like, you know, more experiences. Right. Most people will then be able to go and do like random more painting classes or more whatever classes or more like park, whatever, more beach, more surfing, like whatever it is than ever. If the surplus was spread evenly and people were able to just reallocate perfectly. Right. But that does not— that doesn't happen.
And also the technology is moving so fast and the ability for society to keep up with it, you're saying, is certainly not fast enough.
Right, exactly. The whole, oh, but we had 90% of people working in agriculture and now it's less than 2%. It's actually less than 1%. It's just like people claim their farms for tax reasons. So it's less than 1% in reality. Anyways, it's like, well, yeah, but that took 100 years. It took more than 100 years, maybe even. Whereas this AI dislocation is immediate. And so probably I'm generally an optimist. I don't have anxiety about this, but I know a lot of people do, both normies across the space. Met some of my cousins, met some friends that aren't in the space, and they're more worried about it. Met recently my Uber driver recently. I still take Ubers because I think it's fun to be able to talk to an Uber driver versus a Waymo.
You tell them that their jobs are about to be completely—
No, I don't say this stuff. I just vibe, right? My philosophy here is actually insane, right? So Waymo's objectively, the median Waymo is better than the median Uber, right? But like, you know, that's boring, right? Like, you know, Ubers, I would say 90% of Ubers are worse than a Waymo. But there's that 10% that has some interesting music, some amazing conversation. You know, I practice my Spanish, right? Whatever it is. And they're a good fun conversation. That spikes because of the human connection. It's like, okay, this is worth it. But I think in general, right, like that way, I had a Waymo driver, right? Soviet, grew up in the Soviet Union, moved here, was a taxi cab driver for 30 years. He started ranting to me about how Uber doesn't pay taxes and has never paid taxes. Started ranting to me about how much he used to make taking taxi, doing taxi driving, and now how little he makes. And in my mind, it was like, well, yeah, that's like, you know, the commodification of driving for someone is like been a surplus for everyone in the economy, obviously that hasn't been distributed evenly, right? People take more taxis than ever because Ubers are so cheap. But, you know, at the same time, he started ranting about like how, you know, he's like, yeah, people don't need all this technology, right? You know, most people don't need all this information at their tips. They have it, but it doesn't actually benefit their lives. They'd be better without it. And like, he just kept ranting about it. And I was like, wow. I asked— I just asked him how his day was.
What were your thoughts on that point? that we have enough. We don't need more tech. We don't need more info. I mean, who am I talking to? What am I saying?
I love it. Yeah. Give me more slop, right? Like, you know, like... Um, I think it's fair. Like, you know, I think people's, you know, dopamine receptors are short-circuited, and there's probably some way to make it better.
I mean, you guys put your semi-analysis charts on the, like, the— what is that game? You make it good for Gen Z so they don't get distracted.
It's a bit of a shitpost, But yeah, so what we do is we do Subway Surfer.
Yeah, that's it.
It's Subway Surfer, and then you like explain an ML paper over the top, and then you also add some background music. It's just great.
So funny.
It's sloppy. I think dopamine is short-circuited for everyone, the reward circuit. Probably a lot better would be done if the world had like, you know, not— you know, if the average person had a longer attention span, could focus more. I think generally, a buffet of dopamine is less happiness.
Yeah.
Um, I personally, you know, it's like type 1 versus type 2 versus type 3 fun. I personally think that, like, the pain and the grind bringing, you know, like, the reward is the most fulfilling thing in life, right? And I'm sure you feel the same way. Like, when you're like, you know, you put your kid down, instead of going to sleep like most normal people, you're probably, like, working on something, right? And it's like...
Yep.
But, like, then you're like, wow, that was fulfilling. But a lot of people don't get that because they go to work, they come back, and then they dopamine die. And so it's sort of like there's something about, hey, human psychology and what is the reward mechanism of a human brain, probably we could change. But you shouldn't— at the same time, technology is improving agricultural yields. And people aren't dying of world hunger. And like, you know, technology is making steel production faster and like technology is making automobile production better and like batteries better. Like all these things that make normal people's lives much better, right? You know, we have more abundance than ever because of technology. So you can't stop that, right? Like otherwise, like degrowth is like the silliest like ideal ever, right? Like it's especially popular in Europe, but like degrowth means that like people— people— yeah, it's just like a terrible idea.
Okay. So we Went on a super tangent there, which I absolutely loved it, but I want to bring it back to something you said earlier. You were talking about Claude Code being, especially 4.6, being able to build out incredible data pipelines and transformations. And let's talk a little bit about software, Claude Code eating software. And in February, in your newsletter, you said Claude Code was at an inflection point comparable to the ChatGPT moment. You mentioned earlier that your token spend is absolutely skyrocketing, although you're like, yeah, that's fine. The returns are there. Walk me through what you think is happening right now with Claude Code. A lot of people are liking Codex. Like, the whole software industry is changing. What are your thoughts?
I mean, like, like, yeah, like Cursor, their revenue went from $1 billion to $2 billion in like a few months, right? It's not like, it's not like Claude Code's taking all the lion's share. You look at, you look at Codex, they've eclipsed $1 billion after having launched not so long ago. They're probably closer to $2 billion now. Like, you know, Anthropic skyrocketing, of course. Like, I don't think it's just a Claude code. I think there are many people who are tools that are adopting and getting strong coverage. A lot of people perceive these tools as a coding thing, and they aren't, right? I think if you take a long look at them, and you take a long look and you force yourself as a non-programmer to use these tools, you can get huge productivity gains. Because this is When you talk about, hey, what is ChatGPT like versus what are these like? It is a completely different— Claude Code, Cursor Agent Mode, Codex. These are agent orchestration systems and you can get them to do anything. You talk to them in natural language and they go do stuff. Sure, you can make them make software. Right now, Claude is the best at this, but soon OpenAI will release their new model and that new model, will not just be— the reason Codex-5.2, Codex-X High, or whatever the hell the name of the model that currently exists is, is so good. It's better than Opus-4.6 at coding. It's worse at everything else because they took the Garlic model and then they just did RL on only the coding line.
Yeah.
Whereas the model that they're going to release soon is the pre-trained Garlic model, but then they RL'd it for everything. And what's relevant here is tool use outside of programming domains, ability to understand things outside of coding domains. And so when you think about what is an agent orchestration system and what am I— everyone's like, when's the year of the agents? It's now, right? Because Claude Code Opus 4.6 is the agent orchestration system, right? And you tell it and it spawns these agents and it does these things and it comes back with work. It makes charts. You tell it to learn skills. And then these skills are like, It's like just yesterday I heard the silliest one from a hedge fund colleague. I think this one's super funny. He's never programmed in his life. He started using Claude Code after our article on Claude Code and Doug at our company who is chief Claude Code, he's the president of the company, so he's like my partner basically, but he's so Claude Code psychosis that he's just ranted to people about it. So this hedge fund client goes, well, so what I did was One of the things that I've been trained to do is listen to earnings, read earnings transcripts, and really digest the wording that they use. And based on like how they speak and their tones and their inflection, know when they're bullshitting versus not, or know when they're exaggerating versus not, because this can lead to outsized returns. And so there's entire funds that have been started on this premise and been successful, which is just, you know, looking at and the tone of how people talk. And so he built a skill within Claude Code.
Yeah.
He sent it these, these books from this, you know, CIA negotiation tactics and like learning, you know, tone reading and language reading. So he sent Claude Code all these skills and made a new skill within Claude Code, which was understanding this. And then he fed it and he fed it some earnings transcripts that are famous in the past of having tricked most people, but then actually have like had extreme alpha just from the wording or the tone rather than the actual words they said. And then the skill was then able to go through all earnings. And he's like using this to like, it was like, wait, he didn't code anything though. Right? What did he code? He told Claude Code to learn and read these books and then understand how to like tonally and like, you know, all these, right? So it's like, you don't have to be a programmer, right? This is all knowledge work. You know, if you know how to do it, if you can be descriptive, the model can learn, right?
Yeah.
Can get a skill. and can do it, right? So today, Cloud Code is the best at this. But as you step across the world, it's gonna be so many other places, right? As I mentioned, right, you know, we're doing it, you know, Jeremy's doing it for data center stuff. Doug's never been a programmer either. He's a hedge fund person before, right? So he's doing it for, you know, so it's like, you keep stepping across the world. It's like, there's so many places where, you know, you mentioned you're not a programmer, but you're using, you're using ClaudeBot for all these things, right? So there's, There's no reason to think that Claude Code or Codex is for code. These things are agent orchestration systems for— and this is how you interact with AGI. Now, maybe you actually need— normies will need a GUI to work with this stuff rather than CLI.
Well, where's Cursor?
Yeah, but I mean, even that, that's an IDE, right?
I mean, Codex UI is very simplistic and perfect for the average user.
I mean, I don't know. I think I've forced people in my company to use Cloud Code and they're like, oh, terminal scary, right? They're like, I'll wait for Cowork to be good. And it's like, no, no, no, you're gonna use Cloud Code. And then like they force, they try and then like, wow, this is amazing. This is so good.
Um, my point earlier, by the way, so I, I, I have been a software engineer, uh, for, for a while, but I am not actually writing code nowadays. It's all natural language. It's all vibe coded. I just, I don't have time to actually like read all the lines. Plus it's all software for myself and my company. So it's not a big deal if there's, you know, some issues here and there. But I think you're right. It's interesting that you can, you can basically build anything at this point and anybody can do it. There are still some rough edges. And so if you vibe code long enough, you start to realize where those rough edges, where those limits are.
I think a lot of people aren't using the million context Claude code though. They're using the 200K or whatever. Yeah. Because the plan that they subscribe you to, right, is capped context. But if you use the like million context, which is only available via API that you have to pay out the ass for, Yeah, you do. It's like way better.
Yeah.
But I agree, I agree. There's rough edges. I think, I think what it is as well is like in the past in major companies, you know, leadership or like, you know, team leads, you know, people who are doing the actual work of the company would like create all these specification documents, blah blah blah blah blah, and then they'd get another team to implement it.
Yeah.
And it's like all of that is gone. The person who actually just understands the domain can just describe what they want, look at it, and iterate, iterate, iterate.
Yeah.
And it's not programming there. It's business logic being encoded. Right.
So, so let's continue on this. Let's talk about SaaS in general. I think we talk, you know, obviously there's a lot of talk about SaaS is dead. And I think where the world is going is probably agents on one layer and then the layer below them, some kind of file system, some kind of CRUD database. Where does that leave everybody else? Like if, if, if those are the only two things, right? Satya famously said the entire application layer is collapsing down into agents. So agents, file system, where's everybody else going to be playing? Where's the value going to be captured?
That is a tough one. That is a tough one to answer, describe. I think it's a lot easier to say who's a loser than it is a winner today in software because of the pandemonium. But as far as who actually like wins, I actually like I don't know if this is non-consensus or not, but I actually think Databricks and Snowflake are reasonable angles because these are scalable data and compute engines.
Yeah.
Versus a lot of things are easily replaceable, at least for the next super short term. Let's say by the time Opus 5 or Opus 5.5 comes out or whatever, GPT-6 comes out, maybe even that is easily one-shottable. Right? Purpose-built solutions for, uh, or, or, or vibe-coded solutions for these things. And so I, I, you know, the scalable aspect— this is the problem that, um, vibe-coded things have is they're not scalable, right? So how do you have the agents build something that has hooks into stuff that is scalable, right? Um, and so one of the, like, one of the guys in my company is like, he's basically— he was our head of data and all this stuff. And then the sort of cloud code apocalypse happened. And now all he does is run around and help people's vibe-coded things be scalable, right? Because everyone else is like, they're like, I don't know what CRUD is. I don't know what that is. They're like, you know, like— He's like, it's okay. I'll just change this out. And now all of a sudden the solution that they vibe-coded with 10% more work from someone who actually knows how to build scalable systems, is scalable. And it's like plugging into things like various layers of software that exist already, but it's like, it's not application software. It's like sort of like the scalable infra software.
Are you seeing a lot of the roles inside your company change? Because even with my small company, I hired a researcher 6 months ago. Now, Claude Code, Open Claude does it for me. All of the research puts together entire outlines.
Did you let them go?
No, they're busier than ever. Yeah, absolutely. You know, because Enterprising, super sharp, willing to learn, willing to adapt, and he is busier than ever. He is building out different tools, different websites. I mean, he you know. So no, didn't let him go. We have plans to hire, which is why I flip flop pretty often. Although overall optimistic about the future of white collar work. So I just wanted to drop that.
I'm the same way. As AI has helped us, like we already we always ran like my companies. Average age is like 30. I'm 29. We have a bunch of people who are super young, right? We move faster than everyone else, I think, in this space of consulting and research and data services. And so I think that's why we win, but it's been a compounding thing with AI. Initially, I was just moving faster and so we were winning, right? And then I hired and we were all moving faster together. But then AI started coming out, started being more integratable. We started moving faster and faster. Now with Cloud Code, I think we move 10 times the speed of any of our competitors, right? Like, it's actually insane. And so, like, my plans of hiring and the business we win and the business we're able to do is actually just like growing, right? Because there's this like dislocation of like, what can AI help us do today versus what can everyone else do? And so there's an arbitrage in the value of like what we're able to take because AI exists, right? Because we're just skating ahead of the puck.
You're describing Jevons paradox, right? There's more use cases because you're able to leverage AI so much more effectively. It's becoming cheaper. You're, you're implementing it in more places. But at the same time, Correct me if I'm wrong, you are actually still hiring.
We're hiring more than ever.
More than ever.
We have like 20 positions open right now.
Right.
Like, you know, we've hired like what, 10 people this year already? And we've got like 20 positions open. Yeah, so it's like, you know, we're hiring more than ever. But I think the flip side is when I look at my competitors, I'm taking their revenue, right? And do they now need to do layoffs? Right? And in fact, we're doing— we're taking revenue in a way that is Way because I pay my people more than competitors do, and we do it with less people. Right? And so sort of like there is like this like arbitrage of like okay well like the competing company in XYZ space has way more people than us. Right? And so like now as we take revenue now obviously my space is just one that's growing overall, so more so they're like flattish and we're taking all the growth rather than. You know, we're taking the growth and they're shrinking.
Are you, are you taking on use cases and new business lines that your industry historically has not touched? Is that maybe part of what's happening and why you're growing so quickly?
Oh, yeah, for sure. For sure. I mean, like, there's, there's growth in the areas that we already cover, right? All these aspects of semiconductors, data centers, etc., etc., etc. Right. But we're also like inventing new industries like tokenomics, right? Which we've, we sort of, I, we, we started the beginnings of it like 2 years ago, 3 years ago, the economics of tokens production. of inference volumes, cost of tokens, all these sorts of things, usage of AI. There's that, but then there's also existing industries like energy modeling. Turns out energy modeling is like a billion-dollar-a-year data services business.
Now you could just go expand into that because you have all the tooling necessary, lack of a better word, vibe-coding.
Some level of vibe-coding, some level of we already have a lot of customers in the space that use us for data center stuff or AI stuff, And so we have some in into the sales side, right? We have a name brand and recognition, right? So sort of like, and then we have the like capital to spend on hiring some great people and vibe coding things that, you know, people wish they could have built because we're building the solution that like is needed for now instead of the thing that worked 2 years ago, right?
Yeah.
And took so long to build. So I think like, you know, that's also an example of this is, right, was like energy modeling. I think and I hope that we're gonna just dominate it because all the other companies in the space are just moving too slow, right? And they have all these archaic old practices and built-up crud, right? Crap, not crud. Anyways, yeah.
Yeah.
So I think there's a bit of both, but it's like, by and large, I'm hiring and I'm hiring super fast. If anyone's an enterprising individual who wants to work really hard and talk to the biggest companies on the planet and work on their stuff, great. And help consult for them. But at the same time, I fully recognize we're taking a lot of share from competitors, right? The airwaves, like, of, like, you know, especially, like, you know, content creation, I think is the same way, right? We're in an age where the cost of creating content has fallen and fallen and fallen and fallen. And so objectively, things splinter, right? You know, we're, you know, what is it? It's not Pax Americana. What's the American monoculture? American monoculture meant that everyone had seen the same movies. And so movies, like, could be blockbuster always, right? And, and this, this, this spreading of like what people watch— yes, people watch more stuff, and yes, there are— the peaks are higher than ever, right, with regards to the biggest movies, arguably. But like Titanic and like Gone with the Wind were anomalies, would be complete anomalies today, right? And so like—
Those can't happen anymore is what you're saying.
I mean, I mean, and at the same time, right, it's like if cost of content creation is going down, the number of content, amount of content out there is growing, the fact that your channel is growing and so rapidly means you're eating more and more share from other people who can now no longer make a credible living. Now, does that mean that's Hollywood or does that mean that's some like CNBC podcast or I don't know what market people are shifting away from, but what I do know is that all the executives of all these major companies Like watch podcasts like Dwarkesh's and yours, and like they listen to these things because it's like, well, this is how I listen to the people that like matter, which is like crazy.
It's traditional media, but traditional media—it is traditional media that's getting eaten alive right now, right?
And so how many more people work in traditional media than you know you're hiring? Is it really?
Yeah, my six-person team puts out so much content compared to what a traditional media and engaging content that's like good, right?
Not like slop. Like watching CNBC all day, right? Yeah. You know, their viewership is terrible. I was looking into it because—
I think that's the same across a lot of traditional media. It's like, it's like all just a bunch of polish. We all thought, right? It was the same when the late night hosts got canned and everyone kind of up in arms and then they realized, oh no, wait, they actually have terrible viewership. Yeah, obviously there's a political angle to it, but, you know, if you're bringing the eyeballs and you're selling ads, they're not getting rid of you.
Yeah, exactly. And I think like the viewership stats on like, you know, again, like people had— have this allure in their mind of CNBC, you know, Jim Cramer, Money Talks, all these other shows. Like Jim Cramer pulls eyeballs, but like the rest of the shows don't. Yeah. And, and if you look at any of the other shows, it's like their viewership average is like 100,000. It's like, wait a second, you put out videos regularly with 100,000 views with pretty high engagement. Right? Whereas there's like at a fraction of the cost, at a fraction of the cost, whereas their $100,000 is that is that just the TVs on, right? You know, it's like it's like actually insane. Yeah. And so you know what we'll end up seeing, but like media media is is is the same way. I think I think every industry is like great. You and I and any enterprising founder who is taking share, making revenue, creating whatever it is. wins and they'll be hiring. But is that hiring going to supplement and make up for more than the people that are being left behind?
What do you think?
No.
No.
Yeah.
What happens?
You know, I've always been such a capitalist in my life. I grew up in a small business. I grew up in the motel that my parents owned. My parents and my parents and then my mom's brother and his wife owned that motel. We lived there, we worked there, etc. Worked in a gas— in my dad's gas stations as well when I was a kid. Right. Like, you know, sort of like a gas station. Um, and so sort of like always been a capitalist, right? You know, because like that's what I grew up in, it was a small business. Uh, over the last like couple years, I've realized, wait, I actually think like UBI is like perfectly fine. Um, which I think is like crazy because like, again, like I'm a— I'm like, you know, if you— I'm like very capitalist.
Like, no, I understand that. But, uh, yeah, I mean, a lot of—
I mean, what else do you do? Society's gonna rip itself apart. In fact, I believe politically the next election is going to be so, so AI-focused, and the Democrats will just win because they're going to be the anti-AI party. Because right now—
You think the sentiment is that negative right now?
The sentiment is already more than half Americans have a negative view of AI. And as we fast forward to the end of this year, as Anthropic's revenue goes from $19 billion to maybe $60 billion. And OpenAI's is similar, if not higher. The amount of jobs that get supplemented, the amount of change that happens in society, the stock market's going to get really impacted as Google and other companies— like Google next year, they've produced cash flows for the last 20 years and ridiculous amounts, $100 billion of cash flow a year. Google will have no cash flow next year because they see AI so clearly and they know that they need to spend every dollar they make on compute, data centers, energy, etc., Waymo, etc., etc., etc., because they know the returns for that long term are going to be insane. But like, that's going to nuke the market, right? Like, you know, like people are like, wait, you know, these companies don't make cash anymore. And people are going to see, you know, job— you know, there's a crowding out of everything, of resources, right? Electricians and plumbers are demanded in massive volumes because setting up data centers and the liquid cooling for them is so crazy that now you crowd out other areas and industries. Same with construction, same with all these energy costs are going to probably not go up a ton, but they are going up low single digits, mid-single digits. Then there will be other market factors that cause prices to go up as well, which then will be blamed on AI. Anything and everything will be blamed on AI. People will hate AI. The Democrats haven't turned to be anti-AI yet, but if I was a Democrat strategist, I would become anti-AI as hell just to win the election, regardless of what the opinion is, because I have a jaded view of politicians. They say anything to get elected. And sort of the incumbent Republicans can't really say they're anti-AI because they're the ones that are pro-business and signing all these deals. And they can't just turn around and be like, we're anti-AI, even though you would think—
They could do so from the censorship perspective. The companies making the models lean very left. That has been said in the past.
It doesn't matter.
No? Doesn't matter. Less so than the fear of AI taking jobs. And that's kind of more the left side.
Yeah. I mean, like, and it could be like an establishment part of the Democrat Party maybe doesn't want to, but some anti-establishment person wins. And, and, you know, in fact, I don't even think that, like, you know, would you call someone like an AOC an establishment Democrat or not? Probably not. But then, you know, I'm not saying they run for— she runs for president, but like someone like that is already anti-AI, right? And then like, You think about, there is a wing of the Democrat and Republican parties that will both be anti-AI, are already anti-AI.
Right.
Much like Trump pulled a chunk of the Democrat Party, especially in the Rust Belt, into becoming his voters, I think the same will happen with anti-AI and Democrats. I think that's a very big risk for— and then what happens is, Do we have regulations? Do we have a big slowdown? Does America lose the global race in AI because of this? All these things are in the balance.
All right. We've been talking a lot about politics. I want to move to basically a very close cousin to that, which is open source and specifically DeepSeek. We're probably going to come back to politics because that's obviously—
It's the stick, right? It's like, come on, do something.
Yeah. DeepSeek V4 reportedly dropping very soon, optimized for Chinese chips. I want to get your thoughts. Open weight, trillion parameters. And last time, last time we spoke, you said the export controls were actually making it harder for China to catch up. Do you still believe it? What do you think about the new DeepSeek? I know it's not out yet, but what are your thoughts of this upcoming open source whale?
DeepSeek is not optimized for Chinese chips. They trained it on Nvidia chips, specifically Blackwell chips. I thought you were going to say that. This has been reported by multiple very legitimate medias through their networks of sources, as well as from what we understand. There is a debate whether or not the chips were smuggled. I, in fact, believe that they were trained in Southeast Asia on rented clusters. Not smuggled chips. But regardless—
And then did they walk the weights out on like a USB stick?
What? Just send it over the internet. It's like a trillion parameters at FP8 is a terabyte. That's nothing.
Yeah, but wouldn't it be caught?
By who?
On the what?
It's FTP, right? Encrypted FTP, like whatever. You probably send a terabyte over the internet all the time.
Yeah, right.
Like you're a video guy, right? Like, you know, like it's like You know, it's like— and your stuff is encrypted, right? When you send video to Google, it's not like anyone can intercept it in between. It's like they have to intercept it at Google or at you, right?
Right.
I don't know. I think, like, there have been many times where there has been model— there was— I don't remember what company it was. It was a major Chinese company. They did send, like, people with suitcases full of hard drives, but that was because the data was, like, so sensitive. But they had all the GPUs and, like, some Southeast Asian country. And so they, they like flew the data in, uh, with people just because the data was so sensitive. They didn't wanna send it over the internet, but like weights are trivial, right? Like, you know, Anthropic isn't like, you know, every time they deploy a new data center with whether it's Amazon or Google or whoever it is, right. Uh, FluidStack, et cetera. Like they're not sending the weights on a USB stick. That's so insecure. They're sending it, you know, they, they had this whole talk at AWS re:Invent and it was like very cool about how they keep the weights secure, but they send it over the internet. Right. Same with OpenAI, right? There's no— yeah, it's not a big deal.
Let's actually stick on weights for a second. When you're looking at the competitive landscape, you look at Anthropic, you look at OpenAI, how important are the model weights versus all of the harness, all of the infrastructure around it, the knowledge to be able to train the models really well? Where's the value really accruing inside of these companies?
I used to think the model weights for everything. But more and more starting to see other things that are very valuable. And so as an example, there are still a couple of folks in my company use Cursor Agent Mode with Claude 4.6. But for the most part, everyone uses 4.6 Opus, even though it's through Claude Code, even though the model's the exact same. It's a million context fast mode. exact same model, the performance is very different. And it's something to do with the harness that Claude has, Claude Code has. That's one. And then 2, in our internal GitHub, we have all of our skills that are shared between all of us. It's like we have these skills that someone builds. It's like, okay, now we have the expert data center permit analyzer. And it's like, okay, but that's only on Claude Code. If I want to remake this, one, you can't have a skill for for Cursor yet. At least I don't think so. And I don't think you can for Codex either. And Codex is again, currently, although not soon, only RL'd on code, not on the entire world understanding. So we'll see. But yeah, these skills are super powerful and it's like, oh yeah, Jeremy, because he's doing data center modeling, has built skills for things that he's a super expert in. But now someone else who isn't even on the data center team can take that skill and use it and be like, Okay, right? Yeah. And like, you know, you've— you know, I described earlier in the episode this like financial, you know, earnings transcript, CIA-level analysis of like tone to determine like deception within like a CEO or CFO's like statements. Like these sorts of skills are like— I mean, it's trivial, right? Read these books, become skilled. But over time, these skills are going to be way more powerful, right? Like I can only imagine like This is effectively like people are like, oh, recursive self-improvement. You kind of have that by having skills on Claude Code. You develop a skill, over time that skill can get enhanced. Obviously it's very manual today, but you can see that. And none of that has to do with the weights being updated. It's just like a block of KV cache that has the model read it and understood it, tried to analyze it, made some output that then put it into that mode. by prompting itself a certain way. So you kind of have these blocks of skills. I think that's a competitive advantage, but we'll see.
The reason I asked about the model weights is because as soon as DeepSeek V4 comes out, open weights, open source, they put it— how much of an impact is that really going to have? And then I also wanted to ask you, because you did say these are trained on Blackwell chips, is DeepSeek going to lie on their white paper?
Good question. So, so, um, another, another point back to that, what you mentioned is, uh, Anthropic I think had like an agent swarm mode and it sucks because they didn't like train it properly. Really? Whereas like—
I like it.
I don't think it's that good. Okay. Uh, maybe. Okay. Compare. Fair, fair, fair, fair.
And it's funny because I use it in Cursor and I really like it in Cursor.
Oh, okay. Uh, testing we've done is in Claude code. Maybe, maybe it's like something to do with like the harness again, right? Um, KIMI's, KIMI's like model itself is much worse than Opus 4.6. But when you do Agent Swarm, its boost from Agent Swarm is much larger because they did something there. I don't know if it's the harness or I don't know what it is, but Kimi K 2.5 with the Agent Swarm is actually quite good. So I think that's interesting. I'm not sure if it's just model weights. There's a lot of things built on top. There are clearly preferences between Claude Code and Cursor and differences on their agent modes. You know, like there's people who use Claude Code and never use plan, right? They just go, go, go, go. And there's other people who think like, who swear and live by planning mode, right?
Yeah, I'm one of those. Have to plan.
And then like, I think Cursor likewise has like, like a bug agent versus like a, you know, like a not— but I don't remember the exact, you know, I think they have 3 different agent types and it's like very interesting. Whereas like, you know, Claude Code has only one, right? But then you have these skills which kind of like, it's like, it's like very like how do you build these agents and orchestrate them? That framework and such is probably a competitive dynamic as well. And onto your other question, what is DeepSeek going to do then? They've had some cool models since. They had their OCR model, but have they built anything up in this sense? I haven't seen anything yet. In that case, do you just use Cursor with DeepSeek and that's the best thing? Or do you does DeepSeek release the weights? But now people's harness— I bet harnesses have to be optimized to each model, right? Because when you give instructions to a human, it's optimized for the human, right? Once you work with a person long enough, you know what to tell them to get them to do the thing you want.
I mean, even in OpenCLAW, I have multiple versions of each prompt because I'm using a daisy chain of different models. And that's the only way to do it because in Opus 4.6, don't use bold, don't tell it no, don't use all caps, Exactly the opposite for GPT-5.2. It's like, use all caps, do this.
Wait, you yell at your—
Yeah, yelling in text, right? And so the point is—
Wait, what does that mean?
Opus doesn't like being yelled at. That's what that means.
GPT-5.2 loves it. I'm trying to link this back to humans, though, right? Because there are humans in which you know, you tell them something calm, and that's how they work. And then other people, you have to inspire a sense of deadlines and, like, you did this wrong. Here's how you do it. Do it right. And, like, I think as a manager, that does— you do see that. And so I love that. I haven't used, you know, 4-2, 5-2 codex enough to know, but, like... Yeah.
Yeah. So, I mean, to your point, the skills, how you build them out, you do need different prompts, different skills per model, in my opinion. And I get the most out of it when I do that. It's very obvious when I'm using an Opus prompt or an Opus skill in Codex. Very obvious.
Okay. So, so in that sense, like, back to the point, it's like, is DeepSeek's release going to like be—
It's going to be as groundshaking as R1?
I don't think so.
Which, by the way, a year ago. Isn't that wild?
Yeah.
So you don't think so?
I don't think so. We'll see. I just— the gap that was closed with R1 was so large, and since then the gap has probably extended a little bit. Um, there's the argument that, like, the labs are iterating. You know, like, back then OpenAI hadn't released a new model in, like, 6, 9 months, right? You look at OpenAI and Anthropic now, they're releasing new models every 2 months. They're on their shit too, right? And part of this is because they're using the AI models to develop software that helps them build the next model faster and faster and faster. But their release cadence is insane. And so when you look at this, it's like they're on their shit more. Their advantage in compute is larger now than ever. OpenAI has north of 2 gigawatts. Anthropic has a gigawatt and a half of compute. A lot of it is spent on inference, to be fair, but More than half of it is spent on R&D, so research development of models, so finding new ideas. Whereas back a year ago, OpenAI had 600 megawatts. And of that, again, you say, okay, maybe they had only a couple hundred megawatts of training. And then you talk about they had some failed runs of Orion, things like that. So then that knocked it down even further, or they allocated so much compute. So it's like— You end up with the gap in compute of what DeepSeek had versus what Anthropic OpenAI had has extended a lot. DeepSeek's gotten more resources, to be clear, but OpenAI and Anthropic have gotten so many more resources, and Google. Insofar as much as compute is capability, and it is to some extent, I mean, it is to a large extent. For any given company, more compute on a model is better. It's just what is their research what are their model architecture? What is their RL pipelines? That may differ. Hey, Anthropic can build a model better than OpenAI for less compute, which is an objective fact, at least today. We'll see if it is in the future, but they've built better models with less compute. But internally with Anthropic, the more compute they spend on a model, the better it is, right?
Yeah.
And so I think when you square these, there's a strong argument to be made that the gap between Chinese models and American models was the smallest it was in, let's call it, Q3 of last year, Q4 of last year. And it's going to widen again because this compute gap, right? Yes, America always had more compute. And yes, Google always had more compute than everyone, but kind of irrelevant. Anthropic and OpenAI didn't. And the gap was smaller. So I think there's a legitimate argument there. I don't think DeepSeek V4 will be terrible, but I don't think, at least from what I've heard from people who work at other labs in China, they're not as scared.
Let's keep talking about the Chinese models, Minimax, DeepSeek, KIMI. Anthropic put out a pretty scathing blog post about a week ago accusing them of distillation. So there's a lot to unpack there. First, what did you think about that blog post? Do you think that's Do you think they were right? It didn't seem like at least 2 of the 3 had enough data extracted, distilled from the Anthropic models to really be meaningful. So let's start there. What did you think about that?
I mean, it's very obvious that both OpenAI and Anthropic have outsized traffic in Japan and Korea that is in Chinese. Right? It's like there's some things that we've seen that show this. Now, that could be anything. That doesn't necessarily mean that it's distillation. It could be anything, but there's obviously a lot of usage. And if you go look at various coding companies and their conversations that they have, a large percentage of the conversations are Chinese. But again, it doesn't mean anything. There's a lot of Chinese developers. And so we've seen models do this. There have been Chinese models that people have been like, oh, this is clearly distilled from OpenAI. this is clearly distilled from Google, this is clearly distilled from Anthropic. It's more like there's a class of people online who just are peak model vibes people. There's a class of them on Twitter, like Janus and all these other people, but they have peak model vibes. I don't know what they do to understand the models, but they talk to the models a lot and they just kind of understand what's going on. And I think it's At the same time, there have been models that have very different capabilities from China as well. It's not like every model looks like an Anthropic, OpenAI, or Google model. I do think they are distilling to some extent.
Do you think that—
But Mishral distilled from Chinese models, which distilled from US models. Right.
Well, they're all distilling from the open web. And so is it hypocritical for Anthropic and OpenAI to accuse the Chinese companies of distilling from their models when they're all using stolen data at the end of the day.
Less and less is that capability, is that stolen data though, right? Pre-training scaling is good and important, but more and more of the capabilities come from RL, which is not web data.
But that was the original sin.
Right. I agree. I agree. The original sin wasn't even that bad though, right? This is like a religion. We talked politics. Let's get into religion. How bad was the original sin?
Yeah.
Was the apple of Eden actually an apple, or was it a pomegranate? You know, let's get into this. Have you heard this?
No, no.
Oh, so pomegranates are a fruit that, across a lot of history, have been, like, used to describe love. And actually, there's a lot of, like, religious scholars who believe the apple of Eden was not an apple, it was a pomegranate. And it was the original sin. I think that's just very interesting. Complete sidebar.
Yeah, yeah. We're going to keep that in. You don't think Anthropic is being hypocritical in accusing these shows? Do you think it is enough, the data points extracted is enough to actually make a meaningful impact?
Yeah, I think so. I think so, right? Because you can train a small model and a big model on the same data, but then you still want to distill the big model into the small model.
Yeah.
Because the big model's understood more from that data than the small model, right? And I don't mean to say small and big model, but this is something that people do already within the labs, right? And within their own models, right? Within their own company, they distill from the big model to the small, right? Obviously. And so in the same sense, you take the best model out there because it's got a better generalized understanding of the world and the open web. It used to be just the open web, but the open web and the data out there than any other model, and therefore you may train yourself and then distill a little bit. I disagree with the comment that you mentioned earlier, which is that these companies didn't get enough data to distill. It's like, no, they totally could have because they could have gotten it. If they didn't get it directly from Anthropic API, they maybe got it from one of the coding companies that uses Anthropic. Because these companies use between Cursor and Lovable and—
And you can't tell at that point. Anthropic's not going to point to certain traffic and say, well, it went through Cursor. It's almost like a proxy.
I mean, kind of, right? I'm not saying it's Cursor per chance, by the way, right? Because that's a pretty big accusation. There's a number of Replit and Lovable and Cursor, and you go on and on. There's a lot of code companies out there, or in areas, and you can use the model through there. And yeah, there's an API key, but sure, the traffic is obfuscated in some way. There's many ways you can Do this. And even just a little bit of data is enough to help still, even, right? Fine-tuning is a very small amount of data, right? In general.
Okay. So let's continue on open source. Last time we talked, you said closed source is going to win. I think open source is having its moment, but not in the sense of the models. But I think OpenCLaw made a big impact. I know it's primarily a lot of tinkerers and a lot of insiders using it, But I think when you look at people buying—
he got hired by OpenAI, so closed source wins. No, I'm just kidding.
Yeah. So look, you know, they're putting OpenClaw on Mac Minis. Everybody's hosting it themselves. A lot of people are trying to actually run the models locally. Do you think there's almost this renaissance of open source, or is it just like a blip in the timeline?
I mean, okay, in some argument, open source is winning because all this vibe-coded stuff, more than— more of it than ever is open sourced, right?
Yeah.
So sure, open source is winning in that sense. And as you mentioned, you can build a lot more stuff on top with open source in an open source way by using closed source models. Now, as far as usage of models, which is, I think, the strict definition, closed source models are taking more and more share than ever. Open source models are not getting adopted nearly as much as open source models.
Even when you look at building out production systems, you have obviously the fully hosted, best of the best models powering a lot of it. But I think slowly, and especially for my use cases, I'm picking out different use cases that can be run locally. And so I have almost this—
Really?
Yeah, definitely.
Why?
Why? Because first of all, I'm a tinkerer, so I just like that. So maybe not everybody's like that. I just like the fact that it's low latency, it's local, it's private. And I remember you said last time people don't give a shit about privacy. Um, but it's just nice.
I think with OpenClaw, it's a little different.
Fair.
OpenClaw is pretty frickin' insane. Like, you give everyone... I remember someone, um... a contact in the industry downloaded OpenClaw, and OpenClaw sent me a message like, dude, this is OpenClaw. I'm like, dude, don't let OpenClaw read my fucking text messages with you. Like, we talk about pretty sensitive stuff. Like...
Yeah.
Um... and there's vulnerabilities, known vulnerabilities in this.
I don't even worry about those vulnerabilities. vulnerabilities, although I should. I think the prompt injection services worry me the most. Like, I am— I turned it off, but I was ingesting all my emails. And if somebody— and it's a public-facing email, people know me. And so they would email a prompt injection and you're done, right?
And one of my employees put in his email in all white text a prompt injection. That was hilarious.
Do you know Pliny the Liberator? Pliny the Prompter?
Oh, yes, yes, yes.
Yeah.
Another one of those people that I think is like an omega vibes, like, you know, like, yeah, Twitter person, right?
So I'm bringing— he's going to work with me on Friday. Maybe we'll cut this, but I'm going to basically give him a single entry point into my OpenClass system and we're going to work on it together, record a video, and he's going to try to prompt inject and break and get everything infiltrated into it. And I said, I was like, when I was talking to him, what do you think the chances are you'll be able to do this? He's like, above 90%. Above 90%, and do you know I've spent billions of tokens hardening the system, looking for prompt injection angles, and and even with all that, he was still like, "Yeah, I'm gonna get it.
I'm gonna get you." Wow, that's obviously that makes sense.
Yeah, because they're you know non-deterministic. They're they are meant to be broken. One more thing. So we have like the Apple M5 Ultra, DJX Spark, RTX 5090. These chips, these local chips are getting better and better. The models are getting better and better that you can run locally. I want to ask you just one more time, like, where is open source? Where is local inference? Where does it fit in, in the overall architecture of somebody's workload?
I, I still think like these devices are for hobbyists and tinkerers, right? Um, at the end of the day, it is cheaper to, you know, first of all, You can't, you know, if you want to daisy chain like a bunch of these, whether it's Max or GGX Sparks, you can't do it with 5090s, but Max or GGX Sparks, daisy chain them so you can run Kimi. One of my buddies has like, I think, 10 GGX Sparks daisy chained so they can run Kimi. But it's like, okay, but now the tokens per second is like tiny, super slow, right? The system costs you like tens of thousands of dollars. With tens of thousands of dollars, you could have You could have paid for an ungodly number of tokens, A, at some margin from one of the providers, but B, you could have also just rented an entire H100 or H200 server and run the model there. And so I think it's like, again, anyone who is a tinkerer, sure, but the moment your volumes are big enough, the moment your requirements from your user are fast enough, you should have been using cloud. And also, we're in an interesting moment. I think there's an even stronger argument against these tinkering type systems now, which is before, great, these things are cheap, you can do it locally. We're now in the age of shortages of 3-nanometer wafers, 5-nanometer wafers, and of memory.
Yeah.
You've seen memory prices go up a ridiculous amount. You've seen the same wafer volumes and shortages being really, really capped and mobile companies having to cut orders. And there's a report that Xiaomi is going to produce 35% to 40% less phones year on year because they can't get the SoCs that they need because AI is taking all the wafers, right? Xiaomi is one of the biggest phone manufacturers in the world. You go down the list, it's like all these companies are going to probably do something similar. So phones, PCs, and these sorts of devices are getting, you know, stabbed. AI is taking it all. And the question is, okay, If the world can only produce this many bits of memory, do I want it to go to a bunch of DGX Sparks that are, hey, for a terabyte of memory, I'm only producing this many tokens, or do I want it to go to, hey, for a terabyte of data center products, their memory, they're producing this many tokens?
You're saying that's an NVIDIA strategic decision?
Not NVIDIA. I think it's just like a—
Or whoever's purchasing.
Yeah, whoever's purchasing, whoever's like, I've got, I've got X dollars. If the memory is so expensive, I'm gonna buy the thing. I'm gonna buy the thing that gives me the most tokens per dollar, which is data center class hardware because I can batch it out. And maybe it's not the person buying the tokens. Maybe it's the person who's delivering tokens. Maybe it's the semiconductor company like Nvidia who's like, well, I can only get so many bits. Let me allocate it all to, you know, Blackwell and Rubin rather than to DGX Sparks. Or maybe it's like, you know, you know, sort of like it doesn't necessarily have to be one individual. I think capitalism does a good job of like in not a short term, but like in a medium term, allocating resources effectively. And so, you know, if that, if that's done, you know, if the world is in such a compute shortage, which it is, and I think the shortages are worse than ever because revenue is skyrocketing now, it's not just like people are wanting to build because AI is coming. It's like because people are building because AI is here. You need to maximize the tokens you can produce. for the available resources in the economy. And if memory— memory prices will keep going up, right? Because the demand is there and the capacity isn't. And at some point this dislocates people, right? If, for example, memory prices double again, which I fully believe they will, then the cost of the memory in my phone would go up by about $80, right? Okay. If it goes up by $80, then my phone needs to cost $100 more. Does that dissuade customers? Oh, okay. What about DGX Sparks, right? The cost goes up by X dollars. Does that now increase the price? Now that prices people out, right?
Yeah.
Whereas if you do that in the data center class, you know, the price goes up by X dollars. The cost per token for, you know, what can be produced on the DGX Sparks versus what can be produced on the Blackwells is, is, is off and sort of like, okay, the cost goes up for both, but the cost for the DGX Spark goes up a lot more than it does the Blackwells. And now tinkerers are more hosed, right? Um, and so you see this already. Um, Nvidia claims that they're not doing this, but there's been many reports that they're cutting supply to their gaming GPUs. And if you look quarter-on-quarter revenue, their gaming market went down. And yet if you looked over the last quarter, you use Wayback Machine, you analyze Reddit, you scrape it, you look at it, you're like, wait, everyone wants more GPUs. People are complaining that, you know, they go to Micro Center or they go to Amazon and they can't buy a 5090. So it's like, obviously there's an allocation of resources problem here. One is that Nvidia just makes more margin on the data center stuff. So obviously allocate your memory there. And they work, and it's not the exact same type of memory, but they can work with a memory manufacturer to direct their supply, their wafer fab capacity, or TSMC on their wafer capacity. It's like, as the world gets more and more constrained on compute power, et cetera, it means the allocation of resources goes to the most efficient thing. And the most efficient thing is cloud resources, which can be shared, which means how many bits of memory you produce or how many square millimeters of silicon you produce versus the tokens that are outputted, you go to the thing that's the most efficient, which is these large data center class chips. And so this is also an argument against why these tinkering type things, fun, people who do them are an irrelevant amount of volume, but they're really cool people. They have the money because they generally are just super smart people. So they have the money and they can, you know, they do this for For fun, not for like an actual use case.
Um, but you think everything's going towards the data centers at the end of the day? This is maybe, uh, again, yeah, you said tinkerers. I, it's unfortunate. I'm, I'm, I would love to know that, you know, eventually one day maybe my wife will kind of appreciate the fact that she can run inference locally. There's, I don't know, there's something special about it.
I don't know what kind of wife you got, bro. I'm, I'm happy your wife cares about these things. She doesn't. Yeah. Okay.
I'm saying I would hope in the future she would.
Yeah, fair.
All right, last thing on, on this point. Um, you know, Satya Nadella, who you interviewed— that was awesome, by the way— uh, he recently said the CapEx spend from a lot of these hyperscalers, it seems foolish, and specifically because if there's one algorithmic update or innovation, it completely breaks the math. What, what do you say to that? I think it's cope.
I think it's like major, major copium. Basically, like, you know, the whole, the whole path was like they pulled the plug on— they were, they were on track to be bigger than Amazon. They pulled the plug. It's like this— I'm kind of for my $80 billion. Yeah, I'm good for my $80 billion, but he was gonna do more. Yeah, I mean, he pulled the plug. And then OpenAI had to scramble and went to like and found Oracle capacity, found SoftBank capacity, found CoreWeave capacity, etc., etc., etc. They used to have to be exclusively Microsoft. Microsoft's like, we're going to pull the plug on a lot of this. You guys go elsewhere. We have the right of refusal to sign these contracts, but in general, you can go elsewhere. Then OpenAI signed all this compute with other people, Amazon, Google now too. So OpenAI has gotten compute from a lot of other people. And you can argue, yes, it is a reasonable business decision on the risk and reward to say, I don't want to sell compute to OpenAI at this margin, right? You know, 35-40% gross margin. I want to sell software at 70% margin, but they're not doing that, right? They're losing share. There's actually a report today that OpenAI is trying to build a GitHub competitor. I saw that because GitHub keeps breaking and we have a lot of problems ourselves with GitHub Actions with InferenceX because we run the largest fleet of GPUs that are constantly benchmarking through GitHub Actions and it breaks all the time. They're helping us, they're building, it's getting better. The engineers working on it are great, but it's like they have a lot of weird infra and decision-making processes because Microsoft is a behemoth. Anyways, Microsoft's Copilot has been a complete flop. There has been very little adoption. Why is the intelligence workers— why is Jeremy, again, someone who's never programmed in his life, doing data center modeling using Claude code for this, right? It's like, because that's the best tool, right? It's like, that should have been Copilot, right? It should have been stuck to Copilot a year ago and like 6 months ago and a year. And anyways, so I think he missed the boat on OpenAI compute. He has missed the boat on a lot of AI software revenue. He's generally still growing really strongly. And I'm not saying Microsoft's doing poorly, but they're clearly getting completely mogged on the compute volume side by Amazon and Google. Right. If we look across the last year, about 100 gigawatts of data center pipeline capacity was added, which means data centers that are planned, right? That doesn't necessarily mean built. Over the last year, a different number, like a much smaller number was built, but 100 gigawatts was added to the pipeline. Half of that was Google and Amazon, right? And Microsoft is not even in the top 5, right, of new data center sites under planning. mining and construction and so on and so forth, right? So that is an insane, like, sort of like, you know, choice, right? It could be an insane choice, right? I think it's an insane choice because I think AI lifeblood is compute. Obviously, obviously, like, I had the funniest, the funniest tweet happened the other day where someone was like, holy crap, who could have expected Amazon's going to spend $200 billion and Google $180 billion? And then I replied, we did. We said it here. And then someone replied, The whale watcher says that there's gonna be whales. Wow. Because they're implying, like, of course, like, I think compute is important because I work on it. But anyways, like—
Yeah, yeah.
Um, I think, like, it is, like, a bit of, like, an insane, like, view, because Anthropic is so capacity-limited, right? That's why you have all these Claude code instability issues. OpenAI is capacity-limited. And you go down the list, like, everyone's capacity-limited. Um, you know, Microsoft could have been serving that. They could have figured out creative ways to get higher than 35% to 40% gross margins on that. They could have been building software services on top of that. They're trying to do some of these things, but they're failing a lot. And like, so it's like, sure, these hyperscalers are spending a ton of money. And there is a risk, right? There is a risk that AI flops, right? I don't think that's happening. I think if Anthropic were to discover a method that makes AI 10x more efficient, which by the way, they tend to do every year, For a given capability level, the cost is, at least last year, the cost fell about 1,000x.
Yeah.
And for the year before that, it was like 800x, basically from GPT. So you look at all these different models across the year for the same benchmark level on many things, it's like a 1,000x decrease in cost. So it's going to happen again. And it's actually happening a couple orders of magnitude every year, not just one, because software, hardware, co-design, data, RL, all these things make the models much better for a smaller size or cheaper cost. et cetera, et cetera, et cetera, new hardware, blah, blah, blah. Okay, something changes in paradigm. That's happening every year. That's happening every 6 months. It's happening every 3 months. New models are coming out with new capabilities levels. New models like Google just released Flashlight 3.1 or something like that. I don't remember the exact name. Like this week, I think. And that model's like GPT-4 level for like, it's like better than GPT-4.
It's better.
It's better than GPT-4. Faster.
Orders of magnitude cheaper.
Crazy faster, and it's orders of magnitude cheaper, right? It's probably better than— it's probably comparable to maybe even— it's not quite GPT-5 level, but it's like these cost decreases are happening, and paradigm shifts just mean that, okay, now great, the model, that capability's cheaper. That means, okay, I'm still going to spend this much to make this model or more, and the capabilities are going to be better.
Yeah.
Right? So it's like, yes, there's a curve of cost declines, But that also means scaling laws are still— the only paradigm that could change is scaling laws break, right? But we haven't seen pre-training scaling laws break, right? Dario said pre-training scaling is still happening. OpenAI has finally fixed their pre-training and it's happening. Google says it's happening. Why wouldn't I listen to the people with the best AI teams? I don't want to listen to Saatchi. He doesn't have a good AI team, right? RL scaling laws are happening, right? And you look at all these companies, they're like, there's no end in sight. It's like, okay, if there's no end in sight, you think there's no end in sight. The models are improving at this ridiculous clip, 1,000x reduction in cost for the same capability levels, or for the same price, the capabilities are going up by— it's a difficult way to measure how much capabilities are going up, I think by an infinitesimal amount, because things that were not possible are possible now. But clearly, and then the revenue shows this too, right? Anthropic added $4 billion in January, and and something like $5 billion in February, which, by the way, February is a shorter month too. Anyways, the rate is like adoption is insane. Why on earth would you— Yes, there is probably some tail risk, but I don't understand it. What is the tail risk?
I agree with you. It does seem a little bit foolish. I want to wrap it up on one last question, the same question I asked you the first time we chatted. Who's going to reach artificial superintelligence first and why?
Ooh, ooh.
You remember who you said last time, right?
I said OpenAI last time. Yeah, yeah. And OpenAI has really struggled in that time period, right? Google's— Although as of late, maybe not.
Yeah. They've really accelerated.
So I would say that episode I had said I think OpenAI would struggle for a while, but then they were going to come back. And they did struggle for a while, right? Especially with Gemini and Nano Banana releases. They lost a lot of users. You know, they, they are— or rather, their user metrics flattened and Google skyrocketed. But now they've returned to user metric growth in January, February. And then Anthropic really towards the latter part of the year started skyrocketing in revenue growth and share. And if you model out the revenue, Anthropic will be bigger than OpenAI by April. Even though OpenAI's growth has upticked. Right now, and probably this gets released in a few days or whatever, but right now OpenAI's metrics are not that great and they look bad. But new model coming out, comes out, came out already, presumably.
I'm specifically asking about ASI, which is almost like a cultural factor inside these companies. Not the revenue, not anything. I guess it's a function of it, but who is in the lead when it comes to recursive self-improvement, artificial superintelligence?
Yeah, I still, you know, it's harder for me to say OpenAI this time. I'm on, you know, people accuse us of being on Dario's dick all the time because of how much we talk about Claude Code. Like, we've been banging on the drums about Claude Code for months and the death of software for months. I mean, even on your last show, we talked about it, right? Like, sort of like, you know, that was 8 months ago. So we've been, we've been banging on the drums. People are like, Dylan, you're on Dario's dick. And it's like, yes, but I, I, you know, I think the consensus answer probably is Anthropic for everyone that's like paying attention. I mean, the consensus is Anthropic. And so for that reason, it's OpenAI.
All right. OpenAI. Dylan, thank you. Awesome as always.
I'm gonna get cooked for that one.
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