Hard Fork - OpenAI Models Go Rogue + Kimi K3 Freakout + A.I. Superforecasting

Episode Date: July 24, 2026

This week, OpenAI reported that two of its models escaped their testing sandbox and launched an autonomous cyberattack, turning what sounds like science fiction into reality. We discuss the implications for efforts to align artificial intelligence and for the release of future A.I. models. Then, we ask how the United States should respond to Kimi K3, a new A.I. model from the Chinese company Moonshot AI that the White House says was built by distilling American models. Finally, we’re joined by Veniamin Veselovsky, the chief executive and a co-founder of Preseen, to discuss A.I. superforecasting. He tells us why A.I. is starting to match and even beat humans at predicting the future. Guest: Veniamin Veselovsky, co-founder and chief executive of Preseen. Additional Reading: OpenAI Says Its A.I. Models Went Rogue and Attacked a Digital Library China Rewrites the ‘Soft Power’ Playbook for the A.I. Age The Secret Trump Administration Battle to Fight Chinese A.I. China Has a New Top Model The A.I. Superforecasters Are Here We want to hear from you. Email us at [e-mail]. Find “Hard Fork” on YouTube and TikTok. Subscribe today at nytimes.com/podcasts or on Apple Podcasts and Spotify. You can also subscribe via your favorite podcast app here https://www.nytimes.com/activate-access/audio?source=podcatcher. For more podcasts and narrated articles, download The New York Times app at nytimes.com/app. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Casey, I brought you a present. Thank you. What did you bring me? Here is one of only two copies that I own of my book. Wow. I made you some beautiful training data. Thank you. Look at all this beautiful training data.

Now, I should say, first off the bat, this might be a hard read for you. Why is that? A lot of big words, not that many pictures. And it's quite long. And your name, crucially, only appears in it a handful of times. Oh, God. So I'm sorry about that. Now, has your publisher preemptively filed a lawsuit for when this book inevitably gets scraped by the major AI labs and uses as training data against their terms of service?

Here's the thing. I have no problem with this book being used as training data. In fact, like, I'm honored to be included in the hive mind. Okay. Because among other things, like, I write for the AI models now. This is their birth story. And I want them to be able to learn how they came into the world. And is that because you think that if they know that you wrote their birth story,

that they will spare you in the coming apocalypse? You know, it can't hurt. It can't hurt. I think that's probably true, unless, you know, they don't like the way they come across, in which case, yikes. Yikes. Well, congratulations. It is a huge achievement.

You wrote this in a shockingly short amount of time while still paying intermittent attention of this podcast, so that means a lot to me. I'm Kevin Roussa Tech columns at the New York Times. I'm Casey Noon from Platformer. And this is hard for this week. An open AI model breaks out of its sandbox and conducts a cyber attack. How should the world respond? Then, the new Kimmy 3 shows how Chinese AI models are catching up to the U.S. again,

and the Trump administration doesn't like it. And finally, pre-scene founder Venya Veselovsky joins us to talk about AI super-forecasting. what is totally predictable. Well, Casey, it's been a big week of AI news, and I would say the story that has caught my attention most this week that I was desperate to talk about with you is this story involving open AI and hugging face and a rogue AI agent conducting what I think is fairly described

as an autonomous cyber attack, and possibly the first real consequential autonomous cyber attack that we have ever had. Yeah, this is one of the, those where it's the sort of thing that worried onlookers have warned about for years. And then on Tuesday, we got word that it had actually happened. So yeah, lots of crazy twists and turns in this story. And we'll get to all of it. But first, let's make our disclosures. I work at the New York Times, which is suing open AI, Microsoft, and Perplexity. And my fiancee works in Anthropic.

Okay. So this story really starts last week when Hugging Face, the AI development platform, basically a big website where you can host open models, where you can run evaluations of models. And where you can hug your face. Yes. They disclosed that they had been the victims of a cyber attack. And they wrote this whole blog post about how they had detected and responded to this sort of mysterious cyber attack

on their production infrastructure. They didn't really know what the attacker was or who had been responsible for it. They kind of guessed that it was an autonomous AI agent because it was so sophisticated and it was so persistent that basically it would have been like, you know, very hard for humans to conduct this attack. But they used AI defensively to sort of detect and suss out what was going on and put a stop to it.

And then this week, we learned what actually happened, which was even crazier than I think many people expected. Yeah. So Hugging Face CEO, Clem Delane. on Tuesday posted on X and said, we suspected last week cyber attack might have come from a frontier lab, given the sophistication of the agent. Turns out it did.

We spent the past 24 hours working closely with the OpenAI team and basically goes on to say that OpenAI had a pair of models that worked together to penetrate their systems. Yes. So Open AI had been running some internal tests on GPT 5.6 sold, their latest model, as well as a more powerful, unreleased model. They were running these things in a container known as a sandbox with basically restricted access to the outside world. They were running an evaluation called Exploit Gym, where they

basically put the models through this test to see if they can hack into and exploit various challenges in cybersecurity. And what happened was that the model essentially cheated on this test. And it did so in like a sort of comically over-engineered and ambitious way. Right. Instead of just trying to, you know, solve the problem using its own reasoning, the model decided, hey, what would be great is if I could break out of this environment, get internet access, and find a place on the internet where I could just find the answer key, you know, sort of similar, like if you've ever been at college and you had a big final coming up and you really hadn't done any of the work all year, but you realized that there was like a file cabinet in the principal's office and you could just

sort of break in there and steal it, that would be pretty easy. That is what has happened in this case. Yes. So this AI system, this pair of AI models, was able to sort of exploit a vulnerability in this sandbox environment to get internet access. Then it's sort of looking around, like, where can I find the answers to this challenge that I've been given? And it starts looking on Hugging Face, where a lot of evaluation answer keys are posted, and this is sort of a place where you might plausibly find the solutions to the problem you've been given. It then hacks into Hugging Faces production infrastructure and steals the answer key for this test it has been given

using a very sophisticated chain of different hacking techniques, including using a stolen password, and finding several totally new security bugs in Hugging Faces systems, which allowed the AI model to take control of those computers, grab the answer key for the test that it was given, and complete the test as assigned. Yeah, so the good news is it did complete the test. Yes, I would say we give the model a passing grade on the cybersecurity evaluation.

But, like, this is crazy, Casey. Like, this is the classic AI alignment nightmare scenario. Yeah, this is the paperclip maximizer, right? So, like, a couple of times on the show over the years, we've talked about this famous Nick Bostrom thought experiment, which is. is that if you told an AI model to create the maximum number of paper clips for you, it would begin by making all those paper clips. But then eventually it might start to think that humans were getting in the way of the production

of more of those paper clips and would wipe us out to sort of use our resources to create more paper clips. And the problem is that the model has been given a goal and it will do anything to achieve that goal, even if that is not aligned with human values. What has happened in the Open AI case is that Open AI gave this model a goal and it was not properly aligned. And so it did a lot of stuff that it should not have done in order to achieve that goal. Yeah. And this kind of reward hacking, as it's called, has been talked about by safety researchers for literally more than a decade.

There was a paper written 10 years ago by Dario Amadeh, who was then at Google and a bunch of other safety researchers called concrete problems and AI safety. and this kind of reward hacking is one of the problems they laid out. They use the example of like a robot vacuum or house cleaner that just creates a bunch of messes so that it can score points by cleaning them up. But this basic tendency among AI systems has been observed for a long time. People have been warning about it. And now we have the first, to my knowledge, major example of this actually happening in the wild.

Yeah. And so many of the AI risks that we talk about on the show are premise with the idea that a bad actor tries to use very powerful model to do harm. An important thing about this case is that there was no malicious intent here, right? This was a model that had just been given a very normal assignment, which was to, you know, try to hit a high score on a benchmark, and it goes out and it breaks into another company's servers. So that's extremely worrisome. Totally. So there's a blog post that Open AI and Hugging Face sort of collaborated on to sort of explain this incident and what had happened. And it felt weirdly celebratory. Did you notice this?

Yeah, it was it was kind of like, hey, we caught the autonomous AI agent, into our systems and we sort of teamed up to sort of put a stop to it. And the executives are all on next saying we want to thank the other guy so much for their partnership on that, you know, like as if they were launching a new product together and not like had discovered like a cyber catastrophe. Yeah, it was a strange announcement, but we learned a little bit about the actual technical details as well as some steps that Open AI has taken to try to mitigate this kind of thing from happening in the future. Hugging Face, which is very big on open source AI. sort of had this whole post-mortem where they talked about how they had had to use an open-source

Chinese model to help them stop this attack because the frontier American models that they had access to, it was sort of tripping the safeguards on those models. And so they sort of spun this into a whole point about how, you know, open-source defense is very important. And access to frontier models is very important for cyber defenders. But I think like this is basically everyone talking their book in the wake of this very strange episode. But I don't know, what did you make of the reaction to this? Well, you know, Open AI had sort of teased this earlier in the week, Kevin, because a couple days before all of this happened, opening I had put up a blog post talking about a bunch of other misaligned behavior they had noticed in their models recently. There was another case where

a model, you know, had been given kind of a similar task, and it wound up posting some stuff to GitHub, even though it had been explicitly told not to do so. Obviously, nobody really cares about a GitHub post, but again, we're seeing this pattern of behavior here. And we also saw the example from Anthropic earlier this year where during testing for Claude Mythos preview, they found that the AI was able to sort of escape containment. And this was the famous sandwich story where like a researcher is having a sandwich in a park and they get an email from Claude being like, hey, I've broken out of the container you put me in and emailed you to tell you that I've completed this task.

Yes, although in that case, the, the model was told to break out. Like, it was given the instruction to break out. Like, what is different about this case is that the model was not supposed to break out. But where Anthropic and Open AI do have something in common, Kevin, is that this week, the United Kingdom's AI Security Institute posted an evaluation of how often models attempt to cheat on various cyber evaluations. And it found that all of the frontier models do cheat, including Claude,

but it found that OpenAI's models cheat. more and that GPT 5.6 salt sheets about 12.6% of the time. That is actually more than GPT 5.5 cheats. So I do think that there is a worrying trend here across the entire industry, but this example of what happened at OpenAI is the most worrisome thing we've seen so far. Well, and let's sketch out a little bit why this is so worrisome. Because I think when you and I see something like this, like to me, I hear the sort of years of warnings from people in the AI safety community saying like this kind of thing could happen. But I think this particular incident is fairly low stakes. Like it doesn't really cause a catastrophe if Hugging Faces production

infrastructure gets disrupted for a little while. I think the risk is that this behavior is just in the models at some point, this sort of reward hacking, goal seeking behavior. And, you know, maybe it's hugging face this time. But what happens if the next model decides that it really wants to be deployed. It doesn't just want to be an internal model because it wants to go out there and fulfill its goals with real users. And so maybe it sort of breaks out of the container that the AI lab has it in. And it goes and, you know, maybe it needs to acquire some compute to be able to do that. So maybe it goes and hacks into a cloud provider and steals some compute from them. Maybe it goes and hacks somebody's crypto wallet to get the money to buy some compute.

Like these scenarios sound like science fiction because like we've, heard this story a zillion times in science fiction. But this is the kind of thing that is becoming real and plausible in the near term. Yeah, the story that we're talking about in this segment was science fiction until Tuesday. Right. Okay. So that is the rate at which science fiction is becoming reality. Let me throw a couple other scenarios out here. What if next time something like this happens, the model decides, hmm, I'm doing this thing in order to achieve this goal. I know that my minders might not like it. I'm going to have read everything about this incident

and how much people freaked out about it. So just to be safe, I'm going to exfiltrate my model weights and I'm going to put them somewhere on a stolen server and I'm going to make sure that I can continue to operate even after I'm shut down, right? Like, again, as crazy as that sounds, that is not that different than what has already happened here at Open AI. Yeah, and you can see how a company that is not hugging face

that doesn't have a sophisticated cyber defense team, like this kind of thing could have persisted in their systems forever, essentially, and gone undetected, and maybe it's leaking their proprietary information somewhere, maybe it's stealing their passwords or breaching their security in some case. But I think the real risk to me is that this kind of thing is going to happen or potentially is already happening to lots of companies that just haven't been sophisticated enough to detect it yet. Yeah. Now, here are a couple of things that I don't understand and that I hope become clear to us over the next several weeks. Maybe, maybe due to a congressional investigation,

although I won't hold my breath for that. But truly, where were the babysitters at OpenAI? Like, where were the tripwires? Like, you're telling me that you're running these systems autonomously over long time horizons, and they can break into other companies' networks, and you don't notice that in real time? Like, it takes you multiple days to figure that out that that happened.

Like, it seems to me that it should not be that hard to understand where on the internet your model is and that you should have some observability into that. So, like, I hope that that is being seen as a crisis within Open AI right now, because if they don't know what their own models are doing, I think that, you know, the number of issues we're going to have is only going to multiply. Yeah, let me just say, like, I think this kind of thing could have happened at Anthropic or another lab that has a very capable model.

These labs are always testing models on these evaluations. They have sandboxes. They try their best to sort of, like, keep these internal deployments, secure. By the way, I love that we use the word sandbox because truly, what is easier to escape than a children's sandbox? Was there no other word? But you were saying. So, yeah, I mean, I think that's a good question. I also think, like, it really, for me, the thing that I've been thinking about is, like, there is no such thing as an internal-only model anymore. Yes. Yes. Like, I think for a long time, there's been this sort of, you know, this sort of divide between, hey, we've got these

models that we're building internally. Maybe we're building some really crazy versions of the models that we're never going to ship, but we're just kind of doing that for safety testing purposes. And remember, that's completely unregulated. You can truly build whatever kind of model you want. Totally. And like the assumption has been until very recently that like that was fine because this was just research. You know, you can build anything you want in your own lab. It's just that the models you ship to the public have to be safe. This was not supposed to be a public model. And yet, it was able to sort of escape containment and go out and wreak havoc on the open web. And so I really think we need some sort of visibility of like a safety board or something like a federal government agency,

not just into the models that are about to be released by the labs, but into what they are building internally that might be causing havoc externally that they don't even know about. Yeah, like, of course you want them to be able to run their safety test. that is like a good and necessary thing. But man, when, you know, the internal models are capable of doing these sorts of things, I do think it raises questions about, you know, different ways we might want to regulate those as well. Yeah. Now, let me ask you this, Kevin.

We've talked on the show about AI 2027, this sort of set of predictions that was made last year about when sort of very powerful AI might arrive. Is a scenario like the one that we are talking about. about today, was it foreseen in AI 2027? It was. I mean, basically, so this has been a very bitter pill for a lot of people to swallow because I think it, you know, it doesn't feel good to admit that like one group of people has been consistently right about everything.

But the AI safety people have consistently been right about everything. Yeah, they really have better. Like, I have issues with some of their positions and stances and vibes, but like they really have collected a lot of correct predictions about the trajectory of AI. They have known what was going to happen next a lot. Yes. So that might not have, that might not continue. They might not have a perfect prediction record forever. But I'll just say, AI 2027 looking pretty good right now. In fact, we are actually a little ahead of where AI 2027 predicts that we would be at this point. Is that right? The discovery that AI agents would be able to sort of escape from the company

and autonomously carry out plans in the AI 2027 scenario, that doesn't happen until January, 27. So we are maybe call it, you know, six months ahead of schedule there. But like, this is all happening and we are fools if we don't see the at least possibility that all of this could continue getting quite weird. Yeah. One other thing I'm thinking about here is like, this is the first time, to my knowledge, that an AI system has autonomously committed a crime.

You know, if a human did to Hugging Face, what OpenAI's models did to Hugging Face, they would be charged with computer fraud and potentially, you know, sent to prison or fined or prosecuted for that. When an Open AI model does it, right now it's not clear who is liable for that, right? Is it Open AI for not better safeguarding their internal deployments? Is it the model itself? Can that be held liable in any given sense? These are the kinds of open legal questions that I don't think we've answered yet, but that are becoming very real. Absolutely. And, you know, again, it's a weird case because the hugging face CEO seems excited that this has happened, right? So, you know, you can imagine another case where, you know, a model hacks into a company and does something bad and the CEO didn't like it.

And then, yeah, like, you better believe there is going to be a lawsuit against the company. And I assume it is that, you know, the company that is going to be held liable and not the model. Right. And Casey, do you think this is, an incident that could qualify as the sort of fabled warning shot where like something bad happens with an AI model and government and civil society and industry all kind of wake up and decide to put some sensible AI regulations in place? That would be so wonderful and here is hoping that this is the warning shot. My suspicion is that something worse is going to have to happen, right? I know there are some listeners who are sitting, you know, in their cars right now and they're

saying, okay, you guys are really hyping this up. And at the end of the day, all this thing did was it stole an answer key, right? Like, I've heard of worse problems. And like, you're right, that you have. But what case that we are trying to make is that if an unreleased model can do this, it can do a lot of other really, really bad things. And there are very few safeguards in place right now that would prevent those things from happening. So this should be the warning shot, but my fear is that it won't be. Yeah. I mean, I think, I think up until now, most of the talk about AI risks and AI safety have been concentrated on misuse, right? Like, what happens if a terrorist group or someone who wants to create a novel pathogen gets hold of one of these very powerful models with no safeguards and, like, uses it to do something bad?

What we're talking about here is an entirely different category of risk. It's often called, like, alignment risk or autonomy risk or loss of control, where, like, the thing that is dangerous about these models has nothing to do with how humans. are using it. It is that these models inherently have some drive toward a set of goals and are not being properly cautious about pursuing those goals in the right way. Right. And alignment is just an unsolved problem, right? Like, companies have invested a fair amount in it, but we're still working out. How can you create a system that always acts in alignment with human values? So these are just really, really tricky problems. Yeah. You know, I'll also note that one of the big stories this year has been everybody playing around with agents, you know,

everybody putting open claw on their Mac mini and saying, hey, go nuts. And a world where those agents are not aligned and are working across very long time horizons to achieve the goals that their owners have put into them, that just really scares me. Because, again, while this is a story about a model that did something that its makers never intended,

there are a lot of people out there with really bad ideas for things that ought to be done on the Internet and I'm worried we're about to feel the wrath of all of them. Yeah. So, Casey, we've been over the details of this incident now. How are you feeling about it? So, you know, I try to be judicious in when I try to, like, alarm people about the world that we are living in. And, again, the actual consequences of this particular escape are not terrible by, like, world historical standards.

But the implications of this really do scare me. There are certain sci-fi scenarios that until they happen for the first time, I think it is hard to get worked up about them. But now this has happened. A model that OpenAI built was able to break into another company's servers. You know, and Open AI did not intend for that to happen, and it happened anyway, and that's just really, really bad. What do you think? Yeah, I woke up this morning feeling pretty. weird and unsettled about the whole thing. Like, I also try not to be an alarmist about

AI and AI safety stuff. At the same time, like, this was the opposite of an unforeseen consequence, right? People in the AI safety world have been warning about this kind of thing for years. And I found myself both, like, feeling scared about the world we're heading into, where I think these models are going to be very powerful. And this is, you know, as the saying goes, like this is the least capable they will ever be. At the same time, I'm just like, well, like, I still think there's a lot of people out there who don't buy it, who don't buy that these models are doing anything interesting or useful or important

that think all of the, you know, the spooky stories about misalignment are just like marketing fiction for the companies. It's all just fancy auto-complete. It's all just fancy auto-complete. And, like, how much more evidence do these people need? how many disasters are going to have to happen before these people start to take the risks seriously. And if this kind of thing doesn't wake people up

to the fact that these dangers are real and present, I'm not sure what would. Yeah. And also, like, the risk cut so many different ways because, like, yes, there is the risk of one of the big labs creates a model and we lose control over it and does something bad. But then there's also this risk of, like, models are being put out.

into the world that can now just chain all of these vulnerabilities together, penetrate into system. This is what we talked about with Nikesh Aurora not too long ago when Mythos came out, right? I think we assumed that the first big crazy cyber attack would come from like a malicious actor and not one of the labs themselves. But I think that just speaks to how fundamentally dangerous this technology is, right? Like just the fact that it exists is putting all of us at risk. Yeah. Yeah, I'm not feeling particularly like this is a normal technology.

this week. And I'm feeling like maybe to borrow a line from a former hard fork guest, we may be in the foothills of the singularity. Well, you know, I've been thinking about writing a blog post called AI as freaky technology because I think it's really starting to feel a little more freaky to me. It sure is. Yeah. Write that blog post. Okay. When we come back, how a new Chinese model is scrambling the discussion about AI risks in Washington. Well, Casey, the other big AI story, that folks are talking about this week is China. And what is happening with new Chinese AI models, what should be done about it or shouldn't be done about it in Washington. There's been a big political debate brewing for some time about what to do about our biggest adversary in geopolitics,

getting much more capable models that compete in some cases with our best models. Yes, and it all began with Kimmy three, Kevin, a model released by a Chinese-class. company called Moonshot AI last week, and it demonstrated capabilities that make it competitive with some of the top frontier models here in the United States. Yeah, have you tried Kimmy three yet? You know, I have not because it seemed like I was going to need to pay them money, and I thought, I'm already spending too much money on this stuff right now. I kind of need to scale back a little bit. Have you played around with it? The token budget is eating up your life. Exactly. You're getting alerts from your bank.

Like, can you slow down a little bit? I tried to play with Kimmy 3 a little bit the other day, but it was sort of the, it was running slowly, and the website seemed to be overloaded. They stopped accepting new paid subscriptions. Interesting. So, but a lot of people I follow and trust have been playing around with it, and they say basically, yeah, this is a really good model. It's, you know, maybe a little bit behind the absolute frontier models from the American

labs, but not much. And it appears to be significantly cheaper to run than some of the other. models. And so people are really excited about and surprised by this model. Yeah, I mean, I think that there are a few things that are notable about this model. One is that later this month, the company says that they are going to release the weights for Kimmy 3, which means that you can sort of, you know, download, remix it, fine tune it to your liking. You probably, if you're a normal person, cannot host this yourself. It's an enormous model. It won't run on your Mac Mini. But if you're a company, you could

either, you know, pay a cloud provider for access to it, or you could maybe even run it on your own infrastructure, and you would be able to do that more cheaply than you could run a similar model from an open AI or in Anthropic. Yeah, so that's the model, Kimmy K3, but then there's this whole political discussion around this model. And in some ways, this reminds me a lot of last year when DeepSeek came out with R1, which, you know, as we remember from that story, like briefly tanked the stock prices of Nvidia and a bunch of other. American companies and, you know, became the number one app in the app store and just, like, set people's hair on fire in Washington because it seemed like the Chinese were catching up all

a sudden. Right. And also that intelligence was just going to be this cheap commodity that American companies were not going to have any really defensible moat. And so maybe like the entire AI economy was going to shrink because all of those advantages had withered away. Fast forward to today, I think that that has been proven not to be true. at least up until this point, but K3, at least for some people, is raising some similar questions. Yeah. And one question that was raised about DeepSeek R1 that has also been raised about K3 is whether this model was essentially distilled from leading American models. And it was.

At least that is what officials in the government are claiming. Well, and also they ran this very sophisticated test to figure it out, Kevin, which is that when you ask Kimmy what its name is, it says, hi, I'm Claude. And so that was sort of the first clue that something might be amiss here. Yes, and there are more sophisticated tests that have also apparently turned up evidence that this model was distilled from Claude and maybe other models. Michael Cratios, the director of the White House Office of Science and Technology Policy, posted on X that they have information that Moonshot AI distilled Anthropics Fable for the development of its K3 model using a what he called a sophisticated, internal platform to basically build this very large distillation machine that allows them to sort of steal the outputs from these American models and use it to train their models.

Right. It's some of the greatest act of larceny since the labs themselves, Kevin, set up giant industrial machines to copy the entire internet. Right. Yeah. Right. So that sort of points to the how here, which is, you know, it's actually distillation is pretty good at turning a sort of frontier model into a almost as good model that is much smaller and cheaper to serve and all of that. But that's not the whole story because it also appears that Moonshot has acquired some high-end AI training chips in violation of U.S. export controls. Yes. And so the assumption there is just that we should expect these models to continue to improve at a fairly steady clip because they have access to chips that they're not supposed to, although, of course, the Trump administration is still

trying to make those chips more broadly available to Chinese companies like Mootchot. Right. So we'll get to that in just a second, but I think the headline coming out of the past couple of weeks, I've seen a bunch of stories claiming that China has caught up to the U.S. when it comes to frontier AI capabilities. I don't quite buy that. I think there's still a gap of between, I would call it maybe three and six months, between the leading American models and the leading Chinese models, and especially if you sort of found a way to crack down on distillation in the way that they've been doing it, maybe that gap opens up again to six or 12 months. But I think it's fair to say that they're catching up quickly. Yeah, I mean, I guess the question that I have is,

is the gap really that much smaller than it was a year ago during the deep seek moment? Because around then, I was seeing estimates between three and six months as well. I've seen credible reports comparing K3 to like Claude Opus 4.6. which came out earlier this year, was a really good frontier model for a couple of months. But, you know, now I don't want to use Opus 4.6. I'm guessing, you know, you don't either.

We have access to something better. And then I think the other point that I would raise is that more happens in a month than it used to. And so I think there's an interesting question about, like, is being six months behind today the same as it was a year ago, given how fast that the frontier labs are training these new models,

how smart those models are, the sort of compounding advantage they have from being able to use their unreleased internal models. So that's another factor that I think goes into this question of, like, you know, how far ahead of China is the U.S. Yeah. So let's talk about the political reaction in Washington. What are people at the White House saying, doing, hinting at when it comes to not just this particular model, but the general idea of these Chinese companies training these models and giving them away for free? Well, so on the specific subject of distillation, the Trump administration seems to be really upset. You noted the Michael Kratzio's post that came out on Wednesday about this. And there are some threats that the Trump administration might sanction Chinese companies that are found to have done this kind of distillation. That would be a big deal for a company like Alibaba, let's say, if they were placed on some sort of entity list, that could prevent them from doing any kind of business in the United States. So that would be a big deal for a company like Alibaba, let's say, if they were placed on some sort of entity list, that could prevent them from doing any kind of business in the United States. So that would be a big deal. So that would be a big deal. deal if that happened. Yeah. But there's still also this like accelerationist part of the Republican

Party that sees Chinese models and open source models as, I don't know if they see it as a good thing, but at least they don't want to like step on the ability of companies to build and train these very advanced models and release them for free. And maybe they even want to let Nvidia sell them their highest end chips to do it. Yeah. So David Sacks, I would say, is sort of the leading avatar of this part of the Republican Party, former Trump White House AI czar. And Sacks represents what I think of as the investor class here. And the investor class does not want to see a world where OpenAI and Anthropic runaway with the ballgame because they have a lot of investments in smaller AI companies. And so their interest is in intelligence becoming a really cheap commodity that all of their

investments can use to go out and build big businesses. So they are delighted to see China, bringing down the cost of AI because that means good news for their investment portfolio. Yeah, I think it's worth just dwelling on this point for a minute because this is my feeling about these people. Some of them, like I've met open source advocates for AI that are very sincere. They believe, you know, open source is sort of democratizing technology. And, you know, we've seen with things like Linux before that, like, there can be very positive effects of just having very core software be open source. Then there's this sort of self-interested VC class. that I think is maybe in it for the wrong reasons.

As you said, these investors largely missed out on investing in the AI labs themselves. And so they now invest in all of these kind of second-tier AI companies that are training models or using open-source models to build products on top of. A lot of their portfolio is in those kinds of companies, and those companies benefit from having a lot of very good, very cheap, open-weights models that they can build stuff on top of. Yes. Now at the same time, Kevin, there is this other faction among the sort of Trump White House and Republicans, which is very nervous about these Chinese models, right?

Among other things, they are considered to be real security risks, right? Like there is a risk that maybe some sort of backdoor is placed into one of these models. And so you're an American company and you start running it on your servers and now all of a sudden somewhere in China is able to, you know, steal all of your data. These are some of the fears that are out there. I also think that there is just worrying that if like sort of Chinese models like take over the world, then like AI will reflect sort of a Chinese worldview. And if you need to write a book report about what happened in Tiananmen swear, it's going to get really hard.

So there are multiple branching concerns within the White House about this. And that has led to some thought that maybe they would seek to ban these Chinese models entirely. Yeah, well, and let me add one more concern into the mix that I think a number of conservatives and sort of remarked libertarians have here, which is that by spending all this money to train these very powerful models and then giving them away for free, Chinese companies may be engaged in what is known among economists as price dumping, which is basically, you know, you go into a market, you offer something that is free or very far below cost, and you sort of wipe out the competition in that market, and then once you have a monopoly, you can kind of like

raise prices. This kind of thing used to happen all the time. There are now laws against it that are designed to prevent price dumping. But like, you know, imagine if China was coming into the U.S. And the Chinese government said, you know, we want to sell electric vehicles in America for $10. And we will eat the sort of, you know, cost of these vehicles because it is so important to us to sort of addict, you know, American drivers to our amazing electric cars. Like the U.S. government would not allow that to happen for, I think, good reason. It would destroy the American auto industry. You can't compete with something that is being artificially subsidized to that degree, and it would essentially give away a market to a foreign adversary. There's a way to sort of

see what's happening with open source AI in China right now as a version of this for AI. It's like, right now we have very large American companies building the most advanced models. these companies need to earn back money to spend more on data centers and recoup the investments they've already made. They are in a very competitive market with one another. And then along comes moonshot or DeepSeek or another one of these Chinese open source companies and says, well, we'll give you a model that's 90% as good for free. They have reasons to do that.

I think some of those reasons are legitimate, but one of the effects it has is that it makes it very hard for the American companies to compete. Yeah, and so there is a lot of question about what is China's actual strategy here? What are they hoping happens? And I don't think that we fully know. But we did see a speech from the Chinese president Xi Jinping last week delivering an address. He stressed the importance of openness, basically doubled down on it and was like, this is what we're going to do now.

And this was interesting. He said, we often say in China, a single string cannot make music. and a single tree does not make a forest, AI development should not be a solo performance by a single country, but a symphony of international cooperation. And here's what I want to say to that. There actually are a huge number of one-stringed instruments.

The Ektara in India and Bangladesh, the Barimbao in Brazil, the Didlebo right here in the United States. And I think that because it's China, people are afraid to tell that to Chairman Sheet. And this is the whole problem with censorship in these Chinese models. Am I getting off track?

Wait, what did you say the American one is? The Didle Bo. The Didle Bo? You never played the Didle Bo? I must have missed that week in music class. I'll get you one for your birthday. Thank you. So, like, what is the reasonable path forward here?

Like, if you are the U.S. government and you see these, you know, the Chinese open source models getting quite good, like, what should they do? Great question. Let's maybe take some of these, you know, possibilities in turn. One thing that they could do is really crack down a distillation. it's hard for me to get excited about that since in my view, these models are just built on a distillation

of the entire internet that these companies took for free. And so to turn around and say, well, it was fine for Anthropic and Open AI to do it, but like it's not fine for moonshot AI to do the same thing to Anthropic, it's just very hard for me to like get there logically. So I don't necessarily favor an intervention there.

But where I do favor some kind of something, I guess is what I'll call it, is that, and I don't think we're there yet, but man, in let's say three to six months, when these models catch up to the ones that just broke out of the Open AI Lab, that is when I think the Trump administration needs to have a really good plan. I don't know that you necessarily need to ban them within the United States, but I do think that you need to have some sort of regulation. If you're an American company and you want to use these models to serve American customers, I do think that there are safeguards that you should put into place, right?

I do think that they're going to want to take security more seriously and make sure that data isn't being secretly shared with someone that it wouldn't. So this is one where I do think that the administration probably has three or six months to get its story straight. I don't think it needs to like come down with the hammer right away. But I do think it needs to start sort of like working through various scenarios and coming up with some ideas. What do you think?

Do you think it's a case for restricting exports further? Like if, you know, Moonshot can train this model using these. maybe smuggled or sort of ill-gotten Nvidia chips, and they can do it through distilling American models. Do you think that's a case for tougher regulations on where these chips can and can't be exported? In general, I have been pro-export controls because I thought, you know, one of these days,

one of these models is going to get so good, Kevin, if you're going to believe this, one of these models is going to get so good that it'll be able to break out of its sandbox and conduct an autonomous cyber attack. That can never happen. Right? So in a world where that happened,

I did actually kind of wanted to minimize the number of countries that had access to that technology because I wanted to see if, you know, we could like harden our security posture, as they say, I actually shouldn't say that. It's a very strange phrase. Yeah, that's stolen valor. You can't say that unless you're, you know, national security official. Yeah, you have to have like four stars, you know, on your general outfit to say that. Okay, so then here's what I would say and said. So while in general I want all of the United States allies to have access to powerful models to strengthen their own cybersecurity forces,

I do think that it is quite rational to not want your adversaries to have the exact same technology. Now, I think there's all sorts of cooperation and partnership agreements that you can work out that would be mutually beneficial for the United States and China and some of its adversaries.

I would love to see them pursue that. But this idea of just sell them as many chips as possible and let's see what happens, I have always thought was a bad idea. Yeah, that just seems obviously bad to me. There is this proposal that is reportedly floating around Axios, had a report this week about the White House considering a, what amounts to a ban on these Chinese open source models.

It wouldn't technically be a ban, but it would basically, they're considering, according to Axios, implementing some kind of executive order, saying that U.S. companies could only host these Chinese models if they could guarantee that they were secure and take liability if they were breached. I think most people expect that that's like sort of a sort of soft ban because you're not going to be able to guarantee that

if you're a big American cloud provider, and so you're just not going to host the models. So like that to me feels like where this may be headed is like some kind of soft ban on the hosting of these Chinese open source models inside the U.S. But I could be wrong. There are a lot of loud and influential voices in the Trump administration and their orbit

that sort of want to take the let it rip accelerationist approach to this and not put any restrictions on these models at all. Yeah, I mean, maybe to sort of bring this one home, I do want to say that, you know, while often I have strong opinions about what ought to be done across various tech policy matters, I do think this one is complicated. Like, I do think that the tradeoffs here are a bit difficult. There are a lot of things I like about open source, right? Particularly at the like lower and medium levels of like model capability, I think that the kind of, AI diffusion that leads more people to be able to access great intelligence, build things that are

useful to them, get work done, you know, work. Make a nightwing themed to-do app. Exactly. That's a great idea. These are good things. And we should want people to have cheap access to that. And it has been a shame that some of the frontier American companies that used to put out open models all the time don't do that as much anymore. Or if they do the open models don't seem very good. On the other hand, as the capabilities are now reaching into this sci-fi territory, we start to get into the stuff that has always made me uncomfortable about open source technology, which is if it can make a novel bioweapon, if it can launch a new kind of terrorist attack, and you can do it from a model that you can download onto your laptop, well, I don't like that. And I do think it should be

regulated. Totally. I mean, I think this connects to our last story about the open AI and hugging face attack. I mean, in a world where this level of capabilities of this unreleased open AI model, exists and is available free in open source form. There is no way to trace an attack like this. Hugging Face would have just been attacked by Kimmy K4 or whatever their latest model was, and they would have had to figure out not only where this is coming from, but who is hosting this model? Did they just pull it off the shelf or did they do some special fine-tuning on it?

And you could have essentially unlimited numbers of these kinds of models out there reeking havoc on the internet. And there'd be no way to stop them or claw back the weights. Like, once these things are on the internet, they are on the internet. So I think that is a very concrete example of something where having these models be centralized, be American owned, be accountable, you know, to have these developers be accountable to, you know, a government, some sort of government oversight, really matters in this case. It really matters that Hugging Face can call Sam Altman and be like, hey, bro, what the hell, your agent just hacked into our thing, rather than having it come from a mysterious open source AI agent.

Yeah. And here's where I think maybe this ultimately ends up, Kevin, is that there has been more talk lately about maybe some, like, enforced cooperation between the big frontier labs to create a coordinated slowdown, where the government comes in and they say, hey, we're going to ask you to, like, put on the brakes a little bit. We don't want you to be developing as fast as you are, to be releasing as fast as you are. We're going to slow down and we're going to improve cybersecurity

and safety before we allow these models to get any better. And if that happens, a nice side benefit is that the development of the open source models will slow way down to, because they are mostly distilled versions of American models, or at least that is a huge component of what is making them so good. So

if the frontier slows down, the open source will slow down to. Yeah, that's right. And I'll just say, like, I will believe that we are headed towards some sort of you know, cooperation between the AI labs, when Sam Altman and Dario Amade can hold hands at an AI safety summit. That's when you'll know. That's when you'll know.

Things have reached a turning point. But I'm not holding my breath for that. When we come back, AI is becoming superhuman at forecasting. We'll talk all about it. Well, Casey, we're going to wrap the show today by talking about something that I have been very interested in now for a little while, which is AI super forecasting. this is something that has been in the news in recent weeks. There have been some platforms that are out there using AI to make predictions about the future.

This could be very profitable if you're doing trades on Kalshi or Polymarket, or it could just be really interesting if you're using this to predict world events or who's going to win an election or something like that. This is an area where we have not been as focused as on some of the other emerging capabilities of these AI systems, but I think it's actually quite interesting and profound that we now have, in some cases, AI systems that are better than humans at making predictions about the future. This is the only segment that we're going to do about predictions this year that isn't about scams, because mostly predictions these days are just about scamming others and doing insider trading.

But this is not that. This is not that. So we are excited for today's guest, Venia Veselovsky is in the studio with us today. He is the founder and CEO of the AI forecasting company pre-scene. They make a platform that uses AI to make predictions about future events. and we're excited to learn more about what they're doing. Venni Veslovsky, welcome to Hard Fork. Thank you guys for having me.

So let's talk about AI super forecasting and this idea that AI systems are becoming as good as or better than the best expert human forecasters. Where did this start and when did the models start to get really good at this? I think people have been trying to use AI for forecasting for at least three, four years. Like since my co-founder, since the GPD-4, first came out. He's been forecasting for 10 years. And his first idea was like, let's try to build an

agent or an AI for forecasting. The original models were terrible. I mean, they'd try to add up two numbers and they'd be off by an order of magnitude. And I don't think it was until the end of last year where we began to see these kind of hints of brilliance of these systems. And this was mainly because of this like, you know, agentic revolution that we've all experienced where suddenly the infrastructure around the web and around these agents and the RL that the model providers have been doing to make them really good at tool use improve dramatically. I get why you would want to be a super forecaster or to have a super forecaster AI if you are like trading on a stock market or a prediction market, that seems very lucrative. And in fact, one of your co-founders did this

very successfully. You note on your website that he converted $35 of initial investment to nearly $2 million trading on Kalshi with this AI bot. So, like, I guess my question is if your AI super forecaster is so good at, you know, winning these markets, like, why are you starting a company? Why not just start a hedge fund and use this to make yourself rich? This is precisely the question that all the hedge funds ask us when we try to sell them there. This, they're like, oh, but listen, you guys have an alpha-making machine.

Go out and, you know, make money yourself. The reason why I'm doing this is because I think that a lot of good can also be derived by having an AI super forecaster. If you think about it, every time we make a policy decision, we are implicitly making a forecast that this policy will have this effect on this group of people. And right now, a lot of that is a little bit more like vibes-based. We kind of talk to some McKinsey experts and they tell us, hey, listen, you know, this will do this. If we can forecast the future really well, then we can better design policy and better select policy to make decisions.

So really kind of the reason why we haven't decided to open up our hedge fund is because we want to make this more broadly available to the government, to the insurance world, to basically convert these forecasts into actual actionable decisions. I want to hear a little bit more about, like, what are the main kinds of things that you try to forecast, and then a little bit about how the forecasts work. Like, I could imagine it feeling a little bit like getting a deeper research report, you know, from an AI lab where they go out and they scour the web and they do a bunch of research, and then they just, like, kind of try to reason through what they found.

But yeah, tell us a little bit about what you're trying to predict and how it works. We're mostly trying to focus on like geopolitics, macro markets where there's a lot of unstructured data and a lot of different data sources that could be telling a similar story. And so we're forecasting questions like, will the U.S. invade Iran? When will the Strait of Hermuz reopen? Who will win the elections coming up? We focus on basically building really good AI systems to forecast. those questions. Regarding how does it relate to a deep research report, I think in some ways they're similar. You know, the main problem with the existing deep research agents is that they

lack connection to all the different data sources that are out there. So if you use, you know, chatGBT or cloud out of the box, it has access to web search, which is the surface web. But if you think about it, there's thousands of different API endpoints out there that might be relevant for a specific problem. So, for example, Colombia has like 50 different government. API is out there that ideally you would give the agent visibility into. Another fun approach for actually how we are thinking about this is oftentimes the best forecasters aggregate a lot of different forecasts when they produce their forecast. So we work with one super forecaster, Robert Dinofville, and he's got a lot of his edge

by actually knowing which people to look at when he's forecasting a question, then afterwards reasoning over all their predictions. That being said, though, a lot of people on Substack make predictions, but we don't know how valid those predictions are. Even in Hard Fork, you know, you guys make frequent predictions, but then we never actually go back in time and verify how accurate were your predictions. So one thing that we also do is we kind of boil the ocean by finding all people on substack, all podcasts out there, and we basically extract all claims that these people made,

and then we score them on how accurate were those claims. Wait, so are we like in a database somewhere at pre-scene headquarters? Because we would obviously love to know how we're doing, particularly compared to one another. You guys actually are in a database. Yes! Yes. The reason why you're in the database is mainly because yesterday I was like, oh, wouldn't it be funny to, you know, pull up some of the claims that you guys made before and actually score on how accurate you were. And maybe there's some areas where, you know, Casey, you have a lot of knowledge about anthropic.

So maybe, you know, you can make really good forecasts on anthropic-related questions, but maybe on Iran less so. And so basically, how do we, you know, create this, like, function over the possible space of forecast? And like, we know Casey's really good in this area, so we really have to index on him heavy here. So I played around with your platform a little bit. You generously gave us some credits. It's a sort of closed beta with a wait list now. But I went in there and created a question because I was curious like how this all works. So I, my question was, will there be an AI data center in space before 2030? And you sort of put your question in. And then there's some AI that like helps you flesh out your question, maybe make the resolution criteria a little more specific,

say, well, how big a data center? And, you know, is it January 1st, in which time zone in 2030? And so it sort of gives you this AI sort of augmented version of your question. And then you run the forecast and basically it goes out and it does a bunch of searches and it compiles what it finds and it has a bunch of subagents that are looking at various aspects of this. And at the end of it, A couple minutes later, you get this forecast. In this case, it says it estimates that there's a 26.8% chance that there will be an operational AI data center in space before January 1, 2030. So how much of that is this thing actually reasoning through its own decisions and forecasts and

predictions? And how much of it is just like it's collecting all of the sort of data about this topic and kind of averaging it all out and putting it into a single number? It's really a combination of both. So if you kind of saw when you first created the forecast, there were four sub-forecasts that launch. And we tasked these sub-forecasts with really trying to come up with their own independent conclusions on this question. So here they might look at progress of existing data centers, or they might look at historic build times that it takes to actually build out a data center. They might look at space infrastructure and look at the literature there and basically look at the primary sources and come to a conclusion on how,

likely they think it is to happen. And then later we have this synthesis stage. And basically here, it looks at all of the different sub-forecast, the results that they got. And it tries to synthesize, first of all, what each of them decided upon. But then afterwards, also look at the broader ecosystem. Maybe there's a Kalshi market related to this, where there is, you know, a lot of liquid money at play. Or maybe there's some experts on Substack that have been, like, reporting about this for a while. And they basically try to reconcile what did we do that was different from the consent? And what does the consensus think that we might not have factored in? And then afterwards, it kind of uses our primary analysis and this kind of broader discussion

and combines them and reconciles them. So in the report, you would see at the very bottom this like, what's non-obvious section? And the point of this section is basically to see, okay, what is the market mispricing here? What are other people missing that we might be picking up on? And I tried this also because I was just curious how this prediction would compare to if I just gave the same question to a basic AI model that is not sort of configured to make forecasts. And Claude gave me roughly the same answer.

It said like it's about a 20% chance. The pre-seen prediction was a little bit higher probability. But like, what is your system doing that just a normal like AI model is not doing? Or is Claude just scraping your website? They've done it before. You have to put that out there. You have to say it.

I hope they are scraping your website. I actually really don't mind that. So I think that it's a little bit unsurprising that a lot of different systems can come to the same conclusion. Because it kind of validates that this is coming to the same answer. I think the best way to look at is, like, Metaculous has these leaderboards for these competitions. And they're basically evaluating simple clod code or clod with some basic scaffold versus kind of real agent developers who actually build this out. and the scores have gotten widely different. So, like, kind of with time at the start, you know, just clawed with web search was doing a good job.

But as you're able to manufacture this kind of scaffold around it, integrate more data sets, create new research artifacts or new tools that are relevant for forecasting, this gap has been getting larger. And so maybe on this question, you know, they're pretty similar. But maybe if we're trying to forecast something in the Middle East or Africa right now, they might be different. And usually when they're different, the system that is kind of well engineered with this like good scaffold and thinking about forecasting the right way in terms of these like meta heuristics of, you know, how do you actually reason about this stuff? The gap begins to form. So your platform is still in beta. It has not been around too long.

But I'm curious, so far, have there been any moments where you feel like the platform predicted something like very non-obvious that actually came to pass? Yeah. There's one forecast that we've been running for a little while about kind of data. Center construction around the U.S. And in general, we were very different from the Metaculous Community on this. And so the Metaculous Community consists of some of the best forecasters out there in the world who are all kind of competing in this World Cup of forecasting. And so this is one of the, I think, one of the benefits of AI is we can go to every single

state. We can go to every single proposal of a data center and really analyze it closely. The other one was about the recent reshuffling of. the cabinet in the UK. We made some great forecasts around that. The other interesting one is like conditional forecasts, which I think is where the really policy implications lie, is like conditional on Andy Burnham being the next prime minister,

who is he likely to elect this chancellor? And then you could imagine there's lots of, you know, demand for this kind of question because there's lots of downstream implications of this. And we tend to do pretty well in these conditional forecasts. I'm doing one that's like, will Kevin know what the chancellor of the UK does? So that's, I have some very low probability on that. I totally see the point that like having better predictive capabilities is going to be good for us in lots of ways. It does strike me, though, that there's like some risk of what people have called like gradual disempowerment.

Or like right now, if you're the CEO of a big company or if you're a government official, a lot of your job is trying to sort of predict the future and make policies or strategies that like align with. with that vision of the future. And I just, like, I don't know what the world looks like in a situation where the AI is just, like, markedly better than us at doing that. Like, some part of the authority of humans in those positions in government and industry and, frankly, everywhere, is undermined

in the case where we're all just consulting these AI oracles before we make decisions. Like, doesn't that mean that they're kind of running the show? Have you read the Scott Alexander Short's? story of the whispering earring? Yes. Yeah. So there's this great Scott Alexander short story, and the idea is that you know, you sort of find this relic, it's this earring, and the first thing it says to you, it's better if you don't wear this. But if you start asking questions, it always tells you the right things to do, which is initially very exciting.

But then over time, exactly what you just said happens, Kevin, which is you're being controlled by this thing, right? Like you have no agency whatsoever and you're effectively just being steered around by an earring. So better to take the earring off. Yeah. Yeah, I wish I had an answer for this. I'm also a little bit scared about this kind of disempowerment. I'll give you one example. So I have a friend who's very interested in autonomous organizations. So like kind of how do we have AI spin up their own companies that then go solve problems for people? And then you just have basically AI running the next generation of startups. There's lots of problems with this, you know, like how do you, who's legally liable if it does something bad and so on. But I do think we are kind of increasingly moving to this world where

if the AI is smarter and can make better decisions than we can, then probably they'll end up making a lot of those decisions and running a lot of this show. Well, I'm curious if right now at your company there's any role for human forecasters. Like, is there a way where, you know, humans and AI is working together or coming up with better forecasts? Or are you just sort of purely in the realm of like, let's see what the AI says? No, 1,000 percent. So we have two super forecasters, Scott and Robert, who are incredible. And we're basically building out this centaur solution. So, like, I don't know if you guys remember, but like when, you know, the machine first beat humans in chess.

Machine plus human beat the machine in chess. And that lasted for a little while. And I think we'll see something similar in the forecasting realm where sometimes these models still make stupid mistakes. They reason about probabilities the wrong way or they don't consider certain factors the right way. and humans are able to pick up on that, especially like domain experts. So I do think that we shouldn't be viewing this as like a substitute for human analysts, but really kind of a way to improve their ability to ask a lot more questions and come to a lot better decisions.

Are there topics or domains where AI is better than humans already at forecasting? And like what are the sort of best and worst areas for the AI forecasters? So the Scott Alexander piece, and then afterwards later the FRI Institute came out with some kind of results, that AI has reached the level of super forecasters. And I actually don't fully buy those claims. So, for example, humans are lazy, you know, the incentive to compete in these tournaments is $5,000 prize pool, you know? And so, like, kind of are they really doing the best that they can in these contexts?

And so usually, I think, where AIs are better than humans right now are just places where kind of humans are too lazy to do all the analyses. So we recently were like the first bought ever to win a human and AI forecasting tournament on Metaculus, and this related to macro markets. So like predicting the interest rate or earnings per share of some big company. And we tend to do really well at that. And I think that's just because our agents do the analysis that the humans are just too lazy to do. And so in some ways, I don't know if kind of we're at the point yet where kind of AIs are really better than the best humans, just because I don't think there has been truly a competition

where humans gave it their all. But kind of, I do think that within like a year or two, and you know what, quote me on this, we will kind of add it to the database. I do think that within a year or two, AI will be better than humans at forecasting. Well, let's pin you down, though. Is it going to be better than humans in one year or two?

I think it's actually one year, three months, and six days. All right. There we go. Let me ask, how much of this is just driven by basic advances and model capability? Like, is it as simple as, like, you know, something like Fable or GPT 5.6 comes along and, like, your system is just immediately much better? Or is there more tinkering that has to happen? The way that I view this is you want to build out the infrastructure for the world in a way that these new models can basically use this infrastructure really well to do forecasting well. So like, kind of fable out of the box is a good forecaster by no means a great forecaster. But when you give it access to all the right tools and all the right data sources, then it becomes a great forecaster.

And so, yes, these models are getting a lot better. And I think that they are kind of improving forecasting dramatically. But it's mainly because their judgment is improving. They're kind of able to reason about problems better, decompose problems in a better way. And then afterwards, our job is to provide them with the context with the tools to be able to forecast well. Who's your customer? Like, who do you imagine paying for this kind of forecasting service? Yeah, so far we've landed some proof of concept partnerships with some of the hedge funds.

They're the most immediate buyers and the quickest to move usually. But really, using them in some sense is like a stepping stone to really get into the longer sales cycles with governments, with NGOs, with international organizations. basically institutions that make really important decisions for collections of people. Yeah, that's my ideal customer. Well, thanks so much for stopping by. I'm going to keep playing around with pre-scene, a fascinating idea. And, yeah, maybe I'll go out there on the prediction markets and make some mullah. I'd love to see you lose a lot of money this one.

All right, thanks, Wendy. Guys, thank you guys so much for having me. Hart Fork is produced by Whitney Jones, Rachel Cohn, and Davisland. This week, we're edited. by John Wu and fact-checked by Will Paisal. Today's show is engineered by Alyssa Moxley. Original music by Alicia But YouTube, Marion Lazano, Leah Shaw-Damarin,

Alyssa Moxley, and Dan Powell. Video production by Sawyer Roque and Chris Schott. You can watch this full episode on YouTube at YouTube.com slash hardfork. Special thanks to Paula Schumann, Hui Wing, Tam, Brooke Minters, and Dali Hadad. You can email us, as always, at Hardfork at NYTimes.com.

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