Big Tech's Tariff Chaos + A.I. 2027 + Llama Drama

“How do we navigate this new uncertain climate?”

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure, and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner, Dell, AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

• 0:35Casey Newton?

  What's it do with you?

• 0:37Kevin Roose?

  Oh, you know, just binge buying cheap Chinese stuff online to beat the tariffs.

• 0:43Casey Newton?

  Making your final Shein purchases before that company shuts down?

• 0:48Kevin Roose?

  Yes, no, I actually did buy a bunch of stuff over the weekend because I thought this might be my last chance.

• 0:52Casey Newton?

  Yeah.

• 0:53Kevin Roose?

  Casey, what cheap overseas good are you going to miss most after the tariffs kick in?

• 0:57Casey Newton?

  Oh, I feel, the thing, I was never a big like, oh, I gotta go on to Timu and get like a pressure cooker for $6 or whatever. Like, that was never my journey. But I know that, you know, it's a major pastime for a lot of people.

• 1:10Kevin Roose?

  Yeah.

• 1:10Casey Newton?

  Yeah.

• 1:11Kevin Roose?

  Yeah, well, for me, it's like the ability to buy cheap crap for my kid has been revolutionary. My kid, the other day, started saying the phrase dinosaur unicorn. And I thought, that's not real. And he says, I want a dinosaur unicorn. And I said, well, that's not a thing. We can't have that. But then this little like bell goes off in my mind that says, someone's out there has made a dinosaur unicorn something.

• 1:37Casey Newton?

  Almost certainly.

• 1:38Kevin Roose?

  My wife finds like eight different dinosaur unicorn t-shirts and buys one of them. And now he's got this dinosaur unicorn t-shirt that he absolutely loves. That would not happen in a tariffs world.

• 1:47Casey Newton?

  As of today, that shirt costs over $400.

• 1:51Kevin Roose?

  Yeah.

• 1:51Casey Newton?

  Well, I mean, I'm sure Jude looks great in that.

• 1:54Kevin Roose?

  He does.

• 1:55Casey Newton?

  Yeah.

• 1:55Kevin Roose?

  He does. And he's gonna have to wear it for

• 1:57Casey Newton?

  For ten years.

• 2:00Kevin Roose?

  I hope it stretches.

• 2:06Kevin Roose?

  I'm Kevin Reus, a tech columnist from The New York Times.

• 2:08Casey Newton?

  I'm Casey Newton from Platformer.

• 2:10Kevin Roose?

  And this is Hard Fork.

• 2:11Casey Newton?

  This week, the tech world is in chaos over Trump's tariffs. Then, AI researcher Daniel Cocotello returns to the show to discuss a fascinating new set of predictions for how AI could transform the world in just the next few years. And finally, did Meta cheat on an important AI benchmark?

• 2:37Kevin Roose?

  Well, Casey, for the second week in a row, we have been interrupted by news about these Trump tariffs. Now, there was a time in the history of the Hard Fork podcast where the only thing that would cause us to rip up a segment and rerecord it was if Sam Altman had been fired or rehired. But now we live in this new reality where news can change on a dime. And over the past few days, that is exactly what we've seen.

• 3:03Casey Newton?

  I think it's fair to say Hard Fork has been hit harder by the tariffs than any other company.

• 3:09Kevin Roose?

  That's true. That's true. We are bracing ourselves for, you know, massive impact and getting ready for the new reality.

• 3:17Casey Newton?

  Yeah.

• 3:17Kevin Roose?

  So Casey, every great era deserves a name, and I think we should call this era in the technology industry the chaos meta. Nothing to do with meta, the company, but in video gaming, metas are sort of like the, the overall set of conditions that the players have to navigate. And I think it's fair to say that chaos and the lack of certainty surrounding what Donald Trump is going to do on any given day is the new meta for Silicon Valley's largest companies.

• 3:44Casey Newton?

  Yeah, remember like how when we were talking about whether or not TikTok would be banned, which also had a lot to do with what Trump wanted? We talked about how it was kind of simultaneously alive and dead at the same time. Now that's just the entire US economy, Kevin.

• 3:57Kevin Roose?

  Yes. So as of early this week, it looked like we were going to get these massive tariffs on goods imported to the United States from many, many countries all over the world, larger than any tariffs we've seen in the recent history of this country. Then on Wednesday, as we were taping our episode, we got the news that the Trump administration was pushing pause on most of them. Most of these reciprocal tariffs on countries like Vietnam and India were going to be delayed for 90 days, and there would be a baseline 10% tariff rate applied, but not the much higher rates that people had been fearing, except for China, which would have its tariffs increased.

• 4:39Kevin Roose?

  And on Thursday, we learned that those tariffs would actually be 145% on Chinese goods entering the US.

• 4:45Casey Newton?

  The problem is with a podcast, we can't just have a little ticker on the bottom that shows you what the current tariff is.

• 4:50Kevin Roose?

  Yes, but what we saw early this week was that the stock prices of all the biggest US tech companies took a dramatic nosedive. That was in response to these fears about these very high reciprocal tariffs. Now, after the news that these tariffs are going to be placed on a 90-day hold, except for China, some of these stock prices have rebounded. Apple, in particular, had its biggest trading day in many years after the news of these tariffs being delayed came out. So the stock market whiplash is part of the setting for the tech companies that they have to deal with now. But the bigger picture scenario is that doing business in Trump's America is turning

• 5:30Kevin Roose?

  out to be very difficult, not because the administration is necessarily unfriendly to these businesses, but because there's just so much fast-moving news that it's hard for businesses to do any kind of planning or strategy at all.

• 5:42Casey Newton?

  Well, I mean, I wouldn't say this is a particularly business-friendly set of announcements that have been made. I mean, sure, I guess it's friendlier to pause the tariffs than to continue them, but the general chaos, Kevin, I think, has been really bad for American companies.

• 5:54Kevin Roose?

  Yeah, so even beyond the tariffs, there are a bunch of things that the Trump administration has been doing that have impacted the tech industry: restrictions on immigration, cuts to science funding, these antitrust cases, many of which- Are still going forward. So I wanted to kind of give our listeners a sense of how this instability feels on the ground in Silicon Valley to the biggest tech companies. And you had a really smart idea, which was to look at the new chaos meta of Trump's second term through the lens of four tech companies. So today, we're going to take a look at how Trump's new policies and these tariffs have affected four companies, Apple, Nintendo, TikTok and Meta,

• 6:34Kevin Roose?

  all of which have faced significant challenges since Trump took office and all of which are now trying to figure out, how do we go forward? What do we do? How do we navigate this new uncertain climate?

• 6:44Casey Newton?

  Yeah.

• 6:45Kevin Roose?

  So let's start with Apple. Casey, what is going on with Apple?

• 6:49Casey Newton?

  Well, look, of all of the tech companies, Apple has long been the most dependent on China. That is where 90% of iPhones are made. The company is just heavily dependent on its supply chain relationships that it has in that country. So the fact that these tariffs are now 145% on goods coming out of China has just really sent a shiver through that company. Earlier this week, Apple had its worst four day trading period since the year 2000. Once the pause was announced, its stock has started to come back. But this is a very volatile situation for them and the underlying dynamics are the same,

• 7:29Casey Newton?

  which is that it is simply going to be much more expensive for Apple to sell goods made in China here in the United States, Kevin.

• 7:37Kevin Roose?

  Yeah, and obviously, one of the hopes of these tariffs is that it will drive manufacturing back to the United States. There's some hope among members of the Trump administration that this could even force Apple to consider making the iPhone in the United States. Do you think that is likely and why?

• 7:54Casey Newton?

  No, and in fact, I think it's almost sort of worse, Kevin, because this week, the president's press secretary said that the president believes that iPhones can be made in the United States, despite the fact that we know that it is much more expensive to manufacture things here in this country, right? It's very important to remember that whatever the Trump administration might hope that these tariffs accomplish, they have not accompanied it with any plan to increase the manufacturing capacity in this country. The whole thing is just a wish and a prayer that at some point in the future, Apple might have a magical iPhone factory stocked with Americans who want to do those jobs. As it stands now, that doesn't exist.

• 8:31Kevin Roose?

  Yeah, so I would say Apple is somewhat unique among tech companies because it has also been thinking about tariffs and the effect of Trump's policies on their business for longer than many of their competitors. I mean, if you'll remember during the first Trump term, there was some talk about tariffs on Chinese goods. Apple successfully negotiated its way out of those, sort of got an exemption. And in part, they did that by cozying up to the Trump administration, by promising to build and assemble some of their products in the United States. There was this famous tour that Tim Cook gave Donald Trump of this facility in Austin, Texas, where he said they were going to start making a bunch of stuff. So they sort of managed to get the tariffs off their back during the first term. But in the second term, it's not at all clear that they are going to have the

• 9:15Kevin Roose?

  same kind of success. So Casey, how is Apple dealing with the new chaos meta?

• 9:22Casey Newton?

  Well, they are trying to get as many devices as they can out of China and into places where it's going to be much less expensive to export them to the United States. So there was a great story this week in The Times of India that, according to senior Indian officials, Apple transported five cargo planes full of iPhones and other products from India to the United States, which sort of calls to mind those scenes at the end of the Vietnam War, when you see the last helicopter leaving Saigon, except it's full of iPhones. Actually, Katie Antopoulos had a great joke on Threads today. She said that this whole thing is like the movie Dunkirk, but for iPhones. Reuters reported that Apple transported

• 10:03Casey Newton?

  600 tons of iPhones, Kevin, which would have been about 1.5 million devices. And look, you know, those iPhones will pad Apple's profits a little bit more, but pretty soon there's going to be no more planes out of no more countries to escape these tariffs. It is just going to be a really expensive-ass iPhone.

• 10:21Kevin Roose?

  Do you think the iPhone 16 Pro Maxes get to sit in first class on the plane?

• 10:28Kevin Roose?

  They like put them up front in the lie flat seats.

• 10:30Casey Newton?

  Yeah, they should definitely get the upgrade with what they're paying for those things.

• 10:35Kevin Roose?

  Yeah, so, okay, let's move to our next case study of a company trying to deal with the uncertainty and chaos of the Trump administration, Nintendo. Casey, what is going on with Nintendo?

• 10:46Casey Newton?

  Well, so Kevin, as a hardcore gamer, obviously you know that the Switch 2 is coming out this year. This is the sequel to Nintendo's best-selling console of all time, and it was supposed to become available for pre-orders on this very Wednesday. But then tariff chaos started happening, and Nintendo said, "We are going to pause pre-orders because we don't know what it's actually going to cost to sell a Switch 2 in America anymore."

• 11:13Kevin Roose?

  Yeah, and now that Trump has paused these tariffs on most countries other than China, have they said that actually they're going to start shipping the Switch 2 on time after all?

• 11:23Casey Newton?

  Well, what they've said is that they're not planning to change the launch date, which is June 5th, and it does seem like because they are a Japanese company and make the Switch 2 in Vietnam, they are going to be able to avoid the really tough tariffs that Apple is facing, right? Before Trump initiated the pause, there was going to be a 46% tariff on the Switch 2. Now it's back down to that 10%. But look, the Switch 2 was already planning to go on sale for $450, which is $150 more than the original Switch sold at launch. So I think there's a very real question here of whether the price of this console goes up over time, which would be a reversal of the usual trend, which is

• 12:07Casey Newton?

  a console goes on sale for a high price and that price comes down over time. So, once again, Kevin, there's just real chaos here as we await probably the most hotly anticipated piece of hardware to launch, I would say, in the United States this year.

• 12:19Kevin Roose?

  Yeah, now are they bringing in planes full of Switch 2s from Vietnam or wherever they're manufacturing them?

• 12:26Casey Newton?

  They were actually able to put them in one of those pipes, and they just sort of warp down. It's kind of a really cool little thing they have there.

• 12:34Kevin Roose?

  I got it. Okay. Next company on our list, TikTok. Casey, this is a company we have talked about a lot on this show. They were going to be banned. The deadline for banning them got pushed out by another 75 days last week. Casey, what is the latest on TikTok and how it is coping with this escalating trade war between China and the US?

• 12:57Casey Newton?

  Well, Kevin, what is going on with TikTok is, of course, the question asked most in the history of Hard Fork. And what was going on with it until tariff chaos was that it looked like we might have a deal. There was some great reporting in The Times this week that ByteDance, with the support of the Chinese government, had reached the rough outlines of an agreement in which TikTok would create a new American entity. American investors would own the majority of it. Chinese owners would have about a 20% stake. And the American company would essentially rent the algorithm from ByteDance. And so by Thursday of last week, there was this draft executive order that outlined the deal. And then Trump did the thing with the tariffs.

• 13:40Casey Newton?

  And all of a sudden, ByteDance has to call up the White House and say, that deal that you just helped us negotiate, it's off the table because the Chinese government isn't going to support the deal anymore.

• 13:51Kevin Roose?

  Right. So this was a pretty dramatic reversal. And it does seem like they got very close to a deal before these tariffs. What is happening now that these tariffs are on? Does TikTok have any options left?

• 14:03Casey Newton?

  Well, Kevin, along with a 90-day tariff pause, we also now have a 75-day extension that comes after the original 75-day extension that Trump gave in order to force ByteDance to divest TikTok.

• 14:18Kevin Roose?

  This man loves extensions. Let's just say it. This man loves to come up right against a deadline and say, you know what? You got a little more time.

• 14:25Casey Newton?

  Yeah, well, look, you know, I don't know what's going to happen over these next 75 days. I imagine that if the tariffs against China stand at 145%, there is no way the Chinese government is going to support the sale of TikTok. And I just want to say how self-defeating this is because it was barely more than a week ago that Trump was telling reporters that Beijing, if they would simply go along with his plan to force the divestiture of TikTok, then he would go easy on them on tariffs, right? Like, this was his big bargaining chip of, if you don't want high tariffs, you have to let the Americans have TikTok. And to my surprise, it seemed like the Chinese government was actually going to go along with that. And then before they could even get that deal out,

• 15:08Casey Newton?

  Trump, seemingly out of nowhere, announces a brand-new set of tariffs that completely scuttles the deal. So it is as if the president was essentially negotiating against himself and lost the deal that he had won.

• 15:20Kevin Roose?

  Yeah, it does seem strange that he would not wait until after the TikTok deal was finalized and approved by all the relevant officials to then issue these tariffs, if he was actually interested in getting a deal done.

• 15:30Casey Newton?

  Yeah, I think that's right.

• 15:32Kevin Roose?

  So, OK, TikTok is still in this frustrating state of superposition, where they are both dead and alive at the same time. Do we think that this resolves before the end of the next 75-day extension? Or do we think we will need yet another extension to figure out what we're doing with TikTok?

• 15:48Casey Newton?

  My assumption is that on the day that Donald Trump leaves office, we will still be in the middle of one of these extensions. It'll be sort of like the 15th extension, you know, or the 23rd extension. But no, until this tariff situation gets resolved, I do not expect TikTok's fate to be resolved. It is just going to continue to exist in its weird limbo.

• 16:06Kevin Roose?

  All right, so that is TikTok. Our last company on this list of case studies is Meta. Casey, how is Meta dealing with this new uncertain reality?

• 16:17Casey Newton?

  Well, I would say that things turned out a little bit better for them this week than maybe it looked like things were going because tariffs were going to be a huge problem for them too. They are a digital advertising business and a huge number of their advertisers are small and medium sized businesses that buy ads outside the United States to export goods from foreign countries into the United States. Mike Isaac at The Times had a great piece on this this week. There's one analyst who estimates that about $10 billion of Meta's revenue from ads originates from outside the United States. So in a world where everyone was facing these massive tariffs, we were just expecting Meta to get hit really hard on the ads front. Well, now that has mostly gone away, at least for the next 90 days.

• 16:59Casey Newton?

  So it seems like Meta is going to get some breathing room. But there is this one other outstanding question, Kevin, which is that next week Meta's antitrust case is going to trial, right? So in 2020, during the first Trump administration, the Federal Trade Commission files an antitrust lawsuit and tries to break off Instagram and WhatsApp from Meta. It has been in the planning stages ever since. And on Monday, the case is set to go to trial. So why does all of this have anything to do with Trump? Well, Mark Zuckerberg has been giving Trump the full court press, going so far as to buy a $23 million house in Washington, DC recently, just to get closer to and spend more time with the president. There's been some reporting that Zuckerberg was in the White House trying to negotiate a settlement

• 17:44Casey Newton?

  with Trump just within the past few days. So there's a lot of questions right now about whether Zuckerberg will able to use this relationship that he's apparently been building with Trump in order to get rid of this case, which is in some ways an existential threat to his business.

• 18:00Kevin Roose?

  Yeah, and we should also just say like, this shouldn't be possible, right? The FTC is supposed to be an independent agency that has its own enforcement agenda and brings its own cases that are independent from the president. But of course, nothing is truly independent from the president in Trump's Washington. He recently announced that he was getting rid of the two Democratic commissioners on the Federal Trade Commission. That is historically quite unusual for a president to intervene in FTC commissioner staffing at that level. But now it is sort of going to be staffed with people who are friendly to the Trump administration. And so presumably, if he were to go to them and say, hey, let's back off this Meta case. I don't actually think we need to proceed with this. They might listen.

• 18:41Casey Newton?

  And we should say that another way that Meta tried to ensure that this happened is that after the events of January 6th, Meta suspended Trump from its platform for three years and Trump sued them over that. And so after he won the presidency, Zuckerberg came along and said, hey, why don't we settle this too and paid Trump $25 million, right? And I have to say Meta was completely within its rights to suspend an account. They're allowed to suspend whatever account they want. It's a private company with a private platform, but still, just as a little gesture of goodwill, hey, Trump, here's $25 million. So if this actually happens and this lawsuit just goes away, it will just frankly be an example of open corruption.

• 19:21Kevin Roose?

  Okay, so that is our four-company case study of how tech companies are trying to do business and survive in this new uncertain environment. I have to ask, after going through all these examples, which of these companies would you be if you could be one? Which do you think is in the best position in this new chaotic environment?

• 19:42Casey Newton?

  Hmm. Well, you know, until maybe Wednesday, I think I would have said Apple, right? Apple makes the iPhone. The iPhone is the most lucrative product in the history of the technology industry. And even despite some of the tariffs that we were seeing, it seemed like they were still going to be in a good position to navigate them. I was seeing analysis that they were only going to lose maybe seven points of profitability from all this. But the world looks really different with 145% tariff and in a world where Trump just keeps escalating this fight more and more. And so I actually do think that the picture for Apple just looks really strange. So look, I feel a little crazy saying this, but maybe I actually would just rather be Meta. Their hardware business is still a

• 20:24Casey Newton?

  relatively small part of what they do. Mostly what they do is a digital services business. And it seems like Zuckerberg has been able to make at least some inroads with the Trump administration. Maybe they're about to get rid of this lawsuit against them. So, God, I don't know. Maybe I actually want to be Meta. How about you?

• 20:39Kevin Roose?

  Yeah, I think, I mean, as venal and corrupt as it would be for these naked attempts at flattery and persuasion to actually work and pay off, I would not underestimate how well this stuff works with Donald Trump. And I think that Mark Zuckerberg's motive here is to win at all costs. And if he needs to buy a $23 million mansion, or spend time in the White House, or even make some policy adjustments to appease the Trump administration and get what he wants, I think he's demonstrated very clearly that he's willing to do that. My last question on this, Casey, is about this idea of the tech capitulation to Trump. In the past few months, we've observed, we've talked about the fact that a lot of these

• 21:22Kevin Roose?

  tech companies have been really falling all over themselves to appease the Trump administration. Many of them gave to the inaugural, many of them showed up at inauguration, their CEOs were seated just behind the president's own family. The amount of flattery and ass kissing going on here for months now has been, I would say, notable and historic. Do you think that any of that has worked to the degree that these executives thought it would? Did the tech leaders get what they wanted out of Donald Trump?

• 21:53Casey Newton?

  I think that until the tariffs, the answer was basically yes. And the tariffs are what have changed that equation, right? If you look at how JD Vance was talking when he went to Europe, he was echoing a lot of tech company talking points. He and Trump have criticized European fines against tech companies, saying like, we need to protect and defend our American tech companies against these European fines, which was something that the Biden administration never, ever did. They've talked about getting rid of AI guardrails and just letting these companies do whatever they want with AI, which is like music to Mark Zuckerberg's ears. But look, these companies just rely on stable, normal governance to be able to conduct their business around the world. They are as plugged into

• 22:38Casey Newton?

  the interconnected global economy as anyone else, arguably more than many companies. And Trump just came along and blew that up. And I think that it is probably dawning on them that they are probably just going to be living in chaos for the foreseeable future. And it is just going to make their lives much, much more difficult.

• 22:55Kevin Roose?

  Yeah, I think that's right. And I think that a lot of these executives have underappreciated how important stability and predictability are in their business models. I mean, these were companies, many of them, that had issues with the Biden administration. The Biden administration had issues with them. But at least with the Biden administration, these companies knew where they stood, right? There was not this sort of day-to-day whiplash of stock price moving up 10%, down percent, down 10%, tariffs going up to 145% and then down to 10%. It just was not the kind of frenetic environment that we're seeing today. And so I wonder if any of them are starting to appreciate how good they had it during

• 23:38Kevin Roose?

  the Biden years, where for as much as the Biden administration may have gone after them for various things, including antitrust violations, at least they could wake up every day and understand what the world was going to look like for the next 24 hours.

• 23:51Casey Newton?

  Yeah, I think that's true. I think that most of them would probably still be loathe to admit it, but let's give it another few weeks, Kevin, and another few tariffs. And then let's check back in with them.

• 24:01Kevin Roose?

  Sounds good.

• 24:07Kevin Roose?

  Well, that's enough about tariffs, Casey. When we come back, we're going to talk about a terrifying new report about what AI could look like in 2027.

• 24:35Speaker 00

  Support for this podcast comes from Dell Technologies. It's time to put AI to work. Is your business ready? The challenge isn't getting started with AI. It's scaling beyond pilots while maintaining control of your data, governance, infrastructure, and costs. Dell AI Factory provides the integrated foundation you need to scale AI securely and efficiently with one trusted partner. Dell, AI on your terms. From idea to impact. Learn more at dell.com/yourwaytoAI. That's dell.com/yourwaytoAI.

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• 26:18Kevin Roose?

  Well, Casey, today we're going to talk about a forecast.

• 26:21Casey Newton?

  And that's separate from a fork cast, which is something different.

• 26:24Kevin Roose?

  Yeah, that's what we call our end of the year predictions episode, isn't it?

• 26:28Casey Newton?

  I think so.

• 26:30Kevin Roose?

  But today we're talking about something different, which is this new report called AI 2027. This is a report that I wrote about last week and that has gotten a lot of attention in AI circles and policy circles this week. It was produced by the AI Futures Project, a Berkeley-based nonprofit led by Daniel Cocatello, who, listeners of this show may remember, was a former OpenAI employee who left the company last year and became something of a whistleblower, warning about their reckless culture, as he called it, and is now spending his time trying to predict the future of AI.

• 27:09Casey Newton?

  Yeah, and of course, lots of people are trying to predict the future of AI. But what gives Daniel a lot of credibility here is that in 2020

• 27:19Casey Newton?

  He tried to predict what things would look like about now. And he just got a lot of things right. And so when Daniel said, hey, I'm putting together a new report on what I think AI is going to look like in 2027. A lot of close AI observers said, oh, this is really something to read.

• 27:37Kevin Roose?

  Yeah, and he didn't just do this alone. He also partnered with a guy named Eli Lifland, who is an AI researcher and a very accomplished forecaster. He's won some forecasting competitions in the past. And the two of them, along with the rest of their group, and Scott Alexander, who writes the very popular Astral Codex 10 blog, put together this very detailed, what they call a scenario forecast. Essentially, it's a big rapport at a website. It's got some, you know, sort of research backing it up. And it is basically represents their best attempt to kind of synthesize everything they think is likely to happen in AI over the next few years into a readable narrative.

• 28:17Casey Newton?

  Yeah, and if that sounds a little dull to you, I'm telling you, you should just go check this thing out. It's at ai-2027.com. And it's just super readable. And it blows through stuff that feels very familiar right now, like just sort of basic extrapolating from where we are today into getting to, you know, six months, a year from now, the world starts to look very, very different. And there is a lot of research that they have to support why they think that is plausible.

• 28:42Kevin Roose?

  Yeah, and I can imagine people reading this report or listening to us talking about it and say, well, that sounds like science fiction to me. And we should be clear, it is science fiction. This is a fictionalized narrative that they have put together. But I would say it is also grounded in a lot of empirical predictions that can be tested and confirmed or, you know, verified. It's also true that some science fiction ends up becoming reality, right? If you look at movies about AI from past decades, a lot of the things in those movies did end up actually being built. So I think this report, while it may not be 100% accurate, at least represents a very rigorous and methodical attempt to sketch out what the future of AI might look like.

• 29:26Casey Newton?

  And here's my bet. If you put this conversation into a time capsule and revisited it in two years, in 2027, my guess is we're going to find that a good number of things in that scenario actually did come true. I hope we're still doing a podcast in two years. That'd be good.

• 29:42Kevin Roose?

  That'd be great.

• 29:43Casey Newton?

  Yeah. So my forecast is that this is going to be a good conversation. Let's bring in Daniel Cocotello.

• 29:56Casey Newton?

  Daniel Cocotello, welcome back to Hard Fork.

• 29:59Speaker 05

  Thank you. Happy to be here.

• 30:00Casey Newton?

  So you have just led this group that put together this giant scenario forecast, AI 2027. What was your goal?

• 30:07Speaker 05

  So our goal was to predict the future using the medium of a concrete scenario. There is a small but exciting literature of attempts to predict the future of AI that use other methods, which is also very important. Things like, you know, defining a capabilities milestone. Like, here's my definition of AGI. Here is my forecast for how long we'll have until AGI, based on these reasons and stuff. And that's great. And we've done that stuff before. We did a lot of that in the run up to this scenario. But we thought it would be helpful to have a actual, concrete story that you can read. And part of the reason why we think this is important is that it forces you to like, think about everything and integrate it all into a coherent picture.

• 30:51Casey Newton?

  Well, I want to ask you a bit more about that. So I mean, the first thing I want to say about AI 2027 is it's an extremely entertaining read. Like, it is as entertaining as most of the sci fi that I have read. By the end of it, you get into scenarios where, you know, humanity's survival is threatened. And so whether you think it's true or false, it is like really engaging to read. But my understanding of your aim here is that there is something practical about what you were trying to do, right? Can you tell us about sort of the practical idea of going through this exercise?

• 31:25Speaker 05

  Yeah, well, I mean, important background context. The CEOs of OpenAI, Anthropic and Google D-Mine have all publicly stated that they're building AGI and even that they're building superintelligence and that they think that they can succeed by the end of this decade. And that's a really big deal. And everyone needs to be paying attention to that. Like, I think a lot of people dismiss that as hype. And it's a reasonable reaction to say like, They're just hyping their product. But it's not just the CEOs saying this. It's also the actual researchers at the companies. And it's not just people at the companies. It's also various independent people in academia and so forth. And then also, like, you don't just have to trust people's word for it. If you actually look at the evidence, it really does seem strikingly plausible that this could happen by the end of this decade.

• 32:08Speaker 05

  And then if it does happen, things are going to go crazy in some way or other. We like, it's hard to predict exactly how. But obviously, if we do get super intelligent AGI, what happens next is going to look like sci fi.

• 32:22Casey Newton?

  Right.

• 32:22Speaker 05

  It will be like, it'll be, it'll be straight out of a sci fi book, except that it will be actually happening.

• 32:27Casey Newton?

  You, you mentioned that if what the CEOs of tech companies say comes true, we will be living in a sci fi world. And I think for a lot of people, they're content to sort of stop thinking there. Right. They, they might be willing to admit, okay, yeah. If you invent super intelligence, things will probably be crazy. But like, I'll cross that bridge when we come to it. You're sort of taking a different approach and saying like, no, you're going to want to start thinking right now about what it would be like if some of these claims start to come true. So maybe we could get into what some of those claims are. Sketch out for us what you think is very likely to happen just within the next couple of years.

• 33:09Speaker 05

  Well, I wouldn't say very likely. I should express my uncertainty, right? So past discussion often focuses on a single milestone, like artificial general intelligence or super intelligence. We broke it down into a couple of different milestones, which we call superhuman coders, superhuman AI researchers, superintelligent AI researchers, and then broad superintelligence. So we sort of like make our predictions for each of these stages. Even the very first one, I'm only like 50% confident that it will happen by the end of 2027. So I, I have 50% chance that 2027 will end and there still won't be any autonomous, superhuman coding agents.

• 33:45Casey Newton?

  But it's a coin flip. We might also be living in a world where, yes, you do have.

• 33:48Speaker 05

  Exactly. Exactly. So 50% chance, we do have autonomous, fully autonomous. Artificial intelligences that can basically do the job of the cracked engineers by 2027. And then, okay, well, it's the next milestone after that. After that comes automating the full AI research process instead of just the coding. Because AI research is more than just coding. And how long does it take to get to that? Well, we have our guesses. And in our scenario, it happens like six months later. You know? So in our story, get the superhuman coders, use them to go even faster to get to the superhuman AI researchers that are able to do the whole loop. That really kicks things off. And now you're going much faster. How much faster? We say 25 times faster for the algorithmic progress at least. Of course, your compute scale up is not going any faster at all because you still have the same amount of compute.

• 34:31Speaker 05

  But you're able to do the algorithmic progress 20 times faster, 25 times faster. Then you start getting to the superhuman regime. So you start getting systems that are just like qualitatively superior to the best humans at stuff. And they're also probably discovering new paradigms. So we depict them going through multiple paradigm shifts over the course of the second half of 2027, ending up with something that's just vastly superior to humans in every dimension by the end.

• 34:56Casey Newton?

  Yeah. Let me just sort of pause and maybe underline a couple of things there. I think, you know, most people might not understand why the big AI labs are obsessed with automating coding, right? Most people are not software engineers, so they kind of don't care how much of it is automated. But by the time you get to software that is mostly writing itself, it unlocks this other world of possibilities. And you just sort of sketch out a vision where, once we get to a point where the sort of AI coding systems are better than almost every human engineer, or maybe every human engineer, then this other thing becomes possible, which is now you can just set this thing to work, trying to figure out how to build AI itself, right? Is that what I'm hearing you say?

• 35:39Speaker 05

  Basically, I'd break it down into two stages. So I think the coding is separate from the complete automation, as I previously mentioned. I think that I expect to see systems that are able to do all the coding extremely well, but might lack research taste. For example, they might lack good judgment about what types of experiments to run. And so that's why they can't completely automate the research process. And then you have to make a new system or continually train the old system so that it gets that taste. It gets that judgment. Similarly, they might lack coordination ability. They might be not so good at working together in large organizations of thousands of copies, at least initially, but then you fix that and you come up with new methods and you do additional training environments and get them good at that sort of thing. And that's what we depict happening over the first half of 2027.

• 36:22Speaker 05

  And we depict it happening in only half a year because it goes faster, because they've got all the coding down pat. And so even though humans are still directing the whole process, they just give orders to the coding agents and they quickly make everything actually work. So and then halfway through the year, they've succeeded in making new training runs that train the skills that the AIs were missing. So now they're not just coding agents. They are able to do the research taste as well. They're able to come up with the new ideas, they're able to come up with hypotheses and test them, and they're able to work together in big sort of like hive mind clusters of thousands and thousands of them. And that's when things really kick off. That's when it really starts to accelerate.

• 37:03Kevin Roose?

  In your scenario, you have this sort of choose your own adventure ending, where after this thing you call the intelligence explosion, where the superhuman AI coders get into AI R and D and they start automating the process of building better and better AIs, you sort of have two buttons that you can click and one of them sort of unspools the good place ending where we decide to slow down AI development and really get these things under control and solve alignment. And then the red button, you push that and it goes into this very dark dystopian scenario where we lose control of AI, they start deceiving and scheming against us and ultimately maybe we all die.

• 37:43Kevin Roose?

  Why did you decide to give people the option of choosing one of those two endings rather than just sketching what you believe to be the most probable outcome?

• 37:52Speaker 05

  So we did start by sketching what we believe to be the most probable outcome, and it's the race ending, the one that ends with the miscellaneous AIs in control of everything. So we did that first. And then we were like, well, this is kind of depressing and sad. And there's a whole bunch of stuff that we didn't get to talk about because of that. And so we wanted to then have a different ending that ended differently. In fact, we wanted to have like a whole spread. Of different possible outcomes, but we are limited by time and labor. And we were only able to pull together one other outcome, which is the one that we depaid in the slowdown ending. So in the slowdown ending, they solve the alignment issues and they actually get AIs that are actually, you know, what they say on their tin. They're not faking it. They're, they just actually have the goals and values that were put into them or that

• 38:37Speaker 05

  the company was trying to train into them. And, you know, it takes them a couple of months to like sort that out. That's why it's a slowdown. It had to like pivot a lot of their compute and energy towards figuring that stuff out. But they succeed. And so then in that ending, we still have this crazy arms race with China and we still have this crazy geopolitical crisis. And in fact, it still ends in a similar sort of way with this massive arms buildup on both sides, this massive integration into the economy, and then ultimately a peace treaty.

• 39:03Kevin Roose?

  I'm curious, Andrew, if the events of the last week in Washington, the tariffs, this looming trade war with China have affected your forecast at all?

• 39:13Speaker 05

  I mean, we've been, we've been iterally, iterally improving it, but like the core structure of it was basically done a few months ago. So this is all new to us and wasn't really part of the forecast. How would it change things? Well, if the trade war continues and causes a recession and stuff like that, it might just generally slow the pace of AI progress, but not by much, I think. Like, say it makes compute 30% more expensive so that the companies are able to buy 30% less of it. Maybe that would translate to like a 15% reduction in overall research velocity over the next few years, which would mean that the milestones that we talked about happened like a few months later instead of when they do. So the story would still be basically the same.

• 39:54Casey Newton?

  So one of the things I think is most interesting about your project is the bets and bounties section where you are going to pay people for finding errors in your work, for convincing you to change your mind on key points, or for drafting some alternate scenarios. So talk to me a little bit about how that became part of this project.

• 40:12Speaker 05

  So like, you know, I come from the sort of rationalist community background, which is big into making predictions and making bets, putting your money where your mouth is. So I have sort of aesthetic interest in doing that sort of thing. But then also specifically, one of the goals of this project is to get people to think more about this stuff and to, you know, do more scenario forecasting along the lines of what we've done. We're really hoping that people will counter this with their own reasonably detailed, you know, alternative pathways that represents their vision of what's coming. And so we're going to give out a few thousand dollars of prizes to try to mildly incentivize that. And then as for the bounties thing, already we've gotten dozens of people being like, oh, you say this, but like, isn't this a typo? Or like, you know, this feels wrong. And so I have a backlog of things to process, but I'm going to get through it.

• 40:56Speaker 05

  I'm going to like, you know, pay out the little payments and fix all the little bugs and stuff like that. And I'm just quite heart warmed to see that, that level of engagement.

• 41:06Kevin Roose?

  And have you like taken any bets on like different scenarios so far?

• 41:11Speaker 05

  I think so far I've done like one or two, but mostly there's just a backlog I need to work through.

• 41:15Kevin Roose?

  Got it, got it. Now, Daniel, you said you've been getting some good responses from people at the AI companies to this scenario forecast. I did a bunch of calling around when I was writing about this. And after we spoke, I talked to a bunch of different people, both in the AI research community and outside of it. And I would say the most frequent reaction I got was just kind of disbelief. One person I talked to, a prominent AI researcher, said he thought it was an April Fool's joke when I first showed him this scenario because it just sounded so outlandish. You know, you've got Chinese espionage and the models going rogue and the superhuman coders. And like, it all just seemed fantastical.

• 42:00Kevin Roose?

  And it was almost like they didn't even think it was worth engaging with because it was so far out. I'm curious if you've gotten much of that kind of reaction and what your response is.

• 42:10Speaker 05

  Couple of things. So first of all, well, you know, go write your own damn scenario then. I would say you either will write a scenario that doesn't seem outlandish, which I will completely tear apart as unrealistic and just assuming basically that AI progress hits a wall. Or you'll write a scenario that does feel very outlandish, but perhaps in different ways than ours do. Again, like, are they actually going to get to AGI and superintelligence by the end of this decade? If so, you can't possibly write that in a way that's not outlandish. It's just a question of like, which outlandish thing are you going to write? And if you think maybe this is not going to happen, and it's going to hit a wall, yeah, that's possible too. I think that's reasonable. I don't think it's the most likely outcome. Like, I do actually think that probably by the end of this decade, we're going to have superintelligence. But I think it's... yeah.

• 42:55Casey Newton?

  And then say more about that, because, you know, I assume that a lot of our listeners, like, think, either truly think that it will hit a wall, or they're just sort of counting on it hitting a wall so as not to have to reckon with any of the scenarios that you describe.

• 43:08Speaker 05

  Right.

• 43:09Casey Newton?

  So like, what is your message to the person that's just like, eh, it'll probably hit a wall?

• 43:13Speaker 05

  I mean, I don't know, read the literature? Like, there's...

• 43:16Casey Newton?

  These people are not going to read the literature. They listen to podcasts specifically, so they don't have to read the literature.

• 43:21Speaker 05

  Yeah, fair. Well, I could point to specific parts of the literature, like benchmarks, for example, and the trends on them. So I would say, the benchmarks used to be terrible, but they're actually becoming a lot better. Meter, in particular, has these agentic coding benchmarks, where they actually give AI systems access to some GPUs and say, "Have fun. You have like, eight hours to make progress on this research problem. Good luck." And then they measure how good they are compared to human researchers, given the same setup. And, you know, line goes up on the graph. It seems like in a year or two, they'll have AIs that are able to just autonomously do eight-hour-long ML research tasks, you know,

• 44:05Speaker 05

  on these sorts of things. And that's not AGI, that's not superintelligence, but that is maybe the first milestone that I was talking about, superhuman coder, right? So I point to those sorts of trends. And then separately, I would also just do the appeal to authority. Like, if you're not going to read the literature, if you're not going to look at the... if you're not going to sort of form your own opinion about this, and you're still just deferring to what other people think... Well, then I will say, yeah, there's a bunch of naysayers out there who are saying this is all never going to happen. It's just fantasy. But also, there's a bunch of extremely credible people with amazing track records, both inside the companies and outside the companies, who are, in fact, taking this extremely seriously.

• 44:36Kevin Roose?

  Yeah, I also want to read you-

• 44:37Speaker 05

  Including our scenario. Like, you know, Joshua Benjio, for example, read an early draft of our thing and liked it and gave us some feedback on it. And then we put a quote from him at the top, saying, everyone should read this. It's plausible. Right? So- He's a pioneering AI researcher.

• 44:52Kevin Roose?

  Yeah. Another genre of criticism I've heard of this forecast is from people who just don't, who are just questioning the idea that if you get AIs that are superhuman at coding, they will kind of be able to bootstrap their way to general intelligence. And I just want to read you a quote from an email that I got from David Autor, who is a very well-known economist at MIT. And I had asked him to look at the scenario and sort of react to it. And with a particular eye on like, what might this be missing as far as how it sort of assumes this easy and fast jump from superhuman coding to something

• 45:34Kevin Roose?

  like AGI? And I'll just read you what he said. He said, "Llama and their ilk are superpowered incarnations of one incredibly important and powerful part of our cognition. The reason I say we're not on a glide path to AGI is that simply taking this capability to 11 does not substitute for the parts that are still missing. I think that humanity will get to AGI eventually. I'm not a dualist. I just don't believe that swimming faster and faster allows you to fly." What is your reaction to that?

• 46:03Speaker 05

  I agree. We depict this in the course of the story. So if you read AI 2027, they have something that's like Llama, but with a lot more reinforcement learning to do long horizon tasks. And that is what counts as the first superhuman coder. So it's already somewhat different from the systems of today, but it's still broadly similar. It's still sort of maybe the same fundamental architecture, just a lot more training, a lot more scaling up, and in particular, a lot more training specifically on long horizon agentic coding tasks.

• 46:32Speaker 05

  But that's not itself AGI. I agree. That's just the superhuman coder that you get early on. And then you have to go through several more like paradigm shifts to get to actual superintelligence. And we depict that happening over the course of 2027. So a key thing that I think that everyone needs to be thinking about is this takeoff speeds variable. How much faster does the research go when you've reached the first milestone? And how much faster does the research go when you reach the second milestone and so forth? And we are, of course, uncertain about this, like we are about many things. We say in this scenario that we could easily imagine it being five times slower than we depict and taking sort of like five years instead of one year. But also, we could imagine it being five times faster than we depict and taking like two months, you know? So we want to do a lot more research on that, obviously.

• 47:16Speaker 05

  If you want to know where our numbers are coming from, go to the website. There is a tab that you can click on that lists, has a bunch of sort of like back of the envelope calculations and little mini essays where we like generated the quantitative estimates that are the skeleton of the story.

• 47:30Kevin Roose?

  One other piece of criticism I've seen of this project that I wanted to ask you about was from a researcher at Anthropic named Safran Huang, who argued on X that she thought that your approach in AI 2027 was highly counterproductive. Basically, that you were in danger of creating a self fulfilling prophecy by making these sort of scary outcomes very legible, by sort of, you know, burying some assumptions, that you were essentially making the bad scenario that you're worried about more likely to actually happen. What do you make of that?

• 48:05Speaker 05

  I'm quite worried about that as well. And this is something we've been like fretting about since day one of the project. So let me just say a little bit more about that. So first of all, there is a long history of this sort of thing seeming to happen in the field of artificial general intelligence research, most notably Eliyahu Yarkevsky, who is the sort of like I don't know, er, father of worrying about AGI, at least in this generation. People, Alan Turing also worried about it, but Sam Altman specifically tweeted, you know this tweet? Yeah, Sam specifically said, hats off to the Elias Yudkowsky for raising awareness about AGI. It's happening much faster now because of his doomsaying, because it's caused a bunch

• 48:47Speaker 05

  of people to pay more attention to the possibility and to start investing in these companies and so forth. So I was sort of like, I don't know, twisting the knife at him because he obviously doesn't want this to happen faster. He thinks we need more time to prepare and make it safe and so forth. But it does seem like there's been this effect where people talking about how powerful and scary AGI could be has maybe caused it to come a little bit faster and caused people to wake up and race harder towards it. And similarly, I'm worried about causing something like that with AI 2027. One of the subplots in AI 2027 is this whole concentration of power issue of who gets to control the army of superintelligences, right? And in the race ending, it's sort of a moot question because the army of superintelligences

• 49:31Speaker 05

  is just pretending to be controlled and so is not actually listening to anyone when it counts. But in the slowdown ending, they do actually align the AIs. And so they are actually going to do what they're told. And then who gets to say that, right? And the answer in our slowdown ending is the Oversight Committee, which is this ad hoc group of people that is some CEOs and the president who get together and share power over the army of superintelligences. But what I would like to see is something more democratic than that, something where the power is more distributed. I'm also afraid that it could be less democratic than that. At least we get an oligarchy with this committee, but it could very easily end up a dictatorship where one person has absolute control over the army of superintelligences.

• 50:14Speaker 05

  This is yet another example of how I'm trying to not have the self fulfilling prophecy happen. I don't want people to read this and be like, hmm, I'm a CEO.

• 50:22Kevin Roose?

  I can make a lot of money by building the misaligned AI.

• 50:26Speaker 05

  Maybe, yeah. But all that being said.

• 50:30Kevin Roose?

  Yes, any of our evil villain listeners out there, steepling your fingers in your lair under a mountain, knock it off.

• 50:39Speaker 05

  Yeah. So all that being said, we are taking a gamble that sunlight is the best disinfectant.

• 50:48Speaker 05

  The best way forward is to just generally tell the world about what we think is coming and hope that even though many people will react to that in exactly the wrong ways, enough people will react to that in the right ways, that overall it will be good. Because I am tired of the alternative of like, hush, hush, keep everything secret, do backroom negotiations and hope that we get like the right people in the right rooms at the right time and that they make the right decisions. I think that that is kind of doomed. So I'm sort of placing my faith in humanity and telling it as I see it and hoping that insofar as I'm correct, people will wake up in time and, you know, overall that the outcome will be better.

• 51:30Casey Newton?

  Yeah.

• 51:31Kevin Roose?

  All right.

• 51:32Casey Newton?

  Thank you, Daniel.

• 51:34Kevin Roose?

  Thanks, Daniel.

• 51:35Speaker 05

  Thank you so much.

• 51:36Casey Newton?

  When we come back, Meta decides to fake it till they make it.

• 51:43Kevin Roose?

  We'll talk about the cheating scandal that is rocking the world of AI benchmarks.

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• 53:43Kevin Roose?

  Well, Casey, there's one other big AI story we want to talk about this week, and that is about the drama surrounding Llama.

• 53:51Casey Newton?

  That's right, Kevin. Meta has a new large language model. It was hotly anticipated, but I think it's fair to say it kind of stumbled out of the gate.

• 54:01Kevin Roose?

  Yeah, they had some llama, llama cred drama.

• 54:06Casey Newton?

  How many times are you gonna do the llama drama pun?

• 54:09Kevin Roose?

  Well, there's a very popular children's book called Llama, Llama, Red Pyjama. Are you aware of this?

• 54:14Casey Newton?

  I am.

• 54:15Kevin Roose?

  So let's get into it. There's been a lot of things going on around this new language model, Llama 4, that Meta released last weekend. Casey, you've been writing about this in your newsletter this week. Catch me up. What is going on with Llama 4?

• 54:30Casey Newton?

  Yeah, so look, Meta has invested billions and billions of dollars in AI, and they're taking a very different approach from the AI labs that we most often talk about on this show. Companies like OpenAI, Anthropic, Google, their models are closed. You can't sort of download, fine-tune, re-release them under a sort of very permissive license. But with Metas, you can. And when Llama 3 came out last year, developers said, oh, this thing is actually like pretty good. Like, it's not as good as the state of the art, which is often true of the open models, but it's getting up there.

• 55:08Kevin Roose?

  Right. And so they spent all this money to develop Llama 4. People have been talking for months about how this was gonna sort of blow all the other open weights models out of the water. And then they release it and what happens?

• 55:22Casey Newton?

  Well, two things happened, Kevin. The first is that Meta trumpets this model in the way that companies usually do trumpet their most recent models as being the most powerful ever, the most efficient. They show off a bunch of benchmarks. They say this thing is highly capable and it's the bee's knees. They didn't actually say it was the bee's knees. I'm not sure anyone has said that in the past 70 years, but they said things like that. And one of the benchmarks that really got people's attention was LM Arena. You know LM Arena?

• 55:58Kevin Roose?

  I know of it, but I haven't spent much time on it. What is it?

• 56:01Casey Newton?

  So it's this really interesting project. It is a very small nonprofit that includes some researchers from UC Berkeley. And what they do is they get people to volunteer to help and they'll have people enter a query and then they'll show them the response from two different chatbots that are not labeled. And after they get the answer, the user will say, oh, I liked this one better. And they collect those votes over time. And the more that people vote for one chatbot over another, the higher it rises on LM Arena.

• 56:32Kevin Roose?

  I see. So it's sort of like a crowdsourced leaderboard for which of these models people prefer.

• 56:37Casey Newton?

  Exactly. And Kevin, you know, as well as anyone else, that whenever a new model comes out, the question of how good is it turns out to be weirdly hard to answer.

• 56:47Kevin Roose?

  Yep.

• 56:47Casey Newton?

  Right? Maybe it's really good for what you need it to do. Maybe it's really bad. Or maybe it's about as good as something else, but you just happen to like it better because it has a style that matches with what you're looking for. So in such a world, companies are desperate to be seen as good, but they don't have an easy way of communicating that. And that's when LM Arena enters the picture. Because if you can get high enough on that leaderboard, you can point to it and say, aha, look at how we're doing.

• 57:13Kevin Roose?

  Right. The people have voted.

• 57:14Casey Newton?

  That's right. The people have spoken and look how well we're doing. So do you know how well Llama4 does on LM Arena?

• 57:21Kevin Roose?

  No.

• 57:22Casey Newton?

  Llama4 comes in at number two, just under Gemini 2.5 Pro Experimental, which is the The latest model from Google, which has been through a lot of testing and which basically there is like universal acclaim for this model. People think like this is like a truly great model, not just at this little chatbot contest, but across a bunch of other things, including coding and, you know, a lot of other things.

• 57:44Kevin Roose?

  So Llama 4 sort of immediately zooming up to number two on LM Arena would seem to indicate that Meta has really cooked here. They have built this incredible model. They are releasing it to the public under an open weight structure and they are one of the the leading AI labs when it comes to creating very powerful models.

• 58:03Casey Newton?

  That's right, except there's an asterisk.

• 58:06Kevin Roose?

  Oh boy.

• 58:07Casey Newton?

  This version of Llama 4 is an experimental model. Meta on its website says it has been optimized for chat. People start to look into this. They noticed this is not the version of Llama 4 that is actually available for download.

• 58:25Kevin Roose?

  The one that was included in LM Arena was not the one that people could download?

• 58:29Casey Newton?

  That's right. It had a different name. It was named Maverick 0326 Experimental. And people start to think, oh, wait a minute. What if what happened here isn't what normally happens on LM Arena, which is people make a new model and submit it to LM Arena and see how it does. What if Meta trained a special version of Llama 4 just to be good at LM Arena?

• 58:55Kevin Roose?

  Hmm.

• 58:56Casey Newton?

  Now, I have spent the past week trying to research whether this is true. And on Monday, I got Meta to send me a statement, which I guess I should read. "We experiment with all types of custom variants and this experimental version is, quote, 'a chat-optimized version we experimented with that also performs well on LM Arena. We have now released our final open source version and we will see how developers customize Llama 4 for their own use cases." So this was really interesting to me because when they say, well, it also performs well on LM Arena, it suggests that, well, maybe they just made like, I don't know, 15 of these models and they were just like, oh, look, this one happens to do well on LM

• 59:41Casey Newton?

  Arena. That is like one possibility. I think another possibility is exactly what the cynics think, which is, oh, no, they sort of reverse engineered how LM Arena works and they built a bot that was just gonna beat it.

• 59:54Kevin Roose?

  And how would you do that? Like, if your goal was to create a model that would perform very well on this one specific leaderboard, what would you do?

• 1:00:04Casey Newton?

  So LM Arena has released a lot of chats over the years that sort of show which chats are considered preferable to other chats. And it seems that the users of LM Arena really like it when the bot has a high degree of what they call sycophancy. So basically, you're just like, what should I have for breakfast today? And the chatbot is like, oh, my God, that's such a great question. You're a genius. I love the way you're starting the day off, right? That is the kind of answer that people pick. And so you can build a chatbot that essentially just flatters people constantly, and it tends to do really well on chatbot arena. So anyways, in the aftermath of this confusion, LM Arena, which is a very sort

• 1:00:46Casey Newton?

  of mild-mannered organization that I think is not used to being involved in public controversies, puts out a statement, and I have to read the statement, Kevin, because as gentle as it is, I found it pretty damning. They don't go so far as to say Meta cheated. But what they do say is, quote, "Meta's interpretation of our policy did not match what we expect from model providers. Meta should have made it clear that this experimental model was a customized model to optimize for human preference. As a result of that, we are updating our leaderboard policies to reinforce our commitment to fair, reproducible evaluations so this confusion doesn't occur in the future."

• 1:01:29Casey Newton?

  So why is that statement so interesting to me? Well, you basically just have this tiny group of researchers over at Berkeley and Meta...

• 1:01:38Casey Newton?

  Violates their policies so hard that they have to change the rules for how this competition even works, just to get people to stop breaking the competition.

• 1:01:48Kevin Roose?

  Yeah, I thought this was a really interesting set of stories. I'm still waiting for someone, ideally you, to get to the bottom of like, what actually happened inside Meta. But I think it's worth talking about for two reasons. One, because I think it says something about Meta and its place in the AI race. And the other, because I think it says something about the state of AI and these benchmarks and how useful they are or aren't in making sense of the torrent of new models that are coming out constantly from the big AI labs.

• 1:02:19Casey Newton?

  Totally.

• 1:02:20Kevin Roose?

  So maybe let's take those one by one. What do you think this says about Meta's place in the AI race if it does turn out that they had sort of gamed this leaderboard to make it look like their model was better than it was?

• 1:02:33Casey Newton?

  Here's what I think. I think if you're winning the AI race, you do not waste time trying to beat LM Arena, right? What you do is what Google did, which is just release a very powerful pro version of Gemini, and it just happens to float to the top of the arena, not because it's been optimized for conversation, but just because it's a great model that's really good at a lot of things. If you have to make a custom version of your model just to win this rinky-dink competition, it's like hard for me to think of a more adverse indicator for the quality of Meta's AI program. And we should say, there's been reporting in the information over the past year that the Llama 4 development process has been really frustrating for Meta, that they delayed the release twice because they weren't getting the results that they wanted.

• 1:03:16Casey Newton?

  And when it finally did come out and people started to put it through other evaluations, they found that it just was not hitting the mark. In fact, Kevin, Ethan Mollick, former guest on Hard Fork, compared the versions of the experimental chat that was winning the leaderboard to the chats that were produced by the final open weights model. And what he found was, the open weights model was producing really bad responses. Essentially, that the optimized model was performing so much better than the real one that it wasn't even close.

• 1:03:50Kevin Roose?

  So why don't they just release the optimized model, then?

• 1:03:54Casey Newton?

  That's a great question. I don't know the answer to that, but what I'm going to assume is that whatever fine tuning is necessary to increase the level of sycophancy in the bot might be great for this sort of competition, but maybe it's really bad for coding, or creative writing, or the countless other things that we now expect Llama to be good at, right? You know, fine tuning is a very powerful process that can take a very general purpose model that's kind of mediocre at a bunch of things and make it really good at one thing. But these days, people have a lot of options to choose from with their large language models, and there are a lot of them that just have very high general capability, so they're going to use those instead.

• 1:04:35Kevin Roose?

  Yeah. I mean, I have not done my own reporting on the situation inside Meta with Llama4, but I will just say, from a broad view, if you just step back from this particular scandal, Meta is not one of the top three AI labs in America when it comes to releasing frontier models. They are not in the top tier of frontier AI research. A lot of their key researchers have left the company. Their models are not seen as capable as the models from OpenAI, Anthropic, and Google DeepMind. And I think that really frustrates them, right? I think Mark Zuckerberg and his lieutenants, they really want to be seen as part of the vanguard here.

• 1:05:18Kevin Roose?

  And so I would not be surprised at all if in an effort to kind of juice their numbers and appear to be leapfrogging some of their competition, they may have violated the terms of one particular AI benchmark. And that should make us question how well their overall AI program is doing.

• 1:05:33Casey Newton?

  Absolutely. And by the way, the next time they release a model and come out with a bunch of wild claims, like, you think I'm going to believe any of that? No. It's like, you're going to have to go, you know, try to verify every single claim they make independently. And, look, I assume some people are going to hear this and think that I'm making a mountain out of a molehill, but I just think about what Daniel Cocotella just told us, about how powerful these systems are becoming, and about how powerful they're about to become. And you want them to be like, sort of loyal to human beings, but you also want them to like, not be used for bad behavior. And like, if there is a Company out there that is just like cheating to win benchmarks. What else can that model do?

• 1:06:07Kevin Roose?

  Right.

• 1:06:08Casey Newton?

  So even though this may seem like a small thing, I think it matters that we have companies building AI systems where we have some level of trust in those companies, where we believe they have some amount of integrity when it comes to how they operate. And so this was a moment where I thought, wow, my trust in Meta as an AI company has just been dramatically reduced.

• 1:06:25Kevin Roose?

  Yeah, so the meta of it all aside, I think this does actually raise a really important question about the broader AI industry, which is the value of benchmarks in general. Because one thing that I've heard from AI researchers over the past year or two is that these benchmarks, these tests that are given to these models to figure out how intelligent they are, they all have some flaw built into them, right? There's this issue of data contamination, which is what if some of the answers on these tests are being fed into these models during their training process so that you're really not getting a sense of how capable the model is? They're just kind of regurgitating these answers that they've sort of seen already. That is an issue. There are also just the issue that all these companies are effectively grading their own homework, right?

• 1:07:10Kevin Roose?

  There's no like federal program that sort of puts these things through their paces and releases like standardized benchmark scores that we can actually verify and trust. Some of these AI companies are using different methods to even apply these benchmark tests. There's these things called consensus at 64 and all these different ways that you can kind of cherry pick like the best answer that your model gives if you give it the test a bunch of times and use that for your score. So I think we are just losing our ability to trust the way that we measure these AI models in general.

• 1:07:44Casey Newton?

  Yeah, and it's so frustrating. You know, I was thinking, Kevin, imagine like in the early 2010s, and it's not just that like Instagram comes out as an app in the App Store. You have Instagram, you have Instagram 01, you have Instagram 01 Mini, you have Instagram 01 Deep Research, and it's like, download the one that's best for you. You'd be like, why are you making me do any of this, right? Like, just give me the one thing that works. And while every AI lab is trying to realize that, in the meantime, we're living through this Cambrian explosion. Of large language models. And on one hand, I think that makes it really important for there to be benchmarks so that we can look at a glance to have a basic sense of is this thing even worth my time?

• 1:08:24Casey Newton?

  But on the other hand, that makes the benchmarks such an attractive target for gaming and outright cheating.

• 1:08:31Kevin Roose?

  Yeah.

• 1:08:31Casey Newton?

  And so that's why the researcher Andre Karpathy has said that we have what he calls an evaluation crisis. Where when a new model comes out, the question of how good is it is just very difficult to answer. I've been wondering what we can do as journalists to try to answer those questions better. Like, is this a place for journalists to actually say, okay, new model came out, we're going to have our own custom set of evaluations. Maybe we're going to keep those private in some way to prevent them from being gained. But but what what solutions do you see here to this crisis?

• 1:09:01Kevin Roose?

  Well, at the risk of scooping myself here, I will disclose that I am actually starting to work on my own benchmark. Because I think that part of how we are going to make sense of these AI models is that people will just start developing their own set of tests to give to new models. Not necessarily to determine like their overall intelligence, but to determine how good they are at the things we care about. You know, personally, I don't care much if an AI model is getting a 97% on the graduate level physics exam or a 93%, right? That that does not make a huge difference in my life.

• 1:09:37Casey Newton?

  Because it's still higher than you're going to get.

• 1:09:38Kevin Roose?

  Exactly. And I am not a graduate level physics researcher. So I might care more about whether a model is good at creative writing or not. And I might want a battery of tests to determine that. And so I think that as these things become more critical in people's lives and work, we will start seeing these more personalized tests and evaluations that actually measure if the models are good at the things that we care about. What do you think?

• 1:10:05Casey Newton?

  Yeah, I think that's a great point. And after you told me that you were going to do this, I sort of started a scheme and thought, you know, I want my own benchmarks too, because there are, I don't know, I'm sure I can come up with a list of like 10 things that I wish AI could do for me today that it still can't. And so maybe it's time that I should start scenario planning.

• 1:10:22Kevin Roose?

  What's one of your tests that you want to give AI models to determine if they're capable or not?

• 1:10:27Casey Newton?

  Well, like, for example, you know, I have a newsletter that has customer service issues. People email us, they say, oh, my gosh, can I change my email address?

• 1:10:33Kevin Roose?

  People say the writing, and this is so bad.

• 1:10:35Casey Newton?

  People love the writing. That's all I hear about the writing. People are saying, are humanists writing this? That's insane. But I would love to be able to be able to automate some of that, you know? Make it easier for people. So, oh, you need to download your invoice? Which is a question we get a lot. It's like, okay, yes. Actually, we're just going to sort of handle that in automated ways. So that's just one very easy thing. And, you know, if you're thinking, oh, Casey, I actually have a product that can already do that for you. Please don't email me. It can't. I've done through this.

• 1:11:01Kevin Roose?

  Can I tell you one of the things that I want to test AI on?

• 1:11:05Casey Newton?

  Yeah.

• 1:11:06Kevin Roose?

  So, as you know, I just moved into a new house. And so, as a result, I have spent like between a third and half of my waking hours over the last few weeks thinking about hanging pictures. Hanging pictures is one of my least favorite tasks in the world. You have to do math. You have to bring out the laser level. I mean, it's a huge process.

• 1:11:27Casey Newton?

  The golden ratio?

• 1:11:28Kevin Roose?

  Yes. And I would love for an AI system to be able to hang pictures for me.

• 1:11:34Casey Newton?

  That's beautiful.

• 1:11:35Kevin Roose?

  And as soon as that happens, to me, that's AGI.

• 1:11:37Casey Newton?

  Now, would that involve a robot?

• 1:11:39Kevin Roose?

  Probably, yeah. So we got to make some progress before we get there. But if you're listening to this and you're working at one of these robotics companies, get on it.

• 1:12:13Speaker 00

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• 1:13:56Casey Newton?

  Hard Fork is produced by Rachel Cohn and Whitney Jones. This episode was edited by Matt Colette and fact-checked by Ina Alvarado. Today's show was engineered by Chris Wood. Original music by Rowan Nemesto and Dan Powell. Our executive producer is Jen Poyant. Video production by Soya Roque, Pat Gunther, and Chris Shaw. You can watch this whole episode on YouTube at youtube.com/hardfork. Special thanks to Paul Schuman, Hui Wing Tam, Dahlia Hadad, and Jeffrey Miranda. You can email us at [email protected] with your AI doomsday scenario.

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