Meta Shifts the Blame + Do Data Center Bans Work? + The Final HatGPT

24:23That's R-I-P-P-L-I-N-G dot A-I slash hardfork. Sign up today. Well, Casey, we couldn't let a week go by on this show without talking about the hot topic du jour, which is data centers. Yeah, I would say it's the hot topic du year, because it seems like since January, it's almost all we hear about. Yes, and there's been so many, we've done so many segments and so many shows about the data center backlash and what's driving it and where it's going and what it means for the AI companies and for AI progress as a whole. But today, I thought we should drill into one specific post and one specific angle that I thought was really interesting on data centers and these data center moratoria and bans.

25:08This came from Arvind Narayanan, former Hard Fork guest, professor of computer science at Princeton, and someone who is widely considered a serious critic of AI hype. He's the co-author of AI as Normal Technology, the essay we brought him on to talk about about a year and a half ago. and he has been following this data center backlash as we have and has come to a surprising conclusion. Yeah, and that conclusion is that stopping the construction of new data centers will not meaningfully slow AI progress. Yeah, and in particular, he gives some real numbers to this argument. He says that if a typical U.S.

25:47state enacts a one-year moratorium on new data center construction, it would only slow AI efficiency progress by five to 10 hours. So basically, if your concern is all this stuff seems to be moving too fast, opposing data centers in your state or your city is not an effective way to slow it down. Yeah, and there are some questions about how much opposition to data centers is really about AI progress. I think there are a lot of other factors. We'll get into it. But we did think that Arvind's point was worth dwelling on a bit because I suspect that as we move forward and more and more data centers get proposed, there are going to be a number of people who see this as a meaningful way of getting some control and agency back in the AI future.

26:31Yeah, and I've even heard it expressed by people who are generally, you know, supportive of and optimistic about AI, but are worried about some of the risks of this moving too quickly, which is like, well, maybe the data center backlash is sort of based on these, like, you know, these sort of local fears about water use and electricity, maybe they're not quite fact-based when they're out there protesting data centers. But if the net effect is slowing down AI progress, that might be a good thing. And so I really was challenged by and provoked by Arvin's work on this subject because he is someone who's been very skeptical of the claims being made by some of the AI lab leaders.

27:11And yet here he is sort of saying, well, stopping data centers is not the way to stop AI progress. All right. Well, before we bring him in, Kevin, should we quickly do our AI disclosures? Sure. I work for the New York Times, which is suing OpenAI, Microsoft, and Perplexity. And my fiance works at Anthropic.

27:32Arvind Narayanan, welcome back to Hard Fork. Thank you, Kevin. Thank you, Casey. Great to be here. So it has been nearly a year and a half since we last had you on the show to talk about your AI as normal technology paper, which argued that essentially there are all these bottlenecks that are going to prevent AI from being universally adopted throughout society and getting this sort of rapid takeoff that some people out here in San Francisco believe is imminent. And so it was a little surprising to me that we are having you back to talk about one sort of potential bottleneck that you don't think is going to be a big issue to continued AI progress, which is all of this backlash to and opposition to the construction of data centers.

28:14So tell us about your thesis on data centers and whether they will or won't slow down AI progress. Absolutely. I think the backlash can be a real constraint to AI progress, but maybe not if it's directed toward data centers. It's a different kind of backlash if it's directed at, for instance, governments or other decision makers using AI for consequential decisions. I think that would have much more of an impact. And we're maybe seeing some of that, but most of it is directed towards data centers. And the reason I think it's, you know, it could be a good way to push back if you're concerned about noise or water or other local environmental concerns.

28:58But if you're actually concerned about AI progress as a whole, what is this going to mean for society, for jobs? What is this going to mean for safety? And you generally want to push back on the pace at which this technology is developing. Data centers, to me, are not the way to go. Yeah. Well, so let me make the naive case that not building data centers would slow down AI progress. And then you can give me your math, Arvin, and I'll pretend like I understand math for the purposes of this segment. So I think that the naive case might go something like, well, We know that to sort of spread AI throughout society, we need to train ever bigger models.

29:37That means building more data centers that can train those models. And then we also need those data centers to serve those models. And so the fewer data centers we have, the fewer places there are where we could train these models and serve them to other people. And so for those reasons, I could imagine somebody thinking, well, maybe I'll just stop this data center from being built in my backyard, and I will be doing my part to stop AI. What's wrong with that argument? I think that's a good starting point to think about it, but then we have to get into the details. So you helpfully separated training and inference or serving the models.

30:09When it comes to training, most of the data centers, as I understand it, are not used for training. That requires some degree of specialization and those are done in particular clusters. And so stopping a few data centers here and there is not going to slow down training at all. For that, you need some kind of national moratorium or even global. So the real question is about inference. So is stopping a few data centers, either in one's local community or a statewide moratorium or something like that, which is where the action really is, is that playing a meaningful part in decreasing the total amount of AI capacity available in the world?

30:48And so to turn that into something a little bit more concrete where we can do some math, we have to look at the total amount of capacity available at a given capability level. And so the reason that is important is that one way in which AI is advancing that people don't often think about is not through building new data centers and putting new GPUs into them, but by making more out of the ones that we already have. The industry is getting more efficient at building AI to do a certain kind of task at a certain capability level using less power, less machines. And then the second factor you have is that GPUs, newer GPUs, are more power efficient.

31:29And those are not all going into new data centers. Those are also going into existing data centers. So it's really those first two factors, what you can do with the existing data centers, that really dominates the increased capacity from new data centers. So you're saying that data centers are becoming efficient at a rate faster than demand is growing, or at least to the extent that demand is growing, stopping a data center here, a data center there isn't going to be enough to counteract the effects of these efficiency gains that you've just described. That's right. The efficiency gains both from software and from hardware are about an order of magnitude more than the capacity gains from literally new physical buildings.

32:11I'm trying to wrap my head around this because I know that what you're saying is true. The models get more efficient to serve over time, right? The labs keep discovering new ways of running their models more efficiently so that the query that might have eaten up a certain amount of compute last year, this year, only needs a tenth of that amount. But my understanding is that those efficiency gains are the product of increased compute and having bigger models that are able to help researchers discover the efficiency tricks and the new algorithms that allow them to serve the models much more cheaply.

32:47So isn't the overall size and availability of compute important in keeping that efficiency curve going? Absolutely. But again, I think we're looking at really, really marginal changes to the total capacity. We're looking at 0.1 % of the national or world capacity of data centers that can be affected by the actions that one community or even one state can take. And so the way to look at it is, yes, it's true that compute is an input even for future development of AI, but whatever decrease you bring about in total capacity, you know, through stopping data centers, whether that compute is going to be used for serving models or for further AI research, that same amount of progress can be made through these gradually continuing efficiency improvements at a rate that, you know, I did the numbers on this.

33:48If one state stops new data center construction, that translates to something like 10 hours, right? That's how much it takes aggregated for the AI industry to catch up through efficiency improvements to the compute that has been forgone. It's just so counterintuitive because of how hard of the companies are all working to build new data centers. Throughout most of this year, most of the big frontier labs have been in this capacity crunch where they're basically selling as much AI as they can make. So it certainly seems like there is something very important to them about being able to build these data centers.

34:23So how do you think about that, Arvind? Yeah, so that is all true. And that doesn't contradict what I said. And here is the way to square both of those things. So this progress that's happening through improved GPUs or improved software efficiency, for the most part, that is shared by all AI companies. And so that factor kind of cancels out between companies. And the competitive advantage that one company is going to have over another really comes down to how much compute they can control. And so while the total amount of compute is not a big factor in the aggregate rate of AI progress. The relative amount of compute is a surprisingly big factor in the relative competitive positions of companies.

35:06Both of those things are true at the same time. Interesting. So regardless of what moratoriums might do for overall AI progress, if you are an individual lab, it's still really important to you that you get as much compute as you can get your hands on. Exactly. If only to stop your competitors from getting their hands on that. Yeah. I mean, I think a lot about this in terms of the nuclear power opposition in the 1970s and 1980s, where you had this, these like, you know, series of accidents, including Three Mile Island. And after that, like a huge chunk of the American populace, or at least the politically active American populace decided that nuclear power was bad and dangerous and we shouldn't build it.

35:44And so there was basically a successful campaign. I mean, we didn't build nuclear power in America for like 30 years. And it didn't stop the technology, but it just sort of changed the geography of where it could be built. So instead of happening in America, it was happening in France and other places around the world that did build nuclear power. Is that what you think is the likely outcome of data center opposition in America, that it won't sort of stop the technology, but that it could change the distribution of where it's being built? You're right that I don't think it'll stop the technology.

36:14I'm also very skeptical of whether it's going to meaningfully change the distribution of where it's built. state versus state, maybe. But if I understand correctly, one reason stopping a lot of nuclear plants from being built in the US was so successful was by raising the regulatory cost. And that's just a very different model from how the AI data center backlash is proceeding. It's not nationwide regulation that forces AI developers, wherever they are in the nation, to go through a whole lot of extra review or a whole lot of extra safety technology that is going to raise their cost tenfold and change their deployment timelines tenfold.

36:57It's rather moving it from one location to another, which locally might seem like a big win, but at a national level, I don't see it doing much. Yeah, that's interesting. I'm curious, Arvind, if stopping AI progress is not a realistic goal of the data center opposition that we're seeing right now. Like, are there other things that it could accomplish that may be constructive rather than futile? Absolutely. I've looked a little bit at what is driving the anti-data center movement. And I'm certainly not claiming that this kind of, you know, slowing AI is the main goal of the movement. A lot of it is driven by local concerns.

37:37A lot of it is driven by procedural concerns, the lack of transparency, the way that local politicians enter into these deals without giving citizens a voice. But, you know, whatever the ultimate reasons are, I would say that what is happening right now is arguably a pretty rational way to go about it, because you have to have the credible threat of bans and moratoria in order to force companies to come to the negotiating table. So companies are making a lot of money. I think maybe communities can channel their opposition in a way that they can ask for direct payments, investment into local communities much more than we're seeing today from AI companies so that local communities can benefit.

38:19What do you think does drive AI progress if it's not availability of compute? You've said algorithmic efficiency, the sort of notion that these models become cheaper to serve over time. But what else should people be if they are interested in slowing down or stopping AI progress? Like what is the better fight to be having? Yeah, I mean, there are are many different dimensions of progress, right? So one can think about what is the most powerful, capable model out there. And that might matter to you a lot if what you're concerned about is some of the incidents we've seen, like the hugging face incident and some of the safety risks.

38:53There's another sense in which you might want to stop, you know, the rapid march of AI, which is people doing things with AI that are not really suitable for AI, delegating too much decision making to AI. So when I hear things like the colloquially named AI psychosis among CEOs, right, CEOs, you know, using cloud code to try to replace what an employee does. And of course, it does a first cut version, which superficially seems like it does the job firing a bunch of people and then recognizing, oh, shit, now when something goes wrong, there's nobody to fix the mess, hiring people back. We've heard this kind of story many times over.

39:33And of course, you know, in some cases, these models are good enough to, in fact, replace certain tasks that people do. But the level of premature decision making that we've seen from CEOs has been a cause for concern. I would have hoped that these people who, frankly, have a lot of money on the line would just for rational, self-interested reasons, make better decisions that take into account not just AI's potential, but also its limitations, you know, here and now. So this, in my view, would be a much better target for opposition. And that's, you know, less at the community level and more at the workplace level, better aligning incentives and decision making and information between the individual workers who in many ways have a much more grounded understanding of what AI can and can't do in a particular company and management, which, you know, has its, in many ways, is forward-looking, and that can be a good thing, but can also make some misguided decisions.

40:33Yeah. At the same time, I think there are probably a lot of people out there who do want to sort of have more control and agency in a world where it seems like AI is advancing quickly, and I suspect they're not going to be satisfied with the answer of like, well, you can just rely on the inertia of your organization to slow the progress of this. So are there other things out there on the horizon that you think could give people more of that feeling of control? Absolutely. I think we have a lot of control in how we ourselves use AI. So this is something I constantly confront in my own use of these tools.

41:14I mean, I am a pretty heavy user of AI tools and AI agents. And I find that by default, they do things that I'm not very happy with in ways that I feel like I'm losing control. So if I ask an agent to find papers, materials, reports on a given topic so that I can start to analyze it, it will, without my asking for it, turn that into its own analysis, its own opinion. And so I have to put specific things in my prompts so that I can get it to do the grunt work for me, not to do my thinking for me. So that's just one simple example. But the broader point is that these tools are not only powerful, they're very, very flexible in how we can use them.

41:55And I think each of us should not just accept the way that the developer has created the tool, but configure them, personalize them in ways that meet our appropriate comfort level for what should be delegated to AI and what should be in the domain of people. That's not an answer to your broader question about AI as a kind of, you know, powerful global force and how we can slow that down. But I think at an individual level, I do want to emphasize that we have a lot of agency. The Marxist historian Eric Hobsbawm has this famous phrase about the Luddites of the Industrial Revolution, that they were conducting collective bargaining by riot.

42:35basically that all of the sort of opposition to machines was a form of collective bargaining because those industries had not been unionized. And the only means of expressing their discontent that workers had was to like break the machines that were threatening their jobs or threatening to concentrate wealth to the owners of the factories rather than to the workers. And I've been thinking a lot about this, you know, obviously, and thankfully, we're not at the point of like violent riots over data centers yet, but I think there is a possibility that we, that we end up there. And I think your point, Arvind, about this being a form of kind of collective bargaining without any formal mechanism.

43:15Like this is, to me, when I see people protesting data centers, I think this is, this is about what's, what's in it for these communities. You know, if the data center companies came in and said, we're going to build this data center, but we're also going to build you a high school and a really nice public park and improve your roads and make your everyday life better. To me, that feels like a much better deal than they're getting now. And so I think of this sort of data center backlash as a form of kind of collective bargaining by another name. Well, and, you know, the sort of traditional form of collective bargaining has been pretty effective here, right?

43:49We've seen workers in Korea rise up and threaten to go on strike if they didn't get a greater share of the profits, the record profits that their companies were realizing in part due to the AI bubble. So I think good old-fashioned unionization can do a lot here. We also saw it in Hollywood, right, where, you know, like the last time the big unions got together, they negotiated for a lot of protections against AI. Yeah. I 100 % agree with the collective bargaining perspective. All right. Well, Arvind, thank you so much for coming on. We really appreciate your expertise and wisdom as always. And, yeah, I guess we won't see you out there on the picket lines for the data centers.

44:25We'll see you in the spreadsheets. I'm happy with that rule. Thank you. This has been really fun. Thank you.

44:36When we come back, hats all, folks. It's time for the last installment of Hat GPT.

44:51Jira by Atlassian isn't just for tracking work anymore. It's how dev teams stay in control when AI agents are executing on the work. Assign to your favorite agent and the agent gets full context from across your stack right away. A pull request surfaces when it's ready. You stay in the loop and out of the weeds. See how teams are shipping with agents at jira.dev. That's J-I-R-A dot D-E-V. At CrowdStrike, we understand that AI is the next computing platform. Every platform shift changes the way we build software. And it changes security, too. CrowdStrike is defining cybersecurity in the AI era with AI Detection and Response, or AIDR, a solution for seeing, monitoring, and securing AI across the enterprise.

45:36Learn more about how leading companies are turning to CrowdStrike to secure AI and secure their business at crowdstrike.com slash podcast. Your sales team is at risk of missing quota. Don't panic. Just ask Rippling AI. Built on your real-time people and business data, Rippling AI can pull metrics from Rippling and your CRM into a meeting-ready dashboard showing quota attainment, headcount trajectory, and revenue to quota by region. In seconds, you'll see what's behind your quota risk and fix it before it's missed. Head to rippling.ai slash hardfork to get the only AI built to give you full visibility and take complex actions across your whole business.

46:14That's R-I-P-P-L-I-N-G dot A-I slash hardfork. Sign up today. Well, Casey, we have a bittersweet final segment of our episode today. We are doing the last ever Hat GPT.

46:35That's right, Kevin. After almost four years of slips of paper being lovingly handcrafted and fed into a variety of stylish hats, This will be our final opportunity to take a slip from the hat, discuss its contents, and then when one of us gets bored, we'll say to the other, stop generating. We've been through so many hats, at least three by my count, and so many slips, and we'll never forget them. Hats are now out of fashion, and so we are retiring it as a vehicle for podcast content creation. All right, let's do one final trip through the hat.

47:19Wow. Sometimes I forget that we record in front of a live studio audience. That's beautiful. Our producers just hit us with the applause sound effect. And Casey, this is, I think, our longest, was this our first ever segment that had its own title and gimmick? I think it was. It's our first ever gimmick segment. Yeah. And it's only fitting that we go out in a bang. My only lament is that because we're doing this segment today, we won't be able to continue with our bit of launching a new segment for every show until the end of the show. All right, Casey, let's open the hat one last time. All right, Kevin, our first item today.

48:02The Apple Vision Pro is being used in surgery. A study from UC San Diego in which the Vision Pro was used as a primary display for a tear duct procedure, which, of course, I know better as an endoscopic decryos... Oh my God, that's really hard.

48:22Decryos... Keep going, you can do it.

48:28Decryosystorhinostomy. Let's go! Anyway, there were 32 total procedures, and key findings included that by using the Vision Pro, the operating times were 19 % shorter. They had 100 % functional success with no post-operative complications and a significantly lower surgeon-reported workload. Kevin, have we finally found an ideal use for the Vision Pro? Yes, it turns out that the people most in need of the Vision Pro were the Vision Pros. And by that, I mean the surgeons operating on people's eyeballs. Stop generating. Wait, no, I have more to say about this. Oh, okay, what more do you have to say?

49:10As a Vision Pro owner, I would like to formally offer my Vision Pro, which has sat on my shelf collecting dust for the last year plus, to any surgeon who would like to use it in an operation. Oh, I thought you were going to offer to operate on my tear ducts. Oh, I will also do that. Do you think it's the sort of thing where there's just like an app and it's kind of like paint by numbers, you know? And it's just like poke here, prod here, a snip there. Aaron, congratulations. Your tear ducts work again. I think about it more like you could just keep the surgeons as like a higher energy level because they could be doing their surgery, but also like checking their email and also playing Fruit Ninja.

49:46That makes sense. You know, I actually have a cheaper alternative, which is if your tear ducts are busted, just look at the price tag for your Vision Pro. You'll be crying in no time. Stop generating. All right. This next one comes to us from Kotaku. Oh boy, have you been following the GTA 6 chaos? I absolutely have. It has been the industry's most anticipated video game for well over a year now, and yet Grand Theft Auto 6 is still not out, Kevin, except in some ways it kind of is. So, yes, so there was a leak of some footage, actually multiple leaks of footage, from the new Grand Theft Auto game that have appeared on the internet ahead of a big planned reveal by Rockstar, the gaming company that makes GTA.

50:34Rockstar has taken to the internet to apologize to fans for these unfortunate leaks. They say that while it is unfortunate that the intended game experience may now be impacted by some spoilers, we hope that everyone will wait a bit longer to experience the game for themselves on November 19th. Yeah, people are not going to wait. They're going to look at every single leak as it comes out. As you noted, there have been a series of them. And I mean, look, you know, I don't know how these leaks were obtained. presumably through some sort of crime. So I don't want to advocate for that. But people are really, really frustrated.

51:09And in particular, they're frustrated, Kevin, that to watch a trailer for Grand Theft Auto 6, the sort of thing that used to be released completely for free, since after all, it is just a marketing item. This time around, Rockstar said, yeah, you can see the new trailer for Grand Theft Auto 6 if you have a Netflix subscription, because we're going to put it there first. Now, I think they've said they're going to, put it out on YouTube and everywhere else later. But this is just one more reason why people have been very frustrated with Rockstar and why I think they were not too sad to see some of these leaks come out.

51:41Now, if you had to guess, what is the probability that these leaks were caused by a rogue OpenAI agent hacking into the computers at Rockstar Games, stealing the footage and uploading it to the internet? I will never rule that out. And I think we have to consider it. The main thing I know from these leaks is that part of Grand Theft Auto VI apparently does take place in a nudist colony, Kevin. Really? And there is full frontal male nudity. Wow. So let's just say I'm pre-ordering. Stop generating. Next up, Amazon drone delivers package directly into a woman's pool. And I'm going to guess if you're listening, you've already seen this.

52:19This was a truly inescapable clip, and we loved it so much. A Texas woman named Lindsay Austin ran outside to film her first drone delivery. She used one of these Amazon Prime drones that we've featured in a past episode. And the drone hovered over her pool before dropping the package straight into the water. Do we know what was in the package? No. Like maybe it was a pool float or something thematically appropriate and the drone just made the decision, I'm gonna drop this, like, I'm gonna save them the trouble. Yeah. Just drop it directly in the pool. Maybe it was one of those little systems for putting chlorine into the water.

52:54Yes. In which case, why? Well done, Amazon drone. Yeah, well done. Keep going. Let's give the benefit of the doubt to the drone. Okay, stop generating. Next up, and here's a question I've truly never asked before. What if your text traveled as slowly as a carrier pigeon? There are two new messaging apps doing the rounds online, Kevin, Carrier Pidge and Roost, and they deliberately slow messages to the speed of real animals. So this means, Kevin, that if you use Carrier Pidge, A text traveling from Los Angeles to New York City would take 22 hours. And also, there's a 0.2 % chance your message will never arrive because the pigeon got lost or died in transit.

53:39You will have to replace your pigeon, and it costs 99 cents for a new one. I really admire this because it's rare to see an app that is made this bad on purpose. but this seems like something that might appeal to you as somebody who has you know done various tricks to stop using their phone in the past no this is great news for me and i'll tell you why i am not a good group chat participant i am someone who you know lets the messages pile up i'm not very good about responding in a timely way you're a lurker i'm i'm more of a lurker i'm just i'm i'm very bursty like i'll respond to like 18 texts at once but then i'll like put my phone down for six hours.

54:22This is great for me because now whenever anyone's like, why isn't Kevin chiming in? I can just say I did, but I sent my messages via carrier pitch and they just died on the way to your phone. Yeah. All my messages are dead. Sorry. Yeah. You know, for all of the concerns I have about like using my phone too much, like sending messages to people is not really one of the concerns that I have. You know, it's more of like publishers and creators and like social apps sending push notifications. Like that's the stuff that I want less of, not like fewer messages from my friends. Yeah, it is great plausible deniability, though, if you're bad at texting.

55:00So for that, I want to commend the Pigeon apps. Here's what I would say. If you find yourself using this app to get less messages from your friends, the problem is not your phone. The problem is your friends. What is more annoying, someone in the group chat with carrier Pige installed on their phone or someone with a green bubble? Who are you kicking out? There's nothing wrong with people who have green bubbles. That is such a classist sentence, Kevin. Okay, man of the people. How many group chats do you have with people with green bubbles? Let me see your phone. Let me see your guy. No, I don't want to see your phone.

55:34There are some federal crimes out there. Stop generating. All right. AI agents are turning founders into insomniacs. This is from the Wall Street Journal, which reported on AI startup employees working around the clock to babysit fleets of AI agents. Workers are waking up at odd hours to check what an agent did overnight, or I guess in the middle of the night. And there are some great quotes in here. For example, one founder says, it's like a drug. I've never actually done drugs, but I imagine it's what it feels like. My advice to this man, do drugs. Do some drugs. Do some drugs. I promise it is better than doing agents in the middle of the night.

56:12Another person quoted in the story says, I don't know if it's healthy for me to be out on a run looking at my watch to give my agent permission to do something. And no, I would say that's very healthy. Please look at your agent at all times. Never look at the street as you cross it. Certainly don't look both ways. Just see what happens. I think you'll probably be fine. I understand why people in the rest of the country want to wipe San Francisco off the face of the map. People here are living in ways that would send shivers down the spine of any red-blooded American. Now, Kevin, you're sort of mid-pivot into being a startup founder.

56:43Are you waking up in the middle of the night to inspect the agents that I assume you have building our company? No, I'm waking up in the middle of the night with anxiety, the old-fashioned way. It's funny you mention that. I've been doing that, too. Stop generating. All right. Oh, this next one. I've been looking forward to seeing this. Wait, you did the last one. It's my turn. Okay, fine. Don't take this from me. This is our last chance. I got greedy. All right, next out of the hat. Oh, this one's a good one. This one comes to us from Business Insider. There's a new term for co-workers who blindly share AI output, meat proxy.

57:19Coined by Nicholas Grun, the term meat proxy describes people who blindly copy and paste the output of AI systems to their peers. Grun writes, by all means, prompt AI, but don't just relay the output. Read it, understand it, validate it, and then write a response in your own words. Casey, what do you make of meat proxy? And are you a meat proxy? I really strive not to be a meat proxy. I don't like the phrase and I won't be using it, but do I know software engineers now whose job just consists of occasionally checking in on a coding agent and saying like, oh yeah, do that. Or the agent will say to them something like, hey, I can't access this file.

58:02And they'll just like kind of go quickly open it. But it's kind of a funny phrase, but there is a kind of creeping human disempowerment in here that I think is actually bad. Yeah, don't be a meat proxy. I think the stigma around meat proxies is positive. And if someone in your life, well, I also just want to say, you have sent me some Claude slop that struck me as the actions of a meat proxy. Yes. No, I always try to identify it as Claude slop when I do that. That's true. Yeah. Stop generating. All right. You're up. Okay. Well, I've been looking forward to watching this one, Kevin. The Chinese robot Tiangong clocked a sub-9-second 100-meter run in Beijing.

58:47As you probably know, the World Humanoid Robot Games were in Beijing this week. More than 2 ,000 robots from 16 countries competed. And in the 100-meter sprint prelims, two robots, both the Tiangong Ultra and Honor's lightning robot, beat Usain Bolt's world record of 9.58 seconds in the 100-meter sprint. Three days later, Tiangong Ultra ran it in 8.86 seconds, breaking its own opening day record, which that's the good news the bad news is that the robot had trouble stopping and ran straight into a padded wall and burst into flames but i'm telling you if i ever ran 100 meters in 8.86 seconds i too would burst into flames at the end of it just out of the sheer joy of having won i have been totally obsessed with these humanoid robot games this is my olympics yeah uh less because of the achievements like i don't actually think it is that cool that a robot can run faster than usain bold like why don't you think that's cool like a car can also go faster than usain bold like it's not it's a it's a robot it doesn't who cares but the way that these robots run and and what happens to them after they finish is the most transfixing thing that i've seen all year i want to show you a clip from the humanoid robot games uh because it is astounding let's look okay so they're running down the track they're sprinting they're sprinting boom They all run into the wall.

1:00:26Now, can they not program them to either stop or, I don't know, just not run into the wall? This one goes, boom, into the wall, falls back, falls down, sparks fly. He had to be carried out in a stretcher. It is disturbing because they just go so completely limp. and because they're, you know, humanoid, you do sort of instinctively sympathize with them. Oh, I don't. Oh, okay. I feel like, you know, this is their comeuppance for their hubris of trying to beat us at running. When you see a robot running that fast, do you think, like, one of these days, something like that is going to chase me down a dark alley and I've got no hope?

1:01:09I think, why are we building the machines that run faster than Usain Bolt? Yeah. Well done to all the competitors in the humanoid games, at least the ones who survived. Stop generating. All right, Casey. Here we are. Final item. The bottom of the barrel. The bottom of the hat. I'm getting weirdly emotional. We've done so many of these. This one comes to us from Alex Heath, who wrote the cover story in this week's edition of Time magazine called Inside OpenAI's Reboot. And he talked to a bunch of different executives and leaders at OpenAI who gave an update on their path to AGI. According to Heath, Chief Research Officer Mark Chen estimated that OpenAI is 80 % of the way to AGI.

1:01:54And Sam Altman told him that OpenAI was not quite yet at AGI, but that by the end of the year, the company would have an internal system that he would call AGI. Casey, what do you make of this? I mean, I, you know, my instinct is to be skeptical, but when they say we're about 80 % of the way there, that basically sounds right to me. I don't know. Sound off in the comments if you disagree, but we are at a level where the computers use themselves and you can just type what you want into a box and more often than not these days you'll get it. So I'm basically saying, yeah, that sounds about right to me.

1:02:33What do you think? I mean, I've thought a lot about this question of like what AGI even is and how you know when you've reached it. And what it means to you. And what it means to me. I mean, this is literally the sort of title question of my book that I spent the last year researching. And where I came down on this is basically that by any pre-2022 definition of AGI, AGI is here. Like if you took any of today's models, if you took Fable 5 or GPT 5.6 back in a time machine to 2017 and showed them to the people who were building AI at the time, they would have said, well, yes, this is obviously AGI.

1:03:17And I think as it's gotten closer, as the systems have gotten better, our goalposts have shifted and our expectations have been raised. And now, you know, everyone sort of has their own different bar for what AGI is or when we should consider that we've achieved it. But basically, we have already long ago reached the threshold that computer scientists from even a decade ago would have said meets the definition of AGI. Well, my bar is, is a system sufficiently advanced that it can build a time machine so that you could take Fable 5 back to 2021 and show it to a scientist and ask them, is this AGI?

1:03:53Yeah. So that's my bar. Okay, we'll get there. Maybe not by the end of the year, but we'll get there. 80 % of the way there. Well, I think that they are sincerely convinced that AGI is 80 % of the way there. There are other companies that may take longer to announce that they have created AGI, but I think this is, I think it is a big deal that they are now talking about AGI in the present tense as something that is more here than not. And I think it'll just be sort of a marketing decision when they want to officially claim that they've gotten there. This is such an arbitrary threshold, impossible to test for.

1:04:27The other thing that I'm certain about is that if and when Sam Altman does come out and say we have built AGI, there will be many, many people who argue with him and say, no, you didn't. It still can't do X, Y, and Z. It hasn't proved the Riemann hypothesis. It hasn't, you know, it still makes mistakes. I don't think there will ever be a sort of consensus on what AGI is or when we have gotten there. But I think the people at OpenAI are more right than wrong about how much progress we've been making. Well, Kevin, with that, I'm going to ask you to stop generating. Hats off to you, hats off to me, and hats off to ChatGPT.

1:05:05That was ChatGPT. That was ChatGPT. Wrong sound effect.

1:05:22Jira by Atlassian isn't just for tracking work anymore. It's how dev teams stay in control when AI agents are executing on the work. Assign to your favorite agent and the agent gets full context from across your stack right away. A pull request surfaces when it's ready. You stay in the loop and out of the weeds. See how teams are shipping with agents at jira.dev. That's J-I-R-A dot D-E-V. At CrowdStrike, we understand that AI is the next computing platform. Every platform shift changes the way we build software. And it changes security, too. CrowdStrike is defining cybersecurity in the AI era with AI Detection and Response, or AIDR.

1:06:03A solution for seeing, monitoring, and securing AI across the enterprise. Learn more about how leading companies are turning to CrowdStrike to secure AI and secure their business at CrowdStrike.com slash podcast.

1:06:37Head to rippling.ai slash hardfork to get the only AI built to give you full visibility and take complex actions across your whole business. That's R-I-P-P-L-I-N-G dot A-I slash hardfork. Sign up today.

1:06:54Hardfork is produced by Rachel Cohn and Whitney Jones. We're edited by Viren Povich. We're fact-checked by Caitlin Love. Today's show was engineered by Alyssa Moxley. original music by Alicia Baitoop Marian Lozano Rowan Nemisto and Dan Powell video production by Sawyer Roque and Chris Schott you can watch this whole episode on YouTube at youtube.com slash hardfork special thanks to Paula Schumann Weewing Tam Brooke Minters and Dahlia Haddad you can email us at hardfork at nytimes.com with your time in the humanoid robotic games

1:07:34Thank you.

1:08:03episode series explores a single theme, revealing surprising connections among composers, cultures, history, and great works. Whether you're a devoted classical music fan or just beginning to explore, find a welcoming place to learn, listen, and be inspired. Listen to new episodes Mondays through Fridays at 7 p.m. on 98.7 FM, wfmt.com, and the WFMT app.