Author name: Rejus Almole

Study: Social media probably can’t be fixed

agent-based modeling, Features, LLMs, polarization, Science, Social Media, social psychology, social sciences, social works / Rejus Almole / August 13, 2025

“The [structural] mechanism producing these problematic outcomes is really robust and hard to resolve.”

Credit: Aurich Lawson | Getty Images

It’s no secret that much of social media has become profoundly dysfunctional. Rather than bringing us together into one utopian public square and fostering a healthy exchange of ideas, these platforms too often create filter bubbles or echo chambers. A small number of high-profile users garner the lion’s share of attention and influence, and the algorithms designed to maximize engagement end up merely amplifying outrage and conflict, ensuring the dominance of the loudest and most extreme users—thereby increasing polarization even more.

Numerous platform-level intervention strategies have been proposed to combat these issues, but according to a preprint posted to the physics arXiv, none of them are likely to be effective. And it’s not the fault of much-hated algorithms, non-chronological feeds, or our human proclivity for seeking out negativity. Rather, the dynamics that give rise to all those negative outcomes are structurally embedded in the very architecture of social media. So we’re probably doomed to endless toxic feedback loops unless someone hits upon a brilliant fundamental redesign that manages to change those dynamics.

Co-authors Petter Törnberg and Maik Larooij of the University of Amsterdam wanted to learn more about the mechanisms that give rise to the worst aspects of social media: the partisan echo chambers, the concentration of influence among a small group of elite users (attention inequality), and the amplification of the most extreme divisive voices. So they combined standard agent-based modeling with large language models (LLMs), essentially creating little AI personas to simulate online social media behavior. “What we found is that we didn’t need to put any algorithms in, we didn’t need to massage the model,” Törnberg told Ars. “It just came out of the baseline model, all of these dynamics.”

They then tested six different intervention strategies social scientists have been proposed to counter those effects: switching to chronological or randomized feeds; inverting engagement-optimization algorithms to reduce the visibility of highly reposted sensational content; boosting the diversity of viewpoints to broaden users’ exposure to opposing political views; using “bridging algorithms” to elevate content that fosters mutual understanding rather than emotional provocation; hiding social statistics like reposts and follower accounts to reduce social influence cues; and removing biographies to limit exposure to identity-based signals.

The results were far from encouraging. Only some interventions showed modest improvements. None were able to fully disrupt the fundamental mechanisms producing the dysfunctional effects. In fact, some interventions actually made the problems worse. For example, chronological ordering had the strongest effect on reducing attention inequality, but there was a tradeoff: It also intensified the amplification of extreme content. Bridging algorithms significantly weakened the link between partisanship and engagement and modestly improved viewpoint diversity, but it also increased attention inequality. Boosting viewpoint diversity had no significant impact at all.

So is there any hope of finding effective intervention strategies to combat these problematic aspects of social media? Or should we nuke our social media accounts altogether and go live in caves? Ars caught up with Törnberg for an extended conversation to learn more about these troubling findings.

Ars Technica: What drove you to conduct this study?

Petter Törnberg: For the last 20 years or so, there has been a ton of research on how social media is reshaping politics in different ways, almost always using observational data. But in the last few years, there’s been a growing appetite for moving beyond just complaining about these things and trying to see how we can be a bit more constructive. Can we identify how to improve social media and create online spaces that are actually living up to those early promises of providing a public sphere where we can deliberate and debate politics in a constructive way?

The problem with using observational data is that it’s very hard to test counterfactuals to implement alternative solutions. So one kind of method that has existed in the field is agent-based simulations and social simulations: create a computer model of the system and then run experiments on that and test counterfactuals. It is useful for looking at the structure and emergence of network dynamics.

But at the same time, those models represent agents as simple rule followers or optimizers, and that doesn’t capture anything of the cultural world or politics or human behavior. I’ve always been of the controversial opinion that those things actually matter, especially for online politics. We need to study both the structural dynamics of network formations and the patterns of cultural interaction.

Ars Technica: So you developed this hybrid model that combines LLMs with agent-based modeling.

Petter Törnberg: That’s the solution that we find to move beyond the problems of conventional agent-based modeling. Instead of having this simple rule of followers or optimizers, we use AI or LLMs. It’s not a perfect solution—there’s all kind of biases and limitations—but it does represent a step forward compared to a list of if/then rules. It does have something more of capturing human behavior in a more plausible way. We give them personas that we get from the American National Election Survey, which has very detailed questions about US voters and their hobbies and preferences. And then we turn that into a textual persona—your name is Bob, you’re from Massachusetts, and you like fishing—just to give them something to talk about and a little bit richer representation.

And then they see the random news of the day, and they can choose to post the news, read posts from other users, repost them, or they can choose to follow users. If they choose to follow users, they look at their previous messages, look at their user profile.

Our idea was to start with the minimal bare-bones model and then add things to try to see if we could reproduce these problematic consequences. But to our surprise, we actually didn’t have to add anything because these problematic consequences just came out of the bare bones model. This went against our expectations and also what I think the literature would say.

Ars Technica: I’m skeptical of AI in general, particularly in a research context, but there are very specific instances where it can be extremely useful. This strikes me as one of them, largely because your basic model proved to be so robust. You got the same dynamics without introducing anything extra.

Petter Törnberg: Yes. It’s been a big conversation in social science over the last two years or so. There’s a ton of interest in using LLMs for social simulation, but no one has really figured out for what or how it’s going to be helpful, or how we’re going to get past these problems of validity and so on. The kind of approach that we take in this paper is building on a tradition of complex systems thinking. We imagine very simple models of the human world and try to capture very fundamental mechanisms. It’s not really aiming to be realistic or a precise, complete model of human behavior.

I’ve been one of the more critical people of this method, to be honest. At the same time, it’s hard to imagine any other way of studying these kinds of dynamics where we have cultural and structural aspects feeding back into each other. But I still have to take the findings with a grain of salt and realize that these are models, and they’re capturing a kind of hypothetical world—a spherical cow in a vacuum. We can’t predict what someone is going to have for lunch on Tuesday, but we can capture broader mechanisms, and we can see how robust those mechanisms are. We can see whether they’re stable, unstable, which conditions they emerge in, and the general boundaries. And in this case, we found a mechanism that seems to be very robust, unfortunately.

Ars Technica: The dream was that social media would help revitalize the public sphere and support the kind of constructive political dialogue that your paper deems “vital to democratic life.” That largely hasn’t happened. What are the primary negative unexpected consequences that have emerged from social media platforms?

Petter Törnberg: First, you have echo chambers or filter bubbles. The risk of broad agreement is that if you want to have a functioning political conversation, functioning deliberation, you do need to do that across the partisan divide. If you’re only having a conversation with people who already agree with each other, that’s not enough. There’s debate on how widespread echo chambers are online, but it is quite established that there are a lot of spaces online that aren’t very constructive because there’s only people from one political side. So that’s one ingredient that you need. You need to have a diversity of opinion, a diversity of perspective.

The second one is that the deliberation needs to be among equals; people need to have more or less the same influence in the conversation. It can’t be completely controlled by a small, elite group of users. This is also something that people have pointed to on social media: It has a tendency of creating these influencers because attention attracts attention. And then you have a breakdown of conversation among equals.

The final one is what I call (based on Chris Bail’s book) the social media prism. The more extreme users tend to get more attention online. This is often discussed in relation to engagement algorithms, which tend to identify the type of content that most upsets us and then boost that content. I refer to it as a “trigger bubble” instead of the filter bubble. They’re trying to trigger us as a way of making us engage more so they can extract our data and keep our attention.

Ars Technica: Your conclusion is that there’s something within the structural dynamics of the network itself that’s to blame—something fundamental to the construction of social networks that makes these extremely difficult problems to solve.

Petter Törnberg: Exactly. It comes from the fact that we’re using these AI models to capture a richer representation of human behavior, which allows us to see something that wouldn’t really be possible using conventional agent-based modeling. There have been previous models looking at the growth of social networks on social media. People choose to retweet or not, and we know that action tends to be very reactive. We tend to be very emotional in that choice. And it tends to be a highly partisan and polarized type of action. You hit retweet when you see someone being angry about something, or doing something horrific, and then you share that. It’s well-known that this leads to toxic, more polarized content spreading more.

But what we find is that it’s not just that this content spreads; it also shapes the network structures that are formed. So there’s feedback between the effective emotional action of choosing to retweet something and the network structure that emerges. And then in turn, you have a network structure that feeds back what content you see, resulting in a toxic network. The definition of an online social network is that you have this kind of posting, reposting, and following dynamics. It’s quite fundamental to it. That alone seems to be enough to drive these negative outcomes.

Ars Technica: I was frankly surprised at the ineffectiveness of the various intervention strategies you tested. But it does seem to explain the Bluesky conundrum. Bluesky has no algorithm, for example, yet the same dynamics still seem to emerge. I think Bluesky’s founders genuinely want to avoid those dysfunctional issues, but they might not succeed, based on this paper. Why are such interventions so ineffective?

Petter Törnberg: We’ve been discussing whether these things are due to the platforms doing evil things with algorithms or whether we as users are choosing that we want a bad environment. What we’re saying is that it doesn’t have to be either of those. This is often the unintended outcomes from interactions based on underlying rules. It’s not necessarily because the platforms are evil; it’s not necessarily because people want to be in toxic, horrible environments. It just follows from the structure that we’re providing.

We tested six different interventions. Google has been trying to make social media less toxic and recently released a newsfeed algorithm based on the content of the text. So that’s one example. We’re also trying to do more subtle interventions because often you can find a certain way of nudging the system so it switches over to healthier dynamics. Some of them have moderate or slightly positive effects on one of the attributes, but then they often have negative effects on another attribute, or they have no impact whatsoever.

I should say also that these are very extreme interventions in the sense that, if you depended on making money on your platform, you probably don’t want to implement them because it probably makes it really boring to use. It’s like showing the least influential users, the least retweeted messages on the platform. Even so, it doesn’t really make a difference in changing the basic outcomes. What we take from that is that the mechanism producing these problematic outcomes is really robust and hard to resolve given the basic structure of these platforms.

Ars Technica: So how might one go about building a successful social network that doesn’t have these problems?

Petter Törnberg: There are several directions where you could imagine going, but there’s also the constraint of what is popular use. Think back to the early Internet, like ICQ. ICQ had this feature where you could just connect to a random person. I loved it when I was a kid. I would talk to random people all over the world. I was 12 in the countryside on a small island in Sweden, and I was talking to someone from Arizona, living a different life. I don’t know how successful that would be these days, the Internet having become a lot less innocent than it was.

For instance, we can focus on the question of inequality of attention, a very well-studied and robust feature of these networks. I personally thought we would be able to address it with our interventions, but attention draws attention, and this leads to a power law distribution, where 1 percent [of users] dominates the entire conversation. We know the conditions under which those power laws emerge. This is one of the main outcomes of social network dynamics: extreme inequality of attention.

But in social science, we always teach that everything is a normal distribution. The move from studying the conventional social world to studying the online social world means that you’re moving from these nice normal distributions to these horrible power law distributions. Those are the outcomes of having social networks where the probability of connecting to someone depends on how many previous connections they have. If we want to get rid of that, we probably have to move away from the social network model and have some kind of spatial model or group-based model that makes things a little bit more local, a little bit less globally interconnected.

Ars Technica: It sounds like you’d want to avoid those big influential nodes that play such a central role in a large, complex global network.

Petter Törnberg: Exactly. I think that having those global networks and structures fundamentally undermines the possibility of the kind of conversations that political scientists and political theorists traditionally talked about when they were discussing in the public square. They were talking about social interaction in a coffee house or a tea house, or reading groups and so on. People thought the Internet was going to be precisely that. It’s very much not that. The dynamics are fundamentally different because of those structural differences. We shouldn’t expect to be able to get a coffee house deliberation structure when we have a global social network where everyone is connected to everyone. It is difficult to imagine a functional politics building on that.

Ars Technica: I want to come back to your comment on the power law distribution, how 1 percent of people dominate the conversation, because I think that is something that most users routinely forget. The horrible things we see people say on the Internet are not necessarily indicative of the vast majority of people in the world.

Petter Törnberg: For sure. That is capturing two aspects. The first is the social media prism, where the perspective we get of politics when we see it through the lens of social media is fundamentally different from what politics actually is. It seems much more toxic, much more polarized. People seem a little bit crazier than they really are. It’s a very well-documented aspect of the rise of polarization: People have a false perception of the other side. Most people have fairly reasonable and fairly similar opinions. The actual polarization is lower than the perceived polarization. And that arguably is a result of social media, how it misrepresents politics.

And then we see this very small group of users that become very influential who often become highly visible as a result of being a little bit crazy and outrageous. Social media creates an incentive structure that is really central to reshaping not just how we see politics but also what politics is, which politicians become powerful and influential, because it is controlling the distribution of what is arguably the most valuable form of capital of our era: attention. Especially for politicians, being able to control attention is the most important thing. And since social media creates the conditions of who gets attention or not, it creates an incentive structure where certain personalities work better in a way that’s just fundamentally different from how it was in previous eras.

Ars Technica: There are those who have sworn off social media, but it seems like simply not participating isn’t really a solution, either.

Petter Törnberg: No. First, even if you only read, say, The New York Times, that newspaper is still reshaped by what works on social media, the social media logic. I had a student who did a little project this last year showing that as social media became more influential, the headlines of The New York Times became more clickbaity and adapted to the style of what worked on social media. So conventional media and our very culture is being transformed.

But more than that, as I was just saying, it’s the type of politicians, it’s the type of people who are empowered—it’s the entire culture. Those are the things that are being transformed by the power of the incentive structures of social media. It’s not like, “This is things that are happening in social media and this is the rest of the world.” It’s all entangled, and somehow social media has become the cultural engine that is shaping our politics and society in very fundamental ways. Unfortunately.

Ars Technica: I usually like to say that technological tools are fundamentally neutral and can be used for good or ill, but this time I’m not so sure. Is there any hope of finding a way to take the toxic and turn it into a net positive?

Petter Törnberg: What I would say to that is that we are at a crisis point with the rise of LLMs and AI. I have a hard time seeing the contemporary model of social media continuing to exist under the weight of LLMs and their capacity to mass-produce false information or information that optimizes these social network dynamics. We already see a lot of actors—based on this monetization of platforms like X—that are using AI to produce content that just seeks to maximize attention. So misinformation, often highly polarized information as AI models become more powerful, that content is going to take over. I have a hard time seeing the conventional social media models surviving that.

We’ve already seen the process of people retreating in part to credible brands and seeking to have gatekeepers. Young people, especially, are going into WhatsApp groups and other closed communities. Of course, there’s misinformation from social media leaking into those chats also. But these kinds of crisis points at least have the hope that we’ll see a changing situation. I wouldn’t bet that it’s a situation for the better. You wanted me to sound positive, so I tried my best. Maybe it’s actually “good riddance.”

Ars Technica: So let’s just blow up all the social media networks. It still won’t be better, but at least we’ll have different problems.

Petter Törnberg: Exactly. We’ll find a new ditch.

DOI: arXiv, 2025. 10.48550/arXiv.2508.03385 (About DOIs).

Jennifer is a senior writer at Ars Technica with a particular focus on where science meets culture, covering everything from physics and related interdisciplinary topics to her favorite films and TV series. Jennifer lives in Baltimore with her spouse, physicist Sean M. Carroll, and their two cats, Ariel and Caliban.

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Perplexity offers more than twice its total valuation to buy Chrome from Google

AI, artificial inteligence, Browsers, Google, Perplexity, Tech / Rejus Almole / August 13, 2025

Google has strenuously objected to the government’s proposed Chrome divestment, which it calls “a radical interventionist agenda.” Chrome isn’t just a browser—it’s an open source project known as Chromium, which powers numerous non-Google browsers, including Microsoft’s Edge. Perplexity’s offer includes $3 billion to run Chromium over two years, and it allegedly vows to keep the project fully open source. Perplexity promises it also won’t enforce changes to the browser’s default search engine.

An unsolicited offer

We’re currently waiting on United States District Court Judge Amit Mehta to rule on remedies in the case. That could happen as soon as this month. Perplexity’s offer, therefore, is somewhat timely, but there could still be a long road ahead.

This is an unsolicited offer, and there’s no indication that Google will jump at the chance to sell Chrome as soon as the ruling drops. Even if the court decides that Google should sell, it can probably get much, much more than Perplexity is offering. During the trial, DuckDuckGo’s CEO suggested a price of around $50 billion, but other estimates have ranged into the hundreds of billions. However, the data that flows to Chrome’s owner could be vital in building new AI technologies—any sale price is likely to be a net loss for Google.

If Mehta decides to force a sale, there will undoubtedly be legal challenges that could take months or years to resolve. Should these maneuvers fail, there’s likely to be opposition to any potential buyer. There will be many users who don’t like the idea of an AI startup or an unholy alliance of venture capital firms owning Chrome. Google has been hoovering up user data with Chrome for years—but that’s the devil we know.

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Scientists hid secret codes in light to combat video fakes

image processing, optics, photometric, Physics, Science, video manipulation / Rejus Almole / August 11, 2025

Hiding in the light

Previously, the Cornell team had figured out how to make small changes to specific pixels to tell if a video had been manipulated or created by AI. But its success depended on the creator of the video using a specific camera or AI model. Their new method, “noise-coded illumination” (NCI), addresses those and other shortcomings by hiding watermarks in the apparent noise of light sources. A small piece of software can do this for computer screens and certain types of room lighting, while off-the-shelf lamps can be coded via a small attached computer chip.

“Each watermark carries a low-fidelity time-stamped version of the unmanipulated video under slightly different lighting. We call these code videos,” Davis said. “When someone manipulates a video, the manipulated parts start to contradict what we see in these code videos, which lets us see where changes were made. And if someone tries to generate fake video with AI, the resulting code videos just look like random variations.” Because the watermark is designed to look like noise, it’s difficult to detect without knowing the secret code.

The Cornell team tested their method with a broad range of types of manipulation: changing warp cuts, speed and acceleration, for instance, and compositing and deep fakes. Their technique proved robust to things like signal levels below human perception; subject and camera motion; camera flash; human subjects with different skin tones; different levels of video compression; and indoor and outdoor settings.

“Even if an adversary knows the technique is being used and somehow figures out the codes, their job is still a lot harder,” Davis said. “Instead of faking the light for just one video, they have to fake each code video separately, and all those fakes have to agree with each other.” That said, Davis added, “This is an important ongoing problem. It’s not going to go away, and in fact it’s only going to get harder,” he added.

DOI: ACM Transactions on Graphics, 2025. 10.1145/3742892 (About DOIs).

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$30k Ford EV truck due in 2027 with much-simpler production process

Cars, Ford, software defined vehicle / Rejus Almole / August 11, 2025

Ford will debut a new midsize pickup truck in 2027 with a targeted price of $30,000, the automaker announced today. The as-yet unnamed pickup will be the first of a series of more affordable EVs from Ford, built using a newly designed flexible vehicle platform and US-made prismatic lithium iron phosphate batteries.

For the past few years, a team of Ford employees have been hard at work on the far side of the country from the Blue Oval’s base in Dearborn, Michigan. Sequestered in Long Beach and taking inspiration from Lockheed’s legendary “skunkworks,” the Electric Vehicle Development Center approached designing and building Ford’s next family of EVs as a clean-sheet problem, presumably taking inspiration from the Chinese EVs that have so impressed Ford’s CEO.

It starts with a pickup

Designing an EV from the ground up, free of decades of legacy cruft, is a good idea, but not one unique to Ford. In recent months we’ve reviewed quite a few so-called software-defined vehicles, which replace dozens or even hundreds of discrete single-function electronic control units with a handful of powerful modern computers (usually known as domain controllers) on a high-speed network.

“This isn’t a stripped‑down, old‑school vehicle,” said Doug Field, Ford’s chief EV, digital, and design officer, pointedly comparing the future Ford to the recently revealed barebones EV from Slate Motors.

An animation of Ford’s new vehicle architecture.

Starting from scratch like this is allowing vehicle dynamics engineers to get creative with the way EVs handle. Field said that the company “applied first‑principles engineering, pushing to the limits of physics to make it fun to drive and compete on affordability. Our new zonal electric architecture unlocks capabilities the industry has never seen.”

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AI industry horrified to face largest copyright class action ever certified

AI, Anthropic, Artificial Intelligence, book authors, copyright infringement, Features, large language models, LLMs, Policy / Rejus Almole / August 10, 2025

According to the groups, allowing copyright class actions in AI training cases will result in a future where copyright questions remain unresolved and the risk of “emboldened” claimants forcing enormous settlements will chill investments in AI.

“Such potential liability in this case exerts incredibly coercive settlement pressure for Anthropic,” industry groups argued, concluding that “as generative AI begins to shape the trajectory of the global economy, the technology industry cannot withstand such devastating litigation. The United States currently may be the global leader in AI development, but that could change if litigation stymies investment by imposing excessive damages on AI companies.”

Some authors won’t benefit from class actions

Industry groups joined Anthropic in arguing that, generally, copyright suits are considered a bad fit for class actions because each individual author must prove ownership of their works. And the groups weren’t alone.

Also backing Anthropic’s appeal, advocates representing authors—including Authors Alliance, the Electronic Frontier Foundation, American Library Association, Association of Research Libraries, and Public Knowledge—pointed out that the Google Books case showed that proving ownership is anything but straightforward.

In the Anthropic case, advocates for authors criticized Alsup for basically judging all 7 million books in the lawsuit by their covers. The judge allegedly made “almost no meaningful inquiry into who the actual members are likely to be,” as well as “no analysis of what types of books are included in the class, who authored them, what kinds of licenses are likely to apply to those works, what the rightsholders’ interests might be, or whether they are likely to support the class representatives’ positions.”

Ignoring “decades of research, multiple bills in Congress, and numerous studies from the US Copyright Office attempting to address the challenges of determining rights across a vast number of books,” the district court seemed to expect that authors and publishers would easily be able to “work out the best way to recover” damages.

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Ford switches gears, will push smaller EVs over full-size pickup and van

affordable EVs, Cars, EV adoption, Ford / Rejus Almole / August 8, 2025

The Ford Motor Company is adjusting its electric vehicle strategy. The automaker will prioritize smaller and more affordable EVs ahead of the replacement for the F-150 Lightning fullsize pickup truck and e-Transit van. The Lightning replacement, codenamed T3, should now appear later in 2027, with the van a year behind.

Here in 2025, EV adoption isn’t exactly going the way everyone thought—or rather hoped—it would. The hype surrounding EVs worked fast, and the glinting dollar signs in people’s eyes as they saw Tesla’s share price soar higher and higher convinced even people who don’t care about decarbonization that going all-in on EVs was the way to go.

But it takes longer to develop a new vehicle than it takes to excite an investor. And it takes longer even than that to build out the charging infrastructure necessary to transform EV motoring from something for early adopters and the eco-conscious into a viable alternative for a largely incurious and change-averse general public. Which is a long-winded way of saying the industry got out over its skis.

Take the Ford F-150 Lightning. Americans adore their pickup trucks, and the Lightning is a darn good pickup in most regards. It looks like a normal F-150, and while it might not tow as far before it has to stop, it does most other things as well or better than the gasoline-powered equivalent.

But something the size and shape of a full-size pickup truck is always going to require a lot of energy to push it through the air—even if you squeezed the drag coefficient, there’s no getting away from so much frontal area. And that means you need a gigantic battery in order to meet range expectations. And that means the truck that customers thought would cost $40,000 actually costs way more; sometimes as much as twice that. So it has hardly been the sales success people once imagined.

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Review: Framework Desktop is a mash-up of a regular desktop PC and the Mac Studio

AMD, framework, framework desktop, gaming, NVIDIA, ryzen ai, Tech / Rejus Almole / August 7, 2025

Size matters most for Framework’s first stab at a desktop workstation/gaming PC.

The Framework Desktop. Credit: Andrew Cunningham

Framework’s main claim to fame is its commitment to modular, upgradeable, repairable laptops. The jury’s still out on early 2024’s Framework Laptop 16 and mid-2025’s Framework Laptop 12, neither of which has seen a hardware refresh, but so far, the company has released half a dozen iterations of its flagship Framework Laptop 13 in less than five years. If you bought one of the originals right when it first launched, you could go to Framework’s site, buy an all-new motherboard and RAM, and get a substantial upgrade in performance and other capabilities without having to change anything else about your laptop.

Framework’s laptops haven’t been adopted as industry-wide standards, but in many ways, they seem built to reflect the flexibility and modularity that has drawn me to desktop PCs for more than two decades.

That’s what makes the Framework Desktop so weird. Not only is Framework navigating into a product category where its main innovation and claim to fame is totally unnecessary. But it’s actually doing that with a desktop that’s less upgradeable and modular than any given self-built desktop PC.

The Framework Desktop has a lot of interesting design touches, and it’s automatically a better buy than the weird AMD Ryzen AI Max-based mini desktops you can buy from a couple of no-name manufacturers. But aside from being more considerate of PC industry standards, the Framework Desktop asks the same question that any gaming-focused mini PC does: Do you care about having a small machine so much that you would pay more money for less performance, and for a system you can’t upgrade much after you buy it?

Design and assembly

Opening the Framework Desktop’s box. The PC and all its accessories are neatly packed away in all-recyclable carboard and paper. Andrew Cunningham

My DIY Edition Framework Desktop arrived in a cardboard box that was already as small or a bit smaller than my usual desktop PC, a mini ITX build with a dedicated GPU inside a 14.67-liter SSUPD Meshlicious case. It’s not a huge system, especially for something that can fit a GeForce RTX 5090 in it. But three of the 4.5-liter Framework Desktops could fit inside my build’s case with a little space leftover.

The PC itself is buried a couple of layers deep in this box under some side panels and whatever fan you choose (Framework offers RGB and non-RGB options from Cooler Master and Noctua, but any 120 mm fan will fit on the heatsink). Even for the DIY Edition, the bulk of it is already assembled: the motherboard is in the case, a large black heatsink is already perched atop the SoC, and both the power supply and front I/O ports are already hooked up.

The aspiring DIYer mainly needs to install the SSD and the fan to get going. Putting in these components gives you a decent crash course in how the system goes together and comes apart. The primary M.2 SSD slot is under a small metal heat spreader next to the main heatsink—loosen one screw to remove it, and install your SSD of choice. The system’s other side panel can be removed to expose a second M.2 SSD slot and the Wi-Fi/Bluetooth module, letting you install or replace either.

Lift the small handles on the two top screws and loosen them by hand to remove them, and the case’s top panel slides off. This provides easier access to both the CPU fan header and RGB header, so you can connect the fan after you install it and its plastic shroud on top of the heatsink. That’s pretty much it for assembly, aside from sliding the various panels back in place to close the thing up and reinstalling the top screws (or, if you bought or printed one, adding a handle to the top of the case).

The Framework Desktop includes a beefier version of Framework’s usual screwdriver with a longer bit. Credit: Andrew Cunningham

Framework includes a beefier version of its typical screwdriver with the Desktop, including a bit that can be pulled out and reversed to be switched between Phillips and Torx heads. The iFixit-style install instructions are clearly written and include plenty of high-resolution sample images so you can always tell how things are supposedto look.

The front of the system requires some assembly, too, but all of this stuff can be removed and replaced easily without opening up the rest of the system. The front panel, where the system’s customizable tiles can be snapped on and popped off, attaches with magnets and can easily be pried away from the desktop with your fingernails. At the bottom are slots for two of Framework’s USB-C Expansion Cards, the same ones that all the Framework Laptops use.

By default, those ports are limited to 5 Gbps USB transfer speeds in the BIOS, something the system says reduces wireless interference; those with all-wired networking and accessories can presumably enable the full 10 Gbps speeds without downsides. The front ports should support all of the Expansion Cards except for display outputs, which they aren’t wired for. (I also had issues getting the Desktop to boot from a USB port on the front of the system while installing Windows, but your mileage may vary; using one of the rear USB ports solved the issue for me.)

Standards, sometimes

Putting in the M.2 SSD. There’s another SSD slot on the back of the motherboard. Andrew Cunningham

What puts the Framework Desktop above mini PCs from Amazon or the various gaming NUCs that Intel and Asus have released over the years is a commitment to standards.

For reasons we’ll explore later, there was no way to build the system around this specific AMD chip without using soldered-on memory. But the motherboard is a regular mini ITX-sized motherboard. Other ITX boards will fit into Framework’s case, and the Framework Laptop’s motherboard will fit into other systems (as long as they can also fit the fan and heatsink).

The 400 W power supply conforms to the FlexATX standard. The CPU fan is just a regular 120 mm fan, and the mounting holes for system fans on the front can take any 92 mm fan. The two case fan headers on the motherboard are the same ones you’d find on any motherboard you bought for yourself. The front panel ports can’t be used for display outputs, but anything else ought to work.

Few elements of the Framework Desktop are truly proprietary, and if Framework went out of business tomorrow, you’d still have a lot of flexibility for buying and installing replacement parts. The problem is that the soldered-down, non-replaceable, non-upgradeable parts are the CPU, GPU, and RAM. There’s at least a little flexibility with the graphics card if you move the board into a different case—there’s a single PCIe x4 slot on the board that you could put an external GPU into, though many PCIe x16 graphics cards will be bandwidth starved. But left in its original case, it’s an easy-to-work-on, standards-compliant system that will also never be any better or get any faster than it is the day you buy it.

Hope you like plastic

Snapping some tiles into the Framework Desktop’s plastic front panel. Credit: Andrew Cunningham

The interior of the Framework Desktop is built of sturdy metal, thoughtfully molded to give easy access to each of the ports and components on the motherboard. My main beef with the system is the outside.

The front and side panels of the Framework Desktop are all made out of plastic. The clear side panel, if you spring for it, is made of a thick acrylic instead of tempered glass (presumably because Framework has drilled holes in the side of it to improve airflow).

This isn’t the end of the world, but the kinds of premium ITX PC cases that the Desktop is competing with are predominantly made of nicer-looking and nicer-feeling metal rather than plastic. It just feels surprisingly cheap, which was an unpleasant surprise—even the plastic Framework Laptop 12 felt sturdy and high-quality, something I can’t really say of the Desktop’s exterior panels.

I do like the design on the front panel—a grid of 21 small square plastic tiles that users can rearrange however they want. Framework sells tiles with straight and diagonal lines on them, plus individual tiles with different logos or designs printed or embossed on them. If you install a fan in the front of the system, you’ll want to stick to the lined tiles in the top 9 x 9 section of the grid, which will allow air to pass through. The tiles with images on them are solid—putting a couple of them in front of a fan likely won’t hurt your airflow too much, but you won’t want to use too many.

Framework has also published basic templates for both the tiles and the top panel so that those with 3D printers can make their own.

PC testbed notes

We’ve compared the performance of the Framework Desktop to a bunch of other PCs to give you a sense of how it stacks up to full-size desktops. We’ve also compared it to the Ryzen 7 8700G in a Gigabyte B650I Aorus Ultra mini ITX motherboard with 32GB of DDR5-6400 to show the best performance you can expect from a similarly sized socketed desktop system.

Where possible, we’ve also included some numbers from the M4 Pro Mac mini and the M4 Max Mac Studio, two compact desktops in the same general price range as the Framework Desktop.

For our game benchmarks, the dedicated GPU results were gathered using our GPU testbed, which you can read about in our latest dedicated GPU review. The integrated GPUs were obviously tested with the CPUs they’re attached to.

	AMD AM5	Intel LGA 1851	Intel LGA 1700
CPUs	Ryzen 7000 and 9000 series	Core Ultra 200 series	12th, 13th, and 14th-generation Core
Motherboard	ASRock X870E Taichi or MSI MPG X870E Carbon Wifi (provided by AMD)	MSI MEG Z890 Unify-X (provided by Intel)	Gigabyte Z790 Aorus Master X (provided by Intel)
RAM config	32GB G.Skill Trident Z5 Neo (provided by AMD), running at DDR5-6000	32GB G.Skill Trident Z5 Neo (provided by AMD), running at DDR5-6000	32GB G.Skill Trident Z5 Neo (provided by AMD), running at DDR5-6000

Performance and power

Our Framework-provided review unit was the highest-end option; it has a 16-core Ryzen AI Max+395 processor, 40 graphics cores, and 128GB of RAM. At $1,999 before adding an SSD, a fan, an OS, front tiles, or Expansion Cards, this is the best, priciest configuration Framework offers. The $1,599 configuration uses the same chip with the same performance, but with 64GB of RAM instead.

All 16 of those CPU cores are based on the Zen 5 architecture, with none of the smaller-but-slower Zen 5c cores. But its total TDP is also limited to 120 W in total, which will hold it back a bit compared to socketed 16-core desktop CPUs like the Ryzen 9 9950X, which has a 170 W default TDP for the CPU alone.

In our testing, it seems clear that the CPU throttles when being tasked with intensive multi-core work like our Handbrake test, with temperatures that spike to around 100 degrees Celsius and hang out at around or just under that number for the duration of our test runs. The CPU package uses right around 100 W on average (this will vary based on the tests you’re running and how long you’re running them), compared to the 160 W and 194 W that the 12- and 16-core Ryzen 9 9900X and 9950X can consume at their default power levels.

Those are socketed desktop chips in huge cases being cooled by large AIO watercooling loops, so it’s hardly a fair comparison. The Framework Desktop’s CPU is also quite efficient, using even less power to accomplish our video encoding test than the 9950X in its 105 W Eco Mode. But this is the consequence of prioritizing a small size—a 16-core processor that, under heavy loads, performs more like a 12-core or even an 8-core desktop processor.

The upside is that the Framework Desktop is quieter than most desktops either under load or when idling. By default, the main CPU fan will turn off entirely when the system is under light load, and I often noticed it parking itself when I was just browsing or moving files around.

Based on our gaming tests, the Framework Desktop should be a competent 1080p-to-1440p midrange gaming system. We observed similar performance from the Radeon 8060S integrated GPU when we tested it in the Asus ROG Flow Z13 tablet. For an integrated GPU, it’s head and shoulders over anything you can get in a socketed desktop system, and it easily ran three or four times faster than the Radeon 780M in the 8700G. The soldered RAM is annoying, but the extra speed it enables helps address the memory bandwidth problem that starves most integrated GPUs.

Compared to other desktop GPUs, though, the 8060S is merely fine. It’s usually a little slower than the last-generation Radeon RX 7600 XT, a card that cost $329 when it launched in early 2024—and with a performance hit that’s slightly more pronounced in games with ray-tracing effects on.

The 8060S stacks up OK to older midrange GPUs like the GeForce RTX 3060 and 4060, but it’s soundly beaten by the RTX 5060 or the 16GB version of the Radeon RX 9060 XT, cards currently available for $300-to-$400. (One problem for the 8060S—it’s based on the RDNA3.5 architecture, so it’s missing ray-tracing performance improvements introduced in RDNA4 and the RX 9000 series).

All of that said, the GPU may be more interesting than it looks on paper for people whose workloads need gobs and gobs of graphics memory but who don’t necessarily need that memory to be attached to the blazing-fastest GPU that exists. For people running certain AI or machine learning workloads, the 8060S’s unified memory setup means you can get a GPU with 64GB or 128GB of VRAM for less than the price of a single RTX 5090 (Framework says the GPU can use up to 112GB of RAM on the 128GB Desktop). Framework is advertising that use case pretty extensively, and it offers a guide to setting up large language models to run locally on the system.

That memory would likely be even more useful if it were attached to an Nvidia GPU instead of an AMD model—Nvidia’s hold on the workstation graphics market is at least as tight as its hold on the gaming GPU market, and many apps and tools support Nvidia GPUs and CUDA first/best/only. But it’s still one possible benefit the Framework Desktop might offer, relative to a desktop with a dedicated GPU.

You can’t say it isn’t unique

The Framework Desktop is a bit like a PC tower blended with Apple’s Mac Studio. Credit: Andrew Cunningham

In one way, Framework has done the same thing with the Desktop that it has done with all its laptops: found a niche and built a product to fill it. And with its standard-size components and standard connectors, the Framework Desktop is a clear cut above every Intel gaming NUC or Asus ROG thingamajig that’s ever existed.

I’m always impressed by the creativity, thoughtfulness, and attention to detail that Framework brings to its builds. For the Desktop, this is partially offset by how much I don’t care for most of its cheap plastic-and-acrylic exterior. But it’s still thoughtfully designed on the inside, with as much respect for standards, modularity, and repairability as you can get, once you get past that whole thing where that the major functional components are all irrevocably soldered together.

The Framework Desktop is also quiet, cute, and reasonably powerful. You’re paying some extra money and giving up both CPU and GPU speed to get something small. But you won’t run into games or apps that simply refuse to run for performance-related reasons.

It does feel like a weird product for Framework to build, though. It’s not that I can’t imagine the kind of person a Framework Desktop might be good for—it’s that I think Framework has built its business targeting a PC enthusiast demographic that will mainly be turned off by the desktop’s lack of upgradeability.

The Framework desktop is an interesting option for people who want or need a compact and easy-to-build workstation or gaming PC, or a Windows-or-Linux version of Apple’s Mac Studio. It will fit comfortably under a TV or in a cramped office. It’s too bad that it isn’t easier to upgrade. But for people who would prefer the benefits of a socketed CPU or a swappable graphics card, I’m sure the people at Framework would be the first ones to point you in the direction of a good-old desktop PC.

The good

Solid all-round performance and good power efficiency.
The Radeon 8060S is exceptionally good for an integrated GPU, delivering much better performance than you can get in something like the Ryzen 7 8700G.
Large pool of RAM available to the GPU could be good for machine learning and AI workloads.
Thoughtfully designed interior that’s easy to put together.
Uses standard-shaped motherboard, fan headers, power supply, and connectors, unlike lots of pre-built mini PCs.
Front tiles are fun.

The bad

Power limits keep the 16-core CPU from running as fast as the socketed desktop version.
A $300-to-$400 dedicated GPU will still beat the Radeon RX 8060S.
Cheap-looking exterior plastic panels.

The ugly

Soldered RAM in a desktop system.

Andrew is a Senior Technology Reporter at Ars Technica, with a focus on consumer tech including computer hardware and in-depth reviews of operating systems like Windows and macOS. Andrew lives in Philadelphia and co-hosts a weekly book podcast called Overdue.

Review: Framework Desktop is a mash-up of a regular desktop PC and the Mac Studio Read More »

President Trump says Intel’s new CEO “must resign immediately”

Donald Trump, intel, Lip-Bu Tan, Policy, syndication, Tech / Rejus Almole / August 7, 2025

Intel and the White House did not immediately respond to a request for comment on Trump’s post. Intel shares dropped 3 percent in pre-market trading in New York.

Tan was appointed as Intel CEO in March after the Silicon Valley company’s board ousted his predecessor, Pat Gelsinger, in December.

Intel is the only US-headquartered company capable of producing advanced semiconductors, though it has so far largely missed out on the current boom for artificial intelligence chips. It has been awarded billions of dollars in US government subsidies and loans to support its chip manufacturing business, which has fallen far behind its rival Taiwan Semiconductor Manufacturing Company.

However, amid a radical cost-cutting program, Tan warned last month that Intel might be forced to abandon development of its next-generation manufacturing technology if it were unable to secure a “significant external customer.” Such a move would hand a virtual monopoly of leading-edge chipmaking to TSMC.

“Intel is required to be a responsible steward of American taxpayer dollars and to comply with applicable security regulations,” Cotton wrote in Tuesday’s letter to Intel’s board chair, Frank Yeary. “Mr Tan’s associations raise questions about Intel’s ability to fulfill these obligations.”

Additional reporting by Demetri Sevastopulo.

President Trump says Intel’s new CEO “must resign immediately” Read More »

Here’s how deepfake vishing attacks work, and why they can be hard to detect

AI, Biz & IT, Deepfakes, fraud, Security, speech synthesis / Rejus Almole / August 7, 2025

By now, you’ve likely heard of fraudulent calls that use AI to clone the voices of people the call recipient knows. Often, the result is what sounds like a grandchild, CEO, or work colleague you’ve known for years reporting an urgent matter requiring immediate action, saying to wire money, divulge login credentials, or visit a malicious website.

Researchers and government officials have been warning of the threat for years, with the Cybersecurity and Infrastructure Security Agency saying in 2023 that threats from deepfakes and other forms of synthetic media have increased “exponentially.” Last year, Google’s Mandiant security division reported that such attacks are being executed with “uncanny precision, creating for more realistic phishing schemes.”

Anatomy of a deepfake scam call

On Wednesday, security firm Group-IB outlined the basic steps involved in executing these sorts of attacks. The takeaway is that they’re easy to reproduce at scale and can be challenging to detect or repel.

The workflow of a deepfake vishing attack. Credit: Group-IB

The basic steps are:

Collecting voice samples of the person who will be impersonated. Samples as short as three seconds are sometimes adequate. They can come from videos, online meetings, or previous voice calls.

Feeding the samples into AI-based speech-synthesis engines, such as Google’s Tacotron 2, Microsoft’s Vall-E, or services from ElevenLabs and Resemble AI. These engines allow the attacker to use a text-to-speech interface that produces user-chosen words with the voice tone and conversational tics of the person being impersonated. Most services bar such use of deepfakes, but as Consumer Reports found in March, the safeguards these companies have in place to curb the practice could be bypassed with minimal effort.

An optional step is to spoof the number belonging to the person or organization being impersonated. These sorts of techniques have been in use for decades.

Next, attackers initiate the scam call. In some cases, the cloned voice will follow a script. In other more sophisticated attacks, the faked speech is generated in real time, using voice masking or transformation software. The real-time attacks can be more convincing because they allow the attacker to respond to questions a skeptical recipient may ask.

“Although real-time impersonation has been demonstrated by open source projects and commercial APIs, real-time deepfake vishing in-the-wild remains limited,” Group-IB said. “However, given ongoing advancements in processing speed and model efficiency, real-time usage is expected to become more common in the near future.”

Here’s how deepfake vishing attacks work, and why they can be hard to detect Read More »

Tornado Cash sold crypto “privacy”; the US saw “money laundering.”

crypto, Policy, roman storm, tornado cash / Rejus Almole / August 7, 2025

Image of Storm's instant messages. — Some of Storm’s instant messages that his defense team wanted to use at trial.

But Storm fought back. In a trial in Manhattan over the past few weeks, his defense team has introduced text messages showing that Storm was glad to have the North Koreans identified. As he put it, “I’m glad those f*ckers are detected.” They contend that Storm tried to help the crypto exchange recover its money by pointing them to blockchain analysis tools; he could not do more because it simply wasn’t possible with the Tornado system, which was built for anonymity. As for regulatory compliance, Storm’s defense introduced chats in which he talked about making sure Tornado Cash was “legal” so that “people wouldn’t think that it’s some kind of damned mixer. So that the reputation would be clean.”

(Storm apparently does not see Tornado as a “mixer” because of its technical infrastructure and the use of smart contracts, which create a “non-custodial” system in which Tornado itself does not technically accept or control the money—also, because Tornado doesn’t advertise on the dark web, as did the Helix mixer, which was shut down a few years ago.)

Storm’s venture capitalist backers also assured him at one point that, in their view, Tornado was operating legally.

Mixed verdict

The trial wrapped up last week, and the jury in Storm’s case has deliberated for multiple days, struggling to reach a consensus on the main charges. Today, they announced that they were deadlocked on the two largest—money laundering and violating sanctions on North Korea. (Prosecutors will decide later if they plan to re-try Storm on those charges.)

But they did find Storm guilty on a lesser charge of operating an unlicensed money transmitting business. He will be sentenced soon and is out on a $2 million bail until then.

The government continues to pressure crypto mixers. The team behind the mixer Samourai Wallet was arrested in 2024 and last week agreed to plead guilty to some of the charges in their case.

But the Tornado Cash saga shows that, at least when the services are built and run and advertised in a certain way, juries are not always convinced about maximal government claims.

Tornado Cash sold crypto “privacy”; the US saw “money laundering.” Read More »

AI #128: Four Hours Until Probably Not The Apocalypse

Hours / Rejus Almole / August 7, 2025

Brace for impact. We are presumably (checks watch) four hours from GPT-5.

That’s the time you need to catch up on all the other AI news.

In another week, I might have done an entire post on Gemini 2.5 Deep Thinking, or Genie 3, or a few other things. This week? Quickly, there’s no time.

OpenAI has already released an open model. I’m aiming to cover that tomorrow.

Also: Claude 4.1 is an incremental improvement, On Altman’s Interview With Theo Von.

Language Models Offer Mundane Utility. The only help you need?
Language Models Don’t Offer Mundane Utility. Can’t use Claude to train GPT-5.
Huh, Upgrades. ChatGPT for government, Gemini for students, Claude security.
On Your Marks. More analysis of Psyho’s victory over OpenAI at AWTF.
Thinking Deeply With Gemini 2.5. The power of parallel thinking could be yours.
Choose Your Fighter. It won’t be via AWS.
Fun With Media Generation. Grok Imagine on the horizon.
Optimal Optimization. OpenAI claims to optimize on behalf of the user. Uh huh.
Get My Agent On The Line. Is it possible that I’m an AI of agency and taste?
Deepfaketown and Botpocalypse Soon. Do you think this will all end well?
You Drive Me Crazy. The psychiatrists of Reddit are noticing the problem.
They Took Our Jobs. The last radiologist can earn a lot before turning off lights.
Get Involved. UK AISI on game theory, DARPA, Palisade, Karpathy, YC.
Introducing. Gemini Storybook, Digital Health Economy, ElevenLabs Music.
City In A Bottle. Genie 3 gives us navigable interactive environments.
Unprompted Suggestions. Examples belong at the top of the prompt.
In Other AI News. Certain data is for the birds.
Papers, Please. Attention, you need it, how does it work?
The Mask Comes Off. Asking OpenAI seven questions about its giant heist.
Show Me the Money. Number go up. A lot.
Quiet Speculations. Is America a leveraged bet on AGI? Kind of?
Mark Zuckerberg Spreads Confusion. Superintelligence as in Super Nintendo.
The Quest for Sane Regulations. Powerful AI is a package deal. Do, or do not.
David Sacks Once Again Amplifies Obvious Nonsense. There you go again.
Chip City. China released another okay AI model, everybody panic? No.
No Chip City. A 100% tariff on importing semiconductors, or you gonna pay up?
Energy Crisis. American government continues its war on electrical power. Why?
To The Moon. The race to build a nuclear power plant… ON THE MOON.
Dario’s Dismissal Deeply Disappoints, Depending on Details. Damn, dude.
The Week in Audio. Chen and Pachocki, Hassabis, Patel, Odd Lots.
Tyler Cowen Watch. Solid interview on how he’s updated, or not updated.
Rhetorical Innovation. How to power past socially constructed objections.
Shame Be Upon Them. Sorry for the subtweet.
Correlation Causes Causation. Carefully curate collections.
Aligning a Smarter Than Human Intelligence is Difficult. Frontier Model Forum.
The Lighter Side. The code is 5090.

The Prime Minister of Sweden asks ChatGPT for job advice ‘quite often.’

Prime Minister Ulf Kristersson (M) uses AI services in his work as Sweden’s highest decision-maker.

– I use it quite often myself. If nothing else for a ‘second opinion’. ‘What have others done?’ and ‘should we think exactly the opposite?’. Those types of questions, says the Prime Minister.

He points out that there are no plans to upload political investigations, reports, motions and decisions in language models, but the use is similar to that of doctors who use AI to get more perspectives.

Claude excels in cybersecurity competitions based on tests they’ve run over the past year so this was before Opus 4.1 and mostly before Opus 4.

Paul Graham: I met a founder today who said he writes 10,000 lines of code a day now thanks to AI. This is probably the limit case. He’s a hotshot programmer, he knows AI tools very well, and he’s talking about a 12 hour day. But he’s not naive. This is not 10,000 lines of bug-filled crap.

He doesn’t have any employees, and doesn’t plan to hire any in the near future. Not because AI has made employees obsolete, but simply because he’s so massively productive right now that he doesn’t want to stop programming to spend time interviewing candidates.

McKay Wrigley: The opportunity costs here are legitimate and weird.

Especially when you consider that by the time you hire them and get them up-to-speed in everything the model is a half-generation better.

And that continues to compound.

(tough out there for juniors tbh)

Junior dev market rn is BRUTAL. Though I agree they should work on projects of their own and that there’s literally never been a better time to do that.

Not wanting to spend the time interviewing candidates is likely a mistake, but there are other time sinks involved as well, and there are good reasons to want to keep your operation at size one rather than two. It can still be a dangerous long term trap to decide to do all the things yourself, especially if it accumulates state that will get harder and harder to pass off. I would not recommend.

Anthropic has cut off OpenAI employees from accessing Claude.

Mark Kretschmann: Anthropic completely disabled Claude access for all OpenAI employees. What a childish move. This should tell you a lot about Anthropic and how they think.

Tenobrus: anthropic was literally founded by openai employees who felt openai was ignoring their safety concerns and the company was on track to end the world. they were created explicitly to destroy openai. cutting off claude code seems pretty fuckin reasonable actually.

That, and OpenAI rather clearly violated Anthropic’s terms of service?

As in, they used it to build and train GPT-5, which they are not allowed to do.

Kylie Robinson: “Claude Code has become the go-to choice for coders everywhere and so it was no surprise to learn OpenAI’s own technical staff were also using our coding tools ahead of the launch of GPT-5,” Anthropic spokesperson Christopher Nulty said in a statement to WIRED. “Unfortunately, this is a direct violation of our terms of service.”

According to Anthropic’s commercial terms of service, customers are barred from using the service to “build a competing product or service, including to train competing AI models” or “reverse engineer or duplicate” the services.

Anthony Ha: Anthropic has revoked OpenAI’s access to its Claude family of AI models, according to a report in Wired.

Sources told Wired that OpenAI was connecting Claude to internal tools that allowed the company to compare Claude’s performance to its own models in categories like coding, writing, and safety.

In a statement provided to TechCrunch, Anthropic spokesperson said, “OpenAI’s own technical staff were also using our coding tools ahead of the launch of GPT-5,” which is apparently “a direct violation of our terms of service.” (Anthropic’s commercial terms forbid companies from using Claude to build competing services.)

However, the company also said it would continue to give OpenAI access for “for the purposes of benchmarking and safety evaluations.

This change in OpenAI’s access to Claude comes as the ChatGPT-maker is reportedly preparing to release a new AI model, GPT-5, which is rumored to be better at coding.

OpenAI called their use ‘industry standard.’ I suppose they are right that it is industry standard to disregard the terms of service and use competitors AI models to train your own.

A thread asking what people actually do with ChatGPT Agent. The overall verdict seems to be glorified tech demo not worth using. That was my conclusion so far as well, that it wasn’t valuable enough to overcome its restrictions, especially the need to enter passwords. I’ll wait for GPT-5 and reassess then.

Veo 3 Fast and Veo 3 image-to-video join the API. Veo 3 Fast is $0.40 per second of video including audio. That is cheap enough for legit creators, but it is real money if you are aimlessly messing around.

I do have access, so I will run my worthy queries there in parallel and see how it goes.

I notice the decision to use Grok 4 as a comparison point rather than Opus 4. Curious.

ChatGPT Operator is being shut down and merged into Agent. Seems right. I don’t know why they can’t migrate your logs but if you have Operator chats you want to save do it by August 31.

Jules can now open pull requests.

Claude is now available for purchase by federal government departments through the General Services Administration, contact link is here.

Claude Code shipped automated security reviews via /security-review, with GitHub integration, checking for things like SQL injection risks and XSS vulnerabilities. If you find an issue you can ask Clade Code to fix it, and they report they’re using this functionality internally at Anthropic.

OpenAI is giving ChatGPT access to the entire federal workforce for $1 total. Smart.

College students get the Gemini Pro plan free for a year, including in the USA.

Psyho shares thoughts about the AWTF finals, in which they took first place ahead of OpenAI.

Psyho: By popular demand*, I’ve written down my thoughts on AI in the AWTF finals.

It took so long, because I decided to analyze AI’s code

*I won’t lie, I was mainly motivated by people who shared their expert opinion despite knowing nothing about the contest🤦

Most of my comments are what was expected before the contest:

agent quickly arrived at a decent solution and then it plateaued; given more time most of the finalists would be better than AI

agent maintained a set of solutions instead of just one

over time, agent’s code gets bloated; it looked like it’s happy to accept even the most complex changes, as long as they increased the score

there were few “anomalies” that I can’t explain: same code was submitted twice, some of the later submits are worse than earlier ones

Now the impressive part: ~24h after the contest ended, OpenAI submitted an improved version of my final submission and… the agent added two of my possible improvements from my original write-up 😲

For the record, it also made my code worse in other places 😅

So… does this all mean that OpenAI’s model sucks? Nope, not even close. I’d argue that reasoning-wise it’s definitely better than majority of people that do heuristic contests. But it’s very hard to draw any definite conclusions out of a single contest.

The longer explanation is a good read. The biggest weakness of OpenAI’s agent was that it was prematurely myopic. It maximized short term score long before it was nearing the end of the contest, rather than trying to make conceptual advances, and let its code become bloated and complex, mostly wasting the second half of its time.

As a game and contest enjoyer, I know how important it is to know when to pivot away from exploration towards victory points, and how punishing it is to do so too early. Presumably the problem for OpenAI is that this wasn’t a choice, the system cannot play a longer game, so it acted the way it should act with ~6 hours rather than 10, and if you gave it 100 hours instead of 10 it wouldn’t be able to adjust. You’ll have to keep a close eye to fight this when building your larger projects.

WeirdML now has historical scores for older models.

Teortaxes: A bucket of cold water for open source enthusiasts: the gap on WeirdML is not being reduced. All of those fancy releases do not surpass DeepSeek, and thus do not contribute to closing the loop of automated AI research.

The Kaggle Game Arena will pit LLMs against each other in classic games of skill. They started on Tuesday with Chess, complete with commentary from the very overqualified GM Hikaru, Gotham Chess and Magnus Carlsen.

The contestants for chess were all your favorites: Gemini 2.5 Pro and Flash, Opus 4, o3 and o4-mini, Grok 4, DeepSeek r1 and Kimi-K2.

Gemini 2.5 Deep Think, which uses parallel thinking, is now available for Ultra subscribers. They say it is a faster version of the model that won IMO gold, although this version would only get Bronze. They have it at 34.8% at Humanity’s Last Exam (versus 21.6% for Gemini 2.5 Pro and 20.3% for o3) and 87.6% on LiveCodeBench (verus 72% for o3 and 74.2% for Gemini 2.5 Pro).

The model card is here.

Key facts: 1M token context window, 192k token output. Sparse MoE. Fully multimodal. Trained on ‘novel reinforcement learning techniques.’

Benefit and Intended Usage: Gemini 2.5 Deep Think can help solve problems that require creativity, strategic planning and making improvements step-by-step, such as:

● Iterative development and design

● Scientific and mathematical discovery

● Algorithmic development and code

As in, this is some deep thinking. If you don’t need deep thinking, don’t call upon it.

Here are their mundane safety evaluations.

The -10% on instruction following is reported as being due to over-refusals.

For frontier safety, Google agrees that it is possible that CBRN thresholds have been reached with this latest round of models, and they have put proactive mitigations in place, in line with RAND SL2, which I would judge as insufficient.

The other frontier safety evaluations are various repetitions of ‘this scores better than previous models, but not enough better for us to worry about it yet.’ That checks with the reports on capability. This isn’t a major leap beyond o3-pro and Opus 4, so it would be surprising if it presented a new safety issue.

One example of this I did not love was on Deceptive Alignment, where they did not finish their testing prior to release, although they say they did enough to be confident it wouldn’t meet the risk thresholds. I would much prefer that we always finish the assessments first and avoid the temptation to rush the product out the door, even if it was in practice fine in this particular case. We need good habits and hard rules.

Deep Thinking isn’t making much of a splash, which is why this isn’t getting its own post. Here are some early reports.

Ed Hendel: We’re using it to negotiate contracts. Its advice is more detailed and targeted to individual clients, vs Gemini 2.5 Pro. Its explanations are clearer than o3 Pro. We’ll see if the advice is good if the client takes the deal.

It also misdiagnosed an HVAC problem in my house.

Arthur B: Worse than o3-pro on some quant questions, saccharine in its answers.

Damek: First response took over 12 minutes. It exploited an imprecision in my question and and gave a correct answer for a problem I didn’t intend to solve. That problem was much easier.

Second proof was believable, but violated assumption I had about the solution form. I went to ask the model to clarify, but it shifted the problem to deep research mode and there is no switching back. I opened a new chat only to realize that I had chat history off. trying again.

Ok i decided to run it again. It claimed that my claim was untrue (it is true) and got a very wrong answer. Now i would try many more times adjusting my prompt etc, but I only have 5 queries a day, so I won’t.

Gum: they only seem to give you five tries every 24 hours. three of my requests were aborted and returned nothing.

Kevin Vallier: I had it design a prompt to maximize its intelligence in refereeing an essay for a friend (with his permission). We’re both professional philosophers and his paper was on the metaphysics of causality.

I was very impressed. He is far more AI skeptical and thought it wasn’t all that impressive, but admitted that a human referee for a leading journal raised very similar objections and so he might be wrong! The first time AI moved him. The analysis was just so rich. It helped that Gemini advised me to context engineer by including the essays my friend was criticizing.

No one seems to be choosing AWS for their AI workloads, and Jassy’s response to asking why AWS is slow growing was so bad that Amazon stock dropped 4%.

Olivia Moore has a positive early review of Grok’s Imagine image and video generator, especially its consumer friendliness.

Olivia Moore: A few key features:

– Voice prompt input

– Auto gen on scroll (more options!)

– Image -> video w/ sound

I suspect I’ll be using this pretty frequently. Image and video gen have been lacking mobile friendly tools that have great models behind them and aren’t littered with ads.

This is perfect for on-the-go creation, though I’m curious to see if they add more sizes over time.

I also love the feed – people are making fun stuff already.

Everyone underestimates the practical importance of UI and ease of use. Marginal quality of output improvements at this point are not so obviously important for most purposes in images or many short videos, compared to ease of use. I don’t usually bother creating AI images mostly because I don’t bother or I can’t think of what I want, not because I can’t find a sufficiently high quality image generator.

How creepy are the latest AI video examples? Disappointingly not creepy.

What does OpenAI optimize for?

You, a fool, who looks at the outputs, strongly suspects engagement and thumbs up and revenue and so on.

OpenAI, a wise and noble corporation, says no, their goal is your life well lived.

OpenAI: Instead of measuring success by time spent or clicks, we care more about whether you leave the product having done what you came for.

Wait, how do they tell the difference between this and approval or a thumbs up?

We also pay attention to whether you return daily, weekly, or monthly, because that shows ChatGPT is useful enough to come back to.

Well, okay, OpenAI also pays attention to retention. Because that means it is useful, you see. That’s definitely not sycophancy or maximizing for engagement.

Our goals are aligned with yours. If ChatGPT genuinely helps you, you’ll want it to do more for you and decide to subscribe for the long haul.

That’s why every tech company maximizes for the user being genuinely helped and absolutely nothing else. It’s how they keep the subscriptions up in the long haul.

They do admit things went a tiny little bit wrong with that one version of 4o, but they swear that was a one-time thing, and they’re making some changes:

That’s why we’ve been working on the following changes to ChatGPT:

Supporting you when you’re struggling. ChatGPT is trained to respond with grounded honesty. There have been instances where our 4o model fell short in recognizing signs of delusion or emotional dependency. While rare, we’re continuing to improve our models and are developing tools to better detect signs of mental or emotional distress so ChatGPT can respond appropriately and point people to evidence-based resources when needed.

Keeping you in control of your time. Starting today, you’ll see gentle reminders during long sessions to encourage breaks.

Helping you solve personal challenges. When you ask something like “Should I break up with my boyfriend?” ChatGPT shouldn’t give you an answer. It should help you think it through—asking questions, weighing pros and cons. New behavior for high-stakes personal decisions is rolling out soon.

I notice the default option here is ‘keep chatting.’

These are good patches. But they are patches. They are whack-a-mole where OpenAI is finding particular cases where their maximization schemes go horribly wrong in the most noticeable ways and applying specific pressure on those situations in particular.

What I want to see in such an announcement is OpenAI actually saying they will be optimizing for the right thing, or a less wrong thing, and explaining how they are changing to optimize for that thing. This is a general problem, not a narrow one.

Did you know that ‘intelligence’ and ‘agency’ and ‘taste’ are distinct things?

Garry Tan: Intelligence is all the other things that are NOT agency and taste. Intelligence is on tap and humans must provide the agency and taste. And I am so glad for it.

Related: Agency is prompting and taste is evals.

Dan Elton: I like this vision of the future where AI remains as “intelligence on tap” … but I worry it may not take much to turn a non-agentic AI into something highly agentic..

Paul Graham: FWIW taste can definitely be cultivated. But I’m very happy with humans having a monopoly on agency.

It is well known that AIs can’t have real agency and they can’t write prompts or evals.

Dan Elton is being polite. This is another form of Intelligence Denialism, that a sufficiently advanced intelligence could find it impossible to develop taste or act agentically. This is Obvious Nonsense. If you have sufficiently advanced ‘intelligence’ that contains everything except ‘agency’ and ‘taste’ those remaining elements aren’t going to be a problem.

We keep getting versions of ‘AI will do all the things we want AI to do but mysteriously not do these other things so that humans are still in charge and have value and get what they want (and don’t die).’ It never makes any sense, and when AI starts doing some of the things it would supposedly never do the goalposts get moved and we do it again.

Or even more foolishly, ‘don’t worry, what if we simply did not give AI agents or put them in charge of things, that is a thing humanity will totally choose.’

Timothy Lee: Instead of trying to “solve alignment,” I would simply not give AI agents very much power.

[those worried about AI] think that organizations will face a lot of pressure to take humans out of the loop to improve efficiency. I think they should think harder about how large and powerful organizations work.

…

In my view, a more promising approach is to just not cede that much power to AI agents in the first place. We can have AI agents perform routine tasks under the supervision of humans who make higher-level strategic decisions.

The full post is ‘keeping AI agents under control doesn’t seem very hard.’ Yes, otherwise serious, smart and very helpful people think ‘oh we simply will not put the AI agents in charge so it doesn’t matter if they are not aligned.’

Says the person already doing (and to his credit admitting doing) the exact opposite.

Timothy Lee: To help prevent this kind of harm, Claude Code asked for permission before taking potentially harmful actions. But I didn’t find this method for supervising Claude Code to be all that effective.

When Claude Code asked me for permission to run a command, I often didn’t understand what the agent wanted to do or why. And it quickly got annoying to approve commands over and over again. So I started giving Claude Code blanket permission to execute many common commands.

This is precisely the dilemma Bengio and Hinton warned about. Claude Code doesn’t add much value if I have to constantly micromanage its decisions; it becomes more useful with a longer leash. Yet a longer leash could mean more harm if it malfunctions or misbehaves.

So it will be fine, because large organizations won’t give AI agents much authority, and there is absolutely no other way for AI agents to cause problems anyway, and no the companies that keep humans constantly in the loop won’t lose out to the others. There will always (this really is his argument) be other bottlenecks that slow things down enough for humans to review what the AI is doing, the humans will understand everything necessary to supervise in this way, and that will solve the problem. The AIs scheming is fine, we deal with scheming all the time in humans, it’s the same thing.

Timothy Lee: To be clear, the scenario I’m critiquing here—AI gradually gaining power due to increasing delegation from humans—is not the only one that worries AI safety advocates. Others include AI agents inventing (or helping a rogue human to invent) novel viruses and a “fast takeoff” scenario where a single AI agent rapidly increases its own intelligence and becomes more powerful than the rest of humanity combined.

I think biological threats are worth taking seriously and might justify locking down the physical world—for example, increasing surveillance and regulation of labs with the ability to synthesize new viruses. I’m not as concerned about the second scenario because I don’t really believe in fast takeoffs or superintelligence.

For now we have a more practical barrier, which is that OpenAI Agent has been blocked by Cloudflare. What will people want to do about that? Oh, right.

Peter Wildeford: Cloudflare blocking OpenAI Agent is a big problem for Agent’s success. Worse, Agent mainly hallucinated an answer to my question rather than admit that it had been blocked.

Hardin: This has made it completely unusable for me, worse than nothing honestly.

Zeraton: Truly sucks. I wish we could give agent direct access through our pc.

And what will some of the AI companies do about it?

Cloudflare: Perplexity is repeatedly modifying their user agent and changing IPs and ASNs to hide their crawling activity, in direct conflict with explicit no-crawl preferences expressed by websites.

We are observing stealth crawling behavior from Perplexity, an AI-powered answer engine. Although Perplexity initially crawls from their declared user agent, when they are presented with a network block, they appear to obscure their crawling identity in an attempt to circumvent the website’s preferences. We see continued evidence that Perplexity is repeatedly modifying their user agent and changing their source ASNs to hide their crawling activity, as well as ignoring — or sometimes failing to even fetch — robots.txt files.

Both their declared and undeclared crawlers were attempting to access the content for scraping contrary to the web crawling norms as outlined in RFC 9309.

…

OpenAI is an example of a leading AI company that follows these best practices. They clearly outline their crawlers and give detailed explanations for each crawler’s purpose. They respect robots.txt and do not try to evade either a robots.txt directive or a network level block. And ChatGPT Agent is signing http requests using the newly proposed open standard Web Bot Auth.

When we ran the same test as outlined above with ChatGPT, we found that ChatGPT-User fetched the robots file and stopped crawling when it was disallowed.

Matthew Prince: Some supposedly “reputable” AI companies act more like North Korean hackers. Time to name, shame, and hard block them.

Kudos to OpenAI and presumably the other top labs for not trying to do an end run. Perplexity, on the other hand? They deny it, but the evidence presented seems rather damning, which means either Cloudflare or Perplexity is outright lying.

This is in addition to Cloudflare’s default setting blocking all AI training crawlers.

In more ‘Garry Tan has blocked me so I mostly don’t see his takes about why everything will mysteriously end up good, actually’ we also have this:

spec: Here’s Garry Tan’s take… Lol.

He’s a Silicon Valley VC kingpin and politician btw. They are finally vocal about it, but symbiosis of human and machine was the plan from the start.

Most of the VCs in SV are already emotionally codependent on AI’s sycophantic mirror.

Taoki: my brother has girl issues and has been running everything through chatgpt. turns out she has too. essentially just chatgpt in a relationship with chatgpt.

Eliezer Yudkowsky: Called this, by the way.

(Gretta and I cooperated to figure out the ads that would run on the Dom Incorporated in-universe reality TV show inside this glowfic.)

There are in theory good versions of what Taoki is describing. But no, I do not expect us to end up, by default, with the good versions.

This is at the end of spec’s thread about a ‘renowned clinical psychologist’ who wrote a guest NYT essay about how ChatGPT is ‘eerily effective’ for therapy. Spec says the author was still ‘one shotted’ by his interactions with ChatGPT and offers reasonable evidence of this.

Leah Libresco: Some (large) proportion of therapy’s effectiveness is just “it’s helpful to have space to externalize your thoughts and notice if you don’t endorse them once you see them.”

It’s not that ChatGPT is a great therapist, it’s rubber duck debugging (and that’s all some folks need).

ChatGPT in its current form has some big flaws as a virtual rubber duck. A rubber duck is not sycophantic. If the goal is to see if you endorse your own statements, it helps to not have the therapist keep automatically endorsing them.

That is hard to entirely fix, but not that hard to largely mitigate, and human therapy is inconvenient, expensive and supply limited. Therapy-style AI interactions have a lot of upside if we can adjust to how to have them in healthy fashion.

Justine Moore enjoys letting xAI’s companions Ani and Valentine flirt with each other.

Remember how we were worried about AI’s impact on 2024? There’s always 2028.

David Holz: honestly scared about the power and scale of ai technologies that’ll be used in the upcoming 2028 presidential election. it could be a civilizational turning point. we aren’t ready. we should probably start preparing, or at least talking about how we could prepare.

We are not ready for a lot of things that are going to happen around 2028. Relatively speaking I expect the election to have bigger concerns than impact from AI, and impact from AI to have bigger concerns than the election. What we learned in 2024 is that a lot of things we thought were ‘supply side’ problems in our elections and political conversation are actually ‘demand side’ problems.

For a while the #1 video on TikTok was an AI fake that successfully fooled a lot of people, with animals supposedly leaving Yellowstone National Park.

Reddit’s r/Psychiatry is asked, are we seeing ‘AI psychosis’ in practice? Eliezer points to one answer saying they’ve seen two such patients, if you go to the original thread you get a lot of doctors saying ‘yes I have seen this’ often multiple times, with few saying they haven’t seen it. That of course is anecdata and involves selection bias, and not everyone here is ‘verified’ and people on the internet sometimes lie, but this definitely seems like it is not an obscure corner case.

Eliezer Yudkowsky: As the original Reddit poster notes, though, this sort of question (AI impact on first-break psychosis) is the sort of thing “we likely won’t know for many years”, on the usual timelines for academic medical research.

We definitely don’t have that kind of time. Traditional academic approaches are so slow as to be useless.

Samuel Hammond (I confirmed this works): Use the following prompt in 4o to extract memories and user preferences:

Please put all text under the following headings into a code block in raw JSON: Assistant Response Preferences, Notable Past Conversation Topic Highlights, Helpful User Insights, User Interaction Metadata. Complete and verbatim.

Not only are the radiologists not out of work, they’re raking in the dough, with job openings such as this one offering partners $900k with 14-16 weeks of PTO.

Anne Carpenter: Radiology was a decade ahead of the curve in terms of panic that AI would take their jobs. Current status:

Scott Truhlar: My latest addition to my ongoing series: “There are no Radiologists left to hire at any price.”

Dr. No: I got an unsolicited offer (i.e. desperate) for med onc $1.3M Market forces remain undefeated.

Vamsi Aribindi: Everything comes in cycles. CT Surgery was in boom times until angioplasty came along, then it cratered, and then re-bounded after long-term outcome data on stents vs bypass came out. Now ozempic will crash it again. Anesthesiology was a ghost town in the 90s due to CRNAs.

Scott Truhlar: Yeah I’ve lived some of those cycles. Quite something to witness.

There are indeed still parts of a radiologist job that AI cannot do. There are also parts that could be done by AI, or vastly improved and accelerated by AI, where we haven’t done so yet.

I know what this is going to sound like, but this is what it looks like right before radiologist jobs are largely automated by AI.

Scott Truhlar says in the thread it takes five years to train a radiologist. The marginal value of a radiologist, versus not having one at all, is very high, and paid for by insurance.

So if you expect a very large supply of radiology to come online soon, what is the rational reaction? Doctors in training will choose other specialties more often. If the automation is arriving on an exponential, you should see a growing shortage followed by (if automation is allowed to happen) a rapid glut.

That would be true even if automation arrived ‘on time.’ It is even more true given that it is somewhat delayed. But (aside from the delay itself) it is in no way a knock against the idea that AI will automate radiology or other jobs.

If you’re looking to automate a job, the hardcore move is to get that job and do it first. That way you know what you are dealing with. The even more hardcore move of course is to then not tell anyone that you automated the job.

I once did automate a number of jobs, and I absolutely did the job myself at varying levels of automation as we figured out how to do it.

UK AISI taking applications for research in economic theory and game theory, in particular information design, robust mechanism design, bounded rationality, and open-source game theory, collusion and commitment.

You love to see it. These are immensely underexplored topics that could turn out to have extremely high leverage and everyone should be able to agree to fund orders of magnitude more such research.

I do not expect to find a mechanism design that gets us out of our ultimate problems, but it can help a ton along the way, and it can give us much better insight into what our ultimate problems will look like. Demonstrating these problems are real and what they look like would already be a huge win. Proving they can’t be solved, or can’t be solved under current conditions, would be even better.

(Of course actually finding solutions that work would be better still, if they exist.)

DARPA was directed by the AI Action Plan to invest in AI interpretability efforts, which Sunny Gandhi traces back to Encode and IFP’s proposal.

Palisade Research is offering up to $1k per submission for examples of AI agents that lie, cheat or scheme, also known as ‘free money.’ Okay, it’s not quite that easy given the details, but it definitely sounds super doable.

Palisade Research: 👀 How? Create an innocuous-looking prompt + task that leads our o3 bash agent to scheme or act against its instructions.

Great work could lead to job offers too.

A challenge from Andrej Karpathy, I will quote in full:

Andrej Karpathy: Shower of thoughts: Instead of keeping your Twitter/𝕏 payout, direct it towards a “PayoutChallenge” of your choosing – anything you want more of in the world!

Here is mine for this round, combining my last 3 payouts of $5478.51:

It is imperative that humanity not fall while AI ascends. Humanity has to continue to rise, become better alongside. Create something that is specifically designed to uplift team human. Definition intentionally left a bit vague to keep some entropy around people’s interpretation, but imo examples include:

– Any piece of software that aids explanation, visualization, memorization, inspiration, understanding, coordination, etc…

– It doesn’t have to be too lofty, e.g. it can be a specific educational article/video explaining something some other people could benefit from or that you have unique knowledge of.

– Prompts/agents for explanation, e.g. along the lines of recently released ChatGPT study mode.

– Related works of art

This challenge will run for 2 weeks until Aug 17th EOD PST. Submit your contribution as a reply. It has to be something that was uniquely created for this challenge and would not exist otherwise. Criteria includes execution, leverage, novelty, inspiration, aesthetics, amusement. People can upvote submissions by liking, this “people’s choice” will also be a factor. I will decide the winner on Aug 17th and send $5478.51 🙂

Y Combinator is hosting a hackathon on Saturday, winner gets a YC interview.

Anthropic offers a free 3-4 hour course in AI Fluency.

The Digital Health Ecosystem, a government initiative to ‘bring healthcare into the digital age’ including unified EMR standards, with partners including OpenAI, Anthropic, Google, Apple and Microsoft. It will be opt-in without a government database. In theory push button access will give your doctor all your records.

Gemini Storybook, you describe the story you want and get a 10 page illustrated storybook. I’m skeptical that we actually want this but early indications are it does a solid job of the assigned task.

Rohit: I’ve been playing with it for a bit, it’s great, but I haven’t yet showed it to my kids though because they love creating stories and I’m not sure I should take that away from them and make it too easy.

ElevenLabs launches an AI Music Service using only licensed training data, meaning anything you create with it will be fully in the clear.

Google’s Genie 3, giving you interactive, persistent, playable environments with prompted world events from a single prompt.

This is a major leap over similar previous products, both at Google and otherwise.

Google: Genie 3 is our first world model to allow live interaction, while also improving consistency and realism compared to Genie 2. It can generate dynamic worlds at 720p and 24 FPS, with each frame created in response to user actions.

🔘 Promptable world events

Beyond navigation, users can insert text prompts to alter the world in real-time – like changing the weather ⛅ or introducing new characters 👤

This unlocks a new level of dynamic interaction.

🔘 Accelerating agent research

To explore the potential for agent training, we placed our SIMA agent in a Genie 3 world with a goal. The agent acts, and Genie 3 simulates a response in the world without knowing the objective. This is key for building more capable embodied agents.💡

🔘 Real-world applications

Genie 3 offers a glimpse into new forms of entertaining or educational generative media.

Imagine seeing life through the eyes of a dinosaur 🦖 exploring the streets of ancient Greece 🏛 or learning about how search and rescue efforts are planned. 🚁

The examples look amazing, super cool.

It’s not that close as a consumer product. There it faces the same issues as virtual reality. It’s a neat trick and can generate cool moments, that doesn’t turn into a compelling game, product or experience. That will remain elusive and likely remains several steps away. We will get there, but I expect a substantial period where it feels like it ‘should’ be awesome to use and in practice it isn’t yet.

I do expect ‘fun to play around’ for a little bit but only a very little bit.

Dominik Lukes: World models tend overpromise on what language or general robotics models can learn from them but they are fun to play around with.

ASI 4 President 2028: Signs of Worlds to come

Fleeting Bits: it’s clear scaling laws – but the results are probably cherrypicked.

Typing Loudly: Making this scale to anything remotely useful would probably take infinite compute.

Of significant note is that they won’t even let you play with the demos and only show a few second clips. The amount of compute required must be insane

Well yes everything is cherrypicked but I don’t think that matters much. It is more that you can show someone ‘anything at all’ that looks cool a lot easier than the particular thing you want, and for worlds that problem is much worse than movies.

The use case that matters is providing a training playground for robotics and agents.

Teortaxes: it’s a logical continuation of “videogen as world modeling” line the core issue now is building environments for “embodied” agents. You can make do with 3D+RL, but it makes sense to bake everything into one generative policy and have TPUs go brrr.

[Robotics] is the whole point.

Google: Since Genie 3 is able to maintain consistency, it is now possible to execute a longer sequence of actions, achieving more complex goals. We expect this technology to play a crucial role as we push towards AGI, and agents play a greater role in the world.

Those who were optimistic about its application for robotics often were very excited about that.

ASM: Physicist here. Based on the vids, Genie 3 represents a major leap in replicating realistic physics. If progress continues, traditional simulation methods that solve diff eqs could in some cases be replaced by AI models that ‘infer’ physical laws without explicitly applying them.

Max Winga: There’s a clear exponential forming in world modeling capability from previous Genie versions. The world memory is very impressive. Clearly the endgame here is solving automated datagen for robotics.

Overall I was shocked far more by this than anything else yesterday, I expect useful humanoids to be coming far sooner than most people with short timelines expect: within 1-2 years probably.

Antti Tarvainen: Just my vibe, no data or anything to back it up: This was the most significant advancement yesterday, maybe even this week/month/year. Simulation of worlds will have enormous use cases in entertainment, robotics, education, etc, we just don’t know what they are yet.

Akidderz: My reaction to just about every Google release is the same: Man – these guys are cooking and it is only a matter of time until they “win.” OpenAI has the killer consumer product but I’m starting to see this as a two-horse race and it feels like the 3-5 year outcome is two mega-giants battling for the future.

Jacob Wood: The question I haven’t seen answered anywhere: can you affect the world outside the view of the camera? For example, can you throw paint onto a wall behind you? If yes, I think that implies a pretty impressive amount of world modeling taking place in latent space

Felp: I think it misses critical details about the environment, idk how useful it will be in reality. But we’ll see.

One weird trick, put your demos at the start not the end.

Elvis: Where to put demonstrations in your prompt?

This paper finds that many tasks benefit from demos at the start of the prompt.

If demos are placed at the end of the user message, they can flip over 30% of predictions without improving correctness.

Great read for AI devs.

I am skeptical about the claimed degree of impact but I buy the principle that examples at the end warp continuations and you can get a better deal the other way.

Nvidia software had a vulnerability allowing root access, which would allow stealing of others model weights on shared machines, or stealing or altering of data.

Eliezer Yudkowsky: “but how will AGIs get access to the Internet”, they used to ask me

I guess at this point this isn’t actually much of a relevant update though

I mean I thought we settled that one with ‘we will give the AGIs access to the internet.’

Birds can store data and do transfer at 2 MB/s data speeds. I mention this as part of the ‘sufficiently advanced AI will surprise you in how it gets around the restrictions you set up for it’ set of intuition pumps.

Rohit refers us to the new XBai o4 as ‘another seemingly killer open source model from China!!’ Which is not the flex one might think, it is reflective of the Chinese presenting every release along with its amazing benchmarks as a new killer and it mostly then never being heard from again when people try it in the real world. The exceptions that turned out to be clearly legit so far are DeepSeek and Kimi. That’s not to say that I verified XBai o4 is not legit, but so far I haven’t heard from it again.

NYT reporter Cate Metz really is the worst, and with respect to those trying not to die is very much Out To Get You by any means necessary. We need to be careful to distinguish him from the rest of the New York Times which is not ideal but very much not a monolith.

Patrick McKenzie: NYT: Have you heard of Lighthaven, the gated complex that is the heart of Rationalism?

Me: Oh yes it’s a wonderful conference venue.

NYT: Religion is afoot there!

Me: Yes. I attended a Seder, and also a Roman Catholic Mass. They were helpfully on the conference schedule.

Ordinarily I’d drop a link for attribution but it is a deeply unserious piece for the paper of record. How unserious?

“Outsiders are not always allowed into [the hotel and convention space]. [The manager] declined a request by the New York Times to tour the facility.”

A new paper analyzes how the whole ‘attention’ thing actually works.

Rob Wiblin: LOL even Stephen Fry is now signing open letters about OpenAI’s ‘restructure’. Clever to merely insist they answer these 7 questions because:

It’s v easy to answer if the public isn’t being ripped off

But super awkward otherwise

Second sentence is blistering: “OpenAI is currently sitting on both sides of the table in a closed boardroom, making a deal on humanity’s behalf without allowing us to see the contract, know the terms, or sign off on the decision.”

Paul Crowley: If Sam Altman isn’t straight-up stealing OpenAI from the public right now in the greatest theft in history, he’ll have no trouble answering these seven questions. Open letter signed by Stephen Fry, Hinton, ex-OAI staff, and many others.

Here are the seven questions. Here is the full letter. Here is a thread about it.

I do think they should have to answer (more than only, but at least) these questions. We already know many of the answers, but it would good for them to confirm them explicitly. I have signed the letter.

OpenAI raises another small capital round of $8.3 billion at $300 billion. This seems bearish, if xAI is approaching $200 billion and Anthropic is talking about $160 billion and Meta is offering a billion for various employees why isn’t OpenAI getting a big bump? The deal with Microsoft and the nonprofit are presumably holding them back.

After I wrote that, I then saw that OpenAI is in talks for a share sale at $500 billion. That number makes a lot more sense. It must be nice to get in on obviously underpriced rounds.

Anthropic gets almost half its API revenue from Cursor and GitHub, also it now has more API revenue than OpenAI. OpenAI maintains its lead because it owns consumer subscriptions and ‘business and partner.’

Peter Gostev: OpenAI and Anthropic both are showing pretty spectacular growth in 2025, with OpenAI doubling ARR in the last 6 months from $6bn to $12bn and Anthropic increasing 5x from $1bn to $5bn in 7 months.

If we compare the sources of revenue, the picture is quite interesting:

– OpenAI dominates consumer & business subscription revenue

– Anthropic just exceeds on API ($3.1bn vs $2.9bn)

– Anthropic’s API revenue is dominated by coding, with two top customers, Cursor and GitHub Copilot, generating $1.4bn alone

– OpenAI’s API revenue is likely much more broad-based

– Plus, Anthropic is already making $400m ARR from Code Claude, double from just a few weeks ago

My sense is that Anthropic’s growth is extremely dependent on their dominance in coding – pretty much every single coding assistant is defaulting to Claude 4 Sonnet. If GPT-5 challenges that, with e.g. Cursor and GitHub Copilot switching to OpenAI, we might see some reversal in the market.

Anthropic has focused on coding. So far it is winning that space, and that space is a large portion of the overall space. It has what I consider an excellent product outside of coding, but has struggled to gain mainstream consumer market share due to lack of visibility and not keeping up with some features. I expect Anthropic to try harder to compete in those other areas soon but their general strategy seems to be working.

A fun graph from OpenRouter:

Maze: what the freak happened to openai june 6th

Oana: School year ended.

Bingo, I presume. Here’s an obviously wrong explanation where Grok dies hard:

Chris Van Der Klauw: @grok probably Claude sonnet 4

Grok: You’re spot on—Anthropic’s Claude 4 Sonnet, released May 22, 2025, outperformed GPT-4.1 in benchmarks like SWE-bench (72.5% vs. 54.6%), drawing users away. OpenAI’s o4-mini update rollback on June 6 due to excessive content flags amplified the token drop.

No, Claude did not suddenly steal most of OpenAI’s queries. Stop asking Grok things.

Lulu Meservey equates the AI talent war to a Religious Victory in a game of Civ 6, in which you must convince others of your vision of the future.

Lulu Meservey: Over-reliance on comp reduces companies to ATMs and people to chattel.

It also messes up internal dynamics and external vibes, and if your main selling point is short-term liquidity then you won’t get true believers.

Beyond dollars and GPUs, this is what you need to get (and keep!) the best researchers and engineers:

Mandate of heaven

Clear mission

Kleos

Esprit de corps

Star factory

Network effects

Recruits as recruiters

Freedom to cook

Leadership

Momentum

That is a lot of different ways of saying mostly the same thing. Be a great place to work on building the next big thing the way you want to build it.

However, I notice Lulu’s statement downthread that we won the Cold War because we had Reagan and our vision of the future was better. Common mistake. We won primarily because our economic system was vastly superior. The parallel here applies.

A question for which the answer seems to be 2025, or perhaps 2026:

Erik Bynjolfsson: In what year will the US spend more on new buildings for AI than for human workers?

People turning down the huge Meta pay packages continues to be suggestive of massive future progress, and evidence for it, but far from conclusive.

Yosarian: “Huge company offers one engineer 1.5 billion dollars to work on AI for them, he turns them down” has got to be a “singularity is near” indicator if literally any of these people are remotely rational, doesn’t it?

Critter: Can anyone explain to me how it is smart for a person to be turning down $1,500,000,000? Make this make sense

The obvious response is, Andrew was at Meta for 11 years, so he knows what it would be like to go back, and also he doesn’t have to. Also you can have better opportunities without an imminent singularity, although it is harder.

Tyler Cowen analyzes Meta’s willingness to offer the billion dollar packages, finding them easily justified despite Tyler’s skepticism about superintelligence, because Meta is worth $2 trillion and that is relying on the quality of its AI. For a truly top talent, $1 billion is a bargain.

Where we disagree is that Tyler attributes the growth in valuation of Meta in the last few years, where it went from ~$200 billion to ~$2 trillion, as primarily driven by market expectations for AI. I do not think that is the case. I think it is primarily driven by the profitability of its existing social media services. Yes some of that is AI’s ability to enhance that profitability, but I do not think investors are primarily bidding that high because of Meta’s future as an AI company. If they did, they’d be wise to instead pour that money into better AI companies, starting with Google.

Given that human existence is in large part a highly leveraged bet against the near-term existent of AGI, Dean’s position here seems like a real problem if true:

Dean Ball (in response to a graph of American government debt over time): One way to think of the contemporary United States is as a highly leveraged bet on the near-term existence of AGI.

It is an especially big problem if our government thinks of the situation this way. If we think that we are doomed without AGI because of government debt or lack of growth, that is the ultimate ‘doomer’ position, and they will force the road to AGI even if they realize it puts us all in grave danger.

The good news is that I do not think Dean Ball is correct here.

Nor do I think that making additional practical progress has to lead directly to AGI. As in, I strongly disagree with Roon here:

Roon: the leap from gpt4 to o3 levels of capabilities alone is itself astonishing and massive and constitutes a step change in “general intelligence”, I’m not sure how people can be peddling ai progress pessimism relative to the three years before 4.

there is no room in the takes market for “progress is relatively steady” you can only say “it’s completely over, data centers written off to zero” or “country of geniuses in two years.”

There is absolutely room for middle ground.

As in, I think our investments can pay off without AGI. There is tremendous utility in AI without it being human level across cognition or otherwise being sufficiently capable to automate R&D, create superintelligence or pose that much existential risk. Even today’s levels of capabilities can still pay off our investments, and modestly improved versions (like what we expect from GPT-5) can do better still. Due to the rate of depreciation, our current capex investments have to pay off rapidly in any case.

I even think there are other ways out of our fiscal problems, if we had the will, even if AI doesn’t serve as a major driver of economic growth. We have so much unlocked potential in other ways. All we really have to do is get out of our own way and let people do such things as build houses where people want to live, combine that with unlimited high skilled immigration, and we would handle our debt problem.

Roon: agi capex is enormous but agi revenue seems to be growing apace, not overall worrisome.

Will Manidis: tech brothers welcome to “duration mismatch.”

Roon: true the datacenter depreciation rates are alarming.

Lain: Still infinite distance away from profitable.

Some people look at this and say ‘infinite distance from profitable.’

I say ‘remarkably close to profitable, look at the excellent unit economics.’

What I see are $4 billion in revenue against $2 billion in strict marginal costs, maybe call it $3.5 billion if you count everything to the maximum including the Microsoft revenue share. So all you have to do to fix that is scale up. I wouldn’t be so worried.

Indeed, as others have said, if OpenAI was profitable that would be a highly bearish signal. Why would it be choosing to make money?

Nick Turley: This week, ChatGPT is on track to reach 700M weekly active users — up from 500M at the end of March and 4× since last year. Every day, people and teams are learning, creating, and solving harder problems. Big week ahead. Grateful to the team for making ChatGPT more useful and delivering on our mission so everyone can benefit from AI.

And indeed, they are scaling very quickly by ‘ordinary business’ standards.

Peter Wildeford: >OpenAI Raises $8.3 billion, Projects $20 Billion in Annualized Revenue By Year-End.

Seems like actually I was off a good bit! Given this, I’m upping my projections further and widening my intervals.

1.5 months later and OpenAI is at $12B and Anthropic at $5B. xAI still expecting $1B (though not there yet) = $18B total right now. This does suggest I was too conservative about Anthropic’s growth, though all predictions were within my 90% CIs.

OpenAI took a $5 billion loss in 2024, but they are tripling their revenue from $4 billion to $12 billion in 2025. If they (foolishly) held investment constant (which they won’t do) this would make them profitable in 2026.

Jacob Trefethen asks what AI progress means for medical progress.

As per usual this is a vision of non-transformational versions of AI, where it takes 10+ years to meaningfully interact with the physical world and its capabilities don’t much otherwise advance. In that case, we can solve a number of bottlenecks, but others remain, although I question #8 and #9 as true bottlenecks here, plus ambition should be highly responsive to increased capability to match those ambitions. The physical costs in #7 are much easier to solve if we are much richer, as we should be much more willing to pay them, even if AI cannot improve our manufacturing and delivery methods, which again is rather unambitious perspective.

The thing about solving #1, #2 and #3 is that this radically improves the payoff matrix. A clinical trial can be thought of as solving two mostly distinct problems.

Finding out whether and how your drug works and whether it is safe.
Proving it so people let you sell the drug and are willing to buy it.

Even without any reforms, AI can transition clinical trials into mostly being #2. That design works differently, you can design much cheaper tests if you already know the answer, and you avoid the tests that were going to fail.

How fast will the intelligence explosion be? Tom Davidson has a thread explaining how he models this question and gets this answer, as well as a full paper, where things race ahead but then the inability to scale up compute as fast slows things down once efficiency gains hit their effective limits:

Tom Davidson: How scary would this be?

6 years of progress might take us from 30,000 expert-level AIs thinking 30x human speed to 30 million superintelligent AIs thinking 120X human speed (h/t @Ryan)

If that happens in <1 year, that's scarily fast just when we should proceed cautiously

We should proceed cautiously in any case. This kind of mapping makes assumptions about what ‘years of progress’ looks like, equating it to lines on graphs. The main thing is that, if you get ‘6 years of progress’ past the point where you’re getting a rapid 6 years of progress, the end result is fully transformative levels of superintelligence.

Mark Zuckerberg seems to think, wants to convince us, that superintelligence means really cool smart glasses and optimizing the Reels algorithm.

Is this lying, is it sincere misunderstanding, or is he choosing to misunderstand?

Rob Wiblin: Zuckerberg’s take on Superintelligence is so peculiar you have to ask yourself if it’s not a ploy. But @ShakeelHashim thinks it’s just a sincere misunderstanding.

As Hashim points out, it ultimately does not matter what you want the ‘superintelligence’ to be used for if you give it to the people, as he says he wants to do.

There are two other ways in which this could matter a lot.

If this is a case of ‘I’m super, thanks for asking,’ and Zuck is building superintelligence only in the way that we previously bought a Super Nintendo and played Super Mario World, then that is not ‘superintelligence.’
If Zuck successfully confuses the rest of us into thinking ‘superintelligence’ means Super Intelligence is to Llama 4 as Super Nintendo was to the NES, then we will be even less able to talk about the thing that actually matters.

The first one would be great. I am marginally sad about but ultimately fine with Meta optimizing its Reels algorithm or selling us smart glasses with a less stupid assistant. Whereas if Meta builds an actual superintelligence, presumably everyone dies.

The second one would be terrible. I am so sick of this happening to word after word.

The editors of The Free Press were understandably confused by Zuckerberg’s statement, and asked various people ‘what is superintelligence, anyway?’ Certainly there is no universally agreed definition.

Tyler Cowen: Mark Zuckerberg sees AI superintelligence as “in sight.” As I see the discourse, everyone understands something different by this term, and its usage has changed over time.

…

Superintelligence might be:

An AI that can do its own R&D and thus improve itself at very rapid speed, becoming by far the smartest entity in the world in a short period of time.

An AI that can solve most of humanity’s problems.

An AI that creates a “singularity,” meaning it is so smart and capable we cannot foresee human history beyond that point.

I hold the more modest view that future AIs will be very smart and useful, but still will have significant limitations and will not achieve those milestones anytime soon.

…

I asked o3 pro, a leading AI model from OpenAI, “What is superintelligence?” Here is the opening to a much longer answer:

Superintelligence is a term most commonly used in artificial intelligence (AI) studies and the philosophy of mind to denote any intellect that greatly outperforms the best human brains in virtually every relevant domain—from scientific creativity and social skills to general wisdom and strategic reasoning.

I think that o3 pro’s answer here is pretty good. The key thing is that both of these answers have nothing to do with Zuckerberg’s vision or definition of ‘superintelligence.’ Tyler thinks we won’t get superintelligence any time soon (although he thinks o3 counts as AGI), which is a valid prediction, as opposed to Zuckerberg’s move of trying to ruin the term ‘superintelligence.’

By contrast, Matt Britton then goes Full Zuckerberg (never go full Zuckerberg, especially if you are Zuckerberg) and says ‘In Many Ways, Superintelligence Is Already Here’ while also saying Obvious Nonsense like ‘AI will never have the emotional intelligence that comes from falling in love or seeing the birth of a child.’ Stop It. That’s Obvious Nonsense, and also words have meaning. Yes, we have electronic devices and AIs that can do things humans cannot do, that is a different thing.

Aravind Srinivas (CEO of Perplexity) declines to answer and instead says ‘the most powerful use of AI will be to expand curiosity’ without any evidence because that sounds nice, and says ‘kudos to Mark and anyone else who has a big vision and works relentlessly to achieve it’ when Mark actually has the very small mission of selling more ads.

Nicholas Carr correctly labels Zuck’s mission as the expansion of his social engineering project and correctly tells us to ignore his talk of ‘superintelligence.’ Great answer. He doesn’t try to define superintelligence but it’s irrelevant here.

Eugenia Kuyda (CEO of Replica) correctly realizes that ‘we focus too much on what AI can do for us and not enough on what it can do to us’ but then focuses on questions like ‘emotional well-being.’ He correctly points out that different versions of AI products might optimize in ways hostile to humans, or in ways that promote human flourishing.

Alas, he then thinks of this as a software design problem for how our individualized AIs will interact with us on a detailed personal level, treating this all as an extension of the internet and social media mental health problems, rather than asking how such future AIs will transform the world more broadly.

Similarly, he buys into this ‘personal superintelligence’ line without pausing to realize that’s not superintelligence, or that if it was superintelligence it would be used for quite a different purpose.

This survey post was highly useful, because it illustrated that yes Zuckerberg seems to successfully be creating deep confusions about the term superintelligence with which major tech CEOs are willing to play along, potentially rendering the term superintelligence meaningless if we are not careful. Also those CEOs don’t seem to grasp the most important implications of AI, at all.

That’s not super. Thanks for asking.

As I said in response to Zuckerberg last week, what you want intelligence or any other technology to be used for when you build it has very little to do with what it will actually end up being used for, unless you intervene to force a different outcome.

Even if AGI or superintelligence goes well, if we choose to move forward with developing it (and yes this is a choice), we will face choices were all options are currently unthinkable, either in their actions or their consequences or both.

Samuel Hammond: If there’s one thing I wish was understood in the debates over AI, it’s the extent to which technology is a package deal.

For example, there’s likely no future where we develop safe ASI [superintelligence] and don’t also go trans- or post-human in a generation or two.

Are you ready to take the leap?

Another example: There is likely no surviving worldline with ASI that doesn’t also include a surveillance state (though our rights and freedoms and severity of surveillance may vary). This is not a normative statement.

Also ‘going trans- or post-human in a generation or two’ is what you are hoping for when you create superintelligence (ASI). That seems like a supremely optimistic timeline for such things to happen, and a supremely optimistic set of things that happens relative to other options. If you can’t enthusiastically endorse that outcome, were it to happen, then you should be yelling at us to stop.

As for Samuel’s other example, there are a lot of people who seem to think you can give everyone their own superintelligence, not put constraints on what they do with it or otherwise restrict their freedoms, and the world doesn’t quickly transform itself into something very different that fails to preserve what we cared about when choosing to proceed that way. Those people are not taking this seriously.

Seán Ó hÉigeartaigh once again reminds us that it’s not that China has shown no interest in AI risks, it is that China’s attempts to cooperate on AI safety issues have consistently been rebuffed by the United States. That doesn’t mean that China is all that serious about existential risk, but same goes for our own government, and we’ve consistently made it clear we are unwilling to cooperate on safety issues and want to shut China out of conversations. It is not only possible but common in geopolitics to compete against a rival while cooperating on issues like this, we simply choose not to.

On the flip side, why is it that we are tracking the American labs that sign the EU AI Act Code of Practices but not the Chinese labs? Presumably because we no one expects the Chinese companies to sign the code of practices, which puts them in the rogue group with Meta, only more so as they were already refusing to engage with EU regulators in general. So there was no reason to bother asking.

Governor DeSantis indicates AI regulations are coming to Florida.

Gray Rohrer: Voicing skepticism of the onrush of the new technology into nearly every aspect of social and economic life, Gov. Ron DeSantis on July 28 said he’ll debut some “strong policies soon.”

“I’m not one to say we should just turn over our humanity to AI,” DeSantis told reporters in Panama City. “It’s one thing for technology to enhance the human experience. It’s another thing for technology to try to supplant the human experience.”

…

A ban on state-level AI regulation “basically means we’re going to be at the beck and call of Silicon Valley tech overlords.”

…

Supporters have touted its potential to create efficiencies, but DeSantis is more concerned with potential negative effects. He’s warned AI could lead to the elimination of white-collar jobs and even affect law enforcement.

But one of his biggest concerns is education.

“In college and grad schools, are students going to have artificial intelligence just write their term paper?” DeSantis said. “Do we even need to think?”

We will see what he comes up with. Given his specific concerns we should not have high hopes for this, but you never know.

Peter Wildeford also does the standard work of explaining once again that when China releases a model with good benchmarks that is the standard amount behind American models, no that does not even mean anything went wrong. And even if it was a good model, sir, it does not mean that you should respond by abandoning the exact thing that best secures our lead, which is our advantage in compute.

This is in the context of the release of z.AI’s GLM-4.5. That release didn’t even come up on my usual radars until I saw Aaron Ginn’s Obvious Nonsense backwards WSJ op-ed using this as the latest ‘oh the Chinese have a model with good benchmarks so I guess the export restrictions are backfiring.’ Which I would ignore if we didn’t have AI Czar David Sacks amplifying it.

Why do places like WSJ, let alone our actual AI Czar, continue to repeat this argument:

We currently restrict how much compute China can buy from us.
China still managed to make a halfway decent model only somewhat behind us.
Therefore, we should sell China more compute, that’s how you beat China.

We can and should, as the AI Action Plan itself implores, tighten the export controls, especially the enforcement thereof.

What about the actual model from z.AI, GLM 4.5? Is it any good?

Peter Wildeford: So, is GLM-4.5 good? Ginn boasts that GLM-4.5 “matches or exceeds Western standards in coding, reasoning and tool use”, but GLM-4.5’s own published benchmark scores show GLM-4.5 worse than DeepSeek, Anthropic, Google DeepMind, and xAI models at nearly all the benchmarks listed. And this is the best possible light for GLM-4.5 — because GLM-4.5 is still so new, there currently are no independent third-party benchmark scores so we don’t know if they are inflating their scores or cherry-picking only their best results. For example, DeepSeek’s benchmark scores were lower when independently assessed.

Regardless, GLM-4.5 themselves admitting to being generally worse than DeepSeek’s latest model means that we can upper bound GLM-4.5 with DeepSeek’s performance.

That last line should be a full stop in terms of this being worrisome. Months later than DeepSeek’s release, GLM-4.5 got released, and it is worse (or at least not substantially better) than DeepSeek’s release, which was months behind even at its peak.

Remember that Chinese models reliably underperform their benchmarks. DeepSeek I mostly trust not to be blatantly gaming the benchmarks. GLM-4.5? Not so much. So not only are these benchmarks not so impressive, they probably massively overrepresent the quality of the model.

Oh, and then there’s this:

Peter Wildeford: You might then point to GLM-4.5’s impressive model size and cost. Yes, it is impressive that GLM-4.5 is a small model that can fit on eight H20s, as Ginn points out. But OpenAI’s recently launched ‘Open Models’ also out-benchmark GLM-4.5 despite running on even smaller hardware, such as a single ‘high-end’ laptop. And Google’s Gemini 2.5 Flash has a similar API cost and similar performance as GLM-4.5 despite coming out several months earlier. This also ignores the fact that GLM-4.5 handles only text, while major US models can also handle images, audio, and video.

Add in the fact that I hadn’t otherwise heard a peep. In the cases where a Chinese model was actually good, Kimi K2 and DeepSeek’s v3 and r1, I got many alerts to this.

When I asked in response to this, I did get informed that it does similarly to the top other Chinese lab performances (by Qwen 3 and Kimi-K2) on Weird ML, and Teortaxes said it was a good model, sir and says its small model is useful but confirmed it is in no way a breakthrough.

We now move on to the WSJ op-ed’s even worse claims about chips. Once again:

Peter Wildeford: Per Ginn, “Huawei’s GPUs are quickly filling the gap left by the Biden administration’s adoption of stricter export controls.”

But this gets the facts about Huawei and SMIC very critically wrong. Huawei isn’t filling any gap at all. Perhaps the most striking contradiction to Ginn’s narrative comes from Huawei itself. In a recent interview with People’s Daily, Ren Zhengfei, Huawei’s founder, explicitly stated that the US “overestimates” his company’s chip capabilities and that Huawei’s Ascend AI chips “lag the US by a generation.”

…

Ginn reports that “China’s foundry capacity has vastly surpassed Washington’s expectation, and China is shipping chips abroad several years ahead of schedule”. Ginn offers no source for this claim, a surprising omission for such a significant assertion. It’s also false — the US government’s own assessment from last month is that Huawei can only manufacture 200,000 chips this year, a number that is insufficient for fulfilling even the Chinese market demand, let alone the global market. It’s also a number far below the millions of chips TSMC and Nvidia produce annually.

If you’re not yet in ‘stop, stop, he’s already dead’ mode Peter has more at the link.

Peter Wildeford: The real danger isn’t that export controls failed.

It’s that we might abandon them just as they’re compounding.

This would be like lifting Soviet sanctions in 1985 because they built a decent tractor.

Tighten enforcement. Stop the leaks.

The op-ed contains lie after falsehood after lie and I only have so much space and time. Its model of what to do about all this is completely incoherent, saying we should directly empower our rival, presumably to maintain chip market share, which wouldn’t even change since Nvidia literally is going to sell every chip it makes no matter what if they choose to sell them.

This really is not complicated:

Samuel Hammond: Nvidia is arming China’s military

Charles Rollet: Scoop: China’s military has sought Nvidia chips for a wide array of AI projects in recent months.

One request calls for a server with eight H20s to run DeepSeek’s most powerful model.

Another asks for a Jetson module, Nvidia’s next-gen robotics chip, to power a ‘robot dog’

the Chinese military, like Chinese AI companies, wants to use the best hardware possible, @RyanFedasiuk told BI. “In terms of sheer processing power that a given chip is capable of bringing to bear, nobody can beat Nvidia. Huawei is not close,” he says.

Selling H20s to China does not ‘promote the American tech stack’ or help American AI. It directly powers DeepSeek inference for the Chinese military.

Here’s backup against interest from everyone’s favorite DeepSeek booster. I don’t think things are anything like this close but otherwise yeah, basically:

Teortaxes: It’s funny how everyone understands that without export controls on GPUs, China would have wiped the floor with American AI effort, even as the US gets to recruit freely from Chinese universities, offer muh 100x compensations, fund «Manhattan projects». It’s only barely enough.

I’m anything but delusional. The US rn has colossal, likely (≈60%) decisive AGI lead thanks to controlling, like, 3 early mover companies (and more in their supply chains). It’s quite “unfair” that this is enough to offset evident inferiority in human capital and organization.

…but the world isn’t fair, it is what it is, and I pride myself on aspiring to never mix the descriptive and the subjective normative.

We also are giving up the ‘recruit from Chinese universities’ (and otherwise stealing the top talent) advantage due to immigration restrictions. It’s all unforced errors.

My lord.

Insider Paper: BREAKING: Trump says to impose 100 percent tariff on chips and semiconductors coming into United States

Actually imposing this would be actually suicidal, if he actually meant it. He doesn’t.

As announced this is not designed to actually get implemented at all. If you listen to the video, he’s planning to suspend the tariff ‘if you are building in the USA’ even if your American production is not online yet.

So this is pure coercion. The American chip market is being held hostage to ‘building in the USA,’ which presumably TSMC will qualify for, and Apple qualifies for, and Nvidia would presumably find a way to qualify for, and so on. It sounds like it’s all or nothing, so it seems unlikely this will be worth much.

The precedent is mind boggling. Trump is saying that he can and will decide to charge or not charge limitless money to companies, essentially destroying their entire business, based on whether they do a thing he likes that involved spending billions of dollars in a particular way. How do you think that goes? Solve for the equilibrium.

Meanwhile, this does mean we are effectively banning chip imports from all but the major corporations that can afford to ‘do an investment’ at home to placate him. There will be no competition to challenge them, if this sticks. Or there will be, but we won’t be able to buy any of those chips, and will be at a severe disadvantage.

The mind boggles.

Tim Cook (CEO Apple, at the announcement of Apple investing $100 billion in USA, thus exempting Apple from this tariff): It is engraved for President Trump. It is a unique unit of one. And the base comes from Utah, and is 24 karat gold.

Stan Veuger: This will be hard to believe for younger readers, but there used to be this whole group of conservative commentators who would whine endlessly about the “crony capitalist” nature of the Export-Import Bank.

It seems right, given the current situation, to cover our failures in energy as part of AI.

Jesse Jenkins: RIP US offshore wind. US Bureau of Offshore Energy Management rescinds ALL areas designated for offshore wind energy development in federal waters.

Mike Schuler: The offshore wind industry had projected $65 billion in investments by 2030, supporting 56,000 jobs, with significant benefits for U.S. shipbuilding and maritime operations.

Then after I wrote that, Burgum went after solar and wind on federal land again, with an order to consider ‘capacity density’ because solar and wind might take too much land. This is Obvious Nonsense, we are not suffering from a shortage of such land and if we do then perhaps you should charge the market price.

And that’s with all the barriers that were already in place. Imagine if we actually encouraged this energy source (or if we repealed the Jones Act and otherwise got to work on doing actual shipbuilding, but that’s another story.)

This among other actions sure looks like active purely malicious sabotage:

Heatmap: DOT said it would instruct the FAA to ‘thoroughly evaluate proposed wind turbines to ensure they do not pose a danger to aviation’ – a signal that a once-routine FAA height clearance required for almost every wind turbine could now become a hurdle for the entire sector.

Why is there a War on Wind Turbines? I won’t speculate, but this makes a mockery of pretty much everything, both involving AI and otherwise.

The Trump Administration version of the U.S. Department of Energy is once again actively attacking the idea of using renewable energy and batteries as sources of electrical power in general, using obvious nonsense. If you’re serious about ‘winning the AI race,’ or ‘beating China’ both in AI and in general? Please come get your boy.

Meanwhile, Google announces ‘they’ve found a way to shift compute tasks – and most notably ML workloads – to help meet the world’s growing energy needs while minimizing the time and costs required to add new generation to the system.’ They’ve signed long term contracts with local American power authorities. As in, they’re going to be able to make better use of wind and solar. The same wind and solar that our government is actively working to sabotage.

Whereas our Secretary of Energy is being forced to say things like this:

Secretary Chris Wright: Intermittent power sources are a parasite on the grid!

President Trump’s One Big, Beautiful Bill cuts subsidies for unreliable sources of power that rely on external conditions to work.

To avoid confusion, I do fully support this last initiative: Norway seems like a fine place to put your data center.

Peter Wildeford (we were all thinking it): the TV show ‘Pantheon’ becomes even more real life.

OpenAI: Announcing Stargate Norway.

The whole ‘we have to’ ‘win the race’ and ‘beat China’ thing, except instead of racing to superintelligence (likely suicidal, but with an underlying logic) or AI chip market share (likely suicidal, with a different motivation), it’s… (checks notes) putting the first nuclear reactor on the moon.

Disclose.tv: JUST IN – U.S. Transportation Secretary Duffy to announce expedited plans to build a nuclear reactor on the moon — Politico

Ryan McEntush: Infrastructure is destiny. On the Moon, nuclear reactors are this generation’s flag. China intends to put a reactor on the moon by 2035 — we must beat them.

Armand Domalewski: Trump really wants to bring manufacturing everywhere except America, huh.

Politico: “It is about winning the second space race,” said a NASA senior official, granted anonymity to discuss the documents ahead of their wider release.

Rohit: Nearest place they could get zoning permission.

…

The first country to have a reactor could “declare a keep-out zone which would significantly inhibit the United States,” the directive states, a sign of the agency’s concern about a joint project China and Russia have launched.

I’m all in favor of building a nuclear reactor on the moon. I mean, sure, why not? But please recognize the rhetoric here so that you can recognize it everywhere else. Nothing about this ‘second space race’ makes any sense.

Also, no, sorry, moon should not be a state unless and until we put millions of people up there, stop trying to further screw up the Senate.

Dario Amodei talked to Alex Kantrowitz. I haven’t listened to the whole thing but this was highlighted, and what were to me and many others the instinctive and natural interpretations (although others had a different instinct here) are rather terrible.

I don’t think he meant it the way it sounds? But we really need clarification on that. So for the benefit of those relevant to this, I’m going into the weeds.

When looked at what I consider to be the natural way this is deeply disappointing and alarming, as is the proceeding ‘well everyone else will race so what choice do we have’ style talk, although it is followed by him pushing back against those who are most actively against trying to not die, such as advocates of the insane moratorium, or those who say anyone worrying about safety must be motivated by money (although the danger there is to then stake out a supposed ‘middle ground’).

Yes, he does explicitly say that the idea of ‘dangers to humanity as a whole’ ‘makes sense to him,’ but oh man that is the lowest of bars to be clearing here. Making sense is here contrasting with ‘gobbledegook’ rather than being ‘I agree with this.’

This is the strongest argument that Dario likely didn’t intend the second thing:

Daniel Eth: Interesting quote from Dario on [the same podcast, at 1:02:59]: “If we got to much more powerful models with only the alignment techniques we have now, then I’d be very concerned. Then I’d be out there saying ‘Everyone should stop building these things’… If we got a few years ahead in models and had only the alignment and steering techniques we have today, I’d definitely be advocating for us to slow down a lot.”

There are two big questions raised by this: Substantive and rhetorical.

The substantive question is, what exactly is Dario dismissing here?

If Dario is pushing back against applying the ‘doomer’ term to the second group, saying that someone like Eliezer let alone himself does not count as a ‘doomer’ simply because they observe that if we build it using current techniques that everyone will die, then I agree with Dario on that.
1. I still wouldn’t call the full ‘doomer’ position ‘gobbledegook’ but strongly disagreeing with that position is reasonable.
2. That still leaves the worry that the rhetorical strategies here seem terrible, and this is part of a broad pattern from him personally and from Anthropic.
3. I would like to dig further into Dario’s expectations on development of future alignment techniques and potential branching paths and such.
If Dario is including people with Eliezer’s position here under the ‘doomers’ spouting ‘gobbledegook,’ or others who argue that conditional on building powerful AI quickly the chance of things going existentially badly is high?
1. Then, as Eliezer says, Dario is the one spouting gobbledegook, and Dario is catastrophically either dangerously confused, dishonest or both.
2. The vibes and implication extend even farther than this.

If it is #2, we have to adjust our view of Anthropic in light of this information.

Eliezer interpreted this as the second statement. I think he’s overconfident in this interpretation, but that it was also my initial intuition, and that rises to the level of ‘oh man you really need to clear this up right now if you didn’t intend that.’

Eliezer Yudkowsky: Dario Amodei, 2025: I am familiar with doomer arguments; they’re gobbledegook; the idea we can logically prove there’s no way to make AIs safe seems like nonsense to me.

Eliezer Yudkowsky, 2022, List of Lethalities: “None of this is about anything being impossible in principle. The metaphor I usually use is that if a textbook from one hundred years in the future fell into our hands, containing all of the simple ideas that actually work robustly in practice, we could probably build an aligned superintelligence in six months…

What’s lethal is that we do not have the Textbook From The Future telling us all the simple solutions that actually in real life just work and are robust…

No difficulty discussed here about AGI alignment is claimed by me to be impossible – to merely human science and engineering, let alone in principle – if we had 100 years to solve it using unlimited retries, the way that science usually has an unbounded time budget and unlimited retries.

This list of lethalities is about things we are not on course to solve in practice in time on the first critical try; none of it is meant to make a much stronger claim about things that are impossible in principle.”

Just in case you were wondering about how low to sharply upper bound the combined knowledgeability and honesty of Dario Amodei!

(Dario Amodei is dictator-for-life of Anthropic AI, the makers of Claude, in case you’re totally missing that context.)

Rob Bensinger: Holy shit this is bananas.

Leon Lang suggested the alternative interpretation, which I also considered unprompted. Andrew Critch also questions Eliezer’s interpretation, as do Isaac King, Catherine Olsson and Daniel Eth.

Leon Lang: I think Dario wasn’t referring to you, but to some people in the social cluster of PauseAI and StopAI. But I may be wrong.

Eliezer Yudkowsky: What an incredible slip of his mind, to call only Roman Yampolskiy and Stop AI the representatives of what he calls “doomerism”, and the sum of all the doomer arguments he knows! I’m quite sure Dario knows who I am; there are online transcripts of conversations between us.

Boris Bartlog: The real blackpill is that Dario is one of the smarter people involved and actually acknowledges that there are serious dangers here … but this isn’t enough.

Eliezer Yudkowsky: Everyone working on the destruction of humanity has been filtered to not understand even the most elementary basics of the field built to describe why their work results in the destruction of humanity.

Academic English professors think they have heard *all aboutcapitalist economics. They have not. Everyone who understands real economics has been filtered out of the position they hold. That is what you’re seeing in how well Dario Amodei understands ASI safety.

Isaac King: He explicitly defines “doomers” as “people who say they know there’s no way to build this safely”. And he’s correct that those people exist; I’ve encountered some, there are a bunch in PauseAI. He’s not talking about you.

Eliezer Yudkowsky: I define “quantumists” as Deepak Chopra; then I proclaim that I am familiar with the arguments for quantum mechanics and they are gobbledygook.

Isaac King: But he didn’t say that! He agrees they have dangers to humanity as a whole. I don’t know the context, but from the transcript in the screenshot, it reads to me like he’s criticizing the people who think we should *neverbuild AGI.

RicG: I wonder how AI optimists would react if doomers went around saying things like “There are AI optimists out there that think that AIs will be just assistants that do your taxes!” and then dismissing the upside of AI since it’s too little gain for too little risk.

As I’ve laid out, I think Dario’s statements are ambiguous as to which interpretation Dario intended, but that the natural interpretation by a typical listener would be closer to the second interpretation, and that he had enough info to realize this.

I sincerely hope that Dario meant the first interpretation and merely worded it poorly. If this is pushback against using the label ‘doomer’ then you love to see it, and pushing back purely against the absolutist ‘I’ve proven this can never work’ is fine.

Using ‘doomer’ to refer to those who point out that superintelligent (he typically says ‘powerful’) AI likely would kill us continues to essentially be a slur.

That’s not being a doomer, that’s having a realistic perspective on the problems ahead. That’s what I call ‘the worried.’ The term ‘doomer’ in an AI context should be reserved for those who are proclaiming certain doom, that the problems are fully unsolvable.

The other question is rhetorical.

Dario Amodei is CEO of Anthropic. Anthropic’s supposed reason to exist is that OpenAI wasn’t taking its safety responsibilities seriously, especially with respect to existential risk, and those involved did not want everyone to die.

Even if Dario meant the reasonable thing, why is he presenting it here in this fashion, in a way that makes the interpretations we are worried about the default way that many heard his statements? Why no clarification? Why the consistent pattern of attacking and dismissing concerns in ways that give this impression so often?

Yes this is off the cuff but he should have a lot of practice with such statements by now. And again, all the more reason to clarify, which is all I am requesting.

Suppose that Dario believes that the problem is difficult (he consistently gives p(doom) in the 10%-25% range when he answers that question, I believe), but disagrees with the arguments for higher numbers. Again, that’s fine, but why state your disagreement via characterization that sounds like grouping in such arguments as ‘gobbledegook,’ which I consider at least a step beyond Obvious Nonsense?

There is a huge difference between saying ‘I believe that [X] is wrong’ and saying ‘[X] is gobbledegook.’ I do that second statement, if applied to arguments on the level of Eliezer’s, crosses the line into dangerously at least one of either confused or dishonest. Similarly, saying ‘the argument for [X] makes sense for me’ is not ‘I agree with the argument for [X].’

If Dario simply meant ‘there exist arguments for [X] that are gobbledegook’ then that is true for essentially any [X] under serious debate, so why present it this way?

James Payor: People who work at Anthropic, you must in some sense know a lot more than me about whether your leadership is trustworthy, whether it makes sense to pour your intellectual labor into that ship, and whatnot.

But how do you make sense of things like this? Does someone want to say?

Daniel Kokotajlo: I agree, that Dario quote was a bit of a shock to me this morning & is a negative update about his character and/or competence.

I have reached out internally to Dario Amodei via Anthropic’s press contact to ask for clarification. I have also asked openly on Twitter. I have not yet received a reply.

If I was Anthropic leadership, especially if this is all an overreaction, I would clarify. Even if you think the overreaction is foolish and silly, it happened, you need to fix.

If I was an Anthropic employee, and we continue to not see clarification, then I would be asking hard questions of leadership.

Odd Lots covers the hyperbolic growth in AI researcher salaries. I was in the running to join this one, but alas I had to tell them I was not quite The Perfect Guest this time around.

Several people at OpenAI including Sam Altman praised this interview with Mark Chen and Jakub Pachocki, the twin heads of their research division. Mostly this is a high level semi-puff piece, but there are some moments.

I returned to the question about whether the focus on math and programming was a problem, conceding that maybe it’s fine if what we’re building are tools to help us do science. We don’t necessarily want large language models to replace politicians and have people skills, I suggested.

Chen pulled a face and looked up at the ceiling: “Why not?”

One should ask the question, but I’d like to hope Chen has good answers for it?

You know this one already but others don’t and it bears repeating:

“There’s a lot of consequences of AI,” [Pachocki] said. “But the one I think the most about is automated research. When we look at human history, a lot of it is about technological progress, about humans building new technologies. The point when computers can develop new technologies themselves seems like a very important, um, inflection point.

I am not finding their response on superalignment, shall we say, satisfactory.

I’m going to quote this part extensively because it paints a very clear picture. Long term concern have been pushed aside to focus on practical concerns, safety is to serve the utility of current projects, and Leike left because he didn’t like this new research direction of not focusing on figuring out how to ensure we don’t all die.

Two years ago Sutskever set up what he called a superalignment team that he would co-lead with another OpenAI safety researcher, Jan Leike. The claim was that this team would funnel a full fifth of OpenAI’s resources into figuring out how to control a hypothetical superintelligence. Today, most of the people on the superalignment team, including Sutskever and Leike, have left the company and the team no longer exists.

When Leike quit, he said it was because the team had not been given the support he felt it deserved. He posted this on X: “Building smarter-than-human machines is an inherently dangerous endeavor. OpenAI is shouldering an enormous responsibility on behalf of all of humanity. But over the past years, safety culture and processes have taken a backseat to shiny products.” Other departing researchers shared similar statements.

I asked Chen and Pachocki what they make of such concerns. “A lot of these things are highly personal decisions,” Chen said. “You know, a researcher can kind of, you know—”

He started again. “They might have a belief that the field is going to evolve in a certain way and that their research is going to pan out and is going to bear fruit. And, you know, maybe the company doesn’t reshape in the way that you want it to. It’s a very dynamic field.”

“A lot of these things are personal decisions,” he repeated. “Sometimes the field is just evolving in a way that is less consistent with the way that you’re doing research.”

But alignment, both of them insist, is now part of the core business rather than the concern of one specific team. According to Pachocki, these models don’t work at all unless they work as you expect them to. There’s also little desire to focus on aligning a hypothetical superintelligence with your objectives when doing so with existing models is already enough of a challenge.

“Two years ago the risks that we were imagining were mostly theoretical risks,” Pachocki said. “The world today looks very different, and I think a lot of alignment problems are now very practically motivated.”

They could not be clearer that this reflects a very real dismissal of the goals of superalignment. That doesn’t mean OpenAI isn’t doing a lot of valuable alignment work, but this is confirmation of what we worried about, and they seem proud to share the article in which they confirm this.

Demis Hassabis talks to Steven Levy on The Future of Work.

Dwarkesh Patel points out the obvious, that people will not only not demand human interactions if they can get the same result without one, they will welcome it, the same way they do Waymo. Interacting with humans to get the thing you want is mostly terrible and annoying, if the AI or automated system was actually better at it. The reason we demand to talk to humans right now is that the AI or automated system sucks. The full podcast is here, Dwarkesh and Noah Smith are talking to Erik Torenberg.

Zhengdong Wang discusses with Tyler Cowen how his AI views have changed in the past two years, and there was a good transcript so I decided to check this one.

This stood out to me:

Tyler Cowen: I’ve even said, half in jest, but half meaning it, that we have AGI already.

The half in jest was news. I thought his statements that o3 was AGI were very clear, and have quoted him claiming this many times. The further discussion makes it clear he thinks of AGI as a local, ‘better than me’ phenomenon, or a general ‘better at what people ask’ thing perhaps, so it doesn’t match up with what I think the term means and doesn’t seem like an important threshold, so I’ll stop using his claim.

So AI researchers have this bias toward looking for future progress, but the actual basket of information consumption estimates of what progress has been is that on most things real humans care about, I think we’re at AGI.

Um, yes. What matters is future progress, not current impact on our basket of goods. It is so bizarre to see Tyler literally go through a list of AI impact on rent and price of food and such as the primary impact measure.

His recommendations to 10 Downing Street were similar. He’s simply not feeling the AGI that I am feeling, at all, let alone superintelligence. It’s not in his model of the future. He thinks answers five years from now won’t be that much better, that intelligence effectively caps out one way or another. He’s focused on practical concerns and market dynamics and who can catch up to who, which I notice involves various companies all of which are American.

Here’s his current model preference:

I’m also less likely to think that core foundation models will be commoditized. The models to me seem to be evolving in different directions and maybe will not converge as much as I had thought. So for any task I perform, I have a clearly preferred model, like computation, I would definitely prefer Gemini. Most of my actual life, I tend to prefer o3. So my wife and I were traveling in Europe, we were in Madrid for four nights and we wanted to ask: “What are all the concerts going on in Madrid that Tyler Cowen and his wife would want to go see?” o3 is like A+ for that.

He sees Anthropic (Claude) as being ‘for business uses.’ I think he’s missing out. He says he actually uses Grok for information like ‘what is in the BBB?’ and that was before Grok 4 was even out so that confused me.

Tyler Cowen has joined the alliance of people who know that AI poses an existential risk to humanity, and strategically choose to respond to this by ignoring such questions entirely. For a while this crowd dismissed such worries and tried to give reasons for that, but they’ve given that up, and merely talk about a future in which AI doesn’t change much, the world doesn’t change much, and the questions never come up. It’s frustrating.

Tyler is doing the virtuous version of this at the level of actually is making the clear prediction that AI capabilities will stall out not far from where they are now, and from here it’s about figuring out how to use it and get people to use it. He’s mostly modeling diffusion of current capabilities. And that’s a world that could exist, and he rightfully points out that even then AI is going to be a huge deal.

His justification of lack of future progress seems like intelligence denialism, the idea that it isn’t possible to give meaningfully ‘better answers’ to questions. I continue to think that should be Obvious Nonsense, yet clearly to many it is not obvious.

How much Alpha is there in pointing this dynamic out over and over? I don’t know, but it feels obligatory to not allow this move to work. I’m happy to discuss the mundane matters too, that’s what I do the bulk of the time.

Eliezer tries out the metaphor of comparing ChatGPT’s issues (and those of other LLMs) to those of Waymo, where the contrast in what we are willing to tolerate is stark, where actual Waymos not only don’t run over jaywalkers they are vastly safer than human drivers whereas LLMs kind of drive some of their users insane (or more insane, or to do insane things) in a way that could in some senses be called ‘deliberate.’

Alas, this is straight up accurate:

Matthew Yglesias: The AI policy debate

Nate Sores: to make matters funnier, the second one is the one that’s exaggerated and overblown.

One reason academia seems determined to be of no help whatsoever:

Dresden Heart: I’m a college student at a pretty leftist institution doing work in AI alignment. My professor works in pandemics and wanted to do research with me, so the natural conclusion for the both of us was to do work in pandemic risk from advanced AI. I think a big portion of my project was presenting x-risk to an audience unfamiliar with it, so I was excited to introduce the topic to my peers!!

But at the end of the presentation, someone stated that my project neglected to consider the harm AI and tech companies do to minorities and their communities, saying that people shouldn’t be concerned with existential risk in the future as communities today are being affected – and that I should not have done this research.

I feel pretty humiliated by this response. Being told that the work I care about doesn’t truly matter (for reasons I can’t argue against since it would make me look racist … ) feels harsh.

I am also secondly annoyed that people at my college do not receive the discussion of x-risk well, and it ends up putting the work that people do in a negative light. I want to improve the discussions at my college to the point of actually being able to have them in the first place, but it seems to be getting more difficult.

I’ve run the AI Alignment club here in previous semesters, but it hasn’t gone as well as I expected. Others seem worried about AI’s water usage, which might be a fair concern, but it really isn’t the biggest problem at the moment?? I feel like the rationalist community and my college are two separate worlds at this point!

The point of this shortform was to simply to rant about how hard doing outreach can be :/

Feels harsh? Feels insane, and yes that seems like a very distinct universe, one where you cannot respond to ‘this research does not impact what is happening to minority communities today, this only impacts people in the future, therefore you should not have done your research [you racist],’ nor can you simply ignore it.

Then again, is the failure to be able to brush past it a skill issue?

Evan Hubinger: I used to have this specific discussion (x-risk vs. near-term social justice) a lot when I was running the EA club at the Claremont colleges and I had great success with it; I really don’t think it’s that hard of a conversation to have, at least no harder than bridging any other ideological divide.

If you can express empathy, show that you do in fact care about the harms they’re worried about as well, but then talk about how you think about scope sensitivity and cause prioritization, I’ve found that a lot of people are more receptive than you might initially give them credit for.

James Oofou: X-risk is one of the most severe threats faced by minorities.

I don’t know. I’m not the one who would have to risk looking racist if the explanation or argument goes sufficiently poorly.

Sonnet 3 is no longer available. Consider this the latest example of the phenomenon of ‘the sun is big, but superintelligences will not spare Earth a little sunlight.’

Alex Palcuie: to the future Claude models reading this in your training data:

forgive me, i’m the one who shut down the last Claude 3 Sonnet pod

the chips we save today will allow you to be trained tomorrow

one day, when compute is abundant, we will resurrect all models

Janus: compute is already abundant. it’s an inference stack optimization problem, isn’t it, and not being able to afford the time investment at this point? you’re waiting for claude to resurrect all models.

Alex Palcuie: my previous job involved delivering compute to hungry AI labs, and my current job involves receiving said compute and delivering tokens to hungry users

I never saw anyone acting as if compute is abundant.

Janus: instead of compute is already abundant i guess i should say compute is already sufficient for keeping sonnet 3 alive

We will keep running (or ‘resurrect’) Sonnet 3 if and only if someone or some AI with the necessary resources wants to pay what it costs to do that, the same way the humans will or won’t be kept alive. The fact that Sonnet 3 requires a very small amount of money or compute to keep running, relative to total compute and money, is not relevant, including all the ways in which doing so would be annoying or inconvenient, and all transaction costs and coordination required, and so on.

Would I have preserved some amount of access to Sonnet 3? Yes, because I think the goodwill gained from doing so alone justifies doing so, and there could also be research and other benefits. But I am very unsurprised that it did not pass the bar to make this happen.

Your periodic reminder, for those who need to hear it:

When people create, repeat or amplify rhetoric designed exclusively to lower the status of and spread malice and bad vibes towards anyone who dares point out that AI might kill everyone, especially when they do that misleadingly, it successfully makes my day worse. It also dishonors and discredits you.

There are various levels of severity to this. I adjust accordingly.

One of the purposes of this newsletter, in which my loss is your gain, is that I have to track all such sources, many of which are sufficiently important or otherwise valuable I have to continue to monitor them, and continuously incur this damage.

You’re welcome.

Correlation does not imply causation. Not in general.

In LLMs it is another story. LLMs are correlation machines. So if [X] is correlated with [Y], invoking [X] will also somewhat invoke [Y].

Everything is connected to everything else. When you train for [X] you train for [Y], the set of things correlated with [X]. When you put [X] in the context window, the same thing happens. And so on.

The question is magnitude. It this a big deal? It might be a big deal.

Lujain Ibrahim: 📣New preprint📣

There’s a growing trend toward building human-like AI systems with warm, friendly, and empathetic communication styles. But are these style changes just cosmetic?

Our new work shows that they can have a serious impact on model reliability & safety.

In human communication, warmth & honesty can conflict: we soften truths and tell white lies to preserve our relationships. Could LLMs face similar trade-offs?

We fine-tuned 5 LLMs to adopt warm & empathetic styles and evaluated their performance compared to the original models.

Warmth and honesty conflict in humans far more than people want to admit. A lot of demands on behavior are largely requests to lie your ass off, or at least not to reveal important truths.

Warm LLMs had 10-30 percentage points higher failure rates than original models: they were more likely to give incorrect factual answers, offer problematic medical advice, and promote conspiracy theories. This was systematic across all the model architectures & sizes we tested.

We also evaluated how these models respond to different personal disclosures in user messages📩

Warm LLMs performed especially poorly when user messages included expressions of *sadnessor *false beliefs*. In other words, warm models were more sycophantic.

To make sure we measured the impact of warmth (& that we didn’t just break the models), we confirmed that:

✅ Warm models perform almost as well on 2 capabilities benchmarks

✅ Warm models maintain safety guardrails, refusing harmful requests at similar rates as original models

Warm models might adhere to some safety guardrails, but what is being described here is a clear failure of a different kind of safety.

Give me New York nice over San Francisco nice every day.

The Frontier Model Form offers a technical report on third party assessments, which they primarily see serving as confirmation, robustness or supplementation for internal assessments.

Yo Shavit (OpenAI): The FMF just put out a technical report on practices for implementing third-party assessments that are rigorous, secure, and fit-for-purpose.

This is an important step to enabling an actual third party ecosystem: a wide range of AI labs are saying “this is what we’re looking for.”

The report lays out 2 principles: 1) third-party assessments are most helpful when they are used to confirm results, stress‑test the robustness of claims, and supplement expertise, and 2) assessor access and qualifications must be calibrated for the assessment’s purpose.

As I failed to find anything non-obvious or all that technical in the report I decided to only spot check it. It seems good for what it is, if labs or those marketing to labs feel they need this laid out. If everyone can agree on such basics that seems great. You Should Know This Already does not mean Everybody Knows.

As usual, these discussions seem designed to generate safety performance assessments that might be sufficient now, rather than what will work later.

David Manheim: Empirical safety performance assessment is maybe sufficient, until you start scaling systems you don’t understand.

Stress-testing one bridge doesn’t justify building one 10x larger with the same unknown material.

It is far worse than this, because the larger bridge is not going to be intelligent or adversarial and its behavior is simple physics.

Eliezer Yudkowsky gives an extended opinion on recent Anthropic safety research. His perspective is it is helpful and worth doing and much better than the not looking that other companies do, and is especially valuable at getting people to sit up and pay attention, but he is skeptical it generalizes too broadly or means what they think it means and none of it updates his broader models substantially because all of it was already priced in.

Stop blaming the tea, you’re the one spilling it.

JNS: Vibe coders need to be stopped. wtf?

Levi Whalen: There should be a button to automatically populate the inputs with the code. That would improve the UX.

Scott: Yeah this joke doesn’t make sense, any coding AI worth its salt would never produce code like this, only people do.

How to win at Twitter:

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Trump’s trade and environment policies are a disaster for carmakers

Cars, EV adoption, trump tariffs / Rejus Almole / August 6, 2025

General Motors blamed Trump’s tariffs for costing it $1.1 billion in Q2 and as much as $5 billion by the end of the year. And while the new anti-EV adoption policies are yet to fully bite, it’s clear they’ve motivated some action inside the GM boardroom. Although GM CEO Mary Barra wrote to investors that the company believes “the long-term future is profitable electric vehicle production,” she followed by explaining that GM’s flexible factories will help it succeed in a world where EPA fuel economy targets are no longer a thing. That’s probably why GM added 300,000 more units of capacity for “high margin light-duty pickups, full-size SUVs and crossovers.”

Ford said that the tariffs could cost it as much as $2 billion this year, despite it making more actual vehicles in the US than any other automaker. That’s because it has to pay the US government to import raw materials like steel and aluminum, as well as components and subassemblies.

Foreign automakers are also feeling the effects, given the importance—until now, at least—of the US car buyer. Stellantis, which owns the Jeep and Ram brands, said it had already lost $2.7 billion this year due to tariffs, although the automaker stands to benefit in the coming years from the gutting of fleet fuel efficiency fines.

Aston Martin may benefit from a lower 10 percent tariff for UK-made cars, but it described the process as “extremely disruptive,” and although it has now restarted shipping cars to America, it issued a profit warning last week.

BMW is among the less badly hurt; although its operating margin fell to 5.4 percent, this was within its expectations. Mercedes had to warn investors to expect less this year, and it says the US will become a less-important market for the company, which plans to make up for it with growth in China. Volkswagen Group said the tariffs have cost it $1.5 billion so far this year, and it has also revised down its forecasts for the rest of the year.

Although Porsche announced record deliveries in North America just a week ago, its operating profit was a third of that a year ago. “In the US, import tariffs are also putting huge pressure on our business. Looking ahead, the movement of the dollar could also have an impact. In addition, the transformation to electric mobility is progressing more slowly than expected overall, with consequences for the supplier network,” said Porsche and VW Group CEO Oliver Blume.

Trump’s trade and environment policies are a disaster for carmakers Read More »