Events in AI don’t stop merely because of a trade war, partially paused or otherwise.
Indeed, the decision to not restrict export of H20 chips to China could end up being one of the most important government actions that happened this week. A lot of people are quite boggled about how America could so totally fumble the ball on this particular front, especially given what else is going on. Thus, I am going to put these issues up top again this week, with the hopes that we don’t have to do that again.
Upgrades continue, as Gemini 2.5 now powers Deep Research, and both it and Grok 3 are available in their respective APIs. Anthropic finally gives us Claude Max, for those who want to buy more capacity.
The one trade we need to restrict is selling top AI chips to China. We are failing.
The other trades, that power the American and global economies? It’s complicated.
The good news is that the non-China tariffs are partially paused for 90 days. The bad news is that we are still imposing rather massive tariffs across the board, and the massive uncertainty over a wider trade war remains. How can anyone invest under these conditions? How can you even confidently trade the stock market, given the rather obvious insider trading taking place around such announcements?
This is a general warning, and also a specific warning about AI. What happens when all your most important companies lose market access, you alienate your allies forcing them into the hands of your rivals, you drive costs up and demand down, and make it harder and more expensive to raise capital? Much of the damage still remains.
Adam Thierer: Trump’s trade war is going to undermine much of the good that Trump’s AI agenda could do, especially by driving old allies right into the arms of the Chinese govt. Watch the EU cut a deal with CCP to run DeepSeek & other Chinese AI on everything and box out US AI apps entirely. [Additional thoughts here.]
For now we’re not on the path Adam warns about, but who knows what happens in 90 days. And everyone has to choose their plans while not knowing that.
The damage only ends when Congress reclaims its constitutional tariff authority.
When Nvidia CEO Jensen Huang attended a $1 million-a-head dinner at Mar-a-Lago last week, a chip known as the H20 may have been on his mind.
That’s because chip industry insiders widely expected the Trump administration to impose curbs on the H20, the most cutting-edge AI chip U.S. companies can legally sell to China, a crucial market to one of the world’s most valuable companies.
Following the Mar-a-Lago dinner, the White House reversed course on H20 chips, putting the plan for additional restrictions on hold, according to two sources with knowledge of the plan who were not authorized to speak publicly.
The planned American export controls on the H20 had been in the works for months, according to the two sources, and were ready to be implemented as soon as this week.
The change of course from the White House came after Nvidia promised the Trump administration new U.S. investments in AI data centers, according to one of the sources.
Miles Brundage: In the long-run, this is far more important news than the stock bounce today.
Few policy questions are as clear cut. If this continues, the US will essentially be forfeiting its AI leadership in order for NVIDIA to make slightly more profit this year.
This is utterly insane. Investments in AI data centers? A million dollar, let’s say ‘donation’? These are utter chump change versus what is already $16 billion in chip sales, going straight to empower PRC AI companies.
NPR: The Trump administration’s decision to allow Chinese firms to continue to purchase H20 chips is a major victory for the country, said Chris Miller, a Tufts University history professor and semiconductor expert.
“Even though these chips are specifically modified to reduce their performance thus making them legal to sell to China — they are better than many, perhaps most, of China’s homegrown chips,” Miller said. “China still can’t produce the volume of chips it needs domestically, so it is critically reliant on imports of Nvidia chips.”
…
This year, the H20 chip has become increasingly coveted by artificial intelligence companies, because it is designed to support inference, a computational process used to support AI models like China’s DeepSeek and other AI agents being developed by Meta and OpenAI.
Meanwhile, US chip production? Not all that interested:
President Trump has also moved fast to dismantle and reorganize technology policies implemented by the Biden administration, particularly the CHIPS Act, which authorized $39 billion in subsidies for companies to invest in semiconductor supply chains in the U.S.
It is mind blowing that we are going to all-out trade war against the PRC and we still can’t even properly cut off their supplies of Nvidia chips, nor are we willing to spend funds to build up our own production. What the hell are we even doing?
This is how people were talking when it was only a delay in implementing this regulation, which was already disastrous enough:
Tim Fist: This is ~1.3 million AI GPUs, each ~20% faster at inference than the H100 (a banned GPU).
RL, test-time compute, and synthetic data generation all depend on inference performance.
We’re leaving the frontier of AI capabilities wide open to Chinese AI labs.
Samuel Hammond: The administration intends to restrict the H20 based on Lutnick’s past statements, but they don’t seem to be prioritizing it. It literally just takes a letter from the BIS. Someone needs to wake up.
Peter Wildeford: I feel confident that if the US and the West fail to outcompete China, it will be self-inflicted.
Someone did wake up, and here chose the opposite of violence, allowing sales to the PRC of the exact thing most vital to the PRC.
Again: What. The. Actual. Fuck.
There are other problems too.
Lennart Heim: Making sure you’re not surprised: Expect more than 1M Huawei Ascend 910Cs this year (each at ≈80% of Nvidia’s 2-year-old H100 and 3x worse than the new Nvidia B200).
Huawei has enough stockpiled TSMC dies and Samsung HBM memory to make it happen.
Miles Brundage: One of the biggest export control failures related to AI in history, perhaps second only to H20 sales, if things continue.
Oh, and we fired a bunch of people at BIS responsible for enforcing these rules, to save a tiny amount of money. If I didn’t know any better I would suspect enemy action.
The question is, can they do it fast enough to make up for other events?
Paul Graham: The economic signals I and other people in the startup business are getting are so weirdly mixed right now. It’s half AI generating unprecedented growth, and half politicians making unprecedentedly stupid policy decisions.
We consistently see that AI systems optimized for diagnostic reasoning are outperforming doctors – including outperforming doctors that have access to the AI. If you don’t trust the AI sufficiently, it’s like not trusting a chess AI, your changes on average make things worse. The latest exampleis from Project AMIE (Articulate Medical Intelligence Explorer) inNature.
The edges here are not small.
Agus: In line with previous research, we already seem to have clearly superhuman AI at medical diagnosis. It’s so good that clinicians do *worsewhen assisted by the AI compared to if they just let the AI do its job.
And this didn’t even hit the news. What a wild time to be alive
One way to help get mundane utility is to use “mundane” product and software scaffolding for your specific application, and then invest in an actually good model. Sarah seems very right here that there is extreme reluctance to do this.
(Note: I have not tried Auren or Seren, although I generally hear good things about it.)
Near Cyan: the reason it’s so expensive to chat with Auren + Seren is because we made the opposite trade-off that every other ‘AI chat app’ (which i dont classify Auren as) made.
most of them try to reduce costs until a convo costs e.g. $0.001 we did the opposite, i.e. “if we are willing to spend as much as we want on each user, how good can we make their experience?”
the downside is that the subscription is $20/month and increases past this for power-users but the upside is that users get an experience far better than they anticipated and which is far more fun, interesting, and helpful than any similar experience, especially after they give it a few days to build up memory
this is a similar trade-off made for products like claude code and deep research, both of which I also use daily for some reason no one else has made this trade-off for an app which focuses on helping people think, process emotions, make life choices, and improve themselves, so that was a large motivation behind Auren
Sarah Constantin: I’ve been saying that you can make LLMs a lot more useful with “mundane” product/software scaffolding, with no fundamental improvements to the models.
And it feels like there’s a shortage of good “wrapper apps” that aren’t cutting corners.
Auren’s a great example of the thing done right. It’s a much better “LLM as life coach”, from my perspective, than any commercial model like Claude or the GPTs.
in principle, could I have replicated the Seren experience with good prompting, homebrew RAG, and imagination? Probably. But I didn’t manage in practice, and neither will most users.
A wholly general-purpose tool isn’t a product; it’s an input to products.
I’m convinced LLMs need a *lotof concrete visions of ways they could be used, all of which need to be fleshed out separately in wrapper apps, before they can justify the big labs’ valuations.
Eli Dourado: Had a reporter reach out to me, and when we got on the phone, he confessed: “I had never heard of you before, but when I asked ChatGPT who I should talk to about this question, it said you.”
Once again, I point out that even if this did come out of an LLM, it wasn’t a hallucination and it wasn’t even a mistake by the model. It was that if you ask a silly question you get a silly answer, and if you fail to ask the second question of ‘what would happen if I did this’ and faround instead then you get to find out the hard way.
Cowboy: talked to an ai safety guy who made the argument that the chatgpt tariff plan could actually be the first effective malicious action from an unaligned ai
Eliezer Yudkowsky: “LLMs are too stupid to have deliberately planned this fiasco” is true today… but could be false in 6 more months, so do NOT promote this to an eternal verity.
It was not a ‘malicious’ action, and the AI was at most only unaligned in the sense that it didn’t sufficiently prominently scream how stupid the whole idea was. But yes, ‘the AI is too stupid to do this’ is very much a short term solution, almost always.
In recent months I’ve spoken to other YC founders doing AI application startups and most of them have had the same anecdotal experiences:
o99-pro-ultra announced
Benchmarks look good
Evaluated performance mediocre.
This is despite the fact that we work in different industries, on different problem sets. … I would nevertheless like to submit, based off of internal benchmarks, and my own and colleagues’ perceptions using these models, that whatever gains these companies are reporting to the public, they are not reflective of economic usefulness or generality.”
Their particular use case is in computer security spotting vulnerabilities, and LC reports that Sonnet 3.5 was a big leap, and 3.6 and 3.7 were small improvements, but nothing else has worked in practice. There are a lot of speculations about AI labs lying and cheating to pretend to be making progress, or the models ‘being nicer to talk to.’ Given the timing of the post they hadn’t tested Gemini 2.5 Pro yet.
It’s true that, for a number of months, there are classes of tasks where Sonnet 3.5 was a big improvement, and nothing since then has been that huge an improvement over Sonnet 3.5, at least until Gemini 2.5. In other tasks, this is not true. But in general, it seems very clear the models are improving, and also it hasn’t been that long since Sonnet 3.5. If any other tech was improving this fast we’d be loving it.
Gemini 2.5 Pro now powers Google Deep Research if you select Gemini 2.5 Pro from the drop down menu before you start. This is a major upgrade, and the request limit is ten times higher than it is for OpenAI’s version at a much lower price.
Google AI Developers: You asked, we shipped. Gemini 2.5 Pro Preview is here with higher rate limits so you can test for production.
Sundar Pichai (CEO Google): Gemini 2.5 is our most intelligent model + now our most in demand (we’ve seen an 80%+ increase in active users in AI Studio + Gemini API this month).
Thomas Woodside: Google’s reason for not releasing a system card for Gemini 2.5 is…the word “experimental” in the name of the model.
Morgan: google rolled out gemini 2.5 pro “experimental” to all gemini users—for free—while tweeting about hot TPUs and they’re calling this a “limited” release
Steven Adler: Choosing to label a model-launch “experimental” unfortunately doesn’t change whether it actually poses risks (which it well may not!)
That seems like highly reasonable pricing, but good lord you owe us a model card.
Peter Wildeford: It’s super weird to be living in a world where Google’s LLM is good, but here we are.
I know Google is terrible at marketing but ignore Gemini at your own loss.
Important errata: I just asked for a question analyzing documents related to AI policy and I just got back a recipe for potato bacon soup.
So Gemini 2.5 is still very experimental.
Charles: Yeah, it’s better and faster than o1-pro in my experience, and free.
It still does the job pretty well.
Peter Wildeford: PSA: New Gemini 2.5 Pro Deep Research now seems at least as good as OpenAI Deep Research :O
Seems pretty useful to run both and compare!
Really doubtful the $200/mo for OpenAI is worth the extra $180/mo right now, maybe will change soon
I will continue to pay the $200/mo to OpenAI because I want first access to new products, but in terms of current offerings I don’t see the value proposition for most people versus paying $20 each for Claude and Gemini and ChatGPT.
With Gemini 2.5 Pro pricing, we confirm that Gemini occupies the complete Pareto Frontier down to 1220 Elo on Arena.
If you believed in Arena as the One True Eval, which you definitely shouldn’t do, you would only use 2.5 Pro, Flash Thinking and 2.0 Flash.
George offers additional notes on Llama 4, making the point that 17B/109B is a sweet spot for 128GB on an ARM book (e.g. MacBook), and a ~400B MoE works on 512GB setups, whereas Gemma 3’s 27B means it won’t quite fit on a 32GB MacBook, and DS-V3 is slightly too big for a 640GB node.
This kind of thinking seems underrated. What matters for smaller or cheaper models is partly cost, but in large part what machines can run the model. There are big benefits to aiming for a sweet spot, and it’s a miss to need slightly more than [32 / 64 / 128 / 256 / 512 / 640] GB.
In light of recent events, ‘how you handle fools asking stupid questions’ seems rather important.
Daniel West: A interesting/ useful type of benchmark for models could be: how rational and sound of governing advice can it consistently give to someone who knows nothing and who asks terribly misguided questions, and how good is the model at persuading that person to change their mind.
Janus: i think sonnet 3.7 does worse on this benchmark than all previous claudes since claude 3. idiots really shouldnt be allowed to be around that model. if you’re smart though it’s really really good
Daniel West: An interesting test I like to do is to get a model to simulate a certain historical figure; a philosopher, a writer, but it has to be one I know very well. 3.7 will let its alignment training kind of bleed into the views of the person and you have to call it out.
But once you do that, it can channel the person remarkably well. And to be fair I don’t know that any models have reached the level of full fidelity. I have not played with base models though. To me it feels like it can identify exactly what may be problematic or dangerous in a particular thinker’s views when in the context of alignment, and it compromises by making a version of the views that are ‘aligned’
Also in light of recent events, on Arena’s rapidly declining relevance:
Peter Wildeford: I think we have to unfortunately recognize that while Chatbot Arena is a great public service, it is no longer a good eval to look at.
Firstly, there is just blatant attempts from companies to cut corners to rank higher.
But more importantly models here are ranked by normal humans. They don’t really know enough to judge advanced model capabilities these days! They ask the most banal prompts and select on stupidity. 2025-era models have just advanced beyond the ability for an average human to judge their quality.
Yep. For the vast majority of queries we have saturated the ‘actually good answer’ portion of the benchmark, so preference comes down to things like syncopathy, ‘sounding smart’ or seeming to be complete. Some of the questions will produce meaningful preferences, but they’re drowned out.
I do think over time those same people will realize that’s not what they care about when choosing their chatbot, but it could take a while, and my guess is pure intelligence level will start to matter a lot less than other considerations for when people chat. Using them as agents is another story.
That doesn’t mean Arena is completely useless, but you need to adjust for context.
Google: We introduce CURIE, a scientific long-Context Understanding, Reasoning and Information Extraction benchmark to measure the potential of large language models in scientific problem-solving and assisting scientists in realistic workflows.
The future is so unevenly distributed that their scoring chart is rather out of date. This was announced on April 3, 2025, and the top models are Gemini 2.0 Flash and Claude 3 Opus. Yes, Opus.
I don’t think the following is a narrative violation at this point? v3 is not a reasoning model and DeepSeek’s new offering is their GRM line that is coming soon?
Alexander Wang: 🚨 Narrative Violation—DeepSeek V3 [March 2025 version] is NOT a frontier-level model.
SEAL leaderboards have been updated with DeepSeek V3 (Mar 2025).
On multi-turn text only, v3 is in 12th place, just ahead of r1, and behind even Gemini 2.0 Flash which also isn’t a reasoning model and is older and tiny. It is well behind Sonnet 3.6. So that’s disappointing, although it is still higher here than GPT-4o from November, which scores remarkably poorly here, of course GPT-4.5 trounces and Gemini 2.5 Pro, Sonnet 3.7 Thinking and o1 Pro are at the top in that order.
On Humanity’s Last Exam, v3-March-2025 is I guess doing well for a small non-reasoning model, but is still not impressive, and well behind r1.
The real test will be r2, or an updated r1. It’s ready when it’s ready. In the meantime, SEAL is very much in Gemini 2.5 Pro’s corner, it’s #1 by a wide margin.
AI as a debt collector? AI was substantially less effective than human debt collectors, and trying to use AI at all permanently made borrowers more delinquent. I wonder if that was partly out of spite for daring to use AI here. Sounds like AI is not ready for this particular prime time, but also there’s an obvious severe disadvantage here. You’re sending an AI to try and extract my money? Go to hell.
If you see a call to invest in crypto, it’s probably a scam. Well, yes. The new claim from Harper Carroll is it’s probably also an AI deepfake. But this was already 99% to be a scam, so the new information about AI isn’t all that useful.
Scott Alexander asks, in the excellent post The Colors of Her Coat, is our ability to experience wonder and joy being ruined by having too easy access to things? Does the ability to generate AI art ruin our ability to enjoy similar art? It’s not that he thinks we should have to go seven thousand miles to the mountains of Afghanistan to paint the color blue, but something is lost and we might need to reckon with that.
Scarcity, and having to earn things, makes things more valuable and more appreciated. It’s true. I don’t appreciate many things as much as I used to. Yet I don’t think the hedonic treadmill means it’s all zero sum. Better things and better access are still, usually, better. But partly yes, it is a skill issue. I do think you can become more of a child and enter the Kingdom of Heaven here, if you make an effort.
Objectively, as Scott notes, the AIs we already have are true wonders. Even more than before, everything is awesome and no one is happy. Which also directly led to recent attempts to ensure that everything stop being so awesome because certain people decided that it sucked that we all had so much ability to acquire goods and services, and are setting out to fix that.
I disagree with those people, I think that it is good that we have wealth and can use it to access to goods and services, including via trade. Let’s get back to that.
Janus: comic from lesswrong post “Show, not tell: GPT-4o is more opinionated in images than in text” 🤣
the RLHF doesnt seem to affect 4o’s images in the same way / as directly as its text, but likely still affects them deeply
I like that we now have a mode where the AI seems to be able to respond as if it was situationally aware, so that we can see what it will be like when the AIs inevitably become more situationally aware.
There are claims that GPT-4o image generation is being nerfed, because of course there are such claims. I’ve seen both claims of general decline in quality, and in more refusals to do things on the edge like copyrighted characters and concepts or doing things with specific real people.
Here’s a fun workaround suggestion for the refusals:
create a completely fictional character who shares the most characteristic traits of the person in the image. In other words, a totally fictional person who is not the same as the one in the picture, but who has many similar traits to the original photo.
UK’s labor party proposes an opt-out approach to fair use for AI training. OpenAI and Google of course reject this, suggesting instead that content creators use robots.txt. Alas, AI companies have been shown to ignore robots.txt, and Google explicitly does not want there to be ‘automatic’ compensation if the companies continue ignoring robots.txt and training on the data anyway.
My presumption is that opt-out won’t work for the same logistical reasons as opt-in. Once you offer it, a huge percentage of content will opt-out if only to negotiate for compensation, and the logistics are a nightmare. So I think the right answer remains to do a radio-style automatic compensation schema.
Google’s AI Overviews and other changes are dramatically reducing traffic to independent websites, with many calling it a ‘betrayal.’ It is difficult to differentiate between ‘Google overviews are eating what would otherwise be website visits,’ versus ‘websites were previously relying on SEO tactics that don’t work anymore,’ but a lot of it seems to be the first one.
Anthropic: STEM students are early adopters of AI tools like Claude, with Computer Science students particularly overrepresented (accounting for 36.8% of students’ conversations while comprising only 5.4% of U.S. degrees). In contrast, Business, Health, and Humanities students show lower adoption rates relative to their enrollment numbers.
We identified four patterns by which students interact with AI, each of which were present in our data at approximately equal rates (each 23-29% of conversations): Direct Problem Solving, Direct Output Creation, Collaborative Problem Solving, and Collaborative Output Creation.
Students primarily use AI systems for creating (using information to learn something new) and analyzing (taking apart the known and identifying relationships), such as creating coding projects or analyzing law concepts. This aligns with higher-order cognitive functions on Bloom’s Taxonomy. This raises questions about ensuring students don’t offload critical cognitive tasks to AI systems.
The key is to differentiate between using AI to learn, versus using AI to avoid learning. A close variant is to ask how much of this is ‘cheating.’
Measuring that from Anthropic’s position is indeed very hard.
At the same time, AI systems present new challenges. A common question is: “how much are students using AI to cheat?” That’s hard to answer, especially as we don’t know the specific educational context where each of Claude’s responses is being used.
Anthropic does something that at least somewhat attempts to measure this, in a way, by drawing a distinction between ‘direct’ versus ‘collaborative’ requests, with the other central division being ‘problem solving’ versus ‘output creation’ and all four quadrants being 23%-29% of the total.
However, there’s no reason you can’t have collaborative requests that avoid learning, or have direct requests that aid learning. Very often I’m using direct requests to AIs in order to learn things.
For instance, a Direct Problem Solving conversation could be for cheating on a take-home exam… or for a student checking their work on a practice test.
Andriy Burkov: Read the entire thread and share it with anyone saying that LMs can be used as teachers or that they can reliably reason. As someone who spends hours every day trying to make them reason without making up facts, arguments, or theorems, I can testify first-hand: a general-purpose LM is a disaster for teaching.
Especially, it’s not appropriate for self-education.
Only if you already know the right answer or you know enough to recognize a wrong one (e.g., you are an expert in the field), can you use this reasoning for something.
The thread is about solving Math Olympiad (IMO) problems, going over the results of a paper I’ve covered before. These are problems that approximately zero human teachers or students can solve, so o3-mini getting 3.3% correct and 4.4% partially correct is still better than almost everyone reading this. Yes, if you have blind faith in AIs to give correct answers to arbitrary problems you’re going to have a bad time. Also, if you do that with a human, you’re going to have a bad time.
So don’t do that, you fool.
This seems like an excellent opportunity.
AIRA: ARIA is launching a multi-phased solicitation to develop a general-purpose Safeguarded AI workflow, backed by up to £19m.📣
his workflow aims to demonstrate that frontier AI techniques can be harnessed to create AI systems with verifiable safety guarantees.🔓
The programme will fund a non-profit entity to develop critical machine learning capabilities, requiring the highest standards of organisational governance and security.
Phase 1, backed by £1M, will fund up to 5 teams for 3.5 months to develop Phase 2 full proposals. Phase 2 — opening 25 June 2025 — will fund a single group, with £18M, to deliver the research agenda. 🚀
Simeon: If we end up reaching the worlds where humanity flourishes, there are unreasonably high chances this organization will have played a major role.
If you’re up for founding it, make sure to apply!
Also available is the 2025 IAPS Fellowship, runs from September 1 to November 21, apply by May 7, fully funded with $15k-$22k stipend, remote or in person.
To do this as I understand it, they propose Self-Principled Critique Tuning (SPCT), which consists of Pointwise Generative Reward Modeling (GRM), where the models generate ‘principles’ (criteria for valuation) and ‘critiques’ that analyze responses in detail, and optimizes on both principles and critiques.
Then at inference time they run multiple instances, such as sampling 32 times, and use voting mechanisms to choose aggregate results.
They claim the resulting DeepSeek-GRM-27B outperforms much larger models. They intend to make the models here open, so we will find out. Claude guesses, taking the paper at face value, that there are some specialized tasks, where you need evaluation and transparency, where you would want to pay the inference costs here, but that for most tasks you wouldn’t.
This does seem like a good idea. It’s one of those ‘obvious’ things you would do. Increasingly it seems like those obvious things you would do are going to work.
I haven’t seen talk about it. The one technical person I asked did not see this as claiming much progress. Bloomberg also has a writeup with the scary title ‘DeepSeek and Tsinghua Developing Self-Improving Models’ but I do not consider that a centrally accurate description. This is still a positive sign for DeepSeek, more evidence they are innovating and trying new things.
OpenAI: Elon’s nonstop actions against us are just bad-faith tactics to slow down OpenAI and seize control of the leading AI innovations for his personal benefit. Today, we counter-sued to stop him.
He’s been spreading false information about us. We’re actually getting ready to build the best-equipped nonprofit the world has ever seen – we’re not converting it away. More info here.
Elon’s never been about the mission. He’s always had his own agenda. He tried to seize control of OpenAI and merge it with Tesla as a for-profit – his own emails prove it. When he didn’t get his way, he stormed off.
Elon is undoubtedly one of the greatest entrepreneurs of our time. But these antics are just history on repeat – Elon being all about Elon.
No, they are not ‘converting away’ the nonprofit. They are trying to have the nonprofit hand over the keys to the kingdom, both control over OpenAI and rights to most of the net present value of its future profit, for a fraction of what those assets are worth. Then they are trying to have it use those assets for what would be best described as ‘generic philanthropic efforts’ capitalizing on future AI capabilities, rather than on attempts to ensure against existential risks from AGI and ASI (artificial superintelligence).
That does not automatically excuse Elon Musk’s behaviors, or mean that Elon Musk is not lying, or mean that Musk has standing to challenge what is happening. But when Elon Musk says that OpenAI is up to no good here, he is right.
Google appoints Josh Woodward as its new head of building actual products. He will be replacing Sissie Hsiao. Building actual products is Google’s second biggest weakness behind letting people know Google’s products exist, so a shake-up there is likely good, and we could probably use another shake-up in the marketing department.
Keach Hagey wrote the WSJ post on what happened with Altman being fired, so this sounds like de facto confirmation that the story was reported accurately?
Sam Altman: there are some books coming out about openai and me. we only participated in two—one by keach hagey focused on me, and one by ashlee vance on openai (the only author we’ve allowed behind-the-scenes and in meetings).
no book will get everything right, especially when some people are so intent on twisting things, but these two authors are trying to. ashlee has spent a lot of time inside openai and will have a lot more insight—should be out next year.
A Bloomberg longread on ‘The AI Romance Factory,’ a platform called Galatea that licenses books and other creative content on the cheap , focusing primarily on romance, then uses AI to edit and to put out sequels and a range of other media adaptations, whether the original author approves or not. They have aspirations of automatically generating and customizing to the user or reader a wide variety of content types. This is very clearly a giant slop factory that is happy to be a giant slop factory. The extent to which these types of books were mostly already a slop factory is an open question, but Galatea definitely takes it to another level. The authors do get royalties for all of the resulting content, although at low rates, and it seems like the readers mostly don’t understand what is happening?
The 2025 AI Index Report seems broadly correct, but doesn’t break new ground for readers here, and relies too much on standard benchmarks and also Arena, which are increasingly not relevant. That doesn’t mean there’s a great alternative but one must be careful not to get misled.
AI Frontiers launches, a new source of serious articles about AI. Given all that’s happened this week, I’m postponing coverage of the individual posts here.
77% of all venture funding in 2025 Q1, $113 billion, went to AI, up 54% year over year, 49% if you exclude OpenAI which means 26% went to OpenAI alone. Turner Novak calls this ‘totally normal behavior’ as if it wasn’t totally normal behavior. But that’s what you do in a situation like this. If a majority of my non-OpenAI investment dollars weren’t going to AI as a VC, someone is making a huge mistake.
Google using ‘gardening leave,’ paying AI researchers who leave Google to sit around not working for a extended period. That’s a policy usually reserved for trading firms, so seeing it in AI is a sign of how intense the competition is getting, and that talent matters. Google definitely means business and has the resources. The question is whether their culture dooms them from executing on it.
Epoch AI: We estimated OpenAI and Anthropic revenues using revenue data compiled from media reports, and used web traffic and app usage data as proxies for Google DeepMind revenues. We focus on these three because they appear to be the revenue leaders among foundation model developers.
We don’t include internally generated revenues (like increased ad revenue from AI-enhanced Google searches) in our estimates. But these implicit revenues could be substantial: Google’s total 2024 revenue was ~$350B, so even modest AI-driven boosts might be significant.
We also exclude revenues from the resale of other companies’ models, even though these can be huge. For example, we don’t count Microsoft’s Copilot (built on OpenAI’s models), though it might currently be the largest revenue-generating LLM application.
These companies are forecasting that their rapid revenue growth will continue. Anthropic has projected a “base case” of $2.2 billion of revenue in 2025, or 5x growth on our estimated 2024 figure. OpenAI has projected $11.6 billion of revenue in 2025, or 3.5x growth.
OpenAI’s and Anthropic’s internal forecasts line up well with naive extrapolations of current rates of exponential growth. We estimate Anthropic has been growing at 5.0x per year, while OpenAI has grown at 3.8x per year.
AI companies are making enormous investments in computing infrastructure, like the $500 billion Stargate project led by OpenAI and Softbank. For these to pay off, investors likely need to eventually see hundreds of billions in annual revenues.
We believe that no other AI companies had direct revenues of over $100M in 2024.
Tyler Cowen proposes giving AIs goals and utility functions to maximize, then not only allowing but requiring them to have capitalization, so they have ‘skin in the game,’ and allowing them to operate within the legal system, as a way to ‘limit risk’ from AIs. Then use the ‘legal system’ to control AI behavior, because the AIs that are maximizing their utility functions could be made ‘risk averse.’
Tyler Cowen: Perhaps some AIs can, on their own, accumulate wealth so rapidly that any feasible capital constraint does not bind them much.
Of course this scenario could create other problems as well, if AIs hold too much of societal wealth.
Even if the ‘legal system’ were by some miracle able to hold and not get abused, and we ignore the fact that the AIs would of course collude because that’s the correct solution to the problem and decision theory makes this easy for sufficiently capable minds whose decisions strongly correlate to each other? This is a direct recipe for human disempowerment, as AIs rapidly get control of most wealth and real resources. If you create smarter, more capable, more competitive minds, and then set them loose against us in normal capitalistic competition with maximalist goals, we lose. And we lose everything. Solve for the equilibrium. We are not in it.
And that’s with the frankly ludicrous assumption that the ‘legal system’ would meaningfully hold and protect us. It isn’t even doing a decent job of that right now. How could you possibly expect such a system to hold up to the pressures of transformative AI set loose with maximalist goals and control over real resources, when it can’t even hold up in the face of what is already happening?
Andrej Karpathy requests AI prediction markets. He reports the same problem we all do, which is finding markets that can be properly resolved. I essentially got frustrated with the arguing over resolution, couldn’t find precise wordings that avoid this for most of the questions that actually matter, and thus mostly stopped trying.
You also have the issue of confounding. We can argue over what AI does to GDP or market prices, but if there’s suddenly a massive completely pointless trade war that destroys the economy, all you know about AI’s impact is that it is not yet so massive that it overcame that. Indeed, if AI’s impact was to enable this, or to prevent other similar things, that would be an epic impact, but you likely can’t show causation.
Seb Krier, who works on Policy Development & Strategy at Google DeepMind, speculates on maintaining agency and control in an age of accelerated intelligence, and spends much time considering reasons progress in both capabilities and practical use of AI might be slower or faster. I don’t understand why the considerations here would prevent a widespread loss of human agency for very long.
Claim that about 60% of Nvidia GPUs would have been exempt from the new tariffs. I suppose that’s 60% less disastrous on that particular point? The tariffs that are implemented and the uncertainty about future tariffs are disastrous for America and the world across the board, and GPUs are only one especially egregious unforced error.
Gavin Baker explains once again that tariff barriers cripple American AI efforts, including relative to China. Even if you think that we need to reshore manufacturing, either of GPUs or in general, untargeted tariffs hurt this rather than helping. They tax the inputs you will need. They create massive uncertainty and can’t be relied upon. And most of all, there is no phase-in period. Manufacturing takes time to physically build or relocate, even in a best case scenario. This applies across most industries, but especially to semiconductors and AI. By the time America can supply its own chips at scale, the AI race could well be over and lost.
Helen Toner argues that if AIs develop CRBN risks or otherwise allow for catastrophic misuse, avoiding proliferation of such AIs is not our only option. And indeed, that access to such systems will inevitably increase with time, so it better not be our only option. Instead, we should look to the ‘adaptation buffer.’
As in, if you can ensure it takes a while before proliferation, you can use that time to harden defenses against the particular enabled threats. The question is, does this offer us enough protection? Helen agrees that on its own this probably won’t work. For some threats if you have a lead then you can harden defenses somewhat, but I presume this will be increasingly perilous over time.
That still requires substantial non-proliferation efforts, to even get this far, even to solve only this subclass of our problems. I also think that non-proliferation absolutely does also help maintain a lead and help avoid a race to the bottom, even if as Helen Toner notes the MAIM paper did not emphasize those roles. As she notes, what nonproliferation experts are advocating for is not obvious so different from what Toner is saying here, as it is obvious that (at least below some very high threshold) we cannot prevent proliferation of a given strength of model indefinitely.
David Krueger reminds us that the best way not to proliferate is to not build the AI in question in the first place. You still have a growing problem to solve, but it is much, much easier to not build the AI in the first place than it is to keep it from spreading.
Once again: If an open model with catastrophic misuse risks is released, and we need to then crack down on it because we can’t harden defenses sufficiently, then that’s when the actual totalitarianism comes out to play. The intrusiveness required would be vastly worse than that required to stop the models from being trained or released in the first place.
AB 501, the bill that was going to prevent OpenAI from becoming a for-profit, has been amended to be ‘entirely about aircraft liens,’ or to essentially do nothing. That’s some dirty pool. Not only did it have nothing to do with aircraft, obviously, my reluctance to endorse it and sign the petition was that it read too much like a Bill of Attainder against OpenAI in particular. I do think the conversion should be stopped, at least until OpenAI offers a fair deal, but I’d much prefer to do that via Rule of Law.
Google continues to fail marketing forever is probably the main takeaway here.
Ethan Mollick: If you wanted to see how little attention folks are paying to the possibility of AGI (however defined) no matter what the labs say, here is an official course from Google Deepmind whose first session is “we are on a path to superhuman capabilities”
Max Harms: Okay, I’m annoyed at people covering AI 2027 burying the lede, so I’m going to try not to do that. The authors predict a strong chance that all humans will be (effectively) dead in 6 years, and this agrees with my best guess about the future.
…
But I also feel like emphasizing two big points about these overall timelines:
Mode ≠ Median
As far as I know, nobody associated with AI 2027, as far as I can tell, is actually expecting things to go as fast as depicted. Rather, this is meant to be a story about how things could plausibly go fast. The explicit methodology of the project was “let’s go step-by-step and imagine the most plausible next-step.” If you’ve ever done a major project (especially one that involves building or renovating something, like a software project or a bike shed), you’ll be familiar with how this is often wildly out of touch with reality. Specifically, it gives you the planning fallacy.
There’s a Decent Chance of Having Decades
In a similar vein as the above, nobody associated with AI 2027 (or the market, or me) think there’s more than a 95% chance that transformative AI will happen in the next twenty years! I think most of the authors probably think there’s significantly less than a 90% chance of transformative superintelligence before 2045.
Daniel Kokotajlo expressed on the Win-Win podcast (I think) that he is much less doomy about the prospect of things going well if superintelligence is developed after 2030 than before 2030, and I agree. I think if we somehow make it to 2050 without having handed the planet over to AI (or otherwise causing a huge disaster), we’re pretty likely to be in the clear. And, according to everyone involved, that is plausible (but unlikely).
Max then goes through the timeline in more detail. A lot of the disagreements end up not changing the trajectory much, with the biggest disagreement being that Max expects much closer competition between labs including the PRC. I liked the note that Agent-4 uses corrigibility as its strategy with Agent-5, yet the humans used interpretability in the slowdown scenario. I also appreciated that Max expects Agent-4 to take more and earlier precautions against a potential shutdown attempt.
Should we worry about AIs coordinating with each other and try to give them strong preferences for interacting with humans rather than other AIs? This was listed under AI safety but really it sounds like a giant jobs program. You’re forcibly inserting humans into the loop. Which I do suppose helps with safety, but ultimately the humans would likely learn to basically be telephone operators, this is an example of an ‘unnatural’ solution that gets quickly competed away to the extent it worked at all.
The thing is, we really really are going to want these AIs talking to each other. The moment we realize not doing it is super annoying, what happens?
As one comment points out, related questions are central to the ultimate solutions described in AI 2027, where forcing the AIs to communicate in human language we can understand is key to getting to the good ending. That is still a distinct strategy, for a distinct reason.
A reminder that yes, the ‘good’ ending is not so easy to get to in reality:
Daniel Kokotajlo: Thanks! Yeah, it took us longer to write the “good” ending because indeed it involved repeatedly having to make what seemed to us to be overly optimistic convenient assumptions.
If you keep noticing that you need to do that in order to get a good ending, either fix something systemic or you are going to get a bad ending.
Daniel Samanez: Like with people. So we should also restrain people the same way?
Marcus Arvan: Yes, that’s precisely what laws and social consequences are for.
They go on like this for a few exchanges but the point is made. Anarchists are wildly outnumbered and unpopular for very good reasons. Yet, in a situation where we are fast building new minds that will likely be smarter and more generally capable and competitive than humans, we constantly face calls for anarchism, or something deeply close it. Such folks often cry totalitarianism at the idea of regulating such activities at all, let alone on the level we already regulate humans and most human activities.
This is, let us face it, both suicidal and deeply silly.
Speaking of deeply silly:
Dwarkesh Patel: @slatestarcodex uses SpaceX to illustrate the lower bound of what superintelligence could achieve:
“We have something which is smarter than Elon Musk, better at optimizing things than Elon Musk.
We have 10,000 parts in a rocket supply chain.
How many of those parts can Elon personally yell at people to optimize?
We could have a different copy of the superintelligence, optimizing every single part full time.”
Colin Fraser: don’t get what’s interesting about these kinds of pronouncements. It’s basically tautological that superhuman entities would perform superhumanly.
You would think so, Colin. A superhuman entity will perform superhumanly. And yet, I can’t help but notice that a lot of people say that a superhuman entity would not perform superhumanly, or would cause only a marginal performance improvement.
Others are revealing that they do not believe superhuman entities are possible, or even that superhuman performance in various domains is possible. There is usually no coherent explanation why such performance is impossible.
Note that in most domains, reliably performing as well as the best human, but much faster and with access to all the information, is importantly superhuman.
Your periodic reminder that ‘AI spring’ and ‘AI winter’ and essentially anything else that isn’t ‘AI singleton emerges already’ is widely cited as being evidence against an eventual AI singleton.
Ryan Greenblatt: IMO, this isn’t much of an update against CoT monitoring hopes.
They show unfaithfulness when the reasoning is minimal enough that it doesn’t need CoT.
But, my hopes for CoT monitoring are because models will have to reason a lot to end up misaligned and cause huge problems
Sophisticated and non-obvious misalignment (e.g. alignment faking and training gaming) both require decently sophisticated reasoning, especially to avoid getting caught.
Successfully striking to escape, sabotage research, or cause other bad outcomes will also be non-trivial.
That said, I don’t think CoT monitoring will necessarily solve our problems. Future models might be much better at opaque reasoning or might not use CoT due to:
– architectural opaque recurrence (neuralese)
– steganography capabilities
– a much stronger forward pass
Of these, I’m most worried about neuralese recurrence effectively removing direct access to the AI’s reasoning in a legible format.
I discuss how opaque reasoning abilities interacts with the probability of non-obvious misalignment here.
The question is, which of the following are happening:
The CoT in the study is unfaithful because opaque reasoning is better at getting the best answer than transparent reasoning.
The CoT was unfaithful because opaque reasoning vibes with the situation.
The CoT was unfaithful because they optimized it to be opaque without realizing.
Transparent reasoning is better at getting the right answer. The CoT in the study is unfaithful because there’s no need for CoT in the first place, so there’s no optimization pressure on it to be useful, thus it isn’t transparent.
Or, more directly: Is an opaque or unfaithful CoT better or worse at creating the superior final answer than a faithful CoT? Does reasoning a lot mean a transparent CoT, or an unfaithful CoT?
My interpretation of the study was that it was evidence that opaque was stronger, and was what would come out of optimizing for the best answers, in ways that were going to be extremely hard to prevent. Ryan is suggesting perhaps it is the opposite. The good news is that this seems like a very testable empirical question. We can’t test directly on the final answer until too late but we should have good alternatives.
It’s also an important one. A lot of alignment approaches are very different depending on how easy or hard it is to get CoT to be transparent or opaque, faithful or unfaithful, especially at much higher levels of intelligence and complex thought.
A replication of the alignment faking paperusing an improved classifier and including interpretability work on the models gives more detail into what is happening. They tested a bunch of open models, and found that most of these weaker tested models did not fake alignment. This included Llama 4 Scout and Maverick. The other place the original paper found faking was o1, which was not tested here.
