Whereas illicit AI use was already a well-known problem for the growing ecosystem of online tournaments, we didn’t expect it to affect our unrated, prizeless teaching league. To the contrary, we soon became cognisant of how some of our students were outputting better games than we, their teachers, could ever hope to play.
[...]
They fire up their computer out of idle curiosity and nod along passively as the truths of the universe float by them. They register the insights not one bit more because they can click the sublime moves. People consistently underestimate just how lost they will be when the solution is no longer right in front of them.
[...]
The thing I want to impress with this article is the consistency with which we as a species underestimate our own willingness to give up our culture, economy and autonomy to AI, even without monetary incentives.
https://www.lesswrong.com/posts/nR3DkyivzF4ve97oM/how-go-players-disempower-themselves-to-ai
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After 50 years, it’s time to close this important chapter. The top programs are unbeatable by humans; making them stronger has no real research value. These programs rarely make a mistake. Most games between the programs end in a draw, reinforcing the generally accepted notion that perfect play in chess will lead to a draw. The next challenge? Solving chess! With an estimated 1045 states, this is a daunting challenge for hardware and software technology.
https://icga.org/?page_id=3957
Chinese models are ~8 months behind and are falling further behind: https://www.nist.gov/news-events/news/2026/05/caisi-evaluation-deepseek-v4-pro
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Interesting idea to probe how much “programming ability” is post-training vs. pretraining: https://github.com/RicardoDominguez/talkie-coder
Fine-tuning on modern SWE trajectories takes a 13B “vintage” model trained only on pre-1931 text from 4% pass@100 on HumanEval to 4.5% pass@1 on SWE-bench.
This is evidence that general language modeling learns surprisingly reusable abstractions. Why? Imagine trying to teach someone to fix bugs in a large software project by showing them recordings of expert programmers at work. This approach would only be helpful if the learner already had a lot of mental machinery.
Fine-tuning on modern SWE trajectories takes a 13B “vintage” model trained only on pre-1931 text from 4% pass@100 on HumanEval to 4.5% pass@1 on SWE-bench.
This is evidence that general language modeling learns surprisingly reusable abstractions. Why? Imagine trying to teach someone to fix bugs in a large software project by showing them recordings of expert programmers at work. This approach would only be helpful if the learner already had a lot of mental machinery.
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Claude Opus 4.7 managed to implement an AlphaZero-style self-play pipeline from scratch on consumer hardware in three hours.
No starter code. No full paper to copy. It had to implement the research loop: MCTS, neural policy/value nets, self-play, training, and evaluation.
Across eight trials, Opus 4.7 beat the Pascal Pons Connect Four solver as first player in 7/8 runs. No other tested frontier coding agent cleared 2/8.
Paper: Frontier Coding Agents Can Now Implement an AlphaZero Self-Play Machine Learning Pipeline For Connect Four That Performs Comparably to an External Solver https://arxiv.org/abs/2604.25067
No starter code. No full paper to copy. It had to implement the research loop: MCTS, neural policy/value nets, self-play, training, and evaluation.
Across eight trials, Opus 4.7 beat the Pascal Pons Connect Four solver as first player in 7/8 runs. No other tested frontier coding agent cleared 2/8.
Paper: Frontier Coding Agents Can Now Implement an AlphaZero Self-Play Machine Learning Pipeline For Connect Four That Performs Comparably to an External Solver https://arxiv.org/abs/2604.25067
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Between the 15th and 18th centuries, Crimea’s slave raiders seized millions from villages across Poland, Ukraine and Russia, marched them south in chains, and sold them into Ottoman markets. A new APSR study by Volha Charnysh and Ranjit Lall estimates at least 3.64 million captives (likely about 5 million) from 2,511 raids on 882 locations.
The surprise: raided regions later grew faster than comparable unraided ones.
Why? Unlike parts of West Africa, Russia and Poland-Lithuania did not become suppliers inside the trade. They resisted it. Defence lines, garrisons, forts, roads, taxes and standing armies pulled labour, trade and state capacity toward the exposed frontier. Garrison towns became markets; fortified borderlands became administrative and commercial hubs.
The raids were catastrophic. But long-run effects depended on political structure: societies absorbed into slave production were broken; societies able to resist were forced into state-building.
The surprise: raided regions later grew faster than comparable unraided ones.
Why? Unlike parts of West Africa, Russia and Poland-Lithuania did not become suppliers inside the trade. They resisted it. Defence lines, garrisons, forts, roads, taxes and standing armies pulled labour, trade and state capacity toward the exposed frontier. Garrison towns became markets; fortified borderlands became administrative and commercial hubs.
The raids were catastrophic. But long-run effects depended on political structure: societies absorbed into slave production were broken; societies able to resist were forced into state-building.
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How do we measure 3D spatial intelligence?
We show an agent ~20 photos from inside an apartment and ask it to produce the floor plan. It has to identify rooms, work out connections, and keep scale consistent. It does this for 50 apartments, with a notepad to learn across them.
Read more: https://andonlabs.com/evals/blueprint-bench-2
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This was always destined to happen. The party that controls AI has shaping power over the rest of human history.
https://www.nytimes.com/2026/05/04/technology/trump-ai-models.html
https://www.nytimes.com/2026/05/04/technology/trump-ai-models.html
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The Triangle of Everything: https://commons.wikimedia.org/wiki/File:Triangle_of_everything_simplified_2_triangle_of_everything_-_Planck_Units.png
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As I wrote before, the only way to turn the ship around at this point is to immediately pump €1 trillion into a Manhattan Project for a European competitive AI model and another trillion into a next-generation nuclear buildup.
Obviously, it won't happen. It's over. The only question is now whether an American like Trump will shape the future or an American like AOC. But, without some miracle, it won't be Europe. China still has a chance, though.
Obviously, it won't happen. It's over. The only question is now whether an American like Trump will shape the future or an American like AOC. But, without some miracle, it won't be Europe. China still has a chance, though.
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For comparison, Russia has spent roughly $600 billion since 2022 on one of the largest conventional interstate wars since 1945. Morgan Stanley estimates that U.S. big tech hyperscalers' capital expenditures will total $800 billion in 2026.
Now let's look at Europe. Mistral AI, Europe's only serious general-purpose AI lab, will spend about €1-3 billion in capex in 2026 ($1.2–3.5 billion at current exchange rates). All of European AI-lab capex together is roughly in the €10-15 billion ballpark in 2026.
Now let's look at Europe. Mistral AI, Europe's only serious general-purpose AI lab, will spend about €1-3 billion in capex in 2026 ($1.2–3.5 billion at current exchange rates). All of European AI-lab capex together is roughly in the €10-15 billion ballpark in 2026.
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It looks like the economists were right after all: even in a world ruled by superintelligences, monkeys will still have a job.
Also, note that they censored the monkey's tits.
Also, note that they censored the monkey's tits.
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They are actually underspending. Whoever controls AI controls the universe.
https://www.ft.com/content/ce8a1b9d-1427-472f-9585-294c7af2e0fb?syn-25a6b1a6=1
https://www.ft.com/content/ce8a1b9d-1427-472f-9585-294c7af2e0fb?syn-25a6b1a6=1
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Anthropic seems fully committed to winning this race: https://www.anthropic.com/news/higher-limits-spacex
The most interesting dynamic here is how competitors in the AI race, Google and Musk, are selling compute to Anthropic.
The logic here is probably that Anthropic would be getting the compute anyway, and they'd rather be the ones selling/controlling it. Another angle is that they're effectively getting a paying customer to bear the cost of debugging their hardware platform.
Alphabet also has equity in Anthropic. So even if Anthropic wins, they might benefit. If Anthropic loses but spends the $200B on TPUs first, Google still wins. Google is positioned to profit from a wider range of outcomes than if it bet purely on Gemini.
Then there is the circular revenue mechanic. The capital that Anthropic raises from Google and Nvidia directly flows back to buy compute from them, which helps their valuation, which in turn funds more capex. The cash is largely round-tripping between investors, labs, and clouds.
The most interesting dynamic here is how competitors in the AI race, Google and Musk, are selling compute to Anthropic.
The logic here is probably that Anthropic would be getting the compute anyway, and they'd rather be the ones selling/controlling it. Another angle is that they're effectively getting a paying customer to bear the cost of debugging their hardware platform.
Alphabet also has equity in Anthropic. So even if Anthropic wins, they might benefit. If Anthropic loses but spends the $200B on TPUs first, Google still wins. Google is positioned to profit from a wider range of outcomes than if it bet purely on Gemini.
Then there is the circular revenue mechanic. The capital that Anthropic raises from Google and Nvidia directly flows back to buy compute from them, which helps their valuation, which in turn funds more capex. The cash is largely round-tripping between investors, labs, and clouds.
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I still find it hard to deal with this level of time inconsistency. For example, consider all the people who issued dire warnings about Trump and now work for him. Either their words carry no epistemic content, or these people are consistently and dramatically wrong in their judgment.
ETA: I don't want to discourage people from updating on evidence or making peace with your enemy. Great! This should be encouraged!
ETA: I don't want to discourage people from updating on evidence or making peace with your enemy. Great! This should be encouraged!
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