At least until (if?) rapid improvement stops, it seems less likely someone is going to catch the Big Three AI Labs. Microsoft and Meta released their models, which were fine, but not frontier. SpaceX also hasn't regained its position. Chinese models are improving, but still lag.
@emollick
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New book ‘Co-Existence’ on living and working with AIs
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I have a new book coming out October 20: Co-Existence! It is about how we live & work with AIs that are sometimes (but not always) smarter than we are. And it has a cool cover. You can pre-order: https://
co-existence.ai
And here is a post with context: https://
oneusefulthing.org/p/co-existence
-and-the-end-of-co-intelligence
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Anthropic’s codebase 80% authored by Claude AI in 2026
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"As of May 2026, more than 80% of the code we merge into Anthropic’s codebase was authored by Claude." Matches independent measures. There really is no sign this is slowing down (which doesn't mean there aren't organizational challenges to absorbing this much productivity gain)
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Challenge of feeling AI acceleration despite real model improvements
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A real problem with feeling the acceleration viscerally is that current models are really good and it is hard to feel the vibe difference on most individual tasks with new models, even as AIs continue to increase in ability by large amounts (which they actually are doing).
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OpenAI and Anthropic documentation lags products with obsolete advice
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In general, OpenAI and Anthropic documentation lags their own products by months, and both companies have tons of obsolete and contradictory advice scattered around their sites.
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AI coding tools expand features but remain poorly documented
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The capabilities of Claude Code and Codex have expanded a lot in recent months, they added many ways to approach work (subagents, skills, goal, workflows, plugins, etc). Given the AI labs can use their own AI to help documentation, a surprising amount is effectively undocumented
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Ted Chiang’s moral atrophy concern vs AI’s ethical performance
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Leaving aside the question of consciousness, the Ted Chiang piece has a reasonable point about moral atrophy if you let AI make choices. But it is also interesting in light of the fact that repeated randomized trials find AI is apparently a good ethicist.
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GenAI ROI positive but Bain report opaque on metrics
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There are lots of issues with GenAI implementations but surveys seem to suggest broad positive ROI. Data & stories about AI failures are real and useful but need to be clearer about what they are measuring. The Bain report is super odd & opaque about that. https://
bain.com/insights/your-
ai-budget-is-growing-your-returns-arent-heres-why/
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Prior ML data issues could hinder AI investment, citing fake MIT study
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I actually read this & it is super weird, it appears to be an argument that prior machine learning systems (not generative AI) did not generate savings due to data issues so that will lead to a lack of investment into current AI systems Also it cites the mostly fake “MIT study”
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Superforecasters’ prediction matched by Claude Mythos months early
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In early May, the best superforecasters predicted that, by the end of the year, the longest METR 80% task horizons would reach 3-4 hours. In late May, Claude Mythos achieved that number.