Import AI 470: No rights for machines; automating environment generation with SPADE; and building better GPU kernels with Hawkeye

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AI is accelerating some types of progress but not others: …A nice METR study lays out where acceleration is showing up… Here’s a little analysis from METR which looks at where AI may be accelerating different types of science and technology. The study looks at three different areas: cyber, math, and AI research, and finds that AI has contributed a lot to cyber, a little bit to math, and it’s hard to say for AI. Where have LLMs actually made a difference to scientific discovery?

Cyber vulnerabilities: Major acceleration. “The rate of vulnerabilities reported across many projects has dramatically accelerated in 2026 compared with 2025, both for specific projects (cURL, OpenSSL, Firefox, and Microsoft) and for aggregate vulnerability databases (the US NVD, and OSV)”.

The emergent communication habits of semi-autonomous agents (the ur real-world example here being the OpenAI-HuggingFace hack in 2026, and later the various corporate-driven analyses of the behavior of their own fleets of agents).

Mathematics research: Minor acceleration, but harder to measure. “AI is clearly contributing to more work being done (arXiv submissions have doubled in some areas in less than 12 months) but quantifying the value of those contributions is difficult.” Some math problems from prestigious lists have been solved, e.g., “the Jacobian conjecture from Smale’s list, Problem 44 from Green’s list (the halving sieve), and the sofic half of Green’s Problem 100”. However, it may be too early to determine how sustained a trend this is.

Optimization of AI research: No measurable acceleration. When you look at algorithmic progress across seven significant problem areas (CIFAR-10, Hutter compression, Gurobi mixed-integer programming, MIPLIB, nanoGPT, Stockfish, and the matrix-multiplication exponent) there are a couple of these where LLM-attributable contributions have happened (nanoGPT, CIFAR-10), though the rate of increase of usage of AI here is a lot less than with cybersecurity and mathematics.

Why this matters - differential acceleration: This paper highlights how AI is causing advances in some parts of science and technology, but the effect isn’t unified across fields, rather there are pockets of lumpy acceleration (e.g., cyber) and areas where progress is more gradual (math, AI). My suspicion is that acceleration happens when models go through some kind of ineffable phase change for a given skill, as has evidently happened with day-to-day coding (2025), and cyber (2026). The key question is whether we are going to see phase changes in other parts of science and technology or if we won’t. Read more: Research note: Have We Seen an Acceleration in Discoveries? (METR) . ***