Richard Sutton

Sockpuppet.org 2026-04-01-3

Vulnerability Research Is Cooked

Every IT department runs on a hidden subsidy: the scarcity of people smart enough to hack them. Anthropic's Frontier Red Team just demonstrated 500 validated high-severity vulnerabilities from a trivial bash script and Claude Opus 4.6, no fuzzers, no specialized tooling, just raw model inference. The Bitter Lesson is about to hit security like a brick: 80% of exploit development was jigsaw-puzzle grinding, and now everyone has a universal solver. The scarce resource isn't intelligence anymore; it's the ability to patch faster than agents can find what's broken.

# tags

ai-security agentic-ai cybersecurity bitter-lesson

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Thomas Ptacek Nicholas Carlini Anthropic Frontier Red Team Claude Opus 4.6 Ghost CMS Richard Sutton

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Asimov Press · 2026-03-27 2026-03-27-w3

The Legibility Problem

The legibility piece reframes the entire week's stakes: chess went from centaur to post-human in 20 years, and AI-for-science will follow the same arc, but every output still has to pass through labs, regulators, and clinical infrastructure that speak human. The bottleneck was never discovery — it's the translation layer between what AI generates and what human institutions can actually deploy. That gap is exactly what the measurement problem in tokenmaxxing and the $25 theory pipeline leave open: generation is solved, evaluation is partially solved, but operationalizing the output through organizations that weren't built for machine-speed science is unsolved. Whoever owns that translation infrastructure captures value from every breakthrough that needs to reach the physical world, regardless of which model or lab produced it. The capability race and the legibility race are running at different speeds, and the distance between them is where the real economic value will settle.

# tags

agentic-ai ai-for-science infrastructure reliability

Asimov Press 2026-03-27-3

The Legibility Problem

Everyone's racing to build AI that does science. Nobody's building infrastructure for humans to use what it discovers. The bottleneck isn't discovery: it's deployment through human institutions. Chess went from centaur to post-human in 20 years; science will follow the same arc, but the output must still pass through labs, regulators, and clinical infrastructure that speak human. The entity that owns the translation layer between AI-generated and human-implementable science captures value from every breakthrough that needs to reach the physical world.

# tags

ai-for-science agentic-ai reliability infrastructure