Over a weekend and with ~$760, I (not a biologist) used Claude Code to fine-tune a biological AI model on human-infecting viral sequences. Although my experiment wasn't dangerous, it demonstrates how coding agents are changing the biosecurity risk landscape. In a new @GovAIOrg blog post with @lucafrighetti and James Black, we describe this experiment and its policy implications. Biosecurity has traditionally divided AI risks into two buckets: general LLMs that "raise the floor" by democratizing knowledge and specialized biological AI models (BAIMs) that "raise the ceiling" by enabling experts. Increasingly capable coding agents blur that line via three mechanisms: 1) Coding agents let both novices and experts operate BAIMs more effectively, expanding the pool of potential misusers and letting experts test more designs faster. 2) Data filters on BAIMs are brittle when coding agents can autonomously fine-tune the models, as my experiment shows. 3) Coding agents speed up ML engineering, making it more feasible for threat actors to train new specialized models optimized for harmful capabilities from scratch. Policy recommendations: BAIM developers should move beyond data filtering toward trusted-access programs; LLM developers should test agent interactions with BAIMs; policymakers should prioritize physical chokepoints like DNA synthesis screening. Read the piece: governance.ai/analysis/codin…

So, basically, if Anthropic was not a US company, we’d be facing zero days with multiple unknown points of attack on virtually all of our systems to an adversary who developed this capacity before us.

in the alignment risk update for @AnthropicAI's mythos preview, ~all evaluations look good for anthropic except for @METR_Evals' best strategy (all credit @idavidrein!)

AI models' cyber capabilities keep getting meaningfully better, and fast. To determine how AI capabilities will impact cybercrime, we first need a baseline for global cybercrime damages. In a new @GovAIOrg technical report with John Halstead and @lucafrighetti, we arrive at a baseline estimate of global cybercrime damages: $500B (with 90% CI of $100B-$1T) per year. Existing estimates of global cybercrime damages range from tens of billions to tens of trillions of dollars. Most have serious problems: they rely on reported damages only (missing the vast majority of incidents that go unreported), or they don't publish their methodology at all. We tried to do better by extrapolating mostly from survey data, which captures unreported incidents, and by being transparent about every assumption we make. Our total estimate: ~$500B a year. This includes direct losses to individuals, direct response costs to businesses, and defensive spending. Notably, this does not include costs that are even harder to quantify, such as IP theft, espionage, and national security costs, so the real yearly damages are presumably higher. As AI gets better at cyber, even a modest additive effect on the volume of cybercrime is a big deal. A 20% increase would mean ~$100B in additional yearly damages. Our estimates have extremely high uncertainty ranges. If we want to understand how AI is shaping cybercrime, we'll need to build new ways of measure the effects by looking at real world indicators of threat actor AI usage. Read the full report here: governance.ai/research-paper…

The Red Team at @AISecurityInst is hiring! We work with frontier AI companies to red team their misuse safeguards, control measures, and alignment techniques. As the stakes rise, we need much stronger red teaming and many more talented researchers working within gov 🧵

This is our @METR_Evals chart

It took Nature 13 months to publish Evo 2! 13 months! For reference: Opus 4.5 ended software engineering as we know it 4 months ago. ClawdBot added 2 million users in a single week in January. Academic publishing is so cooked it's not even funny nature.com/articles/s41586-0…