By themselves, these projects will not be sufficient to meet the challenge of advanced AI. But they’re a signal of our intent. Over the coming months and years, we will expand our work on these fronts much further.
@anthropicai
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Anthropic Advanced AI Framework urges government to block unsafe models
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Our Advanced AI Framework sets out how governments should prepare for and prevent catastrophic risks from frontier AI systems. The government should have the authority to block or revoke the release of unsafe models, and invest in societal resilience.
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Anthropic CEO Dario Amodei proposes three initiatives to close AI policy gap
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AI is advancing at a pace our policymaking institutions were never built for—and the gap between the two is becoming the central challenge of the technology. In his latest essay, our CEO Dario Amodei lays out how to close it. We're launching three new initiatives to support the
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Anthropic proposes $200M fund for AI labor disruption evaluations
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An Economic Policy Framework: a proposal for how the US government should manage labor market disruption from advanced AI. We’re contributing $200 million to a new fund to sponsor major evaluations of some of these ideas.
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AI’s coding vs biology: bio databases designed for different traffic
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New Science Blog: Why has AI advanced faster in coding than in biology? To agents, bio databases are like cities built before cars—maddening to drive in because they're designed for different traffic. How do we build infrastructure agents can use?
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Anthropic’s Claude Opus 4.7 matches NMR software for chemistry
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New Anthropic Science Blog: Making Claude a chemist. To manipulate a molecule, chemists first need to understand its structure. Their main tool is NMR spectroscopy. We found Opus 4.7 matches—and on some tasks beats—dedicated NMR software. Read more:
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AI Research: Claude’s Improved Decision-Making Outperforms Humans
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AI research is a series of next-step decisions. We looked at sessions where a human researcher took a wrong turn, showed Claude the session up to that point, and asked it what to do next. Mythos Preview improved on humans 64% of the time—up from 22% in 2024.
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AI Self-Improvement Plausible if Trends Continue, But Research Judgment Lacks
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None of this guarantees recursive self-improvement is on the horizon. It’s not yet clear that Claude is capable of research judgment—of choosing the right problems to work on. But if these trends continue, AI systems designing and building their own successors is plausible. This
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Anthropic’s AI models show massive speedup in code training tasks
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Each time we release a model, we run the same test: give it code that trains a small AI model, ask the new model to speed it up. It takes a skilled human 4-8 hours to reach 4x faster. In May 2024, Claude Opus 4 averaged a ~3x speedup. This April, Mythos Preview achieved ~52x.
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Claude’s coding success jumps 50 points, rivaling human quality
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The speedup isn’t just in volume. On open-ended coding problems where answers are unclear, Claude’s success rate is now 76%—a 50 point jump in just 6 months. Many engineers also say Claude’s code quality is now on par with human code; we expect it to be better within the year.