Replying to @AnthropicAI
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.
Anthropic
1,587 posts
- Replying to @AnthropicAIAnd tomorrow, we’ll launch a $150 million national fellowship program designed to help people early in their careers extend the benefits of AI to communities across America.
- Replying to @AnthropicAIOur 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.
- Replying to @AnthropicAIAn 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.
- 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 theToday I'm publishing a new essay, Policy on the AI Exponential. AI is progressing extremely fast—much faster than the policy process was built to handle. The essay lays out where I think the technology is now, and the action needed to close the gap: darioamodei.com/post/policy-on…
- 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?
- 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:
- Replying to @AnthropicAICorrection: Claude Opus 4's ~3x average speedup dates to May 2025, not May 2024. This evaluation has only existed since September 2024, but we backtested it on earlier models: those from May 2024 showed no speedup whatsoever.
- Replying to @AnthropicAINone 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
- Replying to @AnthropicAIAI 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.
- Replying to @AnthropicAIEach 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.
- Replying to @AnthropicAIThe 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.
