Anthropic (@AnthropicAI) on X (original) (raw)

We're an AI safety and research company that builds reliable, interpretable, and steerable AI systems. Talk to our AI assistant

@claudeai

on claude.ai.

• 
  Replying to @AnthropicAI
  These and other measures will allow us to track consequential shifts in the nature of work as they happen—we'll incorporate some of them into the Anthropic Economic Index going forward. Read the full report:
• 
  Replying to @AnthropicAI
  Domain experts—as judged by the questions they ask and vocabulary they use about a subject—are more likely to see success. But the gap between intermediate and expert users is quite modest, suggesting that proficiency in a domain is sufficient to code successfully within it.
  
• 
  Our latest economic research introduces a framework for tracking Claude Code as it scales. Who is using Claude Code, and what are they using it for? How is the value of tasks changing? And how much does domain expertise shape whether a session succeeds?
  
  Replying to @AnthropicAI
  The average task in Claude Code has grown more valuable. We compared the type of work done in each session to what that same task would cost on a freelance marketplace. From October to April, the monetary value of the average session grew 27%.
  
  
  We compared Claude Code success rates between occupations. On our toughest measure of success—requiring verifiable evidence that a goal was completed, like committed code—every field was within 7 percentage points of software engineering.
  
• 
  The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of
• 
  We’re launching Claude Corps, a national fellowship program matching people early in their careers with US nonprofits. We'll teach 1,000 people to use Claude, and pay them to use AI to advance their hosts’ missions.
• 
  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
  
  Today 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…
  
  Replying to @AnthropicAI
  And 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.
  
  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 reposted
  
  Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use. Its capabilities exceed those of any model we’ve ever made generally available.
  
• 
  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:
• 
  Our internal data shows Claude is accelerating AI development—a possible path to recursive self-improvement, or AI autonomously building a more capable successor. It’s happening faster than we thought, and the implications deserve greater attention.
  
  Replying to @AnthropicAI
  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.
  
  Correction: 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 @AnthropicAI
  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.
  
  
  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