Anthropic just called for a global way to slow frontier AI because its own models may be approaching recursive self-improvement, where a system helps build a stronger version of itself without direct human control.
Future models will become so good at research, experiments, debugging, and training design that humans will stop being the main bottleneck.
Once that loop starts, progress could shift from human-paced engineering to machine-assisted improvement, which makes every safety test, law, and lab policy feel late by default.
Anthropic says this has not happened yet, but warns that the jump may arrive before governments, companies, and researchers have a trusted way to measure or restrain it.
The hard part is verification, because a huge AI training run is easier to hide than a weapons site, and any lab that secretly keeps training while others pause could gain the lead.
Anthropic is now ~$1T, may reach $50B annualized revenue, and competes fiercely with OpenAI, so every safety claim also lands inside a giant business fight.
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anthropic .com/institute/recursive-self-improvement
Anthropic just disclosed that Claude now writes more than 80% of the production code it merges.
Before Claude Code reached research preview in 02-25, Claude wrote only low-single-digit merged code, while output per engineer has since risen to 8x the 2024 baseline.
The shift comes from agents that edit files, run tests, inspect failures, spawn helper agents, and keep working across longer tasks instead of only suggesting snippets.
Anthropic says reliable task length is doubling about every 4 months, with Mythos Preview reaching at least 16 hours and open-ended Claude Code success hitting 76%.
i.e. Claude Mythos Preview could stay useful on a task that would take a skilled human roughly 16 hours of work
Claude also moved from a 3x training-code speedup to 52x, while a skilled human reached about 4x in 4 to 8 hours on the same setup.
The remaining human edge is research judgment: choosing the right problem, trusting the right result, and knowing when an experiment is dead.