Anthropic Revealed a Possible Path to AGI
Anthropic may have just published the closest thing we've seen to a roadmap for AGI. Not because they announced a new model. But because they explained how AI could eventually learn to improve AI.
When AI
Builds Itself
Anthropic is handing more of its own engineering and research to AI. The data says the loop is already starting to close.
01 The idea
Recursive self-improvement
An AI capable enough to design and train the next, better version of itself. Anthropic says we are not there yet — and that it isn't inevitable. But the gap is closing from every direction at once, and the charts below are why they think it could arrive sooner than institutions are ready for.
02 How fast it's moving
Two years ago, minutes of work. Now, a full day.
Task length AI can finish on its own
log scale · METR / Anthropic
By April 2026, Mythos Preview could work 16+ hours — the upper edge of what METR can even measure.
Speedup on a fixed code-optimization test
vs the starting code · same correctness checks
On this narrow task, Claude went from helpful to superhuman in under a year.
03 Inside the lab
Humans now direct and review. Claude writes.
Share of Anthropic's merged code written by Claude
before Claude Code → today
Leadership puts the all-in figure (scripts, experiments) at 90%+.
Success on the hardest, open-ended tasks
no clear spec · Claude Code sessions
Up 50 points in six months — on problems with no obvious answer.
Can the model pick a better next step than the human?
% of real research detours where its move was judged better
A stress test on detours where the human's choice had room to improve.
Three facts from the same period
04 When AI runs the research
Handed an open safety problem, agents designed every experiment themselves.
How much of the target gap got closed
weak-supervises-strong project · % of floor-to-ceiling gap recovered
Humans still chose the problem and the scoring rubric — but the agents ran everything in between.
"The future is now."
An Anthropic researcher, on getting a week's worth of results back from Claude in a day or two — with, in their words, pretty minimal help.
05 The wider reality
It's not just Anthropic, and not just code.
06 Three ways it could go
What happens next
The curve bends
Exponentials turn into S-curves. Compute, energy, or chips become the ceiling — but today's tools still spread widely.
Considered unlikelyCompounding gains
Development is largely automated; humans still set direction. Huge productivity — and real risks of misuse.
The likely pathIt builds itself
AI gains the ingenuity to design its own successors. Pace is set by compute; humans move to oversight.
Hardest to predictOne caution runs through all three — Amdahl's law: speeding up one part of a process just shifts the bottleneck to whatever hasn't sped up. A lab can run at the speed of compute, but drug trials, elections, and trust still move at human pace.
07 The ask
The world should have the option to slow down
Anthropic's position: safety research and institutions need a way to keep pace. The hard part isn't the will to stop — it's verification. Training runs are easier to hide than missile silos, and whoever quietly continues while others pause could inherit the lead.
A pause by one lab only changes who's ahead. A real one needs several frontier labs, in several countries, stopping under the same conditions — and able to check each other. The window to work that out is now.
Distilled from “When AI Builds Itself” — Marina Favaro & Jack Clark, The Anthropic Institute. Charts redrawn from the article's figures; values as of May–June 2026.
Source: anthropic.com/institute/recursive-self-improvement