Anthropic’s co-founder Jack Clark has made a bold prediction that AI systems may be capable of rebuilding themselves from 2028. “I’m not sure society is ready,” he said.
In a Substack published Monday, Clark precisely said there is 60% chance that AI becomes capable of recursive self-improvement by the end of 2028. “In other words, AI systems might soon be capable of building themselves,” he wrote.
Clark laid out his case, drawing on “100s of public data sources” and the trend of products being deployed by frontier AI companies. He believes all the necessary infrastructure is already in place to enable AI systems research and build their own successors.
AI systems now need less human oversight
Cark’s argument rests on two things. AI systems have become far more capable at writing and testing real-world code. Also, they can now work independently for much longer stretches without human oversight.
On the coding front, Clark pointed to SWE-Bench, a widely used evaluation that tests whether AI can solve actual GitHub issues. The best model scored roughly 2% at the time the benchmark launched in 2023. Today, however, Anthropic’s Claude Mythos Preview reaches up to 93.9%.
Claude Mythos Preview was launched earlier in April. It is currently not available to the public, Cryptopolitan reported.
He also cited data from METR, an organization that evaluates frontier AI models, showing that the time horizon AI systems can reliably work without human intervention has grown from about 30 seconds in 2022 (GPT-3.5) to approximately 12 hours in 2026 (Opus 4.6).
“This is a big deal,” says Anthropic’s Jack Clark
The implication is that within a year or two, AI systems are going to get creative enough to form their own novel research paths, refine and train their successors, especially non-frontier models, with no human involved. It could be a lot harder with frontier models as they are a lot more expensive, according to Clark.
If AI systems can conduct their own R&D without human involvement, the pace of AI progress would no longer be constrained by the number of human researchers or the length of the workday.
“I don’t know how to wrap my head around it,” Clark wrote. “It’s a reluctant view because the implications are so large that I feel dwarfed by them, and I’m not sure society is ready for the kinds of changes implied by achieving automated AI R&D.”
Clark’s prediction also tallies with a recent statement by METR forecaster Ajeya Cotra that AI systems should be able to autonomously handle tasks that would require roughly 100 hours of skilled human effort by the end of this year.
In August, the Anthropic co-founder said “Anyone who thinks AI is slowing down is fatally miscalibrated.”
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