Anthropic co-founder maps out how recursive AI improvement could outpace the humans meant to supervise it

Jack Clark argues in a long essay that the building blocks for AI systems training their own successors are largely in place. He puts the odds at 60 percent by the end of 2028.

In his newsletter Import AI, Anthropic co-founder Jack Clark says public data points to an imminent automation of AI research. What he means specifically is a system that can train a more powerful successor on its own, "no-human-involved." He pegs the odds at roughly 60 percent by the end of 2028, and 30 percent by 2027.

Clark builds his case mainly on benchmark trends. On SWE-Bench, which tests how well AI systems handle real-world GitHub issues, success rates jumped from about two percent (Claude 2, late 2023) to 93.9 percent, essentially saturating the benchmark. The METR time horizons measure, which tracks how complex a task an AI can complete at 50 percent reliability based on how many hours a skilled human would need, climbed from about 30 seconds with GPT-3.5 to roughly twelve hours with today's frontier models. METR researcher Ajeya Cotra thinks 100 hours by the end of 2026 is plausible.