Six: Beth Barnes on the most important graph in AI right now — and the 7-month rule that governs its progress

About this episode

In 2024, AI models had a 50% chance of successfully completing a task that would take a human expert one hour. Seven months before that, that number was roughly 30 minutes — and seven months before that, 15 minutes.

These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.

Beth Barnes is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities. Beth’s team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI models perform on the same work.

The resulting paper from METR, “Measuring AI ability to complete long tasks,” made waves by revealing that the planning horizon of AI models was doubling roughly every seven months. It’s regarded by many as the most useful AI forecasting work in years.

The companies building these systems aren’t just aware of this trend — they want to harness it as much as possible, and are aggressively pursuing automation of their own research.

That’s both an exciting and troubling development, because it could radically speed up advances in AI capabilities, accomplishing what would have taken years or decades in just months. That itself could be highly destabilising (as we explored in a previous episode in this series: Will MacAskill on AI causing a “century in a decade” — and how we’re completely unprepared).

And having AI models rapidly build their successors with limited human oversight naturally raises the risk that things could go off the rails, if the models at the end of the process lack the goals and constraints we hoped for.

Beth thinks models can already do “meaningful work” on improving themselves, and she wouldn’t be surprised if AI models were able to autonomously self-improve in as little as two years — in fact, she says: “It seems hard to rule out even shorter [timelines]. Is there 1% chance of this happening in six, nine months? Yeah, that seems pretty plausible.”

While Silicon Valley is abuzz with these numbers, policymakers remain largely unaware of what’s barrelling toward us — and given the current lack of regulation of AI companies, they’re not even able to access the critical information that would help them decide whether to intervene. 

Beth adds: “The sense I really want to dispel is, ‘But the experts must be on top of this. The experts would be telling us if it really was time to freak out.’ The experts are not on top of this. Inasmuch as there are experts, they are saying that this is concerning. … And to the extent that I am an expert, I am an expert telling you you should freak out.