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. And there’s not especially anyone else who isn’t saying this.”
Beth and host Rob Wiblin discuss all that, plus much more.
Learn more and read the full transcript on the 80,000 Hours website.
This episode was originally released in June 2025.
Chapters:
- Cold open (00:00:00)
- Who is Beth Barnes? (00:01:19)
- Can we see AI scheming in the chain of thought? (00:01:52)
- The chain of thought is essential for safety checking (00:08:58)
- Alignment faking in large language models (00:12:24)
- We have to test model honesty even before they're used inside AI companies (00:16:48)
- We have to test models when unruly and unconstrained (00:25:57)
- It's essential to thoroughly test relevant real-world tasks (00:30:40)
- METR's research finds AIs are solid at AI research already (00:49:33)
- AI may turn out to be strong at novel and creative research (00:55:53)
- When can we expect an algorithmic 'intelligence explosion'? (00:59:11)
- Recursively self-improving AI might even be here in two years — which is alarming (01:05:02)
- Could evaluations backfire by increasing AI hype and racing? (01:11:36)
- Governments first ignore new risks, but can overreact once they arrive (01:26:38)
- Do we need external auditors doing AI safety tests, not just the companies themselves? (01:35:10)
- A case against safety-focused people working at frontier AI companies (01:48:44)
- The new, more dire situation has forced changes to METR's strategy (02:02:29)
- AI companies are being locally reasonable, but globally reckless (02:10:31)
- Overrated: Interpretability research (02:15:11)
- Underrated: Developing more narrow AIs (02:17:01)
- Underrated: Helping humans judge confusing model outputs (02:23:36)
- Overrated: Major AI companies' contributions to safety research (02:25:52)
- Could we have a science of translating AI models' nonhuman language or neuralese? (02:29:24)
- Could we ban using AI to enhance AI, or is that just naive? (02:31:47)
- Open-weighting models is often good, and Beth has changed her attitude to it (02:37:52)
- What we can learn about AGI from the nuclear arms race (02:42:25)
- Infosec is so bad that no models are truly closed-weight models (02:57:24)
- AI is more like bioweapons because it undermines the leading power (03:02:02)
- What METR can do best that others can't (03:12:09)
- What METR isn't doing that other people have to step up and do (03:27:07)
- What research METR plans to do next (03:32:09)
Video editing: Luke Monsour and Simon Monsour
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong
Music: Ben Cordell
Transcriptions and web: Katy Moore
