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

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

AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes. (See graph.)

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

Today’s guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.

Links to learn more, video, highlights, and full transcript: https://80k.info/bb

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 “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.

Beth has found models can already do “meaningful work” improving themselves, and she wouldn’t be surprised if AI models were able to autonomously self-improve as little as two years from now — in fact, “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.”

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 a concerning risk. … And to the extent that I am an expert, I am an expert telling you you should freak out.


What did you think of this episode? https://forms.gle/sFuDkoznxBcHPVmX6


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)
  • Each 7 months models can do tasks twice as long (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)

This episode was originally recorded on February 17, 2025.

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

Episoder(299)

#2 - David Spiegelhalter on risk, stats and improving understanding of science

#2 - David Spiegelhalter on risk, stats and improving understanding of science

Recorded in 2015 by Robert Wiblin with colleague Jess Whittlestone at the Centre for Effective Altruism, and recovered from the dusty 80,000 Hours archives. David Spiegelhalter is a statistician at the University of Cambridge and something of an academic celebrity in the UK. Part of his role is to improve the public understanding of risk - especially everyday risks we face like getting cancer or dying in a car crash. As a result he’s regularly in the media explaining numbers in the news, trying to assist both ordinary people and politicians focus on the important risks we face, and avoid being distracted by flashy risks that don’t actually have much impact. Summary, full transcript and extra links to learn more. To help make sense of the uncertainties we face in life he has had to invent concepts like the microlife, or a 30-minute change in life expectancy. (https://en.wikipedia.org/wiki/Microlife) We wanted to learn whether he thought a lifetime of work communicating science had actually had much impact on the world, and what advice he might have for people planning their careers today.

21 Jun 201733min

#1 - Miles Brundage on the world's desperate need for AI strategists and policy experts

#1 - Miles Brundage on the world's desperate need for AI strategists and policy experts

Robert Wiblin, Director of Research at 80,000 Hours speaks with Miles Brundage, research fellow at the University of Oxford's Future of Humanity Institute. Miles studies the social implications surrounding the development of new technologies and has a particular interest in artificial general intelligence, that is, an AI system that could do most or all of the tasks humans could do. This interview complements our profile of the importance of positively shaping artificial intelligence and our guide to careers in AI policy and strategy Full transcript, apply for personalised coaching to work on AI strategy, see what questions are asked when, and read extra resources to learn more.

5 Jun 201755min

#0 – Introducing the 80,000 Hours Podcast

#0 – Introducing the 80,000 Hours Podcast

80,000 Hours is a non-profit that provides research and other support to help people switch into careers that effectively tackle the world's most pressing problems. This podcast is just one of many things we offer, the others of which you can find at 80000hours.org. Since 2017 this show has been putting out interviews about the world's most pressing problems and how to solve them — which some people enjoy because they love to learn about important things, and others are using to figure out what they want to do with their careers or with their charitable giving. If you haven't yet spent a lot of time with 80,000 Hours or our general style of thinking, called effective altruism, it's probably really helpful to first go through the episodes that set the scene, explain our overall perspective on things, and generally offer all the background information you need to get the most out of the episodes we're making now. That's why we've made a new feed with ten carefully selected episodes from the show's archives, called 'Effective Altruism: An Introduction'. You can find it by searching for 'Effective Altruism' in your podcasting app or at 80000hours.org/intro. Or, if you’d rather listen on this feed, here are the ten episodes we recommend you listen to first: • #21 – Holden Karnofsky on the world's most intellectual foundation and how philanthropy can have maximum impact by taking big risks • #6 – Toby Ord on why the long-term future of humanity matters more than anything else and what we should do about it • #17 – Will MacAskill on why our descendants might view us as moral monsters • #39 – Spencer Greenberg on the scientific approach to updating your beliefs when you get new evidence • #44 – Paul Christiano on developing real solutions to the 'AI alignment problem' • #60 – What Professor Tetlock learned from 40 years studying how to predict the future • #46 – Hilary Greaves on moral cluelessness, population ethics and tackling global issues in academia • #71 – Benjamin Todd on the key ideas of 80,000 Hours • #50 – Dave Denkenberger on how we might feed all 8 billion people through a nuclear winter • 80,000 Hours Team chat #3 – Koehler and Todd on the core idea of effective altruism and how to argue for it

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