Epoch After Hours

AI benchmarks saturate quickly, struggle to capture what we care about, and cost more than ever to build. But are they doomed? Greg Burnham, who leads Epoch's benchmarking team, and Tom Adamczewski, who developed MirrorCode, push back on the pessimism and dig into what the next generation of AI benchmarks could look like.

Daniel Litt is a professor of mathematics at the University of Toronto. He has been a careful observer of AI’s progress toward accelerating mathematical discovery, sometimes skeptical and sometimes enthusiastic. 

Topics we cover: the hardest problems models can solve today, whether there is convincing evidence that AI is speeding up math research, and what’s missing before AI might have a shot at solving Millennium Prize problems.

We also discuss how to measure progress in math, including Epoch AI’s new FrontierMath: Open Problems benchmark which evaluates models on meaningful unsolved math research problems.

Professor Luis Garicano isn’t your usual academic economist. Academically, his theories have heavily influenced how modern economists understand the structure of firms and the labor market. But his influence hasn’t been confined to the ivory towers of academia — Luis spent three years in the EU parliament, seeing first-hand how EU policy gets made. This has given him an unusually grounded view of how institutions actually work.

Through this institutional lens, Luis has been keeping an eye on how organizations like the EU have been responding to rapid AI developments — and he’s deeply concerned. 
In this episode, Luis chats with our co-hosts Andrei Potlogea and Anson Ho about: 
Why he disagrees with Daron Acemoglu about the macroeconomics of AI and how policy should orient to this
How AI could disrupt the training ladder for entry-level workers, such that they can’t learn economically valuable skills—with major consequences.
Why he thinks the EU AI Act has many major issues, and what he would like the EU to do instead

-- Episode links --
Youtube: https://youtu.be/L8IRbTab2Fk
Transcript: https://epoch.ai/epoch-after-hours/luis-garicano-not-so-simple-macroeconomics-of-ai

-- Timestamps --

0:00:00 – Will AI trigger explosive growth?
0:06:26 – Short-run macroeconomic effects
0:11:29 – The decline of junior jobs
0:20:21 – The missing training ladder
0:39:31 – Europe’s AI regulation problem
0:52:46 – Who captures AI value?
01:08:17 – AI, interest rates & fiscal future

Stanford economist Phil Trammell has been rigorously thinking about the intersection of economic theory and AI (incl. AGI) for over five years, long before the recent surge of interest in large language models.
In this episode of Epoch After Hours, Phil Trammell and Epoch AI researcher Anson Ho discuss what economic theory really has to say about the development and impacts of AGI: what current economic models get wrong, the odds of explosive economic growth, what “GDP” actually measures, and much more!
-- Episode links --
Transcript: https://epoch.ai/epoch-after-hours/economics-of-ai
-- Timestamps --
00:00 Problems with existing work on the economics of AI
10:18 Declining returns to R&D
18:28 What real GDP misses
26:57 Task-based models & AI automation
49:32 The limits of economic theory
01:09:11 How to detect an economic singularity
01:23:32 Increasing returns to scale
-- Credits --
Design: Robert Sandler
Podcast Production & Editing: Caroline Falkman Olsson & Anson Ho
 
Special thanks to The Producer’s Loft for their support with recording and editing this episode — https://theproducersloft.com/

What will AI progress look like over the next 15 years? Informed by current trends, Epoch AI researchers Jaime Sevilla and Yafah Edelman argue that the default expectation should be wild. They discuss whether AI will solve the Riemann Hypothesis in 5 years, what AI agents will be able to do in 2030, and what happens if we have 100,000 self-improving robots. They also explore what might make progress much faster or slower than they expect. 
0:00:00 - Preview
0:00:41 - Intro: Does 5× compute scaling continue?
0:08:15 - Largest training run in 2030 & what does it imply?
0:12:44 - Impact on Software Engineering & other cognitive tasks
0:23:27 - Economic impacts near the end of the decade
0:31:34 - 2030 bifurcation: Slow down or take off?
0:35:49 - Physical vs cognitive automation
0:44:37 - Timelines and impact of full cognitive automation
1:02:37 - Returns to intelligence
1:08:51 - Three cruxes after 2035 (Robots, technology & intelligence)
1:16:28 - What happens in 2040?  
1:23:16 - Recap: Three eras of forecasting
1:37:42 - Closing remarks
For full transcripts of all Epoch After Hours episodes, visit: https://epoch.ai/epoch-after-hours
-- Credits --
Participants: Jaime Sevilla & Yafah Edelman
Design: Robert Sandler
Podcast Production & Editing: Caroline Falkman Olsson & Anson Ho
 
Special thanks to The Producer’s Loft for their support with recording and editing this episode — https://theproducersloft.com/