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AI may reshape the economy more profoundly than any technology in generations. There are strong arguments that automation and productivity gains will be transformative, but precisely what these effects will be and when they will arrive is still uncertain. Epoch examines these questions through both empirical research and formal economic modeling, covering the effects of automation on jobs and wages as well as whether and to what degree AI-driven productivity gains could accelerate economic growth.
We surveyed over 2,000 Americans on how they use AI at work: who uses it, how much, which services, and whether it's replacing or creating tasks.
A fast increase in go-to-market roles, and hints about upcoming products
These benchmarks track a wide range of digital work. Progress will correlate with economic utility, but tasks are too self-contained to indicate full automation.
Beyond benchmarks as leading indicators for task automation
In this episode, economist Luis Garicano chats with the hosts about macroeconomic and labor market effects of AI, with a focus on the EU.
The existing debate rests on data and assumptions that are shakier than most people realize. To make progress, we need better evidence, and experiments are the best way to get it on the margin.
OpenAI has the inference compute to deploy tens of millions of digital workers, but only on a narrow set of tasks – for now.
Stanford economist Phil Trammell joins Epoch AI to explore AGI, growth, GDP limits, and what economic theory can tells us about the future of AI.
If scaling persists to 2030, AI investments will reach hundreds of billions of dollars and require gigawatts of power. Benchmarks suggest AI could improve productivity in valuable areas such as scientific R&D.
Epoch AI researchers Jaime Sevilla and Yafah Edelman forecast AI progress to 2040: coding automation, 10% GDP growth, and wild uncertainty after 2035.
GATE model shows AI-driven growth surges more easily than expected and supports much larger investments—advocating moderate optimism.
Why don't AIs automate more real-world tasks if they can handle 1-hour ones? Anson Ho explores key capability and context bottlenecks.
In this Gradient Updates weekly issue, Ege discusses the case for multi-decade AI timelines.
The real reason that AI benchmarks haven’t reflected real-world impacts historically is that they weren’t optimized for this, not because of fundamental limitations – but this might be changing.
In this podcast episode, two Epoch AI researchers with relatively long and short AGI timelines candidly examine the roots of their disagreements.
We introduce a compute-centric model of AI automation and its economic effects, illustrating key dynamics of AI development. The model suggests large AI investments and subsequent economic growth.
AI's biggest impact will come from broad labor automation—not R&D—driving economic growth through scale, not scientific breakthroughs.
AI's “train-once-deploy-many” advantage yields increasing returns: doubling compute more than doubles output by increasing models' inference efficiency and enabling more deployed inference instances.
This Gradient Updates issue explores how AGI could disrupt labor markets, potentially driving wages below subsistence levels, and challenge historical economic trends.
Epoch AI presents their first podcast, exploring AI scaling trends, discussing power demands, chip production, data needs, and how continued progress could transform labor markets and potentially accelerate global economic growth to unprecedented levels.
This Gradient Updates issue investigates the economic consequences of fully automating remote work.
AI could accelerate AI R&D, especially in coding and debugging tasks. We explore AI researchers’ differing predictions on automation, and their suggestions for designing AI R&D evaluations.
The returns to R&D are crucial in determining the dynamics of growth and potentially the pace of AI development. Our new paper offers new empirical techniques and estimates for this crucial parameter.
Economists have proposed several different approaches to predicting AI automation of economically valuable tasks. There is vast disagreement between different approaches and no clear winner.
Our new article examines why we might (or might not) expect growth on the order of ten-fold the growth rates common in today’s frontier economies once advanced AI systems are widely deployed.
AI’s potential to automate labor is likely to alter the course of human history within decades, with the availability of compute being the most important factor driving rapid progress in AI capabilities.
We have developed an interactive website showcasing a new model of AI takeoff speeds.
We summarize and compare several models and forecasts predicting when transformative AI will be developed.
I give a visual explanation of Ajeya Cotra’s draft report, Forecasting TAI with biological anchors, summarising the key assumptions, intuitions, and conclusions.