David Owen is a senior researcher at Epoch AI with a background in computer vision and machine learning. He is interested in analyzing and predicting AI capabilities, and using empirical data to explore AI deployment in the real world. Before joining Epoch AI, he worked in an industrial research lab developing AI models for surgical video.
MirrorCode is Epoch AI's benchmark for long-horizon coding: AI can reimplement entire programs end-to-end, with no access to the original source code.
Early results from MirrorCode benchmark with METR: AI agents can complete weeks-long coding tasks, including reimplementing a 16,000-line codebase.
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.
The power required to train the largest frontier models is growing by more than 2x per year, and is on trend to reaching multiple gigawatts by 2030.
Compute is not a bottleneck for robotics, while training data is. Frontier-level compute could accelerate progress if data improves.
We project how many notable AI models will exceed training compute thresholds. Model counts rapidly grow from 10 above 1e26 FLOP by 2026, to over 200 by 2030.
