johncrox

Thanks to Ashwin Acharya, David Schneider-Joseph, and Tom Davidson for extensive discussion and suggestions. Thanks to Aidan O’Gara, Alex Lintz, Ben Cottier, James Sanders, Jamie Bernardi, Rory Erlich, and Ryan Greenblatt for feedback. Part 1: Executive Summary There's growing sentiment in the AI community that artificial general intelligence (AGI[1]) could arrive in the next few years, driven by the release of “reasoning models” like OpenAI’s o1 and DeepSeek’s R1, rising datacenter investments, and public statements from AI insiders. Is this largely hype leading people to panic[2], or is there good evidence to support accelerated timelines? The most detailed formal model we have for analyzing the growth in AI’s impacts and capabilities—Tom Davidson's Takeoff Speeds (TS) model published in 2023—originally predicted AI systems capable of full economic automation[3] around 2040. This model remains our single best tool for analyzing timelines and takeoff dynamics, as it systematically incorporates multiple considerations and feedback loops in AI development. When updated with recent evidence, the TS model now suggests full economic automation is likely to arrive by 2030. Two key changes drive this update: 1. Faster scaling of AI inputs: Analysis from Epoch AI finds that AI has been scaling 5 times faster than the TS model previously estimated. This acceleration stems from both increasing compute investments and faster-than-expected improvements to algorithmic efficiency. 2. Current AI R&D capabilities: Measures like METR's Research Engineering Benchmark (RE-Bench) and surveys of AI researchers suggests that current AI systems can already automate something like 1-10% of AI R&D tasks. This observation, combined with the TS model's framework for relating compute to capabilities, suggests that the compute requirements for AGI are lower than the model’s original estimates. Key Implications Under a range of different assumptions, the model predicts we may be enteri