How Close Are We to AGI? Inside Epoch's GATE Model
On this episode, Ege Erdil from Epoch AI joins me to discuss their new GATE model of AI development, what evolution and brain efficiency tell us about AGI requirements, how AI might impact wages and labor markets, and what it takes to train models with long-term planning.
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Episode Description
On this episode, Ege Erdil from Epoch AI joins me to discuss their new GATE model of AI development, what evolution and brain efficiency tell us about AGI requirements, how AI might impact wages and labor markets, and what it takes to train models with long-term planning. Toward the end, we dig into Moravec's Paradox, which jobs are most at risk of automation, and what could change Ege's current AI timelines.
You can learn more about Ege's work at https://epoch.ai
Timestamps: 00:00:00 – Preview and introduction
00:02:59 – Compute scaling and automation - GATE model
00:13:12 – Evolution, Brain Efficiency, and AGI Compute Requirements
00:29:49 – Broad Automation vs. R&D-Focused AI Deployment
00:47:19 – AI, Wages, and Labor Market Transitions
00:59:54 – Training Agentic Models and Long-Term Planning Capabilities
01:06:56 – Moravec's Paradox and Automation of Human Skills
01:13:59 – Which Jobs Are Most Vulnerable to AI?
01:33:00 – Timeline Extremes: What Could Change AI Forecasts?