An audio version of my blog post, Thoughts on AI progress (Dec 2025)

What It Covers

Dwarkesh Patel examines contradictions between short AGI timelines and current reinforcement learning approaches, arguing that models lack human-like on-the-job learning capabilities essential for broad automation.

Key Questions Answered

• •RL Training Paradox: Labs spend billions having PhDs create training examples for specific tasks like Excel or web browsing, suggesting models cannot learn on-the-job like humans who adapt without rehearsing every software tool beforehand.
• •Deployment Reality Check: If models truly matched human capability, they would generate trillions in annual revenue matching global knowledge worker wages, but current figures fall orders of magnitude short, revealing capability gaps despite benchmark improvements.
• •Continual Learning Timeline: Achieving human-level on-the-job learning may require five to ten years beyond initial continual learning releases, similar to how GPT-3 demonstrated in-context learning in 2020 but improvements continue today across comprehension and context length.