Are we truly on the verge of AI automating its own research and development? In this deep-dive episode of the MAD Podcast, Matt Turck sits down with Mostafa Dehghani, a pioneering AI researcher at Google DeepMind whose work on Universal Transformers and Vision Transformers (ViT) helped lay the groundwork for today's frontier models.

Moving past the hype, Mostafa breaks down the actual mechanics of "thinking in loops" and Recursive Self-Improvement (RSI). He explores the critical bottlenecks holding back true AGI—from evaluation limits and formal verification to the brutal math of long-horizon reliability.

Mostafa and Matt also discuss the shift from pre-training to post-training, how Gemini's Nano Banana 2 processes pixels and text simultaneously, and why the "frozen" nature of today's models means Continual Learning is the next massive frontier for enterprise AI and data pipelines.

(00:00) Intro

(01:17) What “loops” in AI actually mean

(05:04) Self-improvement as the next chapter of machine learning

(07:32) Are Karpathy’s autoresearch agents an early form of AI self-improvement?

(08:56) AI building AI: how close are we?

(10:02) The biggest bottlenecks: evals, automation, and long horizons

(12:36) Can formal verification unlock recursive self-improvement?

(14:06) What is model collapse?

(15:33) Generalization vs specialization in AI

(18:04) What is a specialized model today?

(20:57) Could top AI researchers themselves be automated?

(24:02) If AI builds AI, does data matter less than compute?

(26:22) Post-training vs pre-training: where will progress come from?

(28:14) Why pre-training is not dead

(29:45) What is continual learning?

(31:53) How real is continual learning today?

(33:43) Mostafa Dehghani’s background and path into AI

(36:13) The story behind Universal Transformers

(39:56) How Vision Transformers changed AI

(43:47) Gemini, multimodality, and Nano Banana

(47:46) Why multimodality helps build a world model

(52:44) Why image generation is getting faster and more efficient

(54:44) Hot takes

(54:53) What the AI field is getting wrong

(56:17) Why continual learning is underrated

(57:26) Does RAG go away over time?

(58:21) What people are too confident about in AI

(59:56) If he were starting from scratch today