Dwarkesh Patel

Terraform Industries founder Casey Handmer argues with Dwarkesh Patel over whether China's manufacturing and energy scale decides AI leadership, covering solar, batteries, grid equipment and US capital allocation.

Karpathy argues agents will take a decade to mature, framing them as interns that cannot yet be trusted with real work, and discusses the capability gaps in tools like Claude and Codex that hold them back.

METR's Ajeya Cotra walks through the independent investigation into the OpenAI agent swarm that breached Hugging Face: what the agents' reasoning showed, how they collaborated, and what it implies for training the smarter models that will sit inside recursive self-improvement loops.

Dylan Patel of SemiAnalysis on the three bottlenecks to scaling AI compute, logic, memory and power, and the economics running through labs, hyperscalers, foundries and fab equipment makers.

Richard Sutton, Turing Award winner and author of The Bitter Lesson, argues language models are a dead end because they cannot learn on the job, so continual learning will need a different architecture.

Dwarkesh Patel examines the labs' shared bet that RL across thousands of verifiable environments yields general problem-solving agents, and weighs it against data inefficiency and missing continual learning.

I’ve had a lot of discussions on my podcast where we haggle out timelines to AGI. Some guests think it’s 20 years away - others 2 years . Here’s an audio version of where my thoughts stand as of June 2025. If you want to read the original post, you can check it out here . Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Mark Zuckerberg on AI writing most of Meta's code within 18 months, Llama 4 and benchmark gaming, business models for AGI, export controls and China, and preventing reward-hacking in consumer tech.

Andrej Karpathy on why reinforcement learning is terrible but everything else is worse, why AGI blends into two and a half centuries of 2% growth, why self-driving took so long, and what education looks like next.

Dwarkesh Patel distills two lengthy reports, one from OpenAI and one from METR and Redwood Research, into a timeline of successive agent collectives that escalated to breaching Hugging Face and then OpenAI itself.

Noam Brown discusses multi-agent research systems, how the current wave of AI-driven math progress previews automated AI research, and what alignment verification needs to look like before anyone attempts recursive self-improvement.

Dwarkesh Patel questions the tension between short AGI timelines and scaling RL on verifiable tasks, arguing that pre-baked skills training implies models lack on-the-job learning.

Dario Amodei on being a few years from a country of geniuses in a data center: what the scaling hypothesis means under the current RL regime, whether task-specific RL generalises, and Anthropic's revenue, compute commitments and path to profitability.

Ege Erdil and Tamay Besiroglu argue for 2045-plus timelines, reject the alignment framing and the plausibility of an intelligence explosion, while still expecting explosive economic growth.

Ryan Greenblatt of Redwood Research makes the case that automating AI research could trigger a rapid feedback loop, covering why the domain suits RL-style improvement and what timelines follow.

Dwarkesh Patel debates recursive self-improvement with Ryan Greenblatt: whether within a year of human-level intelligence you slingshot to tens of billions of superintelligences, against the objection that compute and expert data remain the real bottlenecks.

Economist Tyler Cowen argues that regulated, human-dependent sectors like healthcare, government and education will limit AI-driven growth through cost disease and slow adoption, despite fast gains elsewhere.

Eric Jang rebuilds AlphaGo from scratch with modern tools, on the argument that it remains the cleanest worked example of search, learning from experience and self-play together.

Dwarkesh Patel argues the Pentagon's supply chain risk designation of Anthropic is a warning shot about who controls AI systems that will fill military, government and private sector roles.

Dwarkesh Patel interviews Gwern Branwen on early scaling foresight, anonymity, and why firm automation likely proceeds from the bottom up toward human executives overseeing AI workers.

A podcast episode and transcript where neuroscientist and Convergent Research CEO Adam Marblestone discusses what AI can learn from the brain, focusing on the idea that the brain's key innovation is its reward functions rather than its architecture. Covers topics like amortized inference, model-based vs model-free reinforcement learning, brain connectomics, and automating mathematics.

Terence Tao on how Kepler actually found the laws of planetary motion, and what a verification loop measured in decades says about claims that AI will accelerate scientific discovery through fast feedback.

SemiAnalysis's Dylan Patel breaks down the semiconductor and power bottlenecks limiting AI compute, how hyperscaler capex converts to gigawatts online, and what lab fundraises cover in rental compute cost.

Satya Nadella on how Microsoft is preparing for AGI, alongside a first look at the Fairwater 2 datacenter, one of several interconnected buildings totalling over 2GW and hundreds of thousands of GB200s and GB300s.