Interview, Explained
In-depth breakdowns of tech interviews — section-by-section analysis with diagrams, quotes, and insights.
~80 minNoam Brown on why the model, not the swarm, did the hard part
OpenAI's Noam Brown explains how many agents scale test-time compute in parallel, why the underlying model deserves the credit for cracking a Millennium Prize Problem, and why aligning these systems is the unsolved problem that worries him most.
~50 minGreg Brockman on the AGI era and pacing the frontier
OpenAI president Greg Brockman argues the AGI era has quietly arrived as a capability, so the hard problem is now pacing it: keeping safety, security, and access ahead of models powerful enough to run for a full day or be deployed ten thousand at a time.
~65 minMehtaab Sawhney & Mark Sellke on why AI reaches the results humans gave up on
OpenAI's models are producing short, human-like proofs for open math problems, shifting the bottleneck from proving results to understanding and organizing them.
~39 minSam Altman on why intelligence on tap raises the bar
Altman argues AI agents compress startup execution from months to minutes, raising the ambition bar while making the concentration of power the central safety risk to design against.
~71 minAkshay Nathan on One Shared Harness, Two UX Shells
OpenAI merged Codex's agent power into ChatGPT Work behind one shared harness after non-developers quietly adopted the coding agent, betting that as AI blurs job roles the product should route users rather than box them in.
~36 minNoam Brown on Why Reasoning Models Need Budget Curves — Interview, Explained
Noam Brown argues that reasoning models must be evaluated as budget curves, because more test-time compute can unlock capabilities that static benchmark grids hide.
~41 minMark Chen on Why You Can't Cheat the Real World
OpenAI's Chief Research Officer explains why he bets on the exponential, grades progress only against metrics that can't be faked, and runs research like a trader's book of high-risk bets.