Interview, Explained
In-depth breakdowns of tech interviews — section-by-section analysis with diagrams, quotes, and insights.
Bret Taylor on why you'll stop paying per token
OpenAI chairman and Sierra CEO Bret Taylor argues that as frontier intelligence becomes a rented commodity, enterprises should stop paying per token and start paying for outcomes — and build their moat on customer relationships, not models.
Amjad Masad on the story that kept Replit alive
Replit's CEO on how founder-led storytelling — going direct, building in public, and refusing to get canceled — kept the company alive years before the product caught up.
Lila Sciences Is Turning the Lab Into an Infinite Token Generator
Lila Sciences applies the bitter lesson to science: a general reasoning model proposes hypotheses, a flexible automated lab runs the experiments, and nature verifies the results, turning experiments into the next internet-scale dataset while making the model itself the product.
Michael Kratsios on why adoption wins the AI race
The White House's top science advisor lays out how America keeps its AI lead: coordinating a decentralized government, opening classified military AI to every major vendor, using frontier models to harden cyber, regulating by use case, and winning through adoption at home and abroad.
Joyce Ruffell & Chase Holden on why racing's data wars reward the organizer
An OpenAI researcher and a racing-software founder describe a sport drowning in data, where AI earns its keep by making the softer, unstructured material usable alongside the telemetry — and where the edge still lands on the human who knows what to ask for.
Andrew Dai on the Jagged Frontier That Left Vision Behind
Andrew Dai argues AI's progress is a jagged frontier — really strong at math, coding and Go, still poor at counting the glasses on a table — and he raised $55 million to post-train that gap.
NVIDIA Engineers on Why Agents Scaled Only After They Got a Sandbox
Two NVIDIA engineers walk through the AI factory they built for themselves — a slow, long-lived stack underneath, a workload layer that churns on top, and a secure workspace in between that finally let agents run unattended, while internal demand grew to four trillion tokens a month.
Chris Taylor & Eddie Siegel on why four people can transform a business
The founders of Ode (with Anthropic) argue enterprise AI wins when a tiny team of elite generalists rewires real workflows — disciplined by evals every few days and a hard 3-to-6-month ROI clock — because the scarce ingredient was never the model, it's the applied-AI team that turns capability into an outcome.
Nick Bostrom on Why the Intelligence Explosion Must Be Steered, Not Stopped
Philosopher Nick Bostrom tells Joe Rogan that superintelligence may arrive within a few years, that the hard part is steering it — technically and politically — rather than stopping it, and that if we get it right the deepest questions won't be about survival but about what humans become and where they find meaning.
Rick Smith on making the bullet obsolete
Axon's founder is re-engineering lethal force out of public safety: close the reliability gap between a Taser and a gun, then hand the trigger to AI-targeted drones that are paid for by business and operated by police.
Joe Lonsdale on why AI rewards speed and creative destruction
Investor Joe Lonsdale reads AI as a time-compressing, price-resetting industrial revolution, and argues the returns go to whoever moves fastest and tears down slow incumbents — from the FDA to the defense primes.
Dan Biderman on why AI needs a nervous system, not just notes
Dan Biderman argues that long context and RAG alone can't give enterprise AI durable memory — context rot and the KV-cache make memory a systems problem — so models need compact knowledge trained into weights alongside text, not just longer context.