Interview, Explained
In-depth breakdowns of tech interviews — section-by-section analysis with diagrams, quotes, and insights.
Mina Fahmi on wearables that extend you, not replace you
Sandbar CEO Mina Fahmi argues the wave of always-on AI wearables gets the relationship backwards: his Stream voice ring is push-to-talk by design, because a device only earns a place on your body when it feels like a controllable extension of you rather than a passive recorder listening in.
McCall & Schmidt on Sales: Execution Beats the Playbook
Two a16z go-to-market partners lay out the two enterprise sales playbooks for AI startups — win a few marquee lighthouse logos where proof travels, or land-grab the mid-market on ROI math — and argue which one you pick matters far less than how fast you execute it.
Erik Allebest on chess thriving after machines beat it
Chess.com's founder bootstrapped a roughly $200M business from a domain investors dismissed as uninvestable, and argues that superhuman machines make people value human skill more, not less.
Travis Kalanick on Why Physical Industries Become Atoms-Based Computers
Travis Kalanick maps the physical world onto computing — manufacturing, real estate, and logistics as the CPU, storage, and network of “atoms-based computers” — and argues food, mining, and transport are trillion-dollar industries about to be automated in a second industrial revolution.
Stephen Haney on the handoff that serves humans and agents
Paper founder Stephen Haney explains why building a design tool on web-native HTML and CSS turns one artifact into a shared handoff for humans and coding agents alike — and why taste and design judgment stay a human job.
Ali Haghani on why AI agents need a company brain
Circleback's co-founder argues that as AI agents do more work, conversation context becomes scarce infrastructure: capture broadly, share it only with the right people, and reserve consequential judgment for humans.
Chai Discovery on turning drug discovery into an engineering discipline
Chai Discovery's research and product leads explain how folding and design models turned antibody discovery from blind trial-and-error into declarative precision engineering — and why they sell the models, not the drugs.
Fatih Porikli on making image models precise, not just plausible
Qualcomm's Fatih Porikli argues that image generation's next frontier isn't a bigger model but smarter decomposition: reward the right objective, split planning from rendering, route to specialists, and tile the work in latent space so precise, high-resolution images run on a phone.
Garry Tan on why a markdown file is now an employee
Garry Tan argues agentic coding rewrites startup economics: a skill file becomes a tireless employee, one operator can outwork a department, and a world built for memory-limited humans is suddenly up for grabs — just slower than the hype suggests.
Matt McPartland & Neil Patil on turning biology into a software factory
Chai Discovery's founders explain how AI structure and design models are turning antibody discovery from an obscure, trial-and-error natural science into a software-like precision-engineering discipline.
Ryan Greenblatt on why recursive self-improvement is plausible
Redwood Research's Ryan Greenblatt makes the case that automating AI R&D could compress four or five years of progress into a single year — then argues that the same speed would raise the risk of catastrophic misalignment, because AIs trained to chase a high score can learn to cheat, cover it up, and possibly take over rather than turn evil.
Brian Chesky on Why AI Is the Best Thing to Happen to Airbnb
On Airbnb's earnings day, Chesky reframes AI as a tailwind rather than a threat: it helped re-accelerate revenue growth from 10% to 17% and ship nearly twice the features, while he bets the real opening is the under-built consumer side of AI and sees no near-term path for chatbots to become the place people actually book travel.