Interview, Explained
In-depth breakdowns of tech interviews — section-by-section analysis with diagrams, quotes, and insights.
~61 minSeema Amble & Steven Sinofsky on why software isn't losing its head
a16z's Seema Amble and Steven Sinofsky argue that 'headless' software doesn't kill the incumbents — because the value was never the UI but the codified business logic, exceptions, and institutional memory underneath, and agents change how you reach that value, not who has to own it.
~24 minRick 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.
~8 minJudson Althoff on Selling Outcomes, Not AI Adoption
Microsoft is standing up a 6,000-person, ~$2.5B Frontier Company unit to push enterprise AI past adoption pilots into outcome-tied deployments, staffed with industry veterans and built on a model-diverse platform that spans 11,000 models.
~61 minIsaiah Taylor on Why Nuclear's Bottleneck Is Speed, Not Design
Valar Atomics founder Isaiah Taylor argues nuclear's real bottleneck is speed and scale, not physics — so his company manufactures reactors through hardware iteration, minimizes accident consequences instead of odds, and races to make energy cheap enough to feed AI's exploding demand for power.
~15 minHPE and NVIDIA on the AI Factory: Data In, Intelligence Out
HPE's Thierry Piennar and NVIDIA's Kaushik Shirhatti reframe the 'AI factory' from a rebranded GPU cluster into an operating model — where the decisive work is data quality, organizational unity, and intentional prioritization, not the silicon.
~151 minAravind Srinivas on why cheap cognition makes curiosity priceless
When cognition costs the same as compute, value moves to what stays scarce — curiosity and good questions — and Srinivas argues that owning your own AI is how individuals keep that power.
~109 minGenesis: AI drug discovery is a science of resolution
Genesis Molecular AI's Evan Feinberg and Sergey Edunov explain how they ported the LLM scaling playbook to 3D molecular structure — synthetic physics data, inference-time 'thinking' in crystal structures, and agents — betting that sub-angstrom resolution is the threshold that turns AI drug discovery from pattern-matching into real medicines.
~94 minGrant Sanderson on Why Math's Hardest Work Resists the Benchmark
AI is racing through math because math is verifiable and grindable, but asking the right question, coining the right definition, and drawing the improbable connection resist every benchmark, so the mathematician's job shifts toward curation.
~70 minDylan Patel on the 100x hiding in hardware-software co-design
SemiAnalysis founder Dylan Patel argues the biggest AI gains no longer come from faster chips alone — they come from co-designing models, kernels, and silicon together, turning three stacked 2x wins into a single 100x, and reshaping the NVIDIA-vs-TPU, CUDA-moat, and compute-crunch debates in the process.
~23 minGavriel Cohen on Why AI Agents Are Never Deploy-and-Forget
NanoClaw's founder argues that autonomous work agents are living infrastructure, not software you ship once: because the model underneath keeps changing and the agent can be hijacked, you build it small and auditable, isolate its credentials, roll it out one person at a time, and maintain it forever.
~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.