Training Data

The race to build superintelligence is producing models that keep getting better at objective problems, but not at behaving like actual people. Joon Sung Park, founder and CEO of Simile and creator of Stanford's "Smallville" generative agents study, argues that simulating human society requires a fundamentally different kind of model. He frames today's frontier models as the "CPU of intelligence"—rational, superhuman at problems with right answers—and Simile as creating the "GPU of intelligence," built to encode the diversity of people's values, preferences, and tastes. It simulated 1,000 Americans and predicted their behavior 85% as accurately as people reproduce their own answers. CVS uses it for concept testing; some customers simulate their own earnings calls. Joon's larger bet: a "CERN of human society" that could one day model bank runs, climate cooperation, or the early signals of a collapsing democracy.

Hosted by Sonya Huang, Sequoia Capital

The entire startup ecosystem is racing to build agent harnesses. Logan Kilpatrick, who leads Google AI Studio and the Gemini API, argues that scramble has a roughly 12-month shelf life. Models will absorb the scaffolding and run it natively, so the edge moves elsewhere. Google's own bet runs in parallel: a single agent harness, born from the Windsurf team and now called Antigravity, has become the connective tissue across search, the Gemini app, Cloud, and AI Studio — the role Gemini-the-model used to play. Logan makes the case that coding already feels like narrow superintelligence, and that "jagged" vertical superintelligence (in math, finance, and science) will arrive well before AGI. He argues Google's real goal is maximizing outcomes for users, not eyeball time. He unpacks Omni, the single model built to replace multiple separate systems Google once trained for text, audio, music, image, and video. His throughline: AI is an accelerant for human ambition, not a substitute for it.

Hosted by Sonya Huang, Sequoia Capital

Jensen Huang, founder and CEO of NVIDIA, makes the case that computing is undergoing its biggest shift in 60 years: from retrieval, where data centers store files we look up, to generation, where every word, image, and video is produced in real time and customized for whoever is asking. He explains why NVIDIA's AI factories are the dynamos of this era: machines that take in electrons and send out tokens of intelligence, just as Siemens' dynamo once turned motion into electricity. Jensen frames intelligence as the third force to "cocoon" the planet after electricity and the internet. He describes the five-layer cake of AI investment—energy, chips, infrastructure, models, applications—and dismantles the fear that AI will erase jobs, using radiology and software engineering to show how automation raised labor demand instead of killing it. His bottom line: you won't lose your job to AI, but you might lose it to someone who uses AI.

Hosted by Konstantine Buhler, Sequoia Capital

Alfred Wahlforss, co-founder and CEO of Listen Labs, is building an AI agent that interviews your customers at a scale no focus group ever could—thousands of voice conversations at once, drawn from an audience of 30 million people. A year after launch, Listen serves hundreds of Fortune 100s to Startups including Microsoft, Google, NBC Universal, P&G, Anthropic, Cursor, and Cognition. Alfred explains the counterintuitive finding underneath it all: people are often more honest with an AI than a human interviewer, opening up to a non-judgmental entity that costs less and never makes them feel rushed. He walks through why interview transcripts—not credit card data or behavioral logs—turn out to be the richest fuel for predicting how customers will behave, how Listen back-tests its simulations to know which questions it can and can't answer, and why 80% of the company's engineering goes into building the right audience. As AGI makes building trivial, Alfred argues the scarce resource becomes knowing what to build. That's the loop Listen wants to own.

Cursor's Federico Cassano and Fireworks' Dmytro Dzhulgakov explain how they collaborated to build Composer as a specialized foundation model. The core insight: models have finite capacity in their weights, and allocating all those bits to the singular task of software engineering in Cursor frees the model to be both better at the task and far more efficient at inference. Rather than start from pre-training and work up, they took an unconventional top-down approach — mid-training and RL on top of an open-source base to get a useful model into users' hands fast, then specializing the model around real Cursor usage. With Fireworks providing distributed infrastructure, Composer delivers frontier-class coding performance with the speed of a much smaller model.

Hosted by Sonya Huang, Sequoia Capital