Daily Brief

Full episode: https://www.youtube.com/watch?v=6AgOfiZOWiY Me on twitter: https://x.com/dwarkesh_sp

Databricks had $1.5M in revenue and a board interviewing outside CEOs. Ali Ghodsi on why a16z picked him anyway, and the trial run nobody told him about.

Live from STATION F, AI Engineer Paris 2026 opens with welcome remarks and main-stage talks on AI infrastructure, coding agents, software factories, and building frontier AI. AI Engineer Paris returns for its second edition, bringing together 1,000+ engineers, founders, CTOs, and VPs of AI for two days of technical talks, practical workshops, and AI engineering research. Day 1 main-stage lineup: • Jen Person (Mistral) — Welcome to AI Engineer Paris 2026 • Lia McBride (AI Engineer) — Intro to AI Engineer • Lara Khanafer (STATION F) — Welcome from Station F • Clemens Rawert (Langfuse) — What

A good exec search takes 6 to 12 months. Ali Ghodsi on why people settle, why the signals were always there, and why backdoor references beat front doors.

Feed a model one hour of video and roughly a million visual tokens go in, yet the loss lands on about two percent of them, since the only ground truth is a transcript or a few labeled frames. Armen Aghajanyan calls that a humongous waste, and predicting every pixel treats a background pixel with the same weight as a gripper tip or a contact point. Perceptron's answer is a perceptive objective that learns which percepts will matter, rather than hardcoding the gripper. The second problem is context bloat from cameras that never switch off. Patch averaging buys ten times compression, but his fix

Well under one percent of the Common Crawl corpus that frontier models train on is in an Indian language, even as frontier labs call India a fast growing market. Krishna Prasad Srinivasan says the knowledge exists but was never digitized, and Sarvam's answer is a three billion parameter vision language model, small enough for one GPU, that he says beats document AI models a hundred times larger. The model is unusual twice over. Its backbone is a state space model rather than a transformer, because a page can run to ten thousand visual tokens and quadratic attention gets expensive, while an SSM

Live from STATION F, the AI Engineer Paris 2026 Main Stage continues with a full day of technical talks from the teams building and scaling production AI systems. Watch sessions covering model customization, inference infrastructure, voice and realtime AI, coding agents, software factories, and the emerging AI economy. Day 2 main-stage lineup: • Jakob Pörschmann (Black Forest Labs) — From Pixels to Robots • Dorian Lods (ElevenLabs) — Generating Identity at Scale • Harry Mellor (Hugging Face) — How a Transformers Model Loads in vLLM • Arielle Le Bail (Stripe) — Inside the AI Economy: What St

Full episode: https://www.youtube.com/watch?v=6AgOfiZOWiY Me on twitter: https://x.com/dwarkesh_sp

Ali Ghodsi built a connector in two days. His team was taking three quarters. What he learned about where the time actually goes, and how they got to seven connectors a quarter.

Confido (YC S21) is building the AI workforce for consumer brands, automating everything from accounting to sales and demand planning. They work with brands like Olipop and Dude Wipes, as well as divisions of Mars and Unilever, and recently raised a $55 million Series B led by Insight. In this episode of Founder Firesides, Confido co-founders Justin Hunter and Kara Holinski join YC’s Diana Hu to discuss escaping pivot hell, landing their first customer with a Figma prototype, and winning seven-figure deals for AI that does the work. They also explain how they build without product managers.

From inventing textbook machine learning algorithms to winning an Academy Award and discovering two asteroids, John Platt has spent his career at the intersection of computation and science. In this episode, the Google Fellow joins Latent Space to unpack how AI can accelerate scientific discovery, what it takes to turn a research problem into something an agent can optimize, and why better models still need scientists who know how to avoid fooling themselves. We go deep on Google’s Empirical Research Assistance (ERA): an AI research system that grew out of an attempt to automate Kaggle, combi

What happens when AI stops merely *reading* DNA and starts learning how to *write* it? Eric Nguyen, CEO and co-founder of Radical Numerics and a key figure behind Evo, joins us to explain the leap from early genome language models to Omni—a system that can outperform specialized models on disease-variant prediction, reason across biological sequences, and even begin to “show its work” when designing better molecules. The surprising part is *where those capabilities seem to be coming from*: in several cases, the model learns useful biological structure it was never explicitly taught. But the s