Daily Brief

Sequoia Capital partner Sonya Huang opens our Own Your Intelligence event with the case for why more companies are choosing to own their intelligence, down to the weights. She lays out the four forces driving the shift: cost, speed, performance, and controlling your own destiny, and explains why the race for the application layer is becoming the race for the intelligence layer itself. Sonya shares an opinionated framework for deciding what to own vs. rent when assembling your AI stack, and another for how to get going from zero to one. With today's open-weight models near the frontier, she ar

How does an application company compete with frontier labs that have more money, talent, compute, and data? At Sequoia Capital’s Own Your Intelligence event, Harvey co-founder and president Gabe Pereyra shares the playbook: leverage the frontier ecosystem instead of building everything yourself. Gabe walks through how Harvey built its research lab, starting with benchmarks like LegalAgentBench and its open-sourced diligence dataset, using domain experts to guide synthetic data generation, partnering with multiple neolabs for post-training, and building the serving and evaluation infrastructur

a16z's Joel De La Garza is joined by Emilio Escobar, Chief Information Security Officer at Datadog, to discuss what it takes to secure a company where nearly every employee is using AI and more than 4,000 engineers are working with coding agents. Rather than trying to block new tools, Emilio explains why Datadog chose to embrace AI early and build the security infrastructure needed to use it safely. They unpack how AI changes traditional assumptions around data permissions, credentials, developer access, and software supply chains. Emilio shares how Datadog uses role-based MCP servers and eph

Sonnet 4.5 developed what Anthropic's Applied AI team came to call context anxiety: approaching its context window limit, it would wrap work up early and stop with room to spare. They built context resets into the harness to compensate. Then Opus 4.5 shipped without the behavior, and the fix turned into pure overhead, adding latency and discarding cache it should have kept. That is the principle Gagan Bhat and Isabella Kai He build the whole session on: a harness encodes assumptions about what the model cannot do on its own, and those assumptions go stale as models improve. The architectural

When should a company move from prompting to post-training its own models? Fireworks AI co-founder and CEO Lin Qiao lays out the full progression at Sequoia Capital’s Own Your Intelligence event, from prompting and RAG to supervised fine-tuning, preference tuning, reinforcement learning, and distillation. And she explains which technique solves which problem. Lin also covers the pitfalls teams hit along the way: prioritizing data quantity over quality, relying on vibes instead of systematic evals, sloppy RL environments, and reward hacking. She shares how companies like Cursor have used post

RL environments have become the hottest topic in AI training data. But what are they, exactly? At Sequoia Capital’s Own Your Intelligence event, Mercor CEO Brendan Foody breaks down the three components: worlds (the messages, docs, and files of a real project), apps (high-fidelity clones of tools like Salesforce and Google Workspace), and tasks (prompts paired with rubric-based verifiers). He walks through a real legal environment built with lawyers from top firms, and shares post-training results showing dramatic gains on domain-specific tasks from modest compute. Brendan also covers why hum

A Qwen thinking model was taking up to 80 turns to submit on SWE bench. Applied Compute wanted it wrapping up by turn 40 and got the submit tool call rate from 22% to 60% with test pass rate flat. The interesting part is the mechanism: because the rollout was conditioned on an old production trace that never called the tool, the teacher never touched the tool call tokens at all. It moved the reasoning path toward the call instead, and the call followed. Sam Denton's frame is a grid. One axis is how online the traces are, from a single dump of production traces to a unified engine where servin

Build the thousand example eval suite everyone tells you to build, switch harnesses, and 80% of it stops meaning anything. Ben Hylak's complaint is that eval advice is still written for the chatbot era, back when you knew the answer to nearly every question a user would ask. His reframing is that the useful question is not what issues your agent has, since it will have effectively infinite issues, but which ones matter. That turns on the gap between your ceiling, the most impressive thing your agent can do, and your floor, the worst. The floor is what breaks trust: recommending a competitor, d

A profile ChatGPT keeps on Shlok Khemani says he travelled to Turkey in 2025. He never has. The memory came from conversations where he was choosing between Turkey and Thailand, he went to Thailand, and the profile kept both with overlapping dates. What bothers him is not the mistake but the incuriosity: nothing notices the conflict, and the evidence to settle it was sitting in his email as flight and hotel bookings. He calls that a product problem, not a technology one. The rest is a year of reverse engineering how consumer memory systems are built, all of it his reading from the outside rat

Does your agent get dumber after the first compaction? After the second? You cannot read that off the code, only off the traces, and there are far too many to read yourself. So LangChain points agents at the traces of other agents and asks exactly that, alongside questions like where users got upset and what a different model would have done at the same step. Vivek Trivedy's argument is that observability and continual learning are the same problem in different clothing, because an agent acting in an environment produces the only real record of what happened, and that record is the substrate e

Chai Discovery co-founder Matt McPartlon and product lead Neil Patil explain why pharma abruptly started buying AI design tools this year instead of forcing every AI-bio company to go build its own pipeline — and what specifically changed in the models to make drug design teams trust them. We trace the lineage from Chai-1, which they open-sourced for reasons that had almost nothing to do with the model itself, through the Chai-2 campaign that convinced Lilly, Pfizer, Novartis, and argenx to sign. Along the way: why their CEO says the company's real competitor is a mouse; the cryo-EM result so