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Had so much fun giving this talk at @sequoia about @harvey’s moneyball approach to building a research lab. The biggest mistake I made in the early days of Harvey was trying to play the Yankees baseball style of frontier intelligence. I found out the hard way that we were the Oakland As - we couldn’t raise the capital or attract the talent to build a frontier lab. However a lot has changed since then and it now feels possible to build frontier intelligence without a frontier budget. Winston and I’s most quoted line from Moneyball is “We can recreate him in the aggregate” when Billy Bean talks about his strategy for building the team The talk outlines our playbook to building frontier intelligence in the aggregate and how we leveraged the frontier ecosystem to do so. This is only now possible with inference providers like @FireworksAI_HQ and @baseten, neolabs like @trajectorylabs, @appliedcompute, and @EngramLab, data providers like @mercor, eval infra like @LangChain and many more. I talk about how we build training data and benchmarks, work with the neolabs and training infra providers to post-train, and give an overview of our serving and eval infra to ensure post trained models work in our product. At the end of Moneyball Billy says that if they don’t win everyone will dismiss this strategy but “if we win, with this budget, and this team, we will have changed the game”. Every application layer company, software company, frontier ecosystem company and startup now has a massive opportunity to play moneyball for frontier intelligence. Go change the game.

Steve Zad joins Harvey today as Chief Revenue Officer. Steve joins from Rubrik, which he helped scale from $50M to $1.5B ARR through a 2024 IPO. We’re excited to have Steve lead GTM at Harvey after adding $100M+ net new ARR last quarter.

Introducing Harvey Research: We've shared our model strategy. We've open-sourced Legal Agent Benchmark, the largest benchmark for long-horizon legal work spanning 1,200 tasks across 24+ practice areas. And we've collaborated on research with leading neolabs and inference providers like @baseten, @trajectorylabs, @LangChain, @FireworksAI_HQ, @appliedcompute, and @EngramLab. Now we have a home base for it. Live at:

When @mudithj and I met @gabepereyra, we were expecting just another vanilla intro call and instead had the best yarn about research, the state of LLMs, and where intelligence is actually heading. It's rare to meet a founder this deep in the weeds who's also building for one of the most important verticals in this new age of intelligence So it was awesome to sit down with Gabe for an extended discussion on what it take to build agents that can reliably complete work over hours, days, or even longer? We talked about why agents today struggle with search and long context windows and how techniques like KV-cache compaction, synthetic data, and continual learning could help. 0:00 Introduction 0:36 Getting legal agents to review the whole data room 2:08 Data rooms larger than any context window 5:28 How far open-source models can go 7:58 Where specialist models fit in legal AI 10:59 Training legal models when client data is off-limits 13:06 Teaching a model how a law firm works 13:59 What belongs in context vs. model weights 15:36 From firm-wide AI to a model for every lawyer 18:37 What training adds beyond retrieving the right cases 20:26 Why context windows have plateaued 24:01 How models could learn continuously on the job 26:12 Can AI recursively improve AI research? 27:07 Research agents can run experiments but not choose them 30:00 Why open-ended research is hard to train 33:47 Why deployment, not intelligence, is the bottleneck 35:08 The cost of frontier intelligence 36:59 Different neolabs, different paths to intelligence 39:26 Using open datasets to compare research methods 41:13 Conclusion

In 2018, Sarah Guo became the youngest general partner in Greylock's 60-year history. She was 28. Four years later, she quit to launch Conviction, a firm staked entirely on AI. Before ChatGPT shipped, she seeded Baseten and Harvey; each is now worth over $11 billion. In Conviction's first year, she wrote early checks into Sierra, Cognition, and Mistral; those three companies are now worth, together, $54 billion. Andrej Karpathy worked out of Conviction's office until Anthropic hired him in May. Guo has been close to Jensen Huang for over a decade. Her first two calls after starting the firm were to Sam Altman and Nat Friedman. And yet the investor closest to the AI frontier is betting against its biggest companies. The two big frontier labs, worth close to a trillion dollars apiece, no longer just want to build the models. They also want to build every product and company on top of them, leaving nothing for anyone else. The market is paying as though they might succeed. Of the $300 billion in venture capital deployed in the first quarter of 2026, the biggest quarter in the history of the trade, 65% went to only four companies: Anthropic, OpenAI, xAI, and Waymo. Guo is betting the labs can't build everything, and she spends her days making sure of it. She won Harvey its first client. She flew across the country to take a single Baseten candidate to a four-hour lunch. On one wedding anniversary, she spent the whole weekend on back-to-back calls, keeping two founders on the line so they couldn't speak to rival firms. Twice a year, she flies the world's brightest young founders to San Francisco and inducts them into the fight. In the months @domcooke spent reporting this piece, @saranormous had her fourth child, walked the Met Gala in 45 pounds of chainmail, and still answered her founders' texts within minutes. Guo's parents arrived from China in 1987 with $50, built a company, and took it public at $1.2 billion. Then it went bankrupt. Guo grew up inside that startup. She built its first website, did her homework in a cubicle, and slept over for bug bashes. She loved it. If two labs build everything, no one gets to do that again. Welcome to Sarah's Wager. Read it below.

Here's what Harvey Engineering shipped in Q2: - Long Horizon Agents - Contract Intelligence - Command Center - Cloud Agent Platform - Agentic Word - Reliability Sprint - Agent Builder v2 - Legal Agent Benchmark (LAB) - Research Collaborations Thread of highlights:

We are hiring for @Harvey’s model training team. This team will help Harvey expand from the application layer into the model layer and from legal into high end knowledge work more broadly. We are hiring AI researchers of all seniority, particularly those with experience post-training frontier or open source models. Our program is centered around large-scale model training, synthetic data generation, long horizon reinforcement learning, and rigorous evaluation in real world deployments. We are scaling-pilled and believe that nothing beats the combination of larger models and better training data. We’ve been able to generate incredibly realistic legal environments and validated that this allows us to post-train open source models to achieve frontier performance with agents. We plan to scale up these data generation and training efforts significantly across legal to start, and eventually other verticals. As a researcher, you will have access to thousands of GPUs and unique training data from our product and customer relationships. Your research will inform Harvey’s product strategy and power AI used for some of the most economically and societally impactful work in the world.