We are working with @EngramLab to train models that can understand a law firm’s knowledge. A huge source of differentiation for a law firm comes from all the past work the firm has done. When an associate is working on a new deal they have access to decades of similar deals the firm has done and a big part of the job is knowing how to effectively leverage that knowledge. However, a large amount of this data is either client data or derived from client data which means you can’t naively train all of it into a single model because this would mean breaking confidentially. In order to separate the research problem from the data privacy and deployment problem we built a synthetic law firm. @ItsJulioPereyra wrote an awesome article describing the dataset, tasks and approach to creating a synthetic version of the type of data you would expect to find at a large law firm. The firm has 46 clients, 266 client matters and each client matter contains work product, versions, emails, drafts, etc that you might find when searching the DMS of a firm. This dataset allows us to explore how well agents can reason across large corpuses of knowledge (100M+ tokens) and require complicated multi-hop reasoning. For example, answering a query like “in similar deals, how did we structure the reps and warranties” requires the model to understand what makes deals similar and then search over all past deals to find examples. We find that generic agent approaches for this type of reasoning are both expensive and not exhaustive and there is a lot of room for improvement. Excited to open source this dataset and also share more results soon

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.

A&O Shearman was the first law firm to pilot Harvey in late 2022. @GabrielMacht sat down with David Wakeling, the partner and board member who led A&O's early bet on Harvey.

Q2 recap for @harvey - +$100M NNARR - 53% DAU/MAU Key hires (including Q1) - Anique (CPO) - prev VP of Product at Rippling - Rachel (CMO) - prev CMO at Notion - Brooks (CISO) - prev CISO at Roblox - Keith (CSO) - prev CPO at Google Product - Agent unification - cloud agents can use all Harvey product surfaces - Command center (EA) - monitor adoption and ROI by use case - Contract intelligence (EA) - agentic contracting platform for enterprises Eng - Migration to cloud agent infrastructure - Integrating open source inference providers - Scaling document processing (54TB / week) AI - Legal Agent Bench - Open source post training - Published multiple research directions with partners We invested heavily in cloud agent infrastructure at the end of last year and in Q1. In Q2 we also unified many of our product surfaces (collapsed as @winstonweinberg says) by making them all tools accessible by our cloud agents. Prior to this, there were a lot of capabilities in Harvey that were often only discovered by power users. As cloud agents get better and our product becomes more connected we are seeing users discover more of the product by learning from their agents (see plot of product surfaces per user).

Model strategy for @harvey: We are working on the first model in our legal foundation model series, inspired by @cursor_ai's Composer. Two goals: 1. Allow us to serve frontier intelligence across our product surface areas at an affordable price and a strong security posture. 2. Create the foundations for law firms to build their own specialized models and own their own intelligence. The model series will focus on complex client matters that span months and take dozens of associates. The agentic system will learn to control legal tech tools, sub agents and ask for help from frontier models or human partners, much like a senior associate. We’ve open sourced benchmarks for evaluating our initial post training work that represents work done by associates and in-house lawyers. We are scaling these significantly using synthetic and human pipelines as well as building private evals for firms. Open sourcing this data has allowed us to quickly validate the feasibility of post training open weight models for legal work. With our research partners we’ve already shown promising results post training open source models to approach frontier performance: 1. @baseten - novel compaction strategies for analyzing large data rooms. 2. @FireworksAI_HQ - matching frontier performance by using frontier as an advisor. 3. @appliedcompute - improving performance and reducing cost of large scale review tables. 4. @trajectorylabs & @nvidia - sovereign continual learning over client matters. We plan to continue to invest heavily in working with research partners and open sourcing our data, models and research as much as possible. We believe open research in legal will be important to building trust in the frontier ecosystem. We are also scaling our research team. Harvey Labs is our internal research group, responsible for pushing the frontier of legal intelligence and working closely with labs, research partners, and academia to bring the frontier of agent research into Harvey. Labs is run by @nikogrupen and @ItsJulioPereyra - Niko worked on multi-agent RL at Google Brain and Julio clerked and worked in BigLaw. We believe this pairing is crucial for building frontier legal AI systems. Together they have already made significant progress in scaling our data and training efforts. The long term goal of Harvey Labs is to contribute to the research and infrastructure required for the legal industry to create a frontier ecosystem. We believe that the best version of legal super intelligence is one where each law firm, enterprise and government owns their own specialized version. We are hiring for Harvey Labs across the post training, agent and data stack and open to acquiring talented teams / neolabs in this space. If interested please DM me.