Shaw

We’re not aware of any law firm going to this extreme. For example Latham is buying GPUs but is also firm-wide on Harvey and we announced a deeper partnership today. We think that’s the right strategy and what matters more than build vs buy is how heavily a firm is investing in AI. The best way to invest is a portfolio approach of working closely with vertical vendors and the labs and cloud providers while also self-building with open source and the frontier ecosystem. Everything is moving too fast and law firms are too complex for a single binary approach like this post suggests. Law firms will not rely on a single vendor nor will they build the entire stack themselves. My view is that longer term the labs and clouds will build general intelligence infrastructure and vertical players will help customize it for each industry. Then individual firms will leverage their unique data, human expertise and workflows to build differentiated AI to better compete against their peers and serve their clients. Our goal is to build the trusted vertical layer and open it up through APIs so firms can build everything from custom models and context layers to agents and products with us. And most importantly do it in a way they can trust that their data and their clients’ data is safe and only used for their benefit.

Excited to welcome @asadovsky as Harvey’s Chief Research Officer. Before Harvey, Adam co-led post-training at Microsoft AI and Google DeepMind. As a CVP at Microsoft AI, he helped build MAI-Thinking-1, Microsoft’s reasoning model. As part of Gemini’s leadership team he helped train Gemini 1.0 through 2.5, including fine-tuning, RL, data, and evals. His prior work as a Distinguished Engineer at Google spanned Assistant, Search Quality, and Search Infrastructure. I met Adam three years ago when I sent him a cold LinkedIn DM and was surprised he responded. At a time when most dismissed the application layer and legal, Adam was curious and generous with his time. He quickly became someone I regularly turned to for advice on AI as we scaled Harvey over the past three years. When we first met, we were too early to hire someone of his caliber and scale, but I always hoped we’d eventually work together. As Winston and I got to know him better, what stood out even beyond his technical achievements was his character. Despite his incredible technical career, he remains curious, humble, practical, and cares deeply about the teams he builds. We couldn’t think of a better leader to help us build frontier intelligence for the professionals and institutions we serve.

Update on @harvey’s model training effort. We post-trained a model we are calling Tenet: - Achieves SOTA on LAB - Generalizes 3rd party legal benchmarks - Uses sub-agents for domain specific capabilities Tenet uses Kimi K3 as base and was post-trained in collaboration with @FireworksAI_HQ: - Rank-64 LoRA over the full network - GSPO with importance-ratio masking - 134 B300 GPUs for 2 months Despite not being trained on 3rd party legal datasets we found improvements on: - @mercor’s Apex Agents - Corporate Law - @crosbylegal’s Redline Bench - LegalBench Tenet also learned how to use domain-specific subagents (separate post-trained models) for complex tasks: - M&A Diligence: training in an RLM harness for long-horizon tasks (with @baseten) - Review Table: specialist models for high-volume structured data extraction (with @appliedcompute) - Firm Knowledge: parametric memory and structured notes for more efficient enterprise search (with @engram) These results suggest we can significantly scale training and we plan to: - Scale both human and synthetic data significantly and scale training to 1K and then 10K GPUs - This scale will let us move to full parameter fine-tuning and larger models - We are now starting to post train models in our production harnesses - Post-train other open-source base models to provide customers with model choice If these problems sound interesting we are hiring for our post-training team

This is a new paradigm for interacting with Claude that is significantly more "inline" with all the other human activity org-wide. Once you do all of the under the hood engineering work to make this "just work" (e.g. across tools, integrations, compute environments, memory, security, etc.), Claude basically joins the team in a seamless way - you can talk to it as you would talk to a person and it can help with a very large variety of workloads. Imo this is the 3rd major redesign of LLM UIUX. The first paradigm was that the LLM is a website you go to, the second was that it is an app you download to your computer. This third one is that it is a self-contained, persistent, asynchronous entity with org-wide tools and context, working alongside teams of humans. It really takes a while to wrap your head around it, but it works and it is awesome.

Introducing Command Center: the intelligence layer for Harvey deployments. Built for Innovation and Legal Ops leaders, it brings usage analytics, peer benchmarking, agentic insights, and recommendations into one place.