CSO @ Harvey - building the future of legal + AI, board member@GTM, former CPO @ Google, partner@GDC. I write about how AI is reshaping the legal profession.
Joined January 2022
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Keith Enright retweeted
It’s almost incomprehensible how wide the distance is between someone who’s working at the frontier with AI and basically everyone else right now.
The crazy thing is these lines are not converging, but only growing farther apart. If the curve of AI progress had leveled off, it would certainly have given most people and companies time to catch up or implement their workflows in a standardized way. The technology would have neutralized competitive advantage because everyone would be using it the same way.
But what’s happened is the opposite. The curve of AI progress is basically going vertical right now, which means if you had codified your processes 3-6 months ago you’re likely already behind again. Now imagine most companies that kicked into gear a year ago and thought that they were done. Even further behind now.
I’ve never seen a technology in my life and career that improves at this rate, which consequently means you need to upgrade your expectations at a similar rate. Our brains weren’t wired for this, and our organizational change management processes certainly weren’t either.
With any form of recursive self improvement, things just get wilder. It’s a time when you will need to be constantly reevaluating the things that didn’t work just a couple months ago to see if they now can be solved. AI native companies certainly have a strong advantage due to this right now, so now it comes down to can the bigger companies keep up. Wild times.
Keith Enright retweeted
👒H/t to @winstonweinberg and @harvey. This is fantastic PR and crisis response!
This is how you engender positive sentiment about you and your company. "Never let a crisis go to waste," doesn't have to have a negative connotation.
Keith Enright retweeted
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.
Keith Enright retweeted
How @ShawnFarsai uses @figma @ElevenLabs and @claudeai to create hyper personalized marketing videos with agents
Keith Enright retweeted
A closer look at Harvey Graph.
Starting with graphs for US Case Law and Spaces, Harvey Graph maps the connections between matters, documents, and legal knowledge so agents can start work with the context they need.
In early Legal Agent Bench tests, adding a context graph raised rubric pass rates on firm-knowledge questions from 65% to 80%.
Keith Enright retweeted
We’re opening a Boston office this fall.
The office will bring us closer to the law firms and in-house legal teams we work with across New England and help us grow the team serving them.
We’re hiring in Boston across sales, recruiting, legal engineering, and customer success.
Keith Enright retweeted
When I shared @harvey’s model strategy a few months ago, there were two parts:
1. Build our own model
2. Use that to help customers do the same
We’ve done the first. Now we’re hiring for the second: Harvey’s Private Model Program.
We’re seeing huge demand from law firms to own their own intelligence leveraging private data.
This will enable them to become frontier firms that get smarter with every client matter.
You’ll:
- Partner with law firms to build these systems.
- Build the team that delivers this at scale.
- Work with our technical org to define the platform that powers this team.
We’re looking for a technical PM or founder type to own Harvey’s Private Model Program.
DM me if this sounds interesting.
Keith Enright retweeted
The hardest thing about building @harvey is doing what’s best for our customers despite immense pressure to do what’s easy.
The easy thing would have been to force our customers onto consumption pricing before they were ready and serve them worse models to protect our margins.
We chose to help our customers transition on a timeline that works for them and give them the best models in the meantime, even though it hurt our margins.
This meant optimizing our product through routing, harness improvements, and post-training so we could serve frontier intelligence at an affordable price.
It also meant building the infrastructure for customers to monitor and manage spend: usage dashboards, per-matter cost attribution, spend caps, and ROI reporting.
As a result, we improved our gross margins from -50% to positive in a single quarter despite usage doubling month over month and continuing to serve the best models.
Our philosophy is simple: do what’s best for our customers, even when it’s painful, hurts our margins, or draws criticism from competitors, X, and the press.
We believe the most important part of building a company is earning and keeping your customers’ trust. You do that by doing the hard thing for them, even when it costs you.
Keith Enright retweeted
Tenet is @harvey's first model post-trained for legal
It reaches frontier performance on Legal Agent Bench and generalizes to other agentic benchmarks
Tenet comes with three subagents post-trained for specialized tasks: M&A diligence, review tables, and firm knowledge
It's Harvey's first major step towards building frontier legal intelligence and enabling firms to build their own specialized models
More from @gabepereyra:
Keith Enright retweeted
Introducing Harvey II: smarter agents from the start.
- Featuring Harvey Tenet, our first model trained for legal work
- Built around matters and projects
- Agents start with the files, context, permissions, and history they need
- Assign tasks to lawyers or agents, then track and review the work
- Harvey remembers how you work and writes like you
Today we’re launching Harvey II: a whole new design, smarter agents that have memory, and a custom model post-trained for legal work.
Read more here!: harvey.ai/blog/introducing-h…
Harvey is hiring a government affairs and public policy leader. The role: engage policymakers in the US and around the world so AI strengthens the legal profession and expands access to justice. You'd build the program working directly with me.
harvey.ai/company/careers/c5…
I recently had the pleasure of being a guest speaker with Prof. Suraj Srinivasan as part of the Harvard Business School Executive Education AI Strategy and Execution Program. We discussed the case he co-authored on Harvey, how things have changed since the case was written last year, and the state of disruption in the legal services ecosystem. As a student and friend of Prof. Srinivasan, I always appreciate the opportunity to compare notes with him and his class, and to talk about the velocity of change I’m seeing in the business and practice of law. (hbs.edu/faculty/Pages/item.a…)
Honored to join UC Law San Francisco's Advisory Board on Technological Innovation in Legal Practice & Education.
AI is transforming how law is practiced. Excited to help shape how the next generation of lawyers is trained for it.
I can't overstate how exciting it is for me and the team at @harvey to be deployed by @Microsoft across their Corporate, External, and Legal Affairs organization. I've had the privilege of working with Microsoft lawyers and compliance professionals for many years, and they're incredibly sophisticated. Microsoft choosing Harvey to support and accelerate their work is a compelling signal of our capability and value for in house teams.
Keith Enright retweeted
Getting this dynamic right is not trivial and is why vertical AI is valuable --> "The key is to optimize the cost-to-outcome frontier in real world context. In practical terms, that means using the right model for each task, and optimizing the context, skills, tools, and agent harness around it."
It's great to see @harvey's founders @winstonweinberg and @gabepereyra continuing to push the envelope on how technology can better support a rapidly changing legal ecosystem. Post-training, truly massive data room capabilities, synthetic legal matters for model training, and more to come...
Investing heavily in post-training at @harvey.
@gabepereyra, @oneill_c, and @mudithj on what we’re working on including 100M-token data rooms and synthetic legal matters for model training
I'm particularly excited about our work to expand the legal agent bench. Supporting the global legal community with clear, actionable intelligence about how particular models perform against real-world legal work flows is essential to the responsible evolution of the legal profession. @gabepereyra and his team's work is foundational to making prescient, data-driven decisions around where to invest when building for future capabilities.
We're open sourcing 10 diligence RL environments to expand @harvey's legal agent bench:
- The largest one is 80M tokens
- Human review of a dataroom costs ~$300K-$1M lawyer time
- Each one has 100-1000 unit tests for automated evaluation
This dataset will allow us to train diligence agents that can take an entire dataroom, client communication and deal context and product a diligence memo.
Current frontier agents struggle due to dataroom size and domain knowledge around diligence but we are seeing improvements with post training.
Awesome write up by @ItsJulioPereyra
A great write up by @ItsJulioPereyra :
nitter.cf/ItsJulioPereyra/status…
