Moish Peltz

We open-sourced our firm's generative AI policy. Free, public domain, yours to use at the link below. Every law firm (and company) needs an AI policy. Big players have AI counsel and governance teams, but everyone else is forced to reinvent the wheel as AI keeps advancing. So we gave ours away (CC0): a law-firm template and a business template, ready to adapt yourself or customize with a Claude skill, with no permission or attribution. Real credit to my colleagues on the FRB AI Committee who built and battle-tested this with me. This is the policy we actually adopted at our law firm, not a hypothetical. My hope is that it becomes a commons. Find a gap, cover a new jurisdiction, sharpen a section? Open a PR and contribute it back. One firm's policy becomes every business's starting point. Take it, use it, help make it better.

Fable on API is about $600/hour lol, our jobs might be safe for a while.

Our goal isn’t to get a data advantage over our law firm clients and even if we wanted to we can’t for exactly the reason you described - the majority of their data is actually client data so even a law firm like Kirkland can’t take all that data and train a model on it bc it would break confidentiality. The two things firms can do are 1) firm-specific models - encode all of your firm knowledge in client agnostic ways to train models (templates, deal points, using your lawyers feedback on non-client data, disentangle your firm’s trajectories from client data, etc) and 2) client-specific models - for deep enough client relationships work with your client to build a joint model where both parties benefit from converting the relationship into AI. This is especially good for firms with long lasting relationships with their clients to make those relationships stickier. The best way to train either of these models is building a product that centralizes the entire client matter process (i.e. allows you to do an acquisition end-to-end and capture the entire trajectory and feedback across the entire team of associates, partners, client). These products will be specialized by practice area (M&A completely different from fund formation which is completely different from IP litigation). Without something like this it’s very hard to get meaningful signals from individual associate queries on a chat based product. We think this general product / infra will be shared across firms but then highly customized and the customizations and data will be owned by the law firms and their clients. Another problem we want to help law firms and their clients solve is how do you manage client data at scale when you are doing this type of training? The Fortune 500 customers we work with already struggle with professional services providers and data security because most of the work is done over email, downloaded onto desktops and printed out. And it’s not the law firm’s fault - they need to stitch together many different products to support all the needs of their clients' regulatory and security requirements. This is already a huge challenge and model training on top of this is going to make things infinitely more complicated. We’re building a collaborative platform (shared spaces) that allows law firms and clients to securely share data on client matters and build custom agents for their clients in a secure way. Eventually this will become infrastructure that allows firms to train client-specific models and for clients to have the piece of mind that the data they are sharing is isolated from other clients and it’s being used to their exclusive benefit. I think this is a good example of a very vertical product that it probably doesn’t make sense for a single law firm or model provider to build. So I agree there isn’t a market for laptops for lawyers but I think there probably is for infrastructure that enables enterprises to manage all their internal and external legal spend, workforce, agents, processes and the same for law firms. Compute is a great moat but in the cloud era most SAAS companies didn’t build datacenters and many were still wildly successful by building on top of the cloud providers. I think we will see the same thing in the next decade as well with respect to model providers.

A company spent $500M in a month on Claude because no one set a usage limit

Someone built an algorithm to find inactive Bitcoin wallets, reported them to the NYPD as "lost property" - and is now suing to own them. 39,069 wallets. ~3.8M BTC. $286 billion. No private keys. Just a court order. The most audacious Bitcoin lawsuit in history.

Starting June 15, paid Claude plans can claim a dedicated monthly credit for programmatic usage. The credit covers usage of: - Claude Agent SDK - claude -p - Claude Code GitHub Actions - Third-party apps built on the Agent SDK