The Answer to Nine Days Ago Is More Interesting Than Either/Or
Writing about Harvey's $550 million round at a $15.5 billion valuation on September 10, this site posed a question: three years out, does the entry point to legal workflow belong to a vertical vendor like Harvey, or to a legal agent supplied directly by a model lab? The risk flagged then was precisely that OpenAI and Anthropic had started working the legal space directly. Nine days later OpenAI shipped a legal configuration — and in the same announcement, **Harvey is listed as an API customer.** So the answer is not replacement but layering: the model vendor takes the model and the search index; the vertical vendor keeps the workflow, the audit trail and the client relationship. Harvey's round was earmarked mainly for building proprietary models, and that move reads more clearly now — it isn't about surpassing its upstream supplier, it's about not being held by the throat by one. Because it is layered, the advice from that piece matters more now, not less: settle in the contract whether templates, retrieval indexes and audit records can be taken with you. The upstream relationship is now a supplier relationship, and which layer your data lives in — and who it follows — is a question for before you sign.
"Not a New Model" Defines the Capability Boundary
This has to be stated plainly: Astra for Law did not change models. The improvement comes from the layer around it — a professional search index plus instructions for legal reasoning and drafting. So the gap between 54% and 38.7% measures the value of *giving it a professional retrieval source*, not the value of *a model that understands law better*. That distinction is directly useful in procurement: if you already have reliable retrieval over legal corpora, what this product adds on top of that is less than the difference between those two numbers suggests. The 54% needs its terms read too: 200 questions, drawn from a third party's private validation set, run by the vendor. Reporting explicitly cautions that it is not a general measure of legal accuracy. One more thing worth recording is the foundation: the case law comes from the nonprofit Free Law Project's CourtListener. Public data infrastructure carrying a commercial product is a pattern this site also met writing about AlphaGenome Atlas on September 10 — a free public layer as the base, with the commercial layer doing access and distribution.
A Product at Roughly Coin-Flip Accuracy Can Only Ship as "the Buyer Must Check the Work"
Read the 54% literally: on that set of legal research questions, close to half come back wrong. That is not a pejorative — it determines the product's shape. The target customers are Am Law 200 firms, where every citation already gets checked line by line, so the division of labor — model drafts, lawyer verifies — is self-consistent, and the positioning matches the capability. The misreading risk is at the other end: **if anyone expects this to remove the verification step, they are using it wrong.** In legal work, skipping the check does not cost you a rewrite; it costs you a wrong authority. Availability is still narrow: selected law firms first via Trusted Access in ChatGPT and Codex, with API access coming. What an evaluator can usefully do now is assemble their own most typical research questions — once the API opens, running your own set will tell you more than those 200 scores.
via: OpenAI: Introducing Astra for Law, SiliconANGLE, Technology.org