i'm hiring ~1,000 lawyers in the next 90 days to help train frontier AI models. $100–120/hr. 20–40 hrs/week. remote. like reviewing a first year's memo, except the first-year is an agent. interested? reply with your practice area and i'll dm you

client goals set the direction, firm standards set the floor, lawyer taste sets the style. but the same lawyer redlines differently for every client, so the persona worth modeling is the lawyer-client pair, not the lawyer

the next big buyer of legal work is going to be an agent that knows when it's out of its depth.

all of this is already normal in software, where an agent has a PR up before anyone's even read the ticket. coding agents got there first because engineers do their homework in public and get graded by tests in seconds. lawyers do theirs in private and get graded by opposing counsel years later, so models have read the law but barely watched anyone practice it. biglaw invented RLHF a century ago and called it the cravath system. associate drafts, partner redlines, repeat for eight years, except the reasoning behind every redline stays in the partner's head. getting that reasoning out of partners' heads and into the training loop is the real bottleneck for legal AI, and it's why demand for lawyers to train models is the highest i've seen in 10 years

models learned law from what ends up in court, which is like learning marriage from divorce filings. most legal work is deals that close and advice that keeps people out of court, and almost none of it ever ends up anywhere a model can read

AI isn’t taking lawyers out of legal work, it’s moving them from the start of the workflow to the end of it. agents do the first draft, lawyers make the final call, and the final call was always what clients were paying for.

you forgot the strongest one: already being an approved vendor, because nobody goes through procurement twice

the first question about AI was whether it could produce an answer, and the question that matters now is whether it can finish a job, handle the exceptions, and leave behind work someone is willing to sign.

running a marketplace is hard without a balance sheet because the client needs a lawyer by monday and pays you in 60 days. but that lawyer gets paid every two weeks (lawyers have rent, clients have procurement portals). and if one timesheet gets disputed the whole invoice sits, so you're floating payroll for people who aren't on your payroll. that 60-day gap where you're profitable on paper and broke on payday is the part nobody puts in the deck, but it's also the moat, because lawyers stay wherever the money shows up on time. every marketplace is a bank with a matching feature, and the matching was never the hard part.

an AI data company can have recurring revenue and still start from zero on every order. imagine a model flags a risky contract clause but misses an exception in an amendment. lawyers correct it, the lab gets useful data, and it orders another batch. on a revenue chart, that's a great account, but what got easier for the vendor? a vendor that's learning knows which lawyers catch exceptions like that, can tell a missing document from bad legal reasoning, and has a rubric the next group of lawyers can actually use. a vendor that isn't learning rebuilds recruiting, training, and QC on every order. i'd want to see gross margin on the second order, accounting for how hard the work is, and, more than the number, the reason it moved. a new model may need entirely new examples, and the lab's documents stay the lab's, so the learning has to live in the process: choosing experts, designing tasks, and getting work accepted on the first pass. after the experts get paid and the lab gets its data, what does the vendor know that it didn't before? it's the question i'd ask any expert marketplace, ours included. repeat demand can fund a business for years. i'd want to know whether each order makes the company better at the next one.

why isn’t anyone interviewing james cameron on ai?

legal AI runs on models, software and judgment. - the companies building the leading models are worth trillions. - the leading legal AI software companies are worth billions. - @lawtrades is the network of lawyers who supply that judgment, and it's valued at a fraction of either. the market is pricing the tools and underpricing the people who know when the answer is right. judgment is the one input models can't generate and apps can't ship without.

if you're evaluating an AI law firm, ask for revenue and margin instead of deal value or country count, because those are the numbers that tell you whether the model works

jevons paradox, legal edition: when legal work gets cheaper, companies don't buy less of it, they open more matters. matters per company have nearly doubled since 2017, and AI makes that line steeper

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