What r/legaltech is saying about...well, 'Legal Tech'
I was an avid 'redditor' once. The appeal faded around the thousandth argument with an anonymous 19-year-old who'd just discovered he was right about everything. But I still drop in from time to time, though, and lately I've been lurking in r/legaltech and r/localllama.
The loudest signals in legal AI come from press releases: funding rounds, ARR milestones, "custom models." The more useful signal comes from the practitioners on r/legaltech, who buy these tools and run them on live matters and then post, in detail, about what actually happened. The forum tags its vendors and keeps a public list of anyone caught astroturfing, so the discussion is about as unfiltered as it gets.
Nobody believes the "model" story anymore. A year ago, calling these products "wrappers", a general model, your documents, a nicer interface, a large invoice, was a fringe complaint. Now it is the default view, and the vendors are the ones confirming it. In June, Harvey announced its own "custom legal model," and its own representative turned up in the thread to explain what that meant: the company post-trains on open-weight bases because it is "much cheaper per token" than the closed models. A user checked Harvey's blog and confirmed it. The verdict was three words: "They made a harness."
The move on pricing is toward the exit
For most of last year the pricing complaint was a mood with no numbers. That changed when someone on an innovation committee posted the actual quotes: Harvey at $1,200 US dollars a seat a month, $2,400 with Lexis; CoCounsel around $1,600; Legora near $400; and Claude, which you can use directly, at $20 to $200.
The phrase that stuck, and gets reused thread after thread, is "Emperor's New Clothes" where the real thing you are buying is the ability to tell clients you use AI. The argument then hardened into economics: the underlying model APIs cost the vendors real money on every call, and the sharpest question in the forum is what happens to these prices when the credits run out. Firms are unbundling toward narrow, cheaper tools, and nothing in the threads suggests they drift back.
"I'm doing my job PLUS babysitting your AI." The new rule for what AI is actually for
The gain equals time saved minus the cost of checking the output. That is the whole game. Narrow jobs where checking is trivial, search, summarising a document you were going to read anyway, get consistently good reviews. Broad, high-stakes ones, first-draft advice, "contract review" as a category, autonomous "reasoning", consistently do not.
Wherever the answer is cheap to check, sentiment is warm; wherever checking is expensive, it curdles. The "do everything" platforms are already being discussed in the past tense.
Confidentiality has stopped being theoretical
It began as ambient worry about feeding client material to a third-party API and turned into some of the biggest threads on the board. Then a US court settled part of it: in Morgan v V2X, a Colorado federal magistrate barred uploading confidential material into any AI tool unless the provider is contractually prohibited from training on it and required to delete it on request. The practical effect, the court acknowledged, rules out most mainstream low-cost tools, standard ChatGPT, Claude, Gemini and the like. What is driving the concern is not fear of hackers but distrust of the vendors themselves, the kind that produced the thread about a firm whose rollout team was renamed the "Letgora" team after a data-retention failure and then let go.
Open weights are where everyone is looking but few have jumped
A year ago self-hosting barely rated a mention. It is now the clearest directional pull in the whole corpus. In June, Harvey admitted its product runs on open-weight bases, and in the same month someone posted a fully local document tool built on Ollama and Qwen. The same month produced the line that keeps the enthusiasm honest: "You're not gonna put Qwen in front of a multimillion dollar case for a client."
A July thread put the tension plainly, framing self-hosting as "building your own power plant because you need electricity" and asking whether the privacy objection to the big labs is even sincere. The appetite is real; the nerve is not there yet. This is the front to watch, because it is where the gap between what firms want and what they will risk is widest.
Put the five together and the picture is coherent, and it is not the one the vendors are selling. The market is moving away from the "model" story toward show-me-the-base, away from premium seats toward whatever is cheapest, toward a hard rule that AI is for what you can check at a glance, away from any tool it has to trust with client files, and toward running its own models the day they are good enough. Every one of those points the same way, and the vendors' pricing and positioning point the other.
Sentiment has turned against the gap between what the technology does and how it is sold, and the single most common move on r/legaltech is a practitioner reading a claim, checking it, and posting what they found.
Legal AI will be enormous. Most of the companies currently selling it will not be the ones who get there. If you want to know which is which, skip the keynote and read the comments, where the receipts are kept.