they need to pick a lane and optimize for it. coz at their size they can't serve the application layer (a.i startups who can fine-tune models will eat their lunch)
if they gonna do a consumer play - then go ham on that.
otherwise they're gonna get caught in the dreaded middle valley.
You can afford to play silly buggers when you have dumpster trucks of money backing up to your door every day see also: Meta.
OpenAI doesn't have any of these things. They have products that they're paying for customers when the market they're in is rapidly converging on fighting for API reasoning as part of enterprise systems and fighting a race to the bottom for fickle consumer solutions that will be eaten by open source once they have to make money.
Maybe they had a brief window for dominance of information search (or maybe it was only ever going to last as long as Google releasing all their internal research) and maybe they had a brief moment of monopoly till Anthropic got going but theyre not in the same dominance position as Google.
I suspect that half-assed announcements like this are a result of different people internally with conflicting incentives resulting in a split-the-baby solution.
Provisioning and contracts and data retention was just an extension to review of existing ones.
Nobody serious is going to risk sending sensible data to OpenAI/Anthropic, etc because "the benchmarks have shown +8% performance there and +2% there". Irrelevant.
The domain remains hard due to the lack of availability of high quality LLM-ready data providers in legal space.
"Given the same prompt, Astra for Law returned two closely matching precedents; in the litigation example, Claude Fable 5.1 returned a holding that had been reversed on appeal, while in the transactional example it reported finding no such case."
If this is true that must mean there are a good deal of cases settled using precedent "gotchas" where both sides knew that without the "load-bearing" precedent the outcome would've definitely been the opposite. (i.e precedents almost always trump even valid arguments)
You. Don't take legal advice from a word calculator.
That will be a decacorn product or more.
I don't think you can. What I get from this article is that this is not a product they're going to sell to average consumers.
Then again, nobody will have money to buy anything at this rate, so in all liklihood, this is a total non-issue.
And yes, even contracts drafted for millions of $ have oversights and unlawful or unenforceable terms.
Not really. Modern society is complicated, and law is a technology that is a reflection of the complexity of society.
To make an analogy: you wouldn't say that "in a utopian society, engineers are an unnecessary profession. Buildings should be clear and simple enough that a common person can be their own structural engineer," because that would mean that building technology would no longer handle a lot of the problems we expect it to handle. It wouldn't be utopia, it would be primitivism.
> LLMs help with that goal.
Not really. What they'd actually do is help them produce output they don't understand and lack the competence to evaluate.
I have had some success using frontier models from the last 6ish months, but only when I can break up my work into discrete and verifiable tasks. For example, I had ~15k pages of discovery I needed to dig through for a summary judgment motion. Instead of just asking Claude to find the best evidence, I asked it first to run a clean, high quality OCR pass (it was almost entirely PDFs). Then I had it generate embeddings and write some reusable python scripts to make keyword and semantic searching easy for agents. While I was writing the brief, I would routinely ask my agent (Claude Code) to use both keyword and semantic searching to find the best evidence supporting whatever assertion I was trying to make. I trusted it because there were traces I could follow.
In other cases/situations, I’ve tried just giving a model access to all the docs and saying “write a brief arguing X,” but it’s always terrible at this. It writes briefs with lots of evocative jargon and rhetorical flourish, but a low signal-to-noise ratio.
Again, I’m sure others’ experiences differ based on workflow, legal area, etc.
But lately, I’ve been taking hints from the “company brain” models, where it develops a running model of the case, and assesses each new piece as it comes in and updates the file.
I’ve also been using “Ralph Wiggum”-type models where you pass letter or contract drafts back and forth between agents with different goals (rules compliance, grammar, conciseness, ai slop detector, an opposing counsel critic, etc.). After a few rounds, it’s not perfect — but I start with a very good first draft in my hands.
Interesting! In other unrelated domains, models seem more willing to take a position. It may be nuanced, but they do tend to take a stance. I think it's good that it leaves the interpretation to humans, but I wonder if this is also some sort of a guardrail to minimize liability...
It’s cleaner.
https://www.reuters.com/legal/litigation/lawyer-state-farm-f...
So no, I don’t know what they mean.
https://www.reuters.com/legal/litigation/appeals-court-warns...
It's everywhere and not hard to find if you make even the most modest effort.
Honestly, this word has lost all meaning, outside of perhaps “any use of AI”.
Well at least I know where team “lawslop” is coming from. Thanks, I guess?
People confuse slop with "bad", but slop isn't bad per se, it only becomes bad when real effort was required.
> broadly : a product of little or no value
> food waste (such as garbage) fed to animals
> excreted body waste