Sure, but in two years AI has gone from “impressive tool, but not a replacement for knowledge workers” to “the study where it beats our highest caliber of knowledge workers may have some methodological deficits.” In another two years it’s going to be curtains.

More than that, the entire structure of the study is pointless. They set up as a question/response and then had humans rate the response. That's literally what LLM's are trained to do, which ultimately is convincing a human to click the "I like this one better" button on it's response.

Independent of whether it has any meaning (because the entire paper might be a bit iffy), I find it curious that Instructors 3 and 8 have the lowest harmfulness rates, quite a bit lower than even the LLMs, but not the highest preference rates. Harmfulness anticorrelates with preference, but not perfectly. Some amount of charisma appears to be a factor even in selections by professionals?

I think your 3k figure comes from here - It is explained:

> As judges, the professors then completed 2,918 blinded, forced-choice comparisons (median per judge: 200), each time indicating which of the two anonymized responses, from the instructor or the LLM, they would rather give to a student

more and more i see papers. interview 8 ppl, draw conclusions based on their expert opinions. AI and Cybersecurity are full of this.

Even saw some where they just slapped interviews + protocol into chatgpt as 'methodology' to extract the results -_-. Peer reviewed and published.

> There's also really clear bias given that the main results only feature Google models.

The main results also don’t seem to know what a “model” is, as the two “models” it refers to are “stock Gemini 2.5 Pro” and “a retrieval-augmented version of NotebookLM”.

One of which is a model, and the other of which is an interface backed by different models depending on exactly when the analysis was performed.

I never get the same answer from any two lawyers. I hate law as a result. With developers you might get disagreements based on experience, but there's usually a strong consensus on specific things, with lawyers and courts its all over the flipping place. I wouldn't be surprised if LLMs can "pass" on paper (ie college exams) but in practice, they might 'struggle' in different courts.

...On the other hand, if an LLM has access to every transcript of every case a Judge has overseen, they might have an unfair advantage in any case... Hmmm...

This all assuming the AI lawyer doesn't hallucinate and start referencing cases that don't exist.

Reversly viewed ones should ask with what intend the study should be like this. And for obvious reasons it sounds like monetary-nature.

I find it entirely likely that the preference for the AI generated answers is entirely due to the confidence of its assertions. Given the numbers of evaluations each prof had to do, there’s no way they researched the answers thoroughly. But if there’s one thing we all know LLMs can do well, it’s to generate text that sounds extremely confident. And that signal is appealing in choosing which of two statements you’d give to students.

The paper says the professors have a median of 200 comparisons each. It also says they only used 2 models because using more models would require more comparisons and they selected Google models because Google was branded/advertised as being education focused. When you see other models show up elsewhere, that's because they extended the main idea to other models but using LLMs to judge instead of human professors.

Agreed. The study might show something useful, but the headline is doing a lot of work.

But does it really matter? It seems fairly obvious that AI is going to outperform professors. While the studies run, there are three more model releases that change the calculus entirely. I wonder how much we are learning with these studies about what is going on.

The study was conducted by Stanford’s HAI institute, which receives heavy funding from Google (how much I couldn’t find because they don‘t publish their donations in a place I could find it; but I suspect it is alot). And the authors did not declare a non-conflict of interest at the end of the paper.

> That's very high variance

Do you doubt that educational value of a law professor can vary from 0 to somewhat reasonable? You are not studying screws here.

This is the bit I'm suspicious of:

> They calibrated AI responses to match the length and structure of human answers

which I would guess removes AI's hallucinations and errors somewhat.

> confidently say this paper has a smell to it. I can't call it bullshit, but there are red flags all over

You can confidently say that you are unsure?

I've used general purpose LLM AI (e.g. run-of-the-mill Claude, GPT etc) heavily to draft legal documents. The biggest trap is the hallucinated citation. It will easily insert an absolutely authentic sounding quotation from another case that perfectly proves the point you are trying to make, then it'll make up an authentic name for it, e.g. United States v. Shenzhou Electronics Inc or whatever. You can get really comfortable after checking its output a few times and getting no false citations, and then BAM, it'll put three in the next motion it writes.

Any lawyer who isn't using LLMs for research is behind the curve, though. They are unbelievable at finding niche cases you would never have found on your own. Previously it was a lot of exact search term matching, which is inherently useless for a lot of legal research. I need something that can search on vaguer terms, which AI can do incredibly well. Just check the results. I'm sure the LLMs from Lexis Nexis/Westlaw are probably better than the general purpose ones.

LLMs make fantastic paralegals. If you're doing any legal work, you should be using it, even if it's just to shoot ideas at. Have it play devil's advocate. My friend always has it play the other party's lawyer to see what all the counter-arguments are going to be.

Just like you would with software development. If you care about what you are creating, CHECK THE OUTPUT.

I think this is probably true for most skilled professions. AI is best used in the hands of folks already knowledgeable in the skills/professions they are using it for.

I liken it to me googling things as a sysadmin vs. Jane from accounting doing it. The non-tech end user is far more likely to make the problem worse, or install something sketchy from the ad riddled results than I am, or one of my help desk employees are.

I wouldn't trust myself to draft an important legal document using AI without the advice of a lawyer, much like I wouldn't really want to rely on my lawyer to use AI to write code for me.

IME so far (as both a lawyer and a software engineer), LLM error rates when drafting code and legal documents are reasonably comparable, but it's more problematic in the legal context because legal documents do not benefit from many of the structural safeguards available for code. For legal documents, there are no automated tests, no static typing, no test environments, no logging/observability instrumentation, no sandboxing.

The time lag between drafting and "deployment" also makes for much less effective, much more expensive debugging loops. You can deploy your code to prod in seconds, see an error pop up in the logs, and immediately start debugging. But it will take at a minimum days and frequently as long as several years before an error in a contract or a court filing will be detected, and often the error is beyond correction at that point. Thus, the errors are both more difficult to detect and to resolve.

And the consequences of error are often much greater, both because they are not correctable and because a legal error may risk someone's life, liberty, or substantial property. Although that's not categorically the case, obviously bugs in certain safety critical systems can be as bad or even worse than legal mistakes. But in general, most software is lower stakes than most legal writing.

On the flip side, LLMs do seem to do a better job with basic style and structure for legal documents compared to code. Things like following IRAC format, citing assertions of law (although hallucination remains an issue), and writing comprehensible sentences. These would be the equivalents in code to best practices like good comments, cohesion, consistent use of design patterns, test coverage, clear variable names, DRY, etc. Although the better performance on those more qualitative metrics may just be because even the longest legal documents are typically simpler in structure and have fewer lines of text than a large, complex codebase. Or maybe it's because LLMs are trained on natural language text more than on code. Or because natural language is more forgiving than code, in that minor variation in diction or grammar is unlikely to have any significant effect on how the document is interpreted, whereas even single character errors in code can have enormous effects.

> Some things seem harmless, i.e. drafting a will

Absolutely not harmless if you're the executor of an estate forced to deal with a screwed up AI will. I just handler my dad's estate this spring. It's a frustrating and confusing process even with the simplest of estates.

I wouldn't consider drafting a will to be harmless. If its done poorly the next of kin could have to deal with a huge headache and potentially months or years of probate proceedings.

I would think that LLMs would be better at avoiding foot-guns. That’s a situation where you have a list of well known rules and potential pit falls, and the work of the lawyer is to apply those to a fact pattern. That’s something that has been hard to automate programmatically, because the fact patterns are similar but different. LLMs, however, seem to excel at applying general principles to differing fact patterns.

As someone who's been sued frivolously...

Believe it or not...

A lot can go wrong if you have real life human lawyers draft a legal document.

I think that's actually a perfect analogy to AI writing code. Drafting a will seems like not a big deal, until that will is accepted as "good enough" and is then in court and under fire.

There will still need to be a lawyer in the loop to review and stamp and take accountability.

However, the good news is that a whole bunch of laywer positions in drafting docs and research will be able to be eliminated due to AI.

> drafting a will

Such a document may not make a difference to the person that eventually will have died, but it can make or break the life of generations to come in countries that are so heavily optimized for dynasty building like the US.

I think that's the right intuition. Legal AI feels especially dangerous because the output can look competent while hiding jurisdiction-specific footguns

This is why I can’t see how college grads are going to survive the AI apocalypse. domain experts driving LLMs are super powerful because they can spot where they make mistakes. Juniors don’t have that insight and the LLMs then cost them productivity.

I'm afraid since claude cheats in benches, what will it do with law?

> drafting a will

Tell me you've never been the executor of an estate in the United States without telling me.

there’s really no limit to how many times and ways you can review something with AI, except dollars.

cannot IMAGINE letting ai write my will rn.

I imagine it's really hard to spot a comma in the wrong place, or a missing sentence in a 10 page contract unless you wrote it yourself, or you assembled it from some battle tested templates.

To give you some example of what can happen if you use AI in legal battle you can look at Valve vs Rothchild case [1].

TL;DR Its never a good idea and it will bite you.

1. https://finance.yahoo.com/news/valve-wins-trial-against-pate...

Just because it is theoretically the bread and butter of LLMs does not mean LLMs are capable of doing the job. It still needs to be proven, setting prior beliefs aside. Law is a life-critical system and deserves our highest level of scrutiny.

"That is literally the bread of LLMs." correct. However, programming has a large number of advantages RE LLM use compare to law:

You can execute the logic, and set up loops from the output. You can set up more useful RL. It's easier to generate synthetic training data. It naturally supports tool use and agent parallelism. It's easier to integrate with APIs (with what few APIs the court systems provide). Programming explicitly encodes abstractions at the function, module levels etc that are easier to KG/reason/build upon than text chunks.

'Bread *and butter'. The English expression requires the second part—but otherwise fits perfectly in your well-stated point, with which I wholeheartedly agree.

Source: AAL.

I see the same problem with AI in both programming and law though.

AI is like a scab on a wound: it's a temporary filler, it rushes in to fill a void, but it's not going to be the final solution.

Models showed us that there was huuuge unmet demand for literacy, both in software and in law. But now we have a choice to either address the systemic causes of the unmet demand, or just try to paper over them with layers and layers of AI scab.

These are academics. Not to disparage them or their work at all but it is very different to the transaction or litigation work that is done in BigLaw. It is a lot more focused on analysing and summarising existing texts, which are themselves more easily available for LLMs to train on (statutes, case law, legal journals, textbooks). As such it is probably the easiest legal work to LLM-ify but also the least valuable, because I assume law professors aren't getting paid nearly as much as BigLaw lawyers. So this approach won't scale. Not to say AI won't crack BigLaw but it will be a different challenge.

LLMs answered student questions of the top of their heads, without any refresher look into the case law. And systems that were primied with the case law like NotebookLM underperformed when compared to baseline LLMs that you'd as anything about anything.

It's not about what LLMs can or are suited to do. This study shows strengths of what's already in them, innately.

The more I see the evolution, the more it looks to me that any knowledge workers is going to be impacted.

> analyzing large amounts of texts, drawing conclusions and writing other texts based on that and nothing more

The same could be said about programming. Or if you want to be even more reductive, looking at a screen and pressing buttons to make the correct lights light up https://xkcd.com/722/

> On the other hand, I do _not_ think that this study provides any indication that an LLM is prepared to actually provide direct legal counsel

I think it indicates that LLMs are smart enough to be used in the context of law education.

The revised title is spot on. It's odd to me how academics are trying to sound like top research labs' CEOs trying to pump valuations by overreaching claims.

That and the research is done by Stanford’s HAI institute with an obvious bias and the paper is curiously missing a conflict of interest statement.

EDIT: just found out that Google is a major donor to HAI. So this research is at least partially funded by Google. Which is probably the reason the authors fail to declare no conflict of interest.

He who makes the rules…..makes the rules.

As a lawyer, I think your intuition is right re llms. Law is the wordplay that llms thrive at.

However the waves are starting and they ARE going to be huge. Corporate clients are insisting on AI. They don’t want to pay an associate hours to draft anything to be reviewed by a partner. They want top partner to use AI and just proofread.

This is true in the naive case.

There are however LLM context building techniques that anchor completions in data structures that persist the structure of claims that support the conclusion contained in a completion. Lots of different patterns exist —organizing logic in language is a rich domain— but the one I’ve liked the most is something called a Claim Dependency Graph that models the relationships between atomic claims as graph edges.

There’s a whole suite of operations you can perform on these structures, and “reconstruct how you came to this conclusion” is absolutely one of them.

A human has a motive that exists that frames the thought being expressed. An LLM is going to be creating a “de novo” thought in response to a line of questioning.

Same is probably true of humans. In a conversation, we often respond from instinct, then work backwards to a rationalization only when asked. For more considered thoughts, if we’re lucky, we can remember our “reasoning traces” but that’s as deep as our introspection goes. Unless we’re neuroscientists, we don’t even know how many neurons we have, let alone have any understanding of how they generate our thoughts. Motivated reasoning impairs our introspection further, and then dishonesty and communication errors prevent us from relaying the limited remaining information to each other.

Model interpretability work has advanced a lot. Arguably we already can explain AI decision-making better than human brains.

LLMs hallucinate, because humans hallucinate.

Asking the LLM in a way where it annotates its sources, it can greatly increase the pattern matching to closely simulate logic, just like in humans.

I understand the question of why did you say this, not that, I have seen other ways of asking that which do not seem to trigger the LLMs over-response in the other direction.

Pro se litigants are hyper vulnerable to LLM hallucinations.

One wrong advice clump and, like a step onto the wrong path while hiking, all subsequent steps go in the wrong direction. And sycophancy tuning means marginal one-sides takes get presented as sure-fire things.

I’m of the opinion that the big wins aren’t in using the LLMs to do the work (legal, in this case), but rather to refine and improve the dialog and presentation from all parties. A court-centric LLM that could give likely procedural needs to a litigant, and a law-firm-centric LLM could help a pro se litigant create a meaningful and refined set of questions for lawyer consideration, condensed and targeted, saving all parties time and confusion while meeting the clients linguistic needs ‘where they are’.

All the lawyers know things LLMs never will, the law is interpreted, and the written part isn’t engineering grade facts but suggestions interpreted in context. Arguably this is a racket and a thin veneer of plausible deniability for authoritarian rule. But as the law stands even with federal statues and citations from the courts website, practicing lawyers will frequently end up explaining that in this county/country/court/jurisdiction The Way of Things is different.

I think that within a few years, most lawyers will expect that clients will have run contracts through an LLM prior to sending them to outside counsel. Emails will be along the lines of:

Please see attached contract we received from [counterparty]. ChatGPT says blah, blah and blah should be revised. What do you think? Is there anything else that we should change?

i use codex to do initial research and draft texts (in typst). i use files-output skill so that all research contexts are rendered into files md files.

i do second phase on codex, by asking to download all pdfs and extract all text of laws it references. can repeat fully local research step.

after i ask gemini to find issues and criticize.

UPDATE: there many legal skills on github to try, not used so any yet

I'm curious what would be your take on the productions of this year's models.

Also because their reasoning is just a statistical model of whatever they've been fed. No experience of pain, humility, human connection, etc in this.

Isn't "getting greater" the more accurate representation, though?

In 'critical' industries, the error rate is massively important, and if the quality of search is reaching an acceptable error rate, that's quite big news.

Would you take responsibility for missing details about local data retention requirements?

I wonder to what degree the AI was just better at communicating. My experience with attorneys is that they are often some of the worst writers.

Yeah, 75% win rate is a ~200 points Elo difference, which is quite massive.

I do wish they'd used some more objective criteria. Simply being preferable one of the things LLMs have trained for since the beginning, hence its sycophantic nature.

Nobody wants to pay their lawyers more than they have to. There will be a huge market for firms that can use AI to avoid charging clients for $1,000/hour junior associates.

that worked out for artists and translators right ?

If you want human connection the legal system is not where you are going to find it, period.

I don't think there will be any such market for "non ai" law. If I'm involved with the legal system I just want out as quick as possible as cheap as possible.

The profession is walking into a court room 90 minutes late because you know the judge's work pattern then going "hey Mike, how are the kids" after 22 years in the same jurisdiction. Then they old boys haggle based on how much the lawyer is charging. You are basically paying for access to the social club. Better outcomes when part of the in-group of course.

It turns out everybody has at least one right hand, even the people we trust the least.

I had a similar thought. What if the result, statistical and significance critique aside, mostly means that when it comes to first-year tutoring of law students, the vibe, tone and overall presentation of arguments weighs a lot, maybe even more than the factual arguments themselves?

In such a framing I don't find it surprising at all that teachers prefer the more polished answers generated by AI, because if LLMs are good at one thing, it is being confident in whatever they generate and present it convincingly.

A couple of acting classes might be cheaper than a lawyer, then you can go all out representing yourself.

This was an open book test. The real problem with this study is that winning the most head-to-head preference tests is not the right metric. It doesn't much matter if two answers are right, and one is written a little better than the other. It matters quite a lot if one answer is right and another is wrong.

The authors point out that this other metric was computed in prior work and incorrectly dismiss it as being not as good as winning percentage in head to head competitions. The cited prior work shows that the models fare poorly on that metric. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5166938

Except the library outperformed the professors, which is quite a bit more impressive.

This is the state of HN. Created new account. Accused without evidence. Emotional clickbait.

they'll embed it in the weights so it can't be jailbroken

> Marc Andreessen argued that we've already reached AGI. He says that the top AI models give better answers than 99% of people he has access to, and he has access to some of the best people in their field.

He stands to make billions if enough people believe him — unless you also do, consider that you’re the mark. For example, if that was true, it would have to mean that AI companies either aren’t letting customers use the good models or are instructing them to frequently make errors which reveal a fundamental lack of reasoning ability.

Consider also that his wealth means he hasn’t had to defend an idea stringently since the 90s. I wouldn’t be surprised if he does think LLMs give deep answers because it often looks that way until you critically review the response and ask questions like what’s missing which require you to have a decent understanding of the problem domain.

>Marc Andreessen argued that we've already reached AGI. He says that the top AI models give better answers than 99% of people he has access to, and he has access to some of the best people in their field.

He has access to employees and yes-men. What he actually needs to hear, nobody will tell him, AI even less so. Every shit idea he has, would be "what a bright idea"-ed by both everyone around him and AI.

And of course there's the little matter that he makes money and increases his power by selling AI. What seller doesn't promote their stuff as the greatest ever?

Marc Andreessen has a strong financial incentive to feel this way and to convince others to feel this way.

I also think it’s easy to think that AI gives good answers if you don’t know the field well. In fields where I know the material, the answers are pretty variable and can be quite bad.

Knowing the question is half of the answer. LLMs are great at scoping your context and answering precisely what you asked; it's also why they go off the rails when they misunderstand a part of your question. Incidentally, they're great at "knowing" and reaching for knowledge.

Humans have the advantage of perspective. We always lack some knowledge and answer broadly. This is bad if you have a particular goal in mind, but better if you're just generally learning, because you see more and learn to discriminate the correct from the wrong. And most importantly, being wrong is part of human ingenuity - because sometimes we turn something "obviously" wrong into something right.

He would tell you NFTs were AGIs if it might get you to buy them.

Getting the right answers is just half of it, you need to know the right questions to ask. I haven't yet seen AI crack that one.

> Marc Andreessen argued that we've already reached AGI. He says that the top AI models give better answers than 99% of people he has access to, and he has access to some of the best people in their field.

Investor with vested interest in AI companies makes claim of reaching "AGI".

He is one of the last people to listen to about AGI. Unless the term "AGI" means something entirely different to him vs to independent researchers vs to CEOs, since the term has become entirely meaningless.