I find this study quite suspect. I'd have to dive deeper but there's definitely significant alarm bells that should be going off for anyone reading.
Figure 2 (page 6) screams problems. There's only 16 professors (3k comparisons each?!?!) and the professors are all over the place. That's very high variance, suggesting the study has no meaningful statistical power. Poor instructor 16 can't catch a break lol
There's also really clear bias given that the main results only feature Google models. Other models show up elsewhere, why not there?
I'm no lawyer, but I'm a pretty competent statistician and can confidently say this paper has a smell to it. I can't call it bullshit, but there are red flags all over
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.
Sure, but the biggest problem is they have no statistical significance. Variance is too high. How do you distinguish the signal from the noise? Confidence intervals aren't enough.
But is it a surprise law professors aren't great statisticians?
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.
As a software engineer I have some intuition for what the risks are of letting agents do some tasks vs others.
I don't have a similar intuition calibrated for what could go wrong when asking AI to draft a legal document. Some things seem harmless, i.e. drafting a will, but I don't really know- our legal system is notoriously rife with footguns.
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.
> I think this is probably true for most skilled professions.
I agree, BUT I also find that it's easy for experts to atrophy quickly. When the AI is right 80/90% of the time it lulls you into over confidence.
I find those that are best and make the greatest use are the ones who remain skeptical but also use the tool. The same people who were already nuanced and picky before AI. The same people who already doubted and questioned their own work, and used that suspicion to help prevent them from having over confidence in their own work. If you weren't willing to just "lgtm" with your own code, it's difficult to do that with AI.
(To be clear, I'm not saying perfectionists. Some might call them that because the picky people have higher standards, but a good expert has to also understand that perfection doesn't exist. That's often a driving force in the suspicion! This also tends to cause them to continually improve)
I would agree with this point and as I explained in a comment replying to the GP comment above, that atrophy is far more dangerous in the legal field than it is with code because legal documents do not benefit from the structural safeguards available for code, like automated testing, static typing, static analysis tools, etc. IME with legal LLMs so far, they are easily in that most dangerous valley where they can lull you into a false sense of security while still introducing extremely dangerous mistakes that are frequently difficult to detect without very careful reading.
The danger of those mistakes creeping in also grows exponentially the farther a lawyer strays from their core legal expertise. There are a few statutes I know inside and out, and I can spot LLM analytical errors related to them in a split second, but once I venture out into domains where I am not an expert (but where I am nevertheless reasonably qualified to practice), it becomes much harder to spot drafting mistakes because I have not refreshed my own understanding of the law by reviewing the relevant cases or statutes as I would when drafting the analysis myself from scratch.
Vice versa there is also a lot of irresponsible programmers doing stupid things with ai. Irresponsible people stay irresponsible, AI just make them more productive at being irresponsible.
So a software engineer could diagnose an illness with ai, even if they happen to be right that doesn't really prove much about how bad of an idea it could be in a long tail scenario.
It's like that in engineering, for sure. My background is in aerospace and there are lots of things that a reasonably technically-inclined random can probably do passably. It takes an engineer to know which tasks those are, though.
I would imagine it's similar in law, in that it takes a lawyer or judge to know where the foot guns lie.
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.
Judging from reported figures, roughly 80-90% of households in the US [1] have a household net worth of at least $0. That means that most people do in fact have an estate.
Median household net worth is in fact somewhere in the $100k-200k range, which is definitely something that could be meaningfully called an "estate." (Most of this tends to be the house, the median net equity in which is about $190k as of 2022).
It’s just the legal term. If you have a relative die with a bit of stuff and an ancient car, they have an estate and someone needs to execute it even if the total value is less than most lawyers care about.
An "estate" is a legal term for property, assets, and liabilities a person leaves behind upon their death. A family member is a top practitioner in the field of estate planning and resolution, and some of the messiest estates they have handled are pro-bono cases of exactly the type of people you would put in italicized "most people": poor, not really able to upkeep a house they inherited from a relative which hadn't had title properly transferred on a previous death because they didn't have money for an attny, now can't get a loan to fix the roof...
Yeah, if you are homeless, carless, and have only the clothes on your back and a shopping cart of stuff, you don't have an estate. Everyone in the middle class in the US has an estate. Much of the time it passes automatically to their spouse on death, but it's still an estate.
And if you are concerned about where it goes, get a GOOD attny. There are many bad ones hanging out their shingle as "Trust & Estate" attnys, and some of the next messiest cases are fixing problems made by those not-so-good attnys.
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.
Well this is largely the fault of law itself. especially english style law. A legal, parseable code, in which not every single tiny municipality (some less than 1 square mile) has their own set of rules and laws, not all published or available - but which citizens are expected to abide by of course - how could we expect AI to do well and not some typical TV southern lawyer who knows the judge?
I could not agree more. A simple example: it boggles my mind how every state organizes their statutes in entirely dissimilar ways. I'm not sure there's a need for every state to have slightly different wording for a murder statute in the first place, but even assuming there is, why do they all have to be scattered around in different code sections instead of every state just following some consistent convention like always putting the murder statute at Title V, Section 1.4 (or whatever the case may be, that's just a random invented example).
For murder that's not such a huge deal because the statutes are typically easy to track down and don't really differ all that much substantively, but once you get really into the weeds on something like commercial contracts it can be a huge pain to do cross-jurisdictional research.
And that's just a tiny, super obvious example of how impenetrable statutory law is, which isn't even the really pernicious problem. Case law is infinitely worse. It makes me absolutely furious how difficult legal research still is. The Westlaw/LexisNexis duopoly is a moral crime and wildly destructive to the quality of government in this country. Every single written court opinion should be publicly available for free on the internet in an easily searched format. It would cost practically nothing to achieve. We're talking about less text than Wikipedia hosts. Yet still many states make it almost impossible to access case law. Even though these cases are law. Binding law that we are supposed to follow, yet we cannot even easily access. It's insane, and largely perpetuated by the complacency of lawyers who can charge others for what should be free, the lobbying of the duopoly, and the incompetence of politicians.
If all of the laws were consistently available and stored in reasonable, consistent citation formats (I would settle for hyperlinking as a replacement for the rat's nest of wildly varying jurisdiction-specific citation systems), it would even be possible to introduce a form of unit testing for legal drafting that would allow us to automatically verify if the LLM hallucinated a citation.
It also doesn't help that we (for what were at the time very good reasons) moved away from the system of legal writs that used to provide fairly standardized, almost "cut and paste" templates for legal filings. So now every legal document (filings, memos, contracts, court opinions, statutes) is drafted like a bespoke, artisanal creation with few strict structural or stylistic conventions. That makes automated interpretation much harder than it needs to be.
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 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.
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.
Instead, the LLMs create entirely new foot guns like citing non-existent cases. You can't go more than a week without encountering another news report of a lawyer submitting an AI-generated legal brief rife with bogus case citations, which even includes briefs submitted to state supreme courts.
I would categorize this in the "expertise that people internalize but never figure out how to verbalize" department, and that is a department we have no way to teach an LLM because if nobody is writing out those unspoken, subconscious rules then the LLM has nothing to read about them in its training data.
Good point. Same probably applies to code as well, coders much tell us why they write the cde the way they did. And if they have comments in their code, those are highly untrustworthy because noboy fixes comments if the code works.
But can an LLM come up with questions like what the definition of is is? Seems to me there's a lot of "depends on how you read it" type of stuff that lawyers excel at finding novel interpretations. So what coders thinking of as rules are much less straight forward to understand when it comes to laws
I think that’s a different task than the one OP is referring to. To your example, I’m not familiar with the capability of LLMs in that regard. I have struggled with using the AI features of westlaw when it comes to that sort of argument. (Basically, making an argument that strays from typical route, because that’s the position you happen to find yourself representing.)
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.
I do question at what point AI could be useful as a teaching aid.
The quality of LLMs depends heavily on, among other things, how you word your questions.
Knowing the correct questions to ask is not something most students know how to do given that it tends to require a fair bit of pre-existing domain knowledge.
I understand why the conversation on this article looks like it does, but the study is specifically focused on the potential for LLMs to operate as tutors for law students. I enjoy the extrapolation out to whether LLMs will replace lawyers, but did not find that to be discussed in the study itself.
In the framing of using LLMs as legal tutors, with the implication of lowering the cost of legal training, this seems like a socially-positive outcome. Furthermore, it feels kind of intuitive to me that any contemporary system operating with an LLM and access to legal reference material will be prepared to answer _student-originated questions_ comprehensively and with breadcrumbs or direct references to educational/source materials, as seems to have been found in the study.
The authors explicitly and intentionally emphasize that many legal questions require contextualization, as opposed to some discrete calculated answer. The result of the study implies that the LLM-based systems were capable of using what many of us here understand to be the "stochastic best-fit algorithmic generation" of a contemporary language model to adequately contextualize a student's question, providing insight into the trade-offs or complications implicit in the question, while then, critically, _meeting the professional standards of legal educators in explaining that complexity to a student_.
Realistically, I would hope this provides some confidence to readers of HN that they can actually ask a legal question to an LLM and expect the response will explain the complexity of the law in relation to the question. This is great news, and is likely the minimal pre-work any of us should do before actually consulting a lawyer, if time permits.
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. Possibly in the same way that a legal textbook does not replace legal counsel, or perhaps more accurately, the same way that stumbling upon a legal case study for approximately the same situation you're in doesn't guarantee you'll have the same result.
I'm surprised Stanford Law would go along with this over-reaching press release title. How about "For common first-year contracts-law questions, law professors preferred AI-generated answers to professor-generated answers"
What the LLM cannot do is explain why it said what it said, when cross-examined. It simply hallucinates the best account of why someone would have said such a thing as it said, same as it can give a probable account of why someone else said something different. The question 'But why did you say this not that ...?' does not lead it to make explicit its grounds for what it said, but just to make a new more complicated statement.
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.
No, it happens in the immediate context, where e.g. we say 'No I meant Meredith Jones, not Meredith Smith'- and the possibility of this elaboration is actually part of ordinary communication. I did mean Meredith Jones, not Meredith Smith - thus the use of the past tense The LLM will just give the best answer for what one might have meant, completely reopening calculation.
Nonsense, some of my friends are lawyers and they're able to give you consistent interpretations on why they think about a certain aspect of a law a certain way.
The whole thing is that they work with this the entire time, so they have a really consistent 'head model' of how things work and why and how considerations should be weighted/ordered/whatever. LLMs just do not have this, there's no consistent underlying reasoning (the 'reasoning' traces in LLMs are really inconsistent)
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.
No, the hallucination of its reasons follows immediately from the technique of probabilistic inference. You can see this in real time, just ask 'why did you use this word, not that word?' It is in the position of a desperate liar. All its responses are essentially 'rationalizations'
My best guess is that Gemini was trained on the textbooks that the questions are meant to test against, thus they are probably better at explicit recall of those questions or related questions.
This is a pretty limited introductory course based on what it says in the methods of the paper itself.
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.
One way to make legal services more affordable and accessible would be to put the burden of ensuring the AI legal services are accurate on a private-public partnership with the government.
If a person using the service is given inaccurate legal advice and acts on that advice, the person can't be charged with a crime, can't be given any civil penalties, etc., as long as the law in question is non-obvious.
Obviously if by some exploit, some fundamentally obvious crime (murder, theft, obvious fraud, etc.) is said to be legal, that wouldn't apply, but of course the service should try to prevent those kinds of exploits anyway.
Could limit this to something like business regulations to begin with, or even specifically for small businesses, or contracts within some time limit and dollar amount that would otherwise be coverable by small claims court, etc.
In the hands of a domain expert, AI is useful.
In the hands of the naive, it is a foot gun.
I killed my Arch installation and was stuck at the GRUB prompt.Unwilling to brush up my rusty knowledge of GRUB syntax, I asked Gemini for help. The commands Gemini suggested would have wiped my hd...
Once Gemini was told that I was using BTRFS, the suggestion from Gemini looked a bit more sane, but still looked incorrect to me.
It was only after I informed Gemini that I was using a NMVE with BTRFS that it finally produced a sane command.
I beat lawyers twice before generative AI even existed.
Recently I asked Gemini a few questions about personal conflicts in everyday life. It's often too conservative, with views too shallow for the problem. So I still handle human conflicts myself. I only outsource the templated stuff like routine chat replies or marketing copy though it saves me huge amount of time. People who quote AI in serious conflicts are too weak to handle them on their own.
> rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups.
That's the problem, you never know when the 25% deliver a true stink bomb, and that's not considering prompting - while a fair prompt/question maybe considered objective, it's very easy to stray.
I'm going to need some legal help for my startup. But I can't pay much. So I figured I will ask AI all relevant questions, as well as forms filled etc. Perhaps even create a patent-application for me.
THEN I find a human lawyer and give AI's answers to them and say "Can you find any errors in this? Can you improve it?" .
That way I think my legal bills should be smaller because the AI has already done most of the work. What do you think? Which LLM is best for legal work?
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
Yeah this could be interesting. A lot of the spotlight has been on “law firm stuff” like demand letters and writing contracts…
But imagine if a dev team didn’t have to go engineer -> product manager -> legal team to get a question answered on local data retention requirements. You could ship that much faster.
If the only purpose of asking a lawyer is transferring risk (aka cover your ass) while getting the same advice as an LLM, that’s slowing down delivery for purely bureaucratic reasons.
I’ve seen that mentality at big companies where everyone is scared to stick their neck out and be accountable for a decision. And nothing gets done. Drives me crazy.
But the people who move up are the people who take ownership and get shit done (and are right a lot).
(BTW, I have been at companies that were sued by regulators. They never really punish the individual(s) who were in the room when the decision is made. So your worry is kind of misplaced.)
> In a blind evaluation of nearly 3,000 anonymized comparisons, professors rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups.
The writing is always fluid and grammatically flawless. This carries much more weight with us than we believe. I know the illusion well from decades of grading college papers. Many of the highest quality students use English as a second language, and I know this, but an American well trained in writing, grammar, spelling always gives an impression of superiority. (Being well trained in writing, grammar, spelling etc is of course high merit, which is how the illusion forms - it is basically an illusion of global 'intelligence')
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.
The arguments need to be based on actual law, and any cited reference cases need to be real.
There's been a lot of news stories about lawyers using AI, and then getting in trouble for citing hallucinated laws or cases. It doesn't matter if the AI response is "preferred" over the human one if it gets thrown out when put under the scrutiny of a real case.
But did they? Or did they just go off what answer felt better? Did they put in any work to actually confirm the answer? Or did the busy law professors just click through and move on with their life?
Well, they had the data around if the answer would be harmful to the students learning. AI was scored at 3.5% harmful answers and 12% of law professor answers were considered harmful.
I think there will be a market for firms that aggressively market themselves as non-AI, and then as more people turn towards that human connection we'll go full circle
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.
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.
Bad legal advice will keep you dealing with the legal system for much longer and at much greater cost. Something being cheap and quick upfront doesn't mean it will be cheap and quick by the end of the process.
Maybe, although I would be extremely hesitant to extrapolate from this one study and trust my legal life to an LLM. One thing that's worth noting, though, is that regardless of the quality of objective legal advice in the abstract, for a lot of smaller scale stuff the human connection actually is literally what is important. There are ambiguities in the law, which are not resolved deterministically but rather at the individual discretion of judges. Your lawyer, if they're any good at their job, knows the local judges and how they're likely to rule for given circumstances, which can influence their legal advice to you specifically.
Incredible that the common people will be able to wrestle the right to rule of law away from the bloated legal caste, who have built themselves quite the moat.
The inaccessibility of justice is a huge driver of inequality. Any tools which bridge this gap will help make a more just society.
Personally I think this is very good. One of the hardest things out there is maintaining a society in the face of changing times and it's because law is dense and slow.
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.
I'm getting more convinced. I mean, sure it makes dumb mistakes sometimes but its a particular set of self serving mistakes, commenting out tests in order to pass. We obv don't want this behavior but I wouldn't say it's dumb.
It'll be like the Turing test, which we just blew past years ago and no one cared. After all the hand-wringing about sentience and rights of the AI if it passes the Turing test, and now we just have AI bots running 24/7 writing slop.
> 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 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.
> 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.
I find this study quite suspect. I'd have to dive deeper but there's definitely significant alarm bells that should be going off for anyone reading.
Figure 2 (page 6) screams problems. There's only 16 professors (3k comparisons each?!?!) and the professors are all over the place. That's very high variance, suggesting the study has no meaningful statistical power. Poor instructor 16 can't catch a break lol
There's also really clear bias given that the main results only feature Google models. Other models show up elsewhere, why not there?
I'm no lawyer, but I'm a pretty competent statistician and can confidently say this paper has a smell to it. I can't call it bullshit, but there are red flags all over
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.
Sure, but the biggest problem is they have no statistical significance. Variance is too high. How do you distinguish the signal from the noise? Confidence intervals aren't enough.
But is it a surprise law professors aren't great statisticians?
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.
As a software engineer I have some intuition for what the risks are of letting agents do some tasks vs others.
I don't have a similar intuition calibrated for what could go wrong when asking AI to draft a legal document. Some things seem harmless, i.e. drafting a will, but I don't really know- our legal system is notoriously rife with footguns.
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.
I find those that are best and make the greatest use are the ones who remain skeptical but also use the tool. The same people who were already nuanced and picky before AI. The same people who already doubted and questioned their own work, and used that suspicion to help prevent them from having over confidence in their own work. If you weren't willing to just "lgtm" with your own code, it's difficult to do that with AI.
(To be clear, I'm not saying perfectionists. Some might call them that because the picky people have higher standards, but a good expert has to also understand that perfection doesn't exist. That's often a driving force in the suspicion! This also tends to cause them to continually improve)
I would agree with this point and as I explained in a comment replying to the GP comment above, that atrophy is far more dangerous in the legal field than it is with code because legal documents do not benefit from the structural safeguards available for code, like automated testing, static typing, static analysis tools, etc. IME with legal LLMs so far, they are easily in that most dangerous valley where they can lull you into a false sense of security while still introducing extremely dangerous mistakes that are frequently difficult to detect without very careful reading.
The danger of those mistakes creeping in also grows exponentially the farther a lawyer strays from their core legal expertise. There are a few statutes I know inside and out, and I can spot LLM analytical errors related to them in a split second, but once I venture out into domains where I am not an expert (but where I am nevertheless reasonably qualified to practice), it becomes much harder to spot drafting mistakes because I have not refreshed my own understanding of the law by reviewing the relevant cases or statutes as I would when drafting the analysis myself from scratch.
> I wouldn't really want to rely on my lawyer to use AI to write code for me.
Yet that is exactly what a lot of C-Suiters (many of whom are lawyers), are doing.
Vice versa there is also a lot of irresponsible programmers doing stupid things with ai. Irresponsible people stay irresponsible, AI just make them more productive at being irresponsible.
im not so sure
i think devs overestimate their own role and underestimate others
i am seeing lawyers and doctors roll out their own software with AI
but we dont have their training and experience
So a software engineer could diagnose an illness with ai, even if they happen to be right that doesn't really prove much about how bad of an idea it could be in a long tail scenario.
It's like that in engineering, for sure. My background is in aerospace and there are lots of things that a reasonably technically-inclined random can probably do passably. It takes an engineer to know which tasks those are, though.
I would imagine it's similar in law, in that it takes a lawyer or judge to know where the foot guns lie.
> 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.
Most people don't have anything that could even be called an "estate".
Judging from reported figures, roughly 80-90% of households in the US [1] have a household net worth of at least $0. That means that most people do in fact have an estate.
Median household net worth is in fact somewhere in the $100k-200k range, which is definitely something that could be meaningfully called an "estate." (Most of this tends to be the house, the median net equity in which is about $190k as of 2022).
Source: https://www2.census.gov/library/publications/2024/demo/p70br...
[1] This doesn't mean "homeowners," rather it's a recognition that assets for married or cohabitating couples are usually commingled.
It’s just the legal term. If you have a relative die with a bit of stuff and an ancient car, they have an estate and someone needs to execute it even if the total value is less than most lawyers care about.
Everyone has an estate. Only thing is that you have to die first.
Ummm, not quite.
An "estate" is a legal term for property, assets, and liabilities a person leaves behind upon their death. A family member is a top practitioner in the field of estate planning and resolution, and some of the messiest estates they have handled are pro-bono cases of exactly the type of people you would put in italicized "most people": poor, not really able to upkeep a house they inherited from a relative which hadn't had title properly transferred on a previous death because they didn't have money for an attny, now can't get a loan to fix the roof...
Yeah, if you are homeless, carless, and have only the clothes on your back and a shopping cart of stuff, you don't have an estate. Everyone in the middle class in the US has an estate. Much of the time it passes automatically to their spouse on death, but it's still an estate.
And if you are concerned about where it goes, get a GOOD attny. There are many bad ones hanging out their shingle as "Trust & Estate" attnys, and some of the next messiest cases are fixing problems made by those not-so-good attnys.
And NO, AI is not good enough.
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.
Well this is largely the fault of law itself. especially english style law. A legal, parseable code, in which not every single tiny municipality (some less than 1 square mile) has their own set of rules and laws, not all published or available - but which citizens are expected to abide by of course - how could we expect AI to do well and not some typical TV southern lawyer who knows the judge?
I could not agree more. A simple example: it boggles my mind how every state organizes their statutes in entirely dissimilar ways. I'm not sure there's a need for every state to have slightly different wording for a murder statute in the first place, but even assuming there is, why do they all have to be scattered around in different code sections instead of every state just following some consistent convention like always putting the murder statute at Title V, Section 1.4 (or whatever the case may be, that's just a random invented example).
For murder that's not such a huge deal because the statutes are typically easy to track down and don't really differ all that much substantively, but once you get really into the weeds on something like commercial contracts it can be a huge pain to do cross-jurisdictional research.
And that's just a tiny, super obvious example of how impenetrable statutory law is, which isn't even the really pernicious problem. Case law is infinitely worse. It makes me absolutely furious how difficult legal research still is. The Westlaw/LexisNexis duopoly is a moral crime and wildly destructive to the quality of government in this country. Every single written court opinion should be publicly available for free on the internet in an easily searched format. It would cost practically nothing to achieve. We're talking about less text than Wikipedia hosts. Yet still many states make it almost impossible to access case law. Even though these cases are law. Binding law that we are supposed to follow, yet we cannot even easily access. It's insane, and largely perpetuated by the complacency of lawyers who can charge others for what should be free, the lobbying of the duopoly, and the incompetence of politicians.
If all of the laws were consistently available and stored in reasonable, consistent citation formats (I would settle for hyperlinking as a replacement for the rat's nest of wildly varying jurisdiction-specific citation systems), it would even be possible to introduce a form of unit testing for legal drafting that would allow us to automatically verify if the LLM hallucinated a citation.
It also doesn't help that we (for what were at the time very good reasons) moved away from the system of legal writs that used to provide fairly standardized, almost "cut and paste" templates for legal filings. So now every legal document (filings, memos, contracts, court opinions, statutes) is drafted like a bespoke, artisanal creation with few strict structural or stylistic conventions. That makes automated interpretation much harder than it needs to be.
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 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.
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.
Instead, the LLMs create entirely new foot guns like citing non-existent cases. You can't go more than a week without encountering another news report of a lawyer submitting an AI-generated legal brief rife with bogus case citations, which even includes briefs submitted to state supreme courts.
e.g., https://www.npr.org/2026/04/03/nx-s1-5761454/penalties-stack...
I don't know the source off hand, but I've seen llms hallucinating case citations in order to "prove" their premises.
can't get more foot gun than "well according to [fiction] it is a well established practice (that the defendent is guilty)"
I would categorize this in the "expertise that people internalize but never figure out how to verbalize" department, and that is a department we have no way to teach an LLM because if nobody is writing out those unspoken, subconscious rules then the LLM has nothing to read about them in its training data.
Good point. Same probably applies to code as well, coders much tell us why they write the cde the way they did. And if they have comments in their code, those are highly untrustworthy because noboy fixes comments if the code works.
But can an LLM come up with questions like what the definition of is is? Seems to me there's a lot of "depends on how you read it" type of stuff that lawyers excel at finding novel interpretations. So what coders thinking of as rules are much less straight forward to understand when it comes to laws
I think that’s a different task than the one OP is referring to. To your example, I’m not familiar with the capability of LLMs in that regard. I have struggled with using the AI features of westlaw when it comes to that sort of argument. (Basically, making an argument that strays from typical route, because that’s the position you happen to find yourself representing.)
I'm afraid since claude cheats in benches, what will it do with law?
Cheat.
Or worse, use historical data to determine the laws of today.
Hmm, what’s the law equivalent of using docker to bypass sudo?
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.
I do question at what point AI could be useful as a teaching aid.
The quality of LLMs depends heavily on, among other things, how you word your questions.
Knowing the correct questions to ask is not something most students know how to do given that it tends to require a fair bit of pre-existing domain knowledge.
I understand why the conversation on this article looks like it does, but the study is specifically focused on the potential for LLMs to operate as tutors for law students. I enjoy the extrapolation out to whether LLMs will replace lawyers, but did not find that to be discussed in the study itself.
In the framing of using LLMs as legal tutors, with the implication of lowering the cost of legal training, this seems like a socially-positive outcome. Furthermore, it feels kind of intuitive to me that any contemporary system operating with an LLM and access to legal reference material will be prepared to answer _student-originated questions_ comprehensively and with breadcrumbs or direct references to educational/source materials, as seems to have been found in the study.
The authors explicitly and intentionally emphasize that many legal questions require contextualization, as opposed to some discrete calculated answer. The result of the study implies that the LLM-based systems were capable of using what many of us here understand to be the "stochastic best-fit algorithmic generation" of a contemporary language model to adequately contextualize a student's question, providing insight into the trade-offs or complications implicit in the question, while then, critically, _meeting the professional standards of legal educators in explaining that complexity to a student_.
Realistically, I would hope this provides some confidence to readers of HN that they can actually ask a legal question to an LLM and expect the response will explain the complexity of the law in relation to the question. This is great news, and is likely the minimal pre-work any of us should do before actually consulting a lawyer, if time permits.
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. Possibly in the same way that a legal textbook does not replace legal counsel, or perhaps more accurately, the same way that stumbling upon a legal case study for approximately the same situation you're in doesn't guarantee you'll have the same result.
I'm surprised Stanford Law would go along with this over-reaching press release title. How about "For common first-year contracts-law questions, law professors preferred AI-generated answers to professor-generated answers"
What the LLM cannot do is explain why it said what it said, when cross-examined. It simply hallucinates the best account of why someone would have said such a thing as it said, same as it can give a probable account of why someone else said something different. The question 'But why did you say this not that ...?' does not lead it to make explicit its grounds for what it said, but just to make a new more complicated statement.
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.
I’d love to read more about these type of patterns. Do you have any recommendations?
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.
No, it happens in the immediate context, where e.g. we say 'No I meant Meredith Jones, not Meredith Smith'- and the possibility of this elaboration is actually part of ordinary communication. I did mean Meredith Jones, not Meredith Smith - thus the use of the past tense The LLM will just give the best answer for what one might have meant, completely reopening calculation.
The point is familiar but there are good illustrations in the Atlantic article by a book editor. At first it seems abstract AI hate, but then she gets to the details. AI text cannot be edited. https://www.theatlantic.com/technology/2026/05/how-to-tell-a... or https://archive.ph/YJsGK
Nonsense, some of my friends are lawyers and they're able to give you consistent interpretations on why they think about a certain aspect of a law a certain way. The whole thing is that they work with this the entire time, so they have a really consistent 'head model' of how things work and why and how considerations should be weighted/ordered/whatever. LLMs just do not have this, there's no consistent underlying reasoning (the 'reasoning' traces in LLMs are really inconsistent)
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.
Humans hallucinate because they take shrooms or have schizophrenia.
No, the hallucination of its reasons follows immediately from the technique of probabilistic inference. You can see this in real time, just ask 'why did you use this word, not that word?' It is in the position of a desperate liar. All its responses are essentially 'rationalizations'
My best guess is that Gemini was trained on the textbooks that the questions are meant to test against, thus they are probably better at explicit recall of those questions or related questions.
This is a pretty limited introductory course based on what it says in the methods of the paper itself.
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.
One way to make legal services more affordable and accessible would be to put the burden of ensuring the AI legal services are accurate on a private-public partnership with the government.
If a person using the service is given inaccurate legal advice and acts on that advice, the person can't be charged with a crime, can't be given any civil penalties, etc., as long as the law in question is non-obvious.
Obviously if by some exploit, some fundamentally obvious crime (murder, theft, obvious fraud, etc.) is said to be legal, that wouldn't apply, but of course the service should try to prevent those kinds of exploits anyway.
Could limit this to something like business regulations to begin with, or even specifically for small businesses, or contracts within some time limit and dollar amount that would otherwise be coverable by small claims court, etc.
Oh, a "Human-Cented" study by AI lover:
Julian Nyarko
LOL!In the hands of a domain expert, AI is useful. In the hands of the naive, it is a foot gun.
I killed my Arch installation and was stuck at the GRUB prompt.Unwilling to brush up my rusty knowledge of GRUB syntax, I asked Gemini for help. The commands Gemini suggested would have wiped my hd...
Once Gemini was told that I was using BTRFS, the suggestion from Gemini looked a bit more sane, but still looked incorrect to me.
It was only after I informed Gemini that I was using a NMVE with BTRFS that it finally produced a sane command.
I beat lawyers twice before generative AI even existed. Recently I asked Gemini a few questions about personal conflicts in everyday life. It's often too conservative, with views too shallow for the problem. So I still handle human conflicts myself. I only outsource the templated stuff like routine chat replies or marketing copy though it saves me huge amount of time. People who quote AI in serious conflicts are too weak to handle them on their own.
> rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups.
That's the problem, you never know when the 25% deliver a true stink bomb, and that's not considering prompting - while a fair prompt/question maybe considered objective, it's very easy to stray.
Yes, LLMs are great at search. That's not news.
I'm going to need some legal help for my startup. But I can't pay much. So I figured I will ask AI all relevant questions, as well as forms filled etc. Perhaps even create a patent-application for me.
THEN I find a human lawyer and give AI's answers to them and say "Can you find any errors in this? Can you improve it?" .
That way I think my legal bills should be smaller because the AI has already done most of the work. What do you think? Which LLM is best for legal work?
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
Are you a lawyer yourself?
Yeah this could be interesting. A lot of the spotlight has been on “law firm stuff” like demand letters and writing contracts…
But imagine if a dev team didn’t have to go engineer -> product manager -> legal team to get a question answered on local data retention requirements. You could ship that much faster.
Would you take responsibility for missing details about local data retention requirements?
honestly if you just avoid EU and China
you can get away with anything
California too.
And with those three places listed you've ruled out literally 40% of the world economy. Great, you can ship your product in bumfuck Nebraska.
Yes.
If the only purpose of asking a lawyer is transferring risk (aka cover your ass) while getting the same advice as an LLM, that’s slowing down delivery for purely bureaucratic reasons.
I’ve seen that mentality at big companies where everyone is scared to stick their neck out and be accountable for a decision. And nothing gets done. Drives me crazy.
But the people who move up are the people who take ownership and get shit done (and are right a lot).
(BTW, I have been at companies that were sued by regulators. They never really punish the individual(s) who were in the room when the decision is made. So your worry is kind of misplaced.)
> In a blind evaluation of nearly 3,000 anonymized comparisons, professors rated AI responses significantly higher than answers written by other professors, with AI winning 75% of head-to-head matchups.
75% win rate seems pretty good!
Paper link: https://law.stanford.edu/wp-content/uploads/2026/06/salinas_...
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.
The writing is always fluid and grammatically flawless. This carries much more weight with us than we believe. I know the illusion well from decades of grading college papers. Many of the highest quality students use English as a second language, and I know this, but an American well trained in writing, grammar, spelling always gives an impression of superiority. (Being well trained in writing, grammar, spelling etc is of course high merit, which is how the illusion forms - it is basically an illusion of global 'intelligence')
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.
What criteria would you use for judging legal arguments?
The arguments need to be based on actual law, and any cited reference cases need to be real.
There's been a lot of news stories about lawyers using AI, and then getting in trouble for citing hallucinated laws or cases. It doesn't matter if the AI response is "preferred" over the human one if it gets thrown out when put under the scrutiny of a real case.
Who's gonna determine that? A bunch of law professors?
But did they? Or did they just go off what answer felt better? Did they put in any work to actually confirm the answer? Or did the busy law professors just click through and move on with their life?
maybe seeing if the case law it cited was real or imagined? Just one idea, IANAL
Well, they had the data around if the answer would be harmful to the students learning. AI was scored at 3.5% harmful answers and 12% of law professor answers were considered harmful.
I think there will be a market for firms that aggressively market themselves as non-AI, and then as more people turn towards that human connection we'll go full circle
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.
Bad legal advice will keep you dealing with the legal system for much longer and at much greater cost. Something being cheap and quick upfront doesn't mean it will be cheap and quick by the end of the process.
But isn’t this study saying that the legal advice could actually be better with AI?
A bit of extrapolation from the study, but not a crazy stretch.
Maybe, although I would be extremely hesitant to extrapolate from this one study and trust my legal life to an LLM. One thing that's worth noting, though, is that regardless of the quality of objective legal advice in the abstract, for a lot of smaller scale stuff the human connection actually is literally what is important. There are ambiguities in the law, which are not resolved deterministically but rather at the individual discretion of judges. Your lawyer, if they're any good at their job, knows the local judges and how they're likely to rule for given circumstances, which can influence their legal advice to you specifically.
Fair.
But I could also see a world where that, too, is fed to models for hyper-local results.
Could be a way off, but I could see it.
I think you are ignoring that there are bad lawyers and they give bad legal advice too
Even the good ones will not step above and beyond what they are paid to do
but an AI ? it will and can go above and beyond
Incredible that the common people will be able to wrestle the right to rule of law away from the bloated legal caste, who have built themselves quite the moat.
The inaccessibility of justice is a huge driver of inequality. Any tools which bridge this gap will help make a more just society.
Personally I think this is very good. One of the hardest things out there is maintaining a society in the face of changing times and it's because law is dense and slow.
I think, in the right hands, this could be huge.
It turns out everybody has at least one right hand, even the people we trust the least.
AI will never convince a jury though.
A couple of acting classes might be cheaper than a lawyer, then you can go all out representing yourself.
Library outperforms student... more news at 9
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.
I'm getting more convinced. I mean, sure it makes dumb mistakes sometimes but its a particular set of self serving mistakes, commenting out tests in order to pass. We obv don't want this behavior but I wouldn't say it's dumb.
It'll be like the Turing test, which we just blew past years ago and no one cared. After all the hand-wringing about sentience and rights of the AI if it passes the Turing test, and now we just have AI bots running 24/7 writing slop.
How does everyone else feel?
> 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 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.
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.
He would tell you NFTs were AGIs if it might get you to buy them.
> 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.
in mice
More great news from the prestigious university where 40% of students claim they are disabled
https://fortune.com/article/rise-in-elite-students-seeking-a...
and where they wanted to ban words such as "chief", "stupid", "karen" and "American"
https://reason.com/2022/12/21/stanford-elimination-harmful-l...
He is basically an AI professor for law. This study just confirms his existence:
https://juliannyarko.com/
Stanford and its donors of course want to replace anyone but its administrators, so they cheer on such anti-intellectual nonsense.
This is the state of HN. Created new account. Accused without evidence. Emotional clickbait.