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PaperclipPerfector  6d ago (text post)   26975 thread views

Culture War Roundup for the week of January 26, 2026

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I've seen a few examples of lawyers praising AI for its legal mind. What does it get wrong in your experience?

LLMs are generally terrible at any sort of task where accuracy and rigor are core constraints. Imagine the full range of legal opinions that exist on the internet, intelligent, retarded, and everything in between. Now imagine what the average of that mass of opinions would look like. That's effectively what you're getting when you ask an LLM for legal advice.

Otherwise see @gattsuru's comment below.

Necessary starting caveat: Unikowsky is an absolute putz when it comes to anything Trump-related, and his analysis should be recognized as on the "ought" side of any is-ought divide, and, more damningly, an "ought" that will not apply to any case where he doesn't like the victim. That doesn't completely destroy his analysis about AI effectiveness, but it does undermine how and what he's evaluating.

For more specific problems:

  • Hallucinations happen. Newer models are better about avoiding them, but they're still prone to it. This is critical because, whether it's a hallucinated citation, incorrectly summarizing the contents of a citation, or hallucinating facts or claims by parties in the case, it's a central example of sanctionable behavior.
  • Even large-context models struggle with the amount of information in a lawsuit, and most models aren't large-context (and many APIs will obfuscate when you're getting downgraded).
  • Worse, they get loopy under certain nonobvious situations: going toward the last 20% of a context size, repeating a word too often during a prompt, so on.
  • It can be very hard to get the LLM to understand core constraints for a specific environment. Citing out-of-circuit cases as if they were binding, not checking if a case was overruled or constrained to its own four corners, even basic formatting stuff can be a problem, here.
  • Most LLMs, unless very carefully prompted, are people-pleasers. They'll quite happily give you the answer you want, even if you aren't explicitly saying what you want, even when this means disregarding well-known counterexamples

There's a defense that people, even lawyers or judges, make many of these same mistakes, and that's true. It's still a problem and a limitation.

AI can be a useful tool, but it's a tool.

I read the article. It’s somewhat interesting but the interesting question is what did prediction markets say. Most SCOTUS opinions are easily predicted (and frequently written about).

I’d expect an LLM to do better there compared to areas with less items written. In the example I was referencing, if an LLM could step back and try to understand the regulatory scheme, it would have understood its answer was counter to the scheme. Once you know that, you have to really study to make sure you aren’t missing something. But not sure it’s capable of that meta check.

Pretty much everything. Literally yesterday there was a question asked by a generalist that he ran through an AI. The response was the opposite of the right answer as there was a specific reg on point.

I also find that AI is bad at understanding the meta analysis behind cases (ie doesn’t really understand the policy and therefore has a hard time generalizing).

It is a better google but still wrong—sometimes in clear ways like the ref on point and sometimes in less obvious ways where it doesn’t understand nuance.

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