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PaperclipPerfector  10d ago (text post)   25759 thread views

Culture War Roundup for the week of April 13, 2026

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I said this in a comment last week, but the utility would be more like "search all the depositions we have on file and pull all the ones where a witness testified about X". Well, we have tens of thousands of depositions on file, most in PDF but some in a special format used for court transcriptions. Conservatively assuming 100 pages per depo and 140 words per page, that comes out to something like half a billion tokens of context required, before we even consider that PDFs take more tokens than plain text, and a lot more if they haven't been OCR'd (which most of these haven't).

This is well-within the technological capability of 2010 AI systems (using techniques that look nothing like chatgpt and where concepts like "context windows" don't apply). So the lack of a tool that does this for you has nothing to do with technical limits on AI, but only on someone's desire to build it/market it to you.

I'm sure rov wants to search by concept or something not keyword.

I understand. Modern LLMs have not meaningfully impacted search at all. Our ability to semantically search documents in a particular technical domain has not advanced tremendously in the last decade. word2vec (2012) and BERT (2017) were both meaningful step changes (but only a small step). Nothing in the generative/LLM era has meaningfully impacted search.

I want to know how this is going to impact things when one side is using it to write and distribute memo's and other corporate information and the other side is using it to summarize and automate replies to said information and 'no' side in reality is paying attention to what the other is doing. The rift and chasm I imagine opening up is going to bring chaos to organizations and cause more harm than it solves.

I read a story the other day about how new candidates on the job market are using them to build their resumes and in response HR departments are using them in tandem with the applicant tracking systems (ATS) to filter through candidates and reject the ones that are suspected to be influenced by an AI / LLM. It's a lose / lose proposition for both parties in the long run.

I would argue that there's a tremendous gulf in capability between the post-bert world of embeddings plus cross encoder rankings, and the ancient days of 2010.

I agree that the genai era does shit for search though, we barely addressed the needle in the haystack problem, let alone asking more complex queries. And LLMs are horrendously incapable of returning all matches for a query, even a super simple one.

And LLMs are horrendously incapable of returning all matches for a query, even a super simple one.

Good God, that's a frustration that I totally forgot about in my various AI criticisms. I have, on multiple occasions specifically asked it to "Provide a comprehensive list of X", which list would include dozens of items, and it instead provides 5 examples, complete with shitty summaries I didn't ask for, which examples are the most obvious and well-known examples (obviously pulled from the Wikipedia page) that I wouldn't need a trillion-dollar technology to find.

Keyword search doesn't even require AI.

LLMs aren't needed for that:

https://en.wikipedia.org/wiki/Concept_search

They don't work well and have been obsoleted by newer llm based techniques, mostly bert based (not to be confused with gpt/genai)

And that sort of 2010 era AI only needs a decent GPU to run (if even), not massive datacenters dedicated to it.

2010 was before AI on GPU was really a thing.

You would have needed a decent full rack of machines in a data center to do a moderately large scale search task back then. No GPUs though. These days, that same task can be done on a single machine that cost <<$5k. Hardware efficiency gains have been massive.

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