This is one of my major concerns about people trying to use these tools for 'efficiency'. The only plausible value in somebody writing a huge report and somebody else reading it is information transfer. LLM's are notoriously bad at this. The noise to signal ratio is unacceptably high, and you will be worse off reading the summary than if you skimmed the first and last pages. In fact, you will be worse off than if you did nothing at all.

Using AI to output noise and learn nothing at breakneck speeds is worse than simply looking out the window, because you now have a false sense of security about your understanding of the material.

Relatedly, I think people get the sense that 'getting better at prompting' is purely a one-way issue of training the robot to give better outputs. But you are also training yourself to only ask the sorts of questions that it can answer well. Those questions that it will no longer occur to you to ask (not just of the robot, but of yourself) might be the most pertinent ones!

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notahacker 2 hours ago | parent | next [-]

Yep. The other way it can have net no impact is if it saves thousand of hours of report drafting and reading but misses the one salient fact buried in the observations that could actually save the company money. Whilst completely nailing the fluff.

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birdsongs 2 hours ago | parent | prev | next [-]

> LLM's are notoriously bad at this. The noise to signal ratio is unacceptably high

I could go either way on the future of this, but if you take the argument that we're still early days, this may not hold. They're notoriously bad at this so far.

We could still be in the PC DOS 3.X era in this timeline. Wait until we hit the Windows 3.1, or 95 equivalent. Personally, I have seen shocking improvements in the past 3 months with the latest models.

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kombookcha 2 hours ago | parent | next [-]

Personally I strongly doubt it. Since the nature of LLM's does not allow them semantic content or context, I believe it is inherently a tool unsuited for this task. As far as I can tell, it's a limitation of the technology itself, not of the amount of power behind it.

Either way, being able to generate or compress loads of text very quickly with no understanding of the contents simply is not the bottleneck of information transfer between human beings.

	▲	birdsongs an hour ago \| parent [-]
		Yeah, definitely more skeptical for communication pipelines. But for coding, the latest models are able to read my codebase for context, understand my question, and implement a solution with nuance, using existing structures and paradigms. It hasn't missed since January. One of them even said: "As an embedded engineer, you will appreciate that ...". I had never told it that was my title, it is nowhere in my soul.md or codebase. It just inferred that I, the user, was one. Based on the arm toolchain and code. It was a bit creepy, tbh. They can definitely infer context to some degree.

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mcny 2 hours ago | parent | prev [-]

I would like to see the day when the context size is in gigabytes or tens of billions of tokens, not RAG or whatever, actual context.

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kykeonaut 2 hours ago | parent | prev | next [-]

> Those questions that it will no longer occur to you to ask (not just of the robot, but of yourself) might be the most pertinent ones!

That is true, but then again also with google. You could see why some people want to go back to the "read the book" era where you didn't have google to query anything and had to make the real questions.

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crabmusket 26 minutes ago | parent | prev [-]

It reminds me of that Apple ad where a guy just rocks up to a meeting completely unprepared and spits out an AI summary to all his coworkers. Great job Apple, thanks for proving Graeber right all along.