571–580 of 783 posts
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What if LLMs are optimizing the average office worker's productivity but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book.
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Coding is a relatively verifiable and strict task: it has to pass the compiler, it has to pass the test suite, it has to meet the user's requests. There are a lot of white-collar tasks that have far lower quality and correctness bars. "Researching" by plugging things into google. Writing reports summarizing how a trend that an exec saw a report on can be applied to the company. Generating new values to share at a com…
Code may have to compile but that's a lowish bar and since the AI is writing the tests it's obvious that they're going to pass. In all areas where there's less easy ways to judge output there is going to be correspondingly more value to getting "good" people. Some AI that can produce readable reports isn't "good" - what matters is the quality of the work and the insight put into it which can only be ensured by lookin…
I have been doing this with coding agents across LLM providers for a while now, with very successful results. Grok seems particularly happy to tell Anthropic where it’s cutting corners, but I get great insights from O3 and Gemini too.
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You are forgetting that they are now going to use AI to summarize it back.
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…
When AI is doing that, it loses all value as a proof of work (just as it does for a school report).
My AI writes for your AI to read is low value. But there is probably still some value in "My AI takes these notes and makes them into a concise readable doc".
Just to be clear, the article is NOT criticizing this. To the contrary, it's presenting it as expected , thanks to Solow's productivity paradox [1]. Which is that information technology similarly (and seemingly shockingly) didn't produce any net economic gains in the 1970's or 1980's despite all the computerization. It wasn't until the mid-to-late 1990's that information technology finally started to show clear benef…
Wide spread internet access turned expensive toys (PCs) into useful assets.
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> llms can get me started really fast. Basically it distills the time taken to research something > the llm doesn't make good long term decisions What could possibly go wrong, using something you know makes bad decisions, as the basis of your learning something new. It's like if a dietician instructed a client to go watch McDonald's staff, when they ask how to cook the type of meals that have been recommended.
I’m bearish on AI, but I still think this is disingenuous. My grade school math teachers were probably not well-versed in Calculus and Real Analysis, but they helped me learn my time tables just as well. AI is great at exposing you to what you don’t even know you don’t know: your personal unknown unknowns, the complexity you’re completely unaware of.
Are you somehow equating basic multiplication to a "bad long term decision"?
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It’s also pretty wild to me how people still don’t really even know how to use it. On hacker news, a very tech literate place, I see people thinking modern AI models can’t generate working code. The other day in real life I was talking to a friend of mine about ChatGPT. They didn’t know you needed to turn on “thinking” to get higher quality results. This is a technical person who has worked at Amazon. You can’t expec…
I don't think "results don't match promises" is the same as "not knowing how to use it". I've been using Claude and OpenAI's latest models for the past two weeks now (probably moving at about 1000 lines of code a day, which is what I can comfortably review), and it makes subtle hard-to-find mistakes all over the place. Or it just misunderstands well known design patterns, or does something bone headed. I'm fine with…
They vibe-coded a complete rewrite of their products in a few months without any human review. Hundreds of thousands LOC. I feel sorry for the remaining engineers having to learn everything they just generated, and are now having customers use.
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> HR on the surface seems unimportant, but you’d notice if the company stopped having health insurance or sending your taxes to the IRS etc etc. Interesting on how the very example you give for "oh this job isn't really bullshit" ultimately ends up being useless for the business itself, and exists only as a result of regulation. No, health insurance being provided by employers, or tax withholding aren't useful things…
Only result of regulation, that statement invalidates probably a majority of modern work, and like every legal professional.
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The comparison seems flawed in terms of cost. A Claude subscription is 20 bucks per worker if using personal accounts billed to the company, which is not very far from common office tools like slack. Onboarding a worker to Claude or ChatGPT is ridiculously easy compared to teaching a 1970’s manual office worker to use an early computer. Larger implementations like automating customer service might be more costly, but…
What if LLMs are optimizing the average office worker's productivity but the work itself simply has no discernable economic value? This is argued at length in Grebber's Bullshit Jobs essay and book.
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> 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 mo…
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.
I predict that in the next two to five years we're going to see a breakthrough in AI that doesn't involve LLMs but makes them 10x more effective at reasoning and completely eliminates the hallucination problem.
We currently have "high thinking" models that double and triple-check their own output and we call that "reasoning" but that's not really what it's doing. It's just passing its own output through itself a few times and hoping that it catches mistakes. It kind of works, but it's very slow and takes a lot more resources.
What we need instead is a reasoning model that can be called upon to perform logic-based tests on LLM output or even better, before the output is generated (if that's even possible—not sure if it is).
My guess is that it'll end up something like a "logic-trained" model instead of a "shitloads of raw data trained" model. Imagine a couple terabytes of truth statements like, "rabbits are mammals" and "mammals have mammary glands." Then, whenever the LLM wants to generate output suggesting someone put rocks on pizza, it fails the internal truth check, "rocks are not edible by humans" or even better, "rocks are not suitable as a pizza topping" which it had placed into the training data set as a result of regression testing.
Over time, such a "logic model" would grow and grow—just like a human mind—until it did a pretty good job at reasoning.
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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…
> LLM's are notoriously bad at this. The noise to signal ratio is unacceptably high… I keep seeing this statement in threads about AI, and maybe it’s just from you, but high SNR is a good thing. See https://en.wikipedia.org/wiki/Signal-to-noise_ratio I think the rest of your post is very valid. It’s the mental equivalent of this article https://news.ycombinator.com/item?id=47049088
"Skill floor" is another one. People generally interpret that one as "must be at least this tall to ride", but it actually means "amount of effort that translates to result". Something that has a high skill floor (if you write "high floor of skill" it makes more sense) means that with very little input you can gain a lot of result. Whereas a low skill floor means something behaves more linearly, where very little input only gains very little result.
Even though its just the antonym, "skill ceiling" is much more intuitive in that regard.