[palata]

> the exact inverse of how I most often use AI, which is to throw a ton of information at it in a large prompt

It sounds to me that you don't make the effort to absorb the information. You cherry-pick stuff that pops in your head or that you find online, throw that into an LLM and let it convince you that it created something sound.

To me it confirms what the article says: it's not worth reading what you produce this way. I am not interested in that eloquent text that your LLM produced (and that you modify just enough to feel good saying it's your work); it won't bring me anything I couldn't get by quickly thinking about it or quickly making a web search. I don't need to talk to you, you are not interesting.

But if you spend the time to actually absorb that information, realise that you need to read even more, actually make your own opinion and get to a point where we could have an actual discussion about that topic, then I'm interested. An LLM will not get you there, and getting there is not done in 2 minutes. That's precisely why it is interesting.

[_qswe]

You're making a weirdly uncharitable assumption. I'm referring to information which I largely or entirely wrote myself, or which I otherwise have proprietary access to, not which I randomly cherry-picked from scattershot Google results.

Synthesizing large amounts of information into smaller more focused outputs is something LLMs happen to excel at. Doing the exact same work more slowly by hand just to prove a point to someone on HN isn't a productive way to deliver business value.

[palata]

> Doing the exact same work more slowly by hand just to prove a point to someone on HN isn't a productive way to deliver business value.

You prove my point again: it's not "just to prove a point". It's about internalising the information, improving your ability to synthesise and be critical.

Sure, if your only objective is to "deliver business value", maybe you make more money by being uninteresting with an LLM. My point is that if you get good at doing all that without an LLM, then you become a more interesting person. You will be able to have an actual discussion with a real human and be interesting.

[_qswe]

Understanding or being interesting has nothing to do with it. We use calculators and computers for a reason. No one hires people to respond to API requests by hand; we run the code on servers. Using the right tool for the job is just doing my job well.

[palata]

> We use calculators and computers for a reason. No one hires people to respond to API requests by hand; we run the code on servers

We were talking about writing, not about vibe coding. We don't use calculators for writing. We don't use API requests for writing (except when we make an LLM write for us).

> Using the right tool for the job is just doing my job well.

I don't know what your job is. But if your job is to produce text that is meant to be read by humans, then it feels like not being able to synthesise your ideas yourself doesn't make you excellent at doing your job.

Again maybe it makes you productive. Many developers, for instance, get paid for writing bad code (either because those who pay don't care about quality or can't make a difference, or something else). Vibe coding obviously makes those developers more productive. But I don't believe it will make them learn how to produce good code. Good for them if they make money like this, of course.

[tpmoney]

> We were talking about writing, not about vibe coding. We don't use calculators for writing. We don't use API requests for writing (except when we make an LLM write for us).

We do however use them to summarize and transform data all the time. Consider the ever present spreadsheet. Huge amounts of data are thrown into spreadsheets and formulas are applied to that data to present us with graphs and statistics. You could do all of that by hand, and you'd probably have a much better "internalization" about what the data is. But most of the time, hand crafting graphs from raw data and internalizing it isn't useful or necessary to accomplish what you actually want to accomplish with the data.

[palata]

You seem to not make the difference between maths and, say, literature or history.

Do you actually think that an LLM can take, say, a Harry Potter book as an input, and give it a grade in such a way that everybody will always agree on?

And to go further, do you actually use LLMs to generate graphs and statistics from spreadsheet? Because that is probably a bad idea given that there are tools that actually do it right.

[_qswe]

We were talking about writing, not about vibe coding.

No one said anything about vibe coding. Using tools appropriately to accomplish tasks more quickly is just common sense. Deliberately choosing to pay 10x the cost for the same or equivalent output isn't a rational business decision, regardless of whether the task happens to be writing, long division, or anything else.

Just to be clear, I'm not arguing against doing things manually as a learning exercise or creative outlet. Sometimes the journey is the point; sometimes the destination is the point. Both are valid.

I don't know what your job is.

Here's one: prepping first drafts of legal docs with AI assistance before handing them off to lawyers for revision has objectively saved significant amounts of time and money. Without AI this would have been too time-consuming to be worthwhile, but with AI I've saved not only my own time but the costs of billable hours on phone calls to discuss requirements, lawyers writing first drafts on their own, and additional Q&A and revisions over email. Using AI makes it practical to skip the first two parts and cut down on the third significantly.

Here's another one: writing technical reports for my day job. Before they'd integrated AI into their platform, I would frequently get rave reviews for the quality and professionalism of my issue reports. After they added AI writing assistance, nothing changed other than my ability to generate a greater number of reports in the same number of billable hours. What you're suggesting effectively amounts to choosing to deliver less value out of ego. I still have to understand my own work product, or I wouldn't be able to produce it even with AI assistance. If someone thinks that somehow makes the product less "interesting", well then I guess it's a good thing my job isn't entertainment.

[palata]

Don't get me wrong: I don't deny that LLMs can help tricking other humans into believing that the text is more professional than it actually is. LLMs are engineered exactly for that.

I'd be curious to know whether your legal documents are as good as without LLMs. I wouldn't be surprised at all if they were worse, but cheaper. Talking about security audits, that's actually a problem I've seen: LLMs makes it harder to detect bad audits, and in my experience I have been more often confronted to bad security audits than to good ones.

For both examples, you say "LLMs are useful to make more money". I say "I believe that LLMs lower the quality of the work". It's not incompatible.

[johnnyanmac]

> deliver business value.

I think that mindet directly correlates with the kind of AI hat prompted this article: "It doesn't matter" in your eyes. You don't see the task as important, only the output and that it makes you money. the craft is less important than what you can sell it for.

[_qswe]

They're just different use cases. There's a difference between a learning exercise and a contractual engagement to deliver a product to a client.

[johnnyanmac]

Yes, a learning exercise has a goal to extend your own knowledge. Business is, especially as of late, figuring out how to get the cheapest but still acceptable work to the recipient for the highest value. I suppose it's on the recipient for not checking the quality of their commission.

[_qswe]

I'm not sure why you're randomly insinuating otherwise, but I've only received positive feedback on the quality of my work.