Comment by TeMPOraL | Hacker News Reader

Comment by TeMPOraL | Hacker News Reader

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> I feel like we'd be hearing from business that crushed their competition by delivering faster or with fewer people. Where are those businesses?

As if tech part was the major part of getting the product to market.

Those businesses are probably everywhere. They just aren't open about admitting they're using AI to speed up their marketing/product design/programming/project management/graphics design, because a) it's not normal outside some tech startup sphere to brag about how you're improving your internal process, and b) because almost everyone else is doing that too, so it partially cancels out - that is what competition on the market means, and c) admitting to use of AI in current climate is kind of a questionable PR move.

WRT. those who fail to leverage the new tools and are destined to be outcompeted, this process takes extended time, because companies have inertia.

>> But there are also local tools generated

> This is really not the same thing as the original claim

Point is that such wins compound. You get yak shaving done faster by fashioning your own tools on the fly, and it also cuts cost and a huge burden of maintaining relationships with third parties[0]

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[0] - Because each account you create, each subscription you take, even each online tool you kinda track and hope hope hope won't disappear on you - each such case comes with a cognitive tax of a business relationship you probably didn't want, that often costs you money directly, and that you need to keep track of.

> They just aren't open about admitting they're using AI to speed up their marketing/product design/programming/project management/graphics design

Sure… they'd hate to get money thrown at them from investors.

Did you notice that what companies say to investors and what they say to the public are usually entirely different things? When they get mixed up - especially when investor-bound information reaches general public - it's usually a bad day for the company.

And because from the outside everything looks worse than ever. Worse quality, no more support, established companies going crazy to cut costs. AI slop is replacing thoughtful content across the web. Engineering morale is probably at an all time low for my 20 years watching this industry...

So my question is: if so many people should be bragging to me and celebrating how much better things are, why does it look to me like they are worse and everyone is miserable about it...?

I think in context of this discussion you might be confused about what the term "better" refers to.

> And because from the outside everything looks worse than ever. Worse quality, no more support, established companies going crazy to cut costs. AI slop is replacing thoughtful content across the web. Engineering morale is probably at an all time low for my 20 years watching this industry.

That is true and present across the board. But consider, all of that is what "better" means to companies, and most of that is caused by actions that employers call success and reward employees for.

Our industry, in particular, is a stellar example - half of the things we make are making things worse; of the things that seem to make things better, half of them are actually making things worse, but it's not visible because of accounting trickery (e.g. specialized roles cut is legible to beancounters; the workload being diffused and dragging everyone else's productivity down is not).

So yeah, AI is making things better for its users, but expect that what's "better" for the industry whose main product is automating people away from their jobs, is going to translate to a lot of misery down the line.

> Those businesses are probably everywhere. They just aren't open about admitting

"Where's the evidence?" "Probably everywhere."

OK, good luck, have fun

Yup. Or, "Just look around!".

If it was self-evident then I wouldn’t need to ask for evidence. And I imagine you wouldn’t need to be waving your hands making excuses for the lack of evidence.

To me it's self-evident, but is probably one casual step removed from what you'd like to see. I can't point to specific finished or released projects that were substantially accelerated by use of GenAI[0]. But I can point out that nearly everyone I talked with in the last year, that does any kind of white-collar job, is either afraid of LLMs, actively using LLMs at work and finding them very useful, or both.

It's not possible for this level of impact at the bottom to make no change on the net near the top, so I propose that effects may be delayed and not immediately apparent. LLMs are still a new thing in business timelines.

TL;DR: just wait a bit more.

One thing I can hint at, but can't go into details, is that I personally know of at least one enterprise-grade project whose roadmap and scoping - and therefore, funding - is critically dependent on AI speeding up significant amount of development and devops tasks by at least 2-3x; that aspect is understood by both developers, managers, customers and investors, and not disputed.

So, again: just wait a little longer.

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[0] - Except maybe for Aider, whose author always posts how much of its own code Aider wrote in a given release; it's usually way above 50%.

> One thing I can hint at, but can't go into details, is that I personally know of at least one enterprise-grade project whose roadmap and scoping - and therefore, funding - is critically dependent on AI speeding up significant amount of development and devops tasks by at least 2-3x; that aspect is understood by both developers, managers, customers and investors, and not disputed.

Mm. I can now see why, in your other comment, you want to keep up with the SOTA.

It's actually unrelated. I try to keep up with the SOTA because if I'm not using the current-best model, then each time I have a hard time with it or get poor results, I keep wondering if I'm just wasting my time fighting with something a stronger model would do without problems. It's a personal thing; I've been like this ever since I got API access to GPT-4.

My use of LLMs isn't all that big, and I don't have any special early access or anything. It's just that the tokens are so cheap that, for casual personal and professional use, the pricing difference didn't matter. Switching to a stronger model meant that my average monthly bill went from $2 to $10 or something. These amounts were immaterial.

Use patterns and pricing changes, though, and recently this made some SOTA models (notably o3, gpt-4.5 and the most recent Opus model) too expensive for my use.

As for the project I referred to, let's put it this way: the reference point is what was SOTA ~2-3 months ago (Sonnet 3.7, Gemini 2.5 Pro). And the assumptions aren't just wishful thinking - they're based on actual experience with using these models (+ some tools) to speed up specific kind of work.

Schroedingers AI. It's everywhere, but you can't point to it cause it's apparently indistinguishable from humans, except for the shitty AI which is just shitty AI.

It's a thought terminating cliche.

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