Nuance has been completely lost on our society. If it doesn't spark immediate outrage or joy it has no place in our attention economy. I really wish this wasn't the case and I'm not sure how we can reverse it. Most people just aren't interested in the 'balanced takes' because it's just not exciting enough it seems sadly.

It is good to remember that the discourse is not fully real.

There are fake accounts pushing AI everywhere, and people burned out by the marketing that react positioning too aggressively in the opposite direction (particularly when it's their boss who bought into the marketing and makes unreasonable demands).

> Am I able to write things faster with LLMs, yes.

LLM is great at identifying useless writing - if you have llm writing it, it should not exist.

> To say otherwise tells me you haven't actually tried to make use of these tools.

There's also a lot of ideological opposition, which often tries to claim that the tech is useless etc.

> Am I missing out on the work involved in learning things I would be forced to otherwise, also yes.

Yes, but many of those things are things you might not really care about learning about. And if you want to learn about them, AI can be a big help, if you use it appropriately.

The "mixed bag" comes from the way people use it, mostly not from the tech itself.

>It's one thing to get an AI google response and feel like it's dubious, it's another thing to know what you want and have an LLM find the APIs for a framework you're not familiar with

What I'm hearing is that the 0.00001% use case is great while the 99.9999% use case is shit--and we're supposed to think that's reasonable.

Feeding nonsense to pretty much the entire population is just fine with tech bros because a few programmers have an easier time cranking out some code to more effectively sell ads?

That's not a mixed bag, friend. That's a bag of horse shit with one M&M at the bottom.

No, we can't all be friends. Never happened, never will be.

They got duped into it partly because of the enthusiasm and demoware shown by folks like the ones that topped HN with their exhortations about the wonders of AI for coding.

We’re not innocent bystanders here, and it’s important to recognize that. Our hype added to the hype. Our optimism added to the optimism. After layoffs due to Section 172 and interest rates going up, technologists were looking for a reason to be in-demand again, and generative AI as a platform specialization provided that.

We can’t now criticize CEOs for being taken in by the same enthusiasm we pushed for our own purposes.

I have a theory there’s a more nefarious problem that AI changes the incentives around what work is easy to do, in a way that can affect how work hits the bottom line. Aside from the slop problem- people create more documentation more people have to read, etc. it makes it easier for me to say do a bunch of bug fixes or refactors that aren’t in the critical path.

So even if in say 100 person engineering out 10 folks might get 2-3x critical path work. 50 folks Might just add non-critical path work, and the other forty might use it in a way that they end up doing g less critical path work.

But depending on your metrics productivity could look up while the bottom line is unaffected. In which case model quality is a red herring.

AI is a bullshit artist's wet dream. It's bluffing turned up to 11, plausible enough to be believable, especially to the gullible and the greedy who are both succumbing to its psychotic effects.

I'm consistently burning over $100/day in Claude/OpenAI tokens using Codex and Claude - but I'm on the subscriptions so I'm only paying $100/month to each vendor.

If I worked for a larger company that isn't allowed to use those subscriptions and has to pay list price I'd be costing them ~$2,000/month. Now times that by a large engineering team.

They believed and were financially incentivized to embrace the hype and operationalized it because technical leadership thought rapid PoCs would apply to the rest of the tech stack.

IMHO it’s just good old fomo. They fear that the landscape will shift overnight and they’ll be left with a ton of useless meat bags instead of a pay as you go models that you can upgrade instantly every quarter.

Simple answer is that these execs live in a big bubble and goad each other into bigger and bigger investments via groupthink.

Out of touch leadership and management combined with a culture of self-congratulatory "innovation" where companies just plagiarize eachother in an endless loop... It doesn't surprise me at all, tbh.

Apple and Meta (and others) did the same thing with VR. In 2015 all these CEOs were looking for their next app store and, herd-like, settled on VR computing. They couldn't come up with anything more promising so they burned tens of billions on failed goggles that anyone with any sense could have told you was never going to work.

Groupthink. FOMO. Envy. Hubris. Greed. Those fuel Big Tech and we get to pay for the results.

Once you realize most execs are literal sociopaths driven by pure greed and power a lot of things start to make sense. Of course for simple minded folks like us none of it is remotely understandable

I think this probably has more to do with companies switching to API pricing for enterprises, no?

Regardless, the C-suite wouldn’t be performing due diligence if they weren’t at least attempting to perform the calculus of “what are we getting out of this spend?” and what we’re seeing now is them looking for the justification.

Didn’t Uber mention that they’re having a hard time tying all of that spend to any new or improved features?

A CEO unable to predict or even imagine a few months down the road, who bets everything on the current state of things, is a shitty CEO.

We’ve had our AI budget per Engineer cut twice now since the peak mania in early 2026 and Fable was banned even before it was removed by Anthropic due to cost. It’s still used a lot but I think maybe they’re not really seeing the ROI especially when some people spent thousands and thousands per month on dubious AI things.

Personally I truly can’t manage that many tasks (really 1 or 2 max) in parallel with these since you need to think very hard about everything the AI spits out because they’re such natural bullshitters and you end up in places where no one on the team understands anything in the project

Company is a fortune 100 and a client of mine.

Since the switch to API pricing they’ve cut usage limits in half twice and are now saying that anyone with high usage is essentially going to be audited.

They’re down to about 500 a month per person.

I know about a small company with literal competition about who spends more tokens. They had a prize for winner - not too big materially but a lot of status attached.

The competition remained after switch to token pricing and abruptly stopped at seemingly random moment.

Frankly, I want competition about who finds the most expensive work trip hotel.

Probably looks like this:

- teams blitz through Jira tickets

- developers figure out they can't keep up with reviewing the code

- too many new features pushed at once, rushed work to develop training materials

- features don't work as good, many edge cases come out in support tickets

- tickers return to Jira board

- teams spend time triaging and fixing with less AI

- some features turned out to be mistake, but now have to stay

- once mess is cleared, return to "normal" pace, no AI agents, Cursor allowed with budget cap.

CEOs generally suffer zero consequences for their decisions, unless that decision costs someone real money.

> potential productivity” that you can tap into during critical times,

Critical times thinking is considered ineffectivity last 30 years or so. Managers who do that are considered bad.

I would imagine this is the route it will go too, once (if) hardware supply chains catch up and costs can start coming down.

It's too useful to ignore, but too expensive to pay-as-you-go at current pricing for frontier models. Smaller, less capitalized companies will use the subscriptions (if they still exist by then), and larger firms will just build out their own compute for their dev teams.

If AI isn't worth what it costs why are some of these companies allowing $1,000+/employee/month?

https://www.scientificamerican.com/article/google-engineer-c...

it was before ChatGPT

> what breaks the spell?

If throwing more compute at the problem keeps only resulting in incremental gains, I think that should do it. It goes one of 2 ways, really. Either we can throw enough compute at pre-training that results in infinitely more capable models to the point that the cost is now justified [1], or, we hit a scaling wall, get stuck with what we have now (or at that time) and the valuations crash knowing that "this is it" for the foreseeable future without a big breakthrough.

The labs go bankrupt or get acquired by the typical giants (Google, Microsoft, Amazon), the models get rolled into GCP, Azure, and AWS as a service, and that's it. It becomes another dev tool, much like a new IDE.

[1] cost being justified I'd rank as "your average non technical PM can now end to end develop robust, production software free of most serious vulnerabilities." model & tool capabilities that would allow you to hire a small team of non-techincal roles, for half the salary, that can produce the output of a large engineering org. If that doesn't happen, I don't see how the current buildout is sustainable.