62 points by crescit_eundo34 days ago | 50 comments
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Companies are slamming the brakes on AI in a massive reversal that’s unlike anything I’ve seen in the last 25 years in tech.
6 months ago it was use AI all the time go! Now companies are putting use limitations in place, strict budget controls, and the wagons are circling around various “AI labs” teams that cost a ton and have shown little to no ROI.
It was all fun and games until the bill arrived. Now it seems there’s a mad rush for AI companies to IPO before the music truly stops.
I've seen legitimately good outcomes with AI - a backlog has been cleared, features that were left on the cutting room floor have been pulled back in AND delivered all thanks to the use of AI coding tools. AI workflows have brought down processes from weeks of human processing to a couple of minutes with human oversight - and the revenue that it unlocks more than covers the AI bill. This is within a large corporate company - the "No such story exists for AI" feels overplayed. Sure, the wave of (quoting the article) "braindead executives, imbeciles and middle management hall monitors that don’t do any real work" might be bigger than with previous hype cycles because AI as a tool does enable pseudo-intellectualism, but the article overstates its case. I know, 1 counterpoint doesn't make a strong argument - but there's no reason the way we're applying this as a tool can't provide the same gains within other organisations - am I missing something/being delusional/huffing copium?
> AI is more expensive today than it was three years ago, and it is not getting cheaper. Sam Altman’s comments about “intelligence too cheap to meter” were lies. NVIDIA’s Blackwell GPUs didn’t make it cheaper, and its Vera Rubin GPUs won’t either. Google’s TPUs won’t do it, Amazon’s Trainium or Inferentia chips won’t do it, Vera Rubin CPUs won’t do it, OpenAI’s chips won’t do it, and no, DeepSeek won’t do it either.
Has this man ever heard of Jevon’s paradox?
Also all of these claims are objectively wrong today because the goal posts for what AI have been moving this whole time. The models we have today do more, are faster, smaller, and cost less than what was available 3 years ago.
This is exactly what is happening right now. Models are becoming more efficient but at the same time users are starting to tackle tasks they previously didn’t even try to automate.
But Jevons paradox explains the increase in consumption, but it does not necessarily answer the question of business profitability
Saw the same dynamic with relational databases in the 80s. Cheaper storage just meant people kept more data nobody queried. Profitability followed later, unevenly, for specific use cases. Broad "AI ROI" is the wrong frame anyway — always was.
The author conveniently (or perhaps wasn't even aware) left out this quote from Uber's CFO
"What we have done is we have tempered the pace of hiring, and we -- and this is broadly across the company, but specifically from an engineering standpoint -- the hiring ramp we have for the remainder of the year is significantly lower than what we thought it would be when we came into this year."
Uber's response looks to be cutting the number of engineers that generate tokens, not to cut the AI that is generating them. These headlines about Uber are not the victory people are portraying it to be.
I've seen many cases where AI led to ROI with high margins (maybe not enough to justify the entire industry capex though), but they usually share similar features
- AI is a component of a larger product sold
- The product improves the metrics that customers care about, typically autonomously
- The customer is paying for the outcome, regardless of whether or not the product had AI in it
'Copilot' style AI features are much harder to measure ROI on, because they are typically further away from the base metrics that make it easy to measure ROI, and are typically used for specific tasks in a long web of other tasks within a professional job
Healthcare imaging and fraud detection are the two I keep seeing. We ran an AI layer for claims triage at an insurance company - cut manual review time ~40%. The key was it wasn't replacing adjusters, just routing obvious cases. It worked because labor costs are high and the error rate was low enough to not create downstream problems.
Yes it does - the ROI is replacing the global labor market => the replaced workers stop earning income. They cut spending. The businesses they used to patronize see revenue decline => the company that fired its workers to save money discovers that its customers were, in aggregate, other companies’ workers. Revenue growth stalls => dead economy [1]
I'd probably characterise it as more as "AI doesn't have the massively transformational ROI that all the AI salespeople said it would and now I have to pay for my tokens and the humans I though I could replace at the same time". The idea AI would be running whole companies below some weird godlike CEO who won because they were clever just pushed an attractive narrative for the investor class.
I am very bullish on AI as a tool, but not as a way to completely restructure the economy overnight. Doing things is hard, and better tools don't make fundamental problems about change go away.
It seems to me that both sides are starting to drift into extremes. Some promised the replacement of half of office workers, while others are now saying that AI doesn’t create any value at all. The reality is somewhere in between
Also the article seems to be mixing two different things. The pricing as in
>allowed to burn thousands of dollars of tokens on a $39-a-month subscription
and whether the AI is worth it if you do pay what it costs.
I always thought the burn thousands of dollars of tokens without paying bit was unsustainable and harmful for the environment, electricity bills, investors and the like.
But I think it can do worthwhile stuff even if you pay. Like a small job I did was to get the emails of conference attendees off a website. It was tricky as they didn't want to be scrapped so I chatted to gemini and it helped figure out how to use tampermonkey and wrote a script. It was probably <$1 tokens and saved a couple of hours of mucking around. There must be a lot of things like that.
I am considering pinning whatever the earliest version in which this setting was introduced. I can't think of a single feature VSCode has implemented in the last three years that I couldn't go without. The binary for 121 is like 50% larger than 120.
Brilliant article. This is something I've been thinking about for a while. Up until around 2020 I used to work at a company that lived off of games and the economy was, you guessed it, micro-payments. I was the one in charge for developing the system that allowed the people in charge of monetization to configure the games based on your skill to squeeze the most out of you. Suffice to say, it worked great. Fundamentally the business model for all games was identical: cash for virtual currency. Here's the catch: you never knew if spending 50 bucks would make a big difference and you had no way to measure it. In a nutshell, it almost made a difference but just not enough so your brain would go "well what the hell, here's another 50" (classic sunk cost fallacy). And the business knew that and actively exploited it. All the AI slop that is happening now is the evolution of the same thing: exchange cash for virtual currency(tokens) in exchange for immeasurable results and the inevitable "just a few more tokens". Congratulations, you've been played.
The part that stuck with me from working on similar systems was the variable reward timing - not just variable amounts, but unpredictable delay. Players who got consistent rewards churned faster than players who hit dry spells followed by big payouts. We had the data to prove it and it still felt uncomfortable to ship.
Has anyone actually run a controlled experiment here? Every "AI saved us time" claim I've seen compares current output to a remembered baseline, not a matched cohort without the tools. That's not ROI measurement, that's vibes with a spreadsheet attached.
6 months ago it was use AI all the time go! Now companies are putting use limitations in place, strict budget controls, and the wagons are circling around various “AI labs” teams that cost a ton and have shown little to no ROI.
It was all fun and games until the bill arrived. Now it seems there’s a mad rush for AI companies to IPO before the music truly stops.