[0] https://finance.yahoo.com/technology/ai/articles/apple-cuts-...
[1] https://finance.yahoo.com/technology/ai/articles/accenture-j...
Yes. The frontier models are good, indeed, but they are just far too expensive to use much. Recently I was showing my executive leader that we could simply run a certain set of tasks on the top frontier model and we'd be good to go. Oh but it would cost over 15M/year. We're a relatively small company, that's over two magnitudes more than what makes sense for us. So, never going to happen.
What remains then is an optimization exercise to bring the cost down. For months now we've been optimizing usage so we can run nearly all tasks on very cheap models to good success. It's fun to optimize and keeps the CFO from having a heart attack.
But not good news for the AI companies relying in a firehose of cash coming in from their frontier models.
For the edge cases that might need more like running a security audit, it would make more sense to submit that as a project proposal and have the expense approved.
It's easy to spend a lot when writing an entire service, large features, or doing research loops.
They could be tightening the purse strings further until they hit some magical ROI numbers.
You do not need much creativity for that. Just forget to put build artifacts or node_modules in your agent's ignore list and it will happily burn through the cap in thirty minutes
They also should have a smart model supervise the cheaper and less capable ones.
So it's about staying nimble enough so you can easily switch over. Don't tie yourself down to a single product/harness.
That's why those plans are only available to small companies with 150 employees or less.
[0] https://finance.yahoo.com/news/accentures-ceo-says-using-ai-...
I’m not surprised companies are pulling back. That’s a decent chunk of money and CFOs are going to want to know what they’re getting for the money. “More PRs” isn't a good enough answer.
Finally! Gravity and sanity are starting to win.
Or, get this - figuring out what customers really want and need is super hard and churning out code doesn't solve the truly hard problems of software development.
I don't see any indication that LLMs help with requirements gathering or user studies, especially live ones.
Any company, not just Apple, should be tracking whether the extra spending is worth it. There must be a point of diminishing returns where additional AI usage stops increasing productivity at a comparable rate. That point should be your target. Different teams can have vastly different thresholds.
Companies also can't justify ever-increasing costs just because it increases productivity. It's business. You care about profits, not raw productivity. And Apple doesn't seem to be having a particularly great time these days.
Giving people some constraints so they have to actually think about solving their problems properly rather than just throwing money (or tokens) at it is not a bad idea.
Things that drive up spend is mostly doing other people's work. Triage, "that doesn't seem quite right, let's see what happens if I prompt it", properly investigating incidents (I get a lot where the reporter's agent didn't read the code and just guesses at how it works, kind of making the report useless). Fixing those things is what would really get token spend down -- a consistent way of making bug reports against other teams that has the same context as the people that work on it everyday. I don't know if it's possible. But I'm anti-rationing because the first thing you do when your tokens are counted is to stop helping other people, right?
But it's telling that you claim to while treating this as some sort of equivalence.
Smart guy showed up. Yes. Don't do this.
Does anyone know that study? I mean, I can understand other languages takes more tokens but that amount of energy? ...
Most of the programming work I get opus5.5 to do, sometimes with fable coordinating. When I’m building a large feature it will often run over $500 and occasionally on longer sessions over 1k. Everyone else seems to think $200 should get by?
I’m looking for a point of reference here.
For most businesses, that's a no go. At least at the company I work for, we have to wait for new models because they negotiate away the training and retention and the vendors are not too eager to give up their moat.
That's their moat: your data.
You'd think they must be investing heavily into "secret" agentic engineering dogfooding at this point.
Claude tells me the retail price of the tokens I use on a personal plan outside of work. It’s way more than the plan costs me, by a factor of 50 or more. But at work I don’t see such discounts reflected in work’s estimates of my token spend. Something strange is going on.
I have been able to soak up a weeks worth of tokens in 6 hours with astra ultra if I'm not very explicit and bounded in my requests.
Tokens are a resource. We should use as few as possible (Tokengolfing?).
Once you have multiple agents working in parallel, hands off the wheel becomes very attractive. And at that exactly point you lose completely touch of what the heck is going on in your ex-code.
Listen, I dont care if employees scam their bosses by offshoring their work to AI, but its absolutely the case that 90% of people irresponsibly use AI to be lazy, get by doing less work.
Unfortunately, as Ive personally witnessed when you do this and take away the cheating tools engineers just start copying and pasting IP into their own AI subs where you have less garuntees about ZDR. Employees will gladly spend an extra $200 a month so they can work 4hrs less a day and just get by.
Only 10%, maybe less, are actual super contributors with AI. The rest are literally getting a form of diabetes of the brain and the exercise required to reverse that disease is quite uncomfortable, especially when you've been hands off using a cheating tool for a year straight.