They are looking to lower it further to $100 [0]. This is definitely concerning for frontier labs since Accenture also caps their employees' usage to $29 per month [1].
It's only too low if the goal is to use AI as much as possible. If Apple are saying their engineers should not use AI for everything, and that they should still be working as they did but with a small assist from AI, the budget makes a lot of sense.
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.
It's kinda borderline. My dev workflow in my full-time job (Opus 5.5, 5 concurrent worktrees with human checking results and giving instructions) spend exactly $200/day on average. I imagine it could be more friendly if it's $300, as otherwise I might have to move some of my AI usage to weekends, which implicitly increases working hours :[
Apple is paying API prices, not subscription prices, though. $6000/month is half the engineer's monthly salary, it seems ridiculous to say that won't be enough, unless you're being very inefficient with token spend.
The fact that it's expensive has nothing to do with how useful it is though... I know plenty of people who max out their $200 plans every week, and they are working on stuff a lot less financially valuable than what Apple employees work on.
My "AI subscription" is just buying 20$ openrouter credits when I run out and it's costing me less every month then a monthly Claude subscription. 200$ a day is insane
Your data is likely kept and used for training models.
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.
[0] https://finance.yahoo.com/technology/ai/articles/apple-cuts-...
[1] https://finance.yahoo.com/technology/ai/articles/accenture-j...
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.
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.