Apple joins firms putting limits on employees' AI use amid surging costs
Apple has reportedly lowered its employees' daily spending cap on AI use, joining the list of major firms that are limiting spending on AI usage.
The tech firm last month lowered the daily spending cap to $200 for some engineers using AI models, with further cuts planned for some of its engineering teams, Bloomberg reported.
According to the report, which cited people with knowledge of the change, further planned reductions could bring some engineers' AI allowances to as low as $100, which is a daily cap already faced by many groups outside of its engineering division.
Apple, however, is reportedly planning to cut that daily cap to as low as $25 for some teams, with employees who need higher allowances required to request approval from senior leadership.
The reductions are part of Apple's cost-cutting effort, according to Bloomberg, despite a widespread push from the organisation on AI adoption.
Firms reducing AI spending
It joins the list of firms already curbing employees' AI usage spending, such as Microsoft, Uber, Accenture. Here is what each firm has actually done:
- Uber capped employee AI tool spending at $1,500 per engineer per month after blowing its entire AI budget within four months
- Microsoft reportedly reduced internal spending on Anthropic's Claude by more than one-third from a projected annual bill exceeding $1 billion
- Accenture capped centrally funded spending on Claude Enterprise at $29 per employee per month
Faros AI has attributed the surging AI spending from employees to the differences in the complexity of their work.
"The difference may be the complexity of their work. It may also be repeated failed attempts, unnecessary use of expensive models, or agents consuming resources without reaching an acceptable result," it said in a blog post.
"As agents take on more engineering work, organisations need a way to expand useful consumption without allowing costs to grow unchecked. That requires understanding where money goes, improving how AI coding tasks are executed, and applying controls that reflect business priorities."
