Basically, once you’ve invested in on-premises capacity, you can divert mundane AI tasks to those machines for processing — limiting costs, boosting security and releasing capacity, turning to cloud-based models only when higher end AI solutions are required. While that’s good news for Apple, that’s bad news for many AI companies’ revenue models. (Perhaps they should have recognized that even the most advanced LLM’s will run on a standard iPhone eventually.)
The other advantage is that if AI is not used as widely as expected across a company, the same hardware can be used for other company tasks.
What’s actually happening
Enterprises already using AI are learning these lessons, which is why we see more of them buying Macs for these tasks. They do so because Apple’s computers deliver the computational power and performance to run AI effectively, from chip design to power consumption to the OS itself. Apple has intentionally built its platforms to be the best in class for running AI on device, and the Unified Memory architecture Apple has created in Apple Silicon scales really well, meaning you can run ever larger LLMs on Macs.
