According to reports from The Information cited by English media, OpenAI has purchased tens of thousands of Mac mini and Mac Studio units for reinforcement learning and computer-using agent training. The procurement focuses on headless units without screens or keyboards, rather than MacBooks.

These machines are used to train agents that can operate computers autonomously, covering multi-step tasks such as coding, testing code, organizing emails, and summarizing documents. Most of the time in reinforcement learning is spent generating rollouts, and unified memory hosts are used for long-term reasoning rather than replacing large model pre-training in data centers.

OpenAI is still urging Apple to expedite delivery, indicating that current inventory is insufficient. Apple has just released the M6 version of the Mac mini and the M5 Ultra version of the Mac Studio, with high-capacity unified memory and multi-machine chaining capabilities directly aimed at this type of workload.

Anthropic has not made equivalent large-scale purchases but instead rents Mac minis through Amazon Web Services (AWS) for similar tasks. AWS itself has provided cloud hosts based on Mac mini and Mac Studio, allowing labs to access macOS environments without having to build their own server rooms.

Apple executives have previously attributed the high demand for Mac mini and Mac Studio to AI and agent demand exceeding expectations, with high memory specifications being out of stock for months. On the developer side, there are also practices of clustering multiple Macs using Thunderbolt interfaces to run larger models locally.

In market mechanisms, the buyers are labs needing massive real computer interface rollouts, while the sellers are consumer-grade hosts with unified memory and cloud-based Macs. This event is driven by agent training demand, with funds flowing from NVIDIA clusters to Apple desktop machines and AWS Mac instances; benefiting are Apple's high-spec Mac shipments and cloud Mac rentals, while pressured are already tight high-capacity memory configurations and paths that tie all training to discrete graphics cards.

At the same time, NVIDIA still views Apple as a major competitor in local AI inference, with consumer-grade AI computers and lab purchases overlapping on the same memory shortage chain.

Source: Public Information