Anthropic set out its compute strategy in an investor video that is part of its initial public offering roadshow, which began on 10 November. The next day, software developer Simon Willison described the video in a blog post, and The Decoder, an AI news site, reported on it. There is no official transcript, so Willison transcribed the video himself with the speech recognition program MacWhisper.

In the video, Anthropic co-founder Tom Brown said the company trains and runs its Claude model on chips from four manufacturers. The Decoder describes Brown as the company’s head of compute. The chips are AWS’s Trainium, Google’s TPU and GPUs (graphics processing units) from Nvidia and AMD. Each chip type needs its own software, which is expensive and difficult to build, Brown said.

Three days instead of months

Anthropic began using AMD GPUs this year. In the past, Brown said, bringing up a new chip platform took dozens of engineers months. This time the company’s Mythos model did the work, getting Claude running on AMD chips in three days with virtually no human help.

The model also ported or wrote almost all the kernels, Brown said. Kernels are small programs that carry out a model’s calculations on a particular chip.

A chart in the investor presentation compares the three days with 6–12 months, The Decoder reported. The chart gives that as the time traditional engineering needs to bring new hardware to usable performance. Its figures concern prompt processing on AMD MI355X GPUs, so the comparison is limited to one stage of inference on one GPU model.

The Decoder notes that the figures are Anthropic’s own and have not been independently verified. In the publication’s view, if the figures hold, Nvidia’s strongest competitive advantage, the CUDA software ecosystem that ties developers to its chips, would lose importance. Companies could then switch chip suppliers more easily than they can today.

Compute multipliers

Brown named compute multipliers as a second advantage. These are research findings that let a company get more out of the same physical computing capacity. In Brown’s example, a fourfold multiplier means a model that needs 100 units of compute can be trained with 25. The multipliers reinforce one another, Brown said.

Anthropic believes Claude will eventually find multipliers on its own as development approaches recursive self-improvement, Brown said. The Decoder notes that the idea is disputed among researchers and has not been proven.

Both GPU makers have financial ties to Anthropic, The Decoder reported. In November 2025 Nvidia committed to invest up to $10 billion in the company. In July 2026 AMD announced an investment of up to $5 billion. In return, Anthropic plans to deploy up to two gigawatts of AMD MI455X GPUs, with the first gigawatt phase beginning in the first half of 2027.