AI is affecting how many of us software engineers build ; we’re prompting more code and producing much more of it. The tools are also adapting, with command-line interfaces gradually becoming more popular than IDEs. But what about operating systems ? To find out, the Pragmatic Engineer newsletter reached out to the leading Linux distribution – the team at Ubuntu – and the Windows team, about how AI is changing their operating systems.
Today’s article focuses on Linux and Ubuntu, and we’ll cover Windows in a follow-up issue.
Jon Seager is VP of Engineering at Canonical – the company behind Ubuntu – and has provided new details about what the team there has built for AI support, and some new ideas that they’re brewing up.
According to Jon Seager, here are globally how they adapt by using AI support :
A) Hardware enablement : support for GPUs, NPUs and DPUs. When you turn on a machine with AI accelerators, Ubuntu aims for the hardware to perform at its full potential. This means having proper driver support for PCs and cloud data centers’ computing units.
B) Hardware partnerships. Working closely with NVIDIA, AMD, and Intel means Ubuntu can support those vendors’ new hardware from release day.
C) CPU architecture variants. New versions in a CPU family add to, or change, features. An operating system needs to support a new version of the CPU architecture variant in order to fully utilize it. Ubuntu does this for the x86‑64 family, making it a lot more performant on newer CPUs – while still supporting older CPUs.
D) Local-first bet & plans for agentic workflows. There’s a big focus on running local models and using “inference snaps” which help choose the right model with the right quantization. There is the intention to support agentic workflows at the OS level, one day, which is currently at the early exploration stage.
E) Developer ecosystem. There’s a plan to add more support for AI dev tools, a focus on sandboxing at the OS level, a push to support ARM64 laptops more, and we touch on the popularity of Windows Subsystem for Linux (WSL).
F) Engineering culture. A skeptical attitude to AI at Canonical has given way to one where experimentation is encouraged and devs lean into AI tools, but there are no targets for token usage or amounts of AI-generated code.
G) What other Linux distributions are doing. Arch Linux takes the “DIY your AI setup” approach, Omarchy makes it easy to install AI tools, while Red Hat Enterprise Linux ships with AI integrated into the command-line and support for AI accelerators & popular AI tools.
