The morning focused mainly on the question: what is a software developer's job in 2026? This culminated in a panel discussion on "building trust in AI". Already, I would say the conference's mind was made up, AI is here to stay; it is just a tool; we should learn how to hold it properly. I think, in part, this was mainly due to the group of speakers who were all, more or less, executives within for-profit businesses that incorporate open source in some way: Github, BIMP.ai and Budibase. The only exception was a speaker from the Institute of Directors. I think this gave the morning session a commercial flavour which suggested the tech industry's large-scale adoption of AI tooling, a position I'm not sure is as common among, or available to, smaller open-source maintainers.
One stream of thought that was popular was arguing that languages like C, where it is easy to introduce memory bugs and vulnerabilities, are on the way out. Copilot's runtime rewrite in Rust was championed as a success story for the kinds of productivity that can be unlocked for highly-skilled developers. A pinch of salt is needed though, as Microsoft did sponsor the event. My own thoughts, however, are that there are quite a few success stories we can learn from where software was translated from one programming language to another using an LLM. Migration seems well-defined and well-scoped, often with large, pre-existing test suites that can easily guide a tool towards some form of correctness. In the same breath, it is not necessarily that straightforward, for example, see some lobste.rs commentary on the Bun rewrite in Rust.
Another undercurrent throughout the morning talks was how LLMs could free up developers to actively engage with users. Some quotes from the talks included "experiment with empathy" and "AI doesn't write the story". On the surface, this seems like a reasonable argument. But, shouldn't these large teams of people have already been thinking about their users, with or without AI? I'm also not convinced this free time will appear. When the metric of success is at least proportional to the rate of new features, it would seem that a tool, like an LLM, would only crank out more widgets rather than better widgets.
A final worrying conversation from the panel, discussed the fact that companies are not hiring graduates. This was self-evident from a few other people I met in and around the conference: computer science graduates still looking for jobs.