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.

The post-lunch session included a talk about the future of community open source, a talk about AI data centres and finally a panel about the "vulnpocalypse".

The first talk focused on the realities of doing community open source and how, in effect, AI and LLMs are not (yet) a part of our shared open culture, citing large, anti-AI open-source projects, some which have banned AI contributions (e.g. NLNet Labs, Codemirror) and pro-AI projects which have closed contributions (e.g. Ladybird). Of course, open source never meant the right to contribute, but it still signals a, perhaps, worrying shift in open-source, cultural norms. An arXiv paper collected some AI policies on popular repositories which is worth a skim.

Another topic in the talk was the "time to open". The speaker argued that it was now possible to completely reverse engineer closed-source products and tools in as little as 48 hours. This could lead to more open source business models, as closed models are no longer viable. The speaker suggested that the open-source community around a project is now more important than the code itself. I think I fall somewhere closer to believing that they are both important. I worry about the proliferation of agentically-managed software stacks which companies and individuals use with little incentive to upstream patches back to the community (or release their new code for the community). This kind of isolationism seems more likely given the ability to create your own implementation of any piece of software.

Unfortunately, I missed a good portion of the final session as I was helping clear up. The talks were: "The cost of Sovereignty", "Knowledge Graphs for Digital Transformation" and "You can't download trust".

I managed to have a good chat with Phil Weir, director of Flax and Teal, about some of their work including Alizarin, a tool for working with knowledge graphs and Arches data. It was refreshing to hear about the details of an actual project someone was building.

Ultimately, I would say the day left me disheartened for the immediate future of (open-source) software. What I found most unsettling about SOOCon is what I find unsettling about the tech industry at large. It would seem that the industry is leaving new graduates in the lurch. Additionally, environmental concerns are falling somewhere (far?) below commerical concerns.

I'm also anxious that open source's ethos is being eclipsed by the need for companies (and individual employees) to demonstrate their relevance in an AI-focused economy, and that that performance of relevance is really about integrating, into whatever you're doing, the products coming out of a few powerful companies, which are not invested in the values which make open source make sense.

I think conferences like State of Open are important, and we need more conversations from all kinds of open-source community members. I'd like to see more talks from students, sole maintainers of highly depended-on software, folks from other non-profit-driven open-source initiatives like Outreachy or the Processing Foundation (these are just examples I know). I recognise that, perhaps, SOOCon26 Belfast was suffering from being in Belfast, hardly the most connected metropolis in the world... but it is the home of The Big Fish. I'm excited to hopefully see it return in the years to come.

My friends, Maddie and Emma, enjoying the splendour of Belfast!

Thanks to Meredith, my partner, for helping me edit this and form some much more coherent ideas.