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30 Jun 2026 3:33pm GMT

Anxiety about AI

The Drupal and broader software community are getting overly anxious about the new kids on the block… AI. I wanted to step back and explore this anxiety through the analogy of AI as the new kids on the block, or, more specifically, the new kids entering our software teams and community. It is important to view AI not as a single kid because AI consists of multiple LLMs and harnesses.

Therefore, our immediate expectation when working with AI is that there is no single way to prepare for or interact with AI that always works across all AIs. The inconsistency and unpredictability of the current state of AI, and how it impacts our work, is making people anxious, which is leading them to want tools and processes to prepare to collaborate with AI. I'm writing this post because I think people's anxiety about AI is making them overprepare.

Overpreparing for AI

A large part of the AI narrative centers on the tooling you need to use AI. I feel that most of the AI tooling is overbuilt or overplanned. At the same time, harnesses like OpenCode provide essential tools and methodologies for an LLM to write code and perform tasks. Still, a harness is just a tool for AI, like the computer and software I am using to write this post.

The tooling and planning I am concerned about involve AI-specific processes for managing and orchestrating AI agents. Many people in the software community are developing AI best practices to address the challenge of integrating AI into our software development process.

A quick aside. I think having AI best practices for Drupal as a collaborative, community-led initiative is essential to Drupal's proper adoption of agent-driven development. Before someone starts using Drupal's AI best practices, they should understand what AI is.

What exactly is AI?

I don't know...Read More

30 Jun 2026 3:13pm GMT

Back in 2019, I wrote that Open Source is not a meritocracy. Meritocracy says talent is the only thing that counts, but that is not true. To contribute, you also need time, a steady income, and a flexible schedule. Plenty of people lack one or more of these.

Some people can give their nights and weekends to learning a codebase, clearing the issue queue, or reviewing patches. Some are paid to do it on the clock. A lot of people can't do either. Their hours go to a second job, caring for family, or simply making it through the week.

That doesn't make these people less talented. It means they have less opportunity.

AI changes that math. A contributor might have the skill to fix a bug, but not the time to learn an unfamiliar codebase. AI can explain unfamiliar code and point them to where the fix belongs.

On paper, that should be great news for Open Source. In practice, AI will only help if access and skill become shared, not private advantages.

AI access is not equal. The most capable models and coding agents cost real money, and using them well takes real skill. I pay hundreds of dollars a month for these tools and have spent countless hours learning when to trust them, when to doubt them, and how to turn their output into useful work. Many contributors do not have that money or that time.

We learned once that "anyone can contribute" is not the same as "everyone has the same opportunity to contribute". AI can repeat that mistake in a new form.

Powerful technologies rarely share their benefits evenly at first. Electricity did not create equal opportunity the moment it was invented. It only changed lives broadly when people built the infrastructure to make it widely available.

The internet followed a similar path: it started with privileged access, then became useful to millions more people as infrastructure, tools, and skills spread.

AI is following the same pattern. It is powerful, imperfect, and unevenly distributed. We should not ban AI or pretend it has no flaws. If we want it to become broadly useful, more people need access to it and the knowledge to use it well.

If we want AI to reduce privilege in Open Source instead of reinforcing it, we have to close two gaps.

The first is cost. Contributors should be able to do meaningful work without paying for the most expensive AI tools. As lower-cost models, including open-weight models, improve, Open Source projects should make them practical for contribution work.

The second is skill. Knowing how to use AI well should become shared knowledge within Open Source projects so more people can learn faster and make better contributions.

Contributing with AI should come down to talent, not to who can afford the best tools or who has the time to learn them.

Open Source already moves many things from private advantage to shared infrastructure: code, documentation, test suites, best practices, decision-making processes, and even the financial side of our projects. The ability to use AI well for contribution should move in the same direction.

The opportunity is not just to make today's contributors faster. It is to help many more people become Open Source contributors: people with the talent, but not the free time, the insider knowledge, the employer backing, or the money for the best tools.

But more contribution is not automatically progress. As I wrote in AI creates asymmetric pressure on Open Source, AI can make it cheaper to contribute without making it cheaper to review.

The test is whether it helps more people move from issue to tested patch while making the result easier for maintainers to trust and merge.

If we do this well, AI can reduce the privilege of free time, insider knowledge, and expensive tools. If we do it poorly, it will widen the gap for contributors and increase the burden on maintainers.

In 2019, I argued that Open Source communities should create opportunity by paying contributors. I still believe that. Paying contributors gives people time. But AI gives us another way to reduce the privilege of free time: it helps people do more with the time they have.

I want Drupal to help explore what that looks like in practice: not because we have all the answers, but because this is the kind of problem Open Source should help solve.

30 Jun 2026 2:41pm GMT