Hannah Foxwell, who has spent over a decade in DevOps and platform engineering, draws a striking parallel to earlier transformations: “It used to be that testers didn’t trust developers and ops didn’t trust testers and there were all these silos. Now we’re putting AI agents in the mix. Can we trust them? Should we trust them?”

This isn’t just déjà vu—it’s a fundamental challenge that resurfaces with every major shift in how we build software. As Robert Werner points out, management had to give up control and push trust to the edges of organizations during the agile transformation. With cloud adoption came self-service and automation. Now, with AI, we’re dealing with non-deterministic black boxes that we need to trust to be “right often enough.”

One of the biggest challenges isn’t the technology itself—it’s the lack of shared understanding. Hannah launched “AI for the Rest of Us,” a community now with over 1,000 members, after realizing that AI fluency is essential for making good decisions about where and how to use these tools.

“I went to a talk at a conference thinking I’d learn about AI in one talk and become an expert by tomorrow,” Hannah recalls. “It just didn’t happen like that. There’s a whole new domain with new vocabulary, new concepts, new techniques.”

The community focuses on making AI accessible without dumbing it down—providing talks and content that explain complex concepts in simple language so more people can participate in the conversation about AI’s role in software development.

The technology is evolving so rapidly that best practices barely have time to solidify before they’re obsolete. Robert describes how hiring strategies at startups are changing every few weeks as new capabilities emerge. “Things that weren’t feasible last week are suddenly possible,” he notes.

But this speed creates a dangerous tension. Organizations are pushing hard for AI adoption while the guardrails, workflows, and cultural practices needed to use it safely are still being figured out. As Matty observes, this leads to perverse incentives—developers required to “use AI” who find ways to tick the box without actually deriving value, just like teams that once added meaningless tests to meet sprint requirements.

A critical question emerges: if AI generates the code, who owns it? Who’s responsible when something goes wrong?

Hannah frames it in familiar DevOps terms: “Does anybody really want to own a service if they didn’t write it and they don’t understand how it works? It’s the ops challenge again—AI throwing code over the wall to us.”

Robert’s answer is pragmatic and honest: humans will need to take responsibility for validating AI-generated code, even if it’s tedious work most developers won’t enjoy. His company, Leap, is building tools specifically to make that verification process as convenient and enjoyable as possible, because he believes there’s simply no other way to do it safely.

There’s an ironic twist in how AI agents work best: they need excellent documentation. Organizations improving their documentation to support AI-powered development are inadvertently following DevOps best practices that benefit human developers too.

But as Matty discovered building his own project, AI-generated documentation can be dangerously unreliable. The tools will confidently document features that don’t exist, pulling from incomplete PRDs or speculative notes in the codebase. Great documentation trains better agents, but agents shouldn’t write that documentation—creating a challenge that requires human judgment and oversight.

The parallels to earlier shifts are instructive. Hannah remembers enterprise clients who insisted continuous delivery would “never work here.” Now it’s common practice. The same resistance appeared with cloud adoption and agile methodologies.

What worked then still matters now:

For developers and teams trying to navigate this transformation, Hannah and Robert offer grounded guidance:

Keep your eyes open: Watch for patterns of success and failure. Who’s making this work, and what do they have in common?

Build community: Find or create spaces where people can share honestly about what’s working and what isn’t, without the pressure to pretend everything’s perfect.

Be selective about information sources: With so much noise and hype, focus on quality outlets. Ignore things for a few weeks, and if they keep coming up, that’s when to invest your time.

Practice regularly: The technology evolves so fast that hands-on experience goes stale quickly. Even if it’s not your main job, refresh your skills every few months.

Be specific and constrained: AI coding assistants work best with clear, narrow requests. Frustration comes from asking too much or being too vague.

We’re in the Nokia phone stage of AI-assisted development, as Robert puts it—the technology will look completely different in just a few years. But unlike waiting passively for that future to arrive, developers and teams are actively creating it through the choices they make today about how to integrate these tools.

The question isn’t whether AI will transform software development—it already is. The question is whether we’ll learn from past transformations to build better practices, stronger safety nets, and more trustworthy systems. Or whether we’ll repeat old mistakes at unprecedented speed.

As Hannah emphasizes, having more people with AI fluency means better conversations and better decisions at a pivotal moment in history. The rollercoaster is moving whether we’re ready or not. The best approach is to keep your eyes open, stay connected to community, and remain thoughtfully critical about what works and what doesn’t.

Learn more about Hannah’s work at AI for the Rest of Us. Use code ADO20 for 20% off tickets to their London conference on October 15-16, 2025.

Security: the one topic that’s guaranteed to turn any DevOps conversation into a mix of fear, eye rolls, and nervous laughter. In this episode of Arrested DevOps, Matty welcomes back Kat Cosgrove to talk about the “never not hot” world of security and why it’s always lurking just over your shoulder (like that one compliance auditor who swears they’re just “observing”).

Kat and Matty cover:

Along the way, expect the usual mix of snark, sarcasm, and the occasional tangent about how everything in tech eventually becomes a security problem.

If you’ve ever patched the same vulnerability three times in a week, or found yourself yelling at a CVE like it’s a personal enemy, this one’s for you.

We’ve all been there: burning out on volatile tech jobs, tangled in impossible systems, and wondering what our work actually means. On this episode of Arrested DevOps, Matty Stratton sits down with Kat Morgan for a heartfelt, funny, and sharply observant conversation about AI: what it helps with, what it hurts, and how we navigate all of that as humans in tech.

They dive deep into how large language models (LLMs) both assist and frustrate us, the ethics of working with machines trained on the labor of others, and why staying kind—to the robots and to ourselves—might be one of the most important practices we have.

“We actually have to respect our own presence enough to appreciate that what we put out in the world will also change ourselves.” – Kat Morgan

Openness plays a significant role in propelling DevOps and organizational processes forward. This is not to imply that everything must be open, but the default should be openness unless a valid reason indicates otherwise.

Andrew Zigler, developer advocate at Mattermost, and Matty from Arrested DevOps recently shared insights on this subject. They discussed creating impactful developer advocates, managing community writing programs, and dealing with the challenges of open source communities.

Andrew emphasizes that the loudest and most contributory voices in open source projects are usually the paid internal staff. However, he champions setting up pathways in the community to validate the experience of all contributors and reward them with anything from thought leadership, platforms, or even swag. The key is to influence individuals at all levels of engagement and ensure that they feel they own part of what they are contributing.

One of the challenges he identified is over-influencing which often stems from the fact that the paid staff are the ones driving the open source project vehicle. This imbalance usually drowns out the voices of other contributors, particularly those who may not have the luxury of dedicating as much time and energy to the project as the paid staff.

Andrew suggests a solution: the company creating more developer advocates through the multiplier effect. This means ensuring that everyone across the board understands the importance of the open-source community and empowers them to contribute. The more developers contribute, the larger and more diversified the community becomes, leading to better outcomes and solutions.

Matty highlights how vital leadership is in these initiatives. By allocating resources, prioritizing open source community engagement, and maintaining a strategic focus, leaders can do much to foster a healthy open-source community. Successful leaders understand that engagement levels differ, so they create opportunities for different levels of contributors to partake and contribute to the community.

To ensure the project remains harmonious and aligned with company goals, the leadership should give equal weight to both staff and contributors’ voices. In the end, everyone involved in the project is part of the community.

The conversation took an interesting turn when they started discussing engineering blogs, a tricky subject for many organizations. Matty points out that these blogs have the tendency to publish sporadically, often dominated by lengthy droughts of content or a sudden overflow of posts.

Such inconsistency happens when the contributors, mostly engineers, write when they can spare the time. Balancing this dynamic is crucial, and one suggested solution is to involve people whose primary job is creating content. They can collaborate with subject matter experts to create consistent, relevant content.

Operating under a default open environment for your projects does not mean that everything has to be open. Nevertheless, transparency and openness should be the norm unless necessary otherwise. By dealing with the occasional echo chamber and understanding that contributions will always ebb and flow, the community will thrive and keep moving forward.

In line with the open source spirit, scaling advocacy is crucial in DevOps. It involves not only the individuals whose title is developer advocate but everyone within the company. By creating more advocates and amplifying community efforts, the DevOps movement continues seamlessly.

Read more of Chelsea Troy’s writing here!