I just spent a few days at a retreat on the future of software development. Small rooms. Unconference format. Some of the sharpest people in our industry - the kind of names that show up on the spines of the books on your shelf. I’m not going to tell you who was there or who said what. We ran the whole thing under Chatham House Rule: use what you learn, attribute none of it. This is my initial take-away.
Here’s a question I can’t stop chewing on: why is some software so damn good, and most of it so… not? Not buggy. Not slow. Just bad. Bad in a way you feel in your gut the moment you touch it. The answer isn’t tooling, or budget, or headcount. It’s taste. And I’m starting to think taste is the whole ballgame in the age of AI.
A couple weeks ago I wrote that your organization has to learn as fast as your best AI adopter. Externalize, combine, socialize, internalize. Run Nonaka’s spiral or get left behind. A bunch of you wrote back with the same question: okay, but where does the knowledge actually go?
Everyone is asking the wrong question. “How do we adopt AI agents?” “Which tool?” “What’s the right policy?” Wrong questions. The right question is: how does your team turn what one person learned on Tuesday into something everybody on the team uses by Friday?
Next week I get to watch one of my favorite things: see a room full of smart people handed a hard problem and watch them figure it out in real time. I’m headed to InfoComm to mentor, judge, and sit on a panel for an idea sprint about AI at the edge. And I could not be more excited.
A response to “LLMs are Eroding My Software Engineering Career and I Don’t Know What To Do” - and to everyone I keep hearing this same anguish from. You’re not wrong that the ground is moving. You’re wrong about which ground.
There’s a thing going around the engineering blogs and Hacker News right now: AI killed Agile, Waterfall is back, write your specs up front, welcome to “Waterfall 2.0.” You may have seen the Medium piece titled Agile Is Dead. AI Killed It. Welcome Back, Waterfall.. Hmmm.
And once I followed the logic far enough, I ran into a second question that nobody seems to want to answer out loud: if the agent writes the code, who actually reads it?
I’ve been dorking around with local AI coding agents for a while now. At some point curiosity got the better of me and I had to know: what the heck are these things actually doing under the hood?
In recent years, digital signage has become a solved problem: push content to screens reliably, at scale, on schedule. That’s not enough anymore. The market is now demanding screens that perceive — that understand what’s in front of them and respond in real time. Most of the hardware deployed in the field today will fail at that job because the foundational assumptions are wrong. Here’s what’s actually required and why the infrastructure choices you make now will define your deployment for the next decade.
A recent industry retreat of senior engineering practitioners published their findings on where software development is heading. Reading it felt like someone had been reading my mind — and the minds of the people I follow online — and synthesized it all into one document. When a lot of people arrive at the same conclusions independently, that’s signal worth paying attention to. Here’s my take on each major theme.