Hamish Songsmith – Blog & Links
February 5, 2026
A growing chasm separates those building around AI from those still debating it—and it has nothing to do with model size or vendor choice.
On one side: people who aren’t fixated on measuring or justifying it first. They have experimented enough and understand that, applied well, AI generally means more productivity. They’re already building tools like Ralph loops, OpenClaw, and AI factories such as GSD and Gas Town.
If those names sound like “random internet projects,” that’s exactly the problem: capability is moving outside your organisation faster than you recognise.
The transformation of software engineering—and more—has kind of already happened. Like the future, it’s not evenly distributed, but it has arrived.
What it actually looks like in months and years to come, no one knows. All we can do is experiment and explore. The waterfall methodology held sway for the first half of my software engineering and technology career. Agile has held sway for the second half.
These things weren’t brought into being with a click of the fingers. They emerged from lessons software engineers were learning. The mistakes they were making. The errors they saw—human and structural. And over time, a set of practices, a set of patterns, and built on top of them, tools and even programming languages, emerged over years and decades.
Just as Agile eclipsed Waterfall, a new set of practices will eclipse Agile. It’s happening already, driven by a profound change in what computation is—one that has happened more quickly than we’ve ever seen before.
You have the opportunity to be part of that conversation, to help shape what happens next. But that window of opportunity is closing.
