In the first two posts, I looked at how AI is reshaping outcomes at a global level.
But this dynamic doesn’t just play out between countries, it plays out within them, because even where access exists, control is uneven.
In the first post, I looked at the idea that AI is pulling us in two directions at once.
Massively accelerated disruption on one side, and the promise of prosperity on the other.
What both framings gloss over is a more uncomfortable question:
Who actually gets access to that prosperity?
Because AI isn't arriving evenly.
It looks like the Labour party is once again preparing to shoot itself in the foot, seemingly because the current Prime Minister is boring.
It's true that Kier Starmer lacks the charisma of... well... cold rice pudding, but after the clown car politics of the previous 14 years of Tory governments, I'll take boring and beige over braying and backbiting.
There’s a framing I’ve been coming back to recently, borrowed from Mo Gawdat.
During the Cold War, the dominant paradigm was MAD — Mutually Assured Destruction — but Gawdat suggests that with AI, we have the potential for something different:
MAP — Mutually Assured Prosperity.
It’s a neat idea, but I’m not convinced it’s that simple.
Every engineering organisation has its stories.
Nobody is ever entirely sure whether these are:
Over the last few posts, I’ve been writing about AI from a fairly close range — how it changes engineering teams, delivery dynamics, capability development, and the shape of organisations.
In other words, the micro view — what happens inside teams when capability, speed, and decision-making all shift at once.
But the more I’ve thought about it, the harder it’s becoming to ignore the bigger picture, as the same forces are playing out at a much larger scale.
Most discussions about AI focus on capability — what it can do, how fast it's improving, and where it might go next.
My last post focused on the fact that less attention is paid to something more subtle, but arguably more disruptive.
Time.
Not just in the sense of speed, but in how time is experienced across different parts of a system.
Because one of the emerging challenges is that time is no longer behaving consistently.
In my most recent posts, I’ve explored how AI is starting to reshape both the structure of engineering teams and the shape of individual capability.
There’s a third dimension that sits across both: Time.
In my last post, I explored the idea that we may be moving from a knowledge-based economy towards something that places greater emphasis on judgement, framing, and leverage.
That shift doesn’t just affect individuals. It has implications for how we structure teams, how we develop capability, and how we think about the long-term sustainability of engineering organisations.
Most of the conversation around AI today is anchored in the near term.
Engineers are asking how it changes their workflow. Product teams are experimenting with copilots. Founders are looking for leverage. There’s a steady undercurrent of anxiety about junior roles disappearing, but it tends to be framed as a tactical problem, something to manage, mitigate, or route around.
I’m less focused on that layer.
Not because it isn’t important, but because it feels like we are still looking at the first-order effects of a much larger shift.