Across this series, I've traced a clear, compounding trajectory.
Uneven global access feeds into platform dependency, which rapidly hardens into Asymmetric Corporate Expansion — a reality where a tiny handful of commercial boardrooms dictate the boundaries of global productivity.
None of this points to a single inevitable outcome.
It points to a system under pressure.
In the previous posts, I looked at how AI is reshaping access and dependency.
Capability is unevenly distributed across countries, platforms are becoming control points within them, and what starts as leverage can easily become dependency.
What happens when capability, access, and dependency all concentrate in the same place?
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.