Whose future is it anyway?
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.
Not just within organisations — across economies, across countries, and across systems that were never designed to move this quickly.
A wider lens
Long before I moved into technology, I studied Economics and Politics at university.
At the time, that felt like a different world — markets, incentives, governance, power structures — academically useful, but abstract.
Over the years, working in technology, I’ve mostly been focused on systems at a different scale:
- Teams
- Platforms
- Delivery
- Architecture
But increasingly, those worlds are starting to overlap.
AI is not just another tool, it represents a fundamental shift in:
- Capability
- Agency
- Speed
- Time
Clearly those are not just engineering concerns, they are economic and political ones too.
From micro to macro
The same questions I’ve been asking about teams:
- Who has capability?
- Who makes decisions?
- How fast are things moving?
- Do we have time to adapt?
Apply just as well at a global level:
- Which countries have access to AI capability?
- Who controls the platforms and infrastructure?
- How quickly are advantages compounding?
- Do governments and societies have time to respond?
A new series
The answers are complex and the potential outcomes are not evenly distributed, so I’m going to explore that.
This series looks at AI from a broader perspective — not just how it changes engineering, but how it has the potential to reshape economies, power, and opportunity.
It starts with a simple question:
Whose future is it anyway?
The more I look at what’s happening, the harder it is to believe that the answer is “everyone”.
If you’re interested in the micro view
If you’re more interested in how these changes play out inside teams and organisations, the previous posts are a good place to start:
They cover:
- Engineering judgement
- Team structure
- Delivery vs capability
- The impact of AI on how software actually gets built
This series builds on that, just at a different scale.
What to expect
The next few posts will explore:
- Why the “AI revolution” may be more compressed than previous shifts
- How access to capability is uneven across countries
- The risk of dependency on platforms
- The concentration of power in a small number of organisations
- What it would actually take to move towards a more balanced outcome
It’s less about prediction or prognostication, and more about understanding the fundamental forces at play and asking whether we’re comfortable with where they might lead.
Starting point
The first post looks at a framing borrowed from Mo Gawdat: Mutually Assured Destruction vs Mutually Assured Prosperity.
It’s a compelling idea, but I’m not convinced it reflects what’s actually happening, so I’ll start there!