The Great Rewiring
AI isn’t just adding capability, it’s rewiring how we decide, connect and create meaning at work.
Last week in Beyond Intelligence, I looked at how AI was beginning to reshape how we think. Focusing on how cognition itself was being extended by machines. But thinking was only ever half the story. The deeper change is what happens when those new forms of thought start flowing through the wiring of our organisations, our teams, and our own sense of meaning at work. In this Weekend Read, I want to talk about that wiring, how it’s being ripped up and re-laid beneath our feet.
Every era of technological change begins as a tool story and ends as a systems story. When we first learned to harness horses, we changed the pace of movement. When we dug canals, we changed the geography of trade. When we built railroads, we changed time itself. Each shift rewired how value, trust and power flowed through society. Today, AI and data systems are doing the same to our world, only this time, the tracks are invisible. They run through workflows, decision rights, culture, and code. And once again, those who cling to old infrastructure risk being left behind with the barge while the train whistles past.
Across every industry, the symptoms are visible. Executives feel they’re spending heavily on AI, yet few can point to a genuine return. Dominic Price recently captured this perfectly, synthesising a piece of research from Molly Sands, PhD: 96% of companies admit they’re not seeing ROI on their AI investment, even though employees report feeling markedly more productive. It’s a paradox born of partial rewiring; the individuals have upgraded, but the systems they inhabit have not. Productivity now moves faster than the organisations built to contain it.
Meanwhile, new organisational species are evolving. Simone Cicero calls them “tiny, powerful teams”: small, generalist groups with end-to-end context and an AI multiplier. I’ve seen them inside enterprises and start-ups alike — five-person teams achieving what once required entire departments. They move fast, not because they have more tools, but because they have fewer dependencies. They act like autonomous cells in a living network, each wired to shared data and common purpose.
Alex Danco, in a piece that’s stayed with me, argues that we’ve entered the age of prediction. An age where value creation comes from writing futures that others subscribe to. That’s what these small teams are doing in practice: using data and models to predict needs, simulate outcomes and build ahead of demand. Prediction becomes the new design language. Planning fades; sensing rises.
A related point from the world of software engineering: technical execution is table stakes now; the differentiator is how you encode intent. “Spec-driven development”: the practice of articulating exactly what you’re trying to achieve and why, in a structured form that humans and machines can both interpret. I see that same principle emerging in leadership. The best teams write down their intent before they automate. They clarify assumptions, outcomes, and constraints, then they let AI help them execute within those boundaries. (also see our Amplitude AI Agents for how we are building towards that future)
In other words, they’re not just plugging AI into the old machine. They’re designing new circuitry where human purpose and machine capability can coexist.
The rewiring is changing more than workflow; it’s changing the physics of organisations. For decades, we’ve lived under a kind of managerial gravity where decisions fell down hierarchies and data climbed slowly upward. Now, the field is shifting. Decisions form where data lives. Authority flows toward those closest to the context (or customer). The centre doesn’t disappear, but it becomes an orchestrator rather than a controller. More conductor than commander. More creator than coordinator in Product teams and ‘at the edge’ closest to the customers
That’s what leadership looks like in a rewired world: architecture rather than instruction, context rather than control.
Inside this gravity shift, accountability is being rediscovered. James Shore calls it the end of the “accountability problem” in agile, that awkward era when teams were empowered but not responsible, and management was responsible but disconnected. With AI-enabled transparency and richer data flows, teams can now make bets, measure results, and own the outcome. They negotiate with leadership more like investors than employees. The contract of work becomes mutual and measurable again.
But the human side of this story is just as important. For many people, the rewiring feels less like empowerment and more like vertigo. The ground is moving. Skills are no longer stable; they’re fluid, modular, recombinable. The linear career arc — learn, apply, master — is breaking apart. A marketer learns to prompt models. A designer learns to script logic. A product manager becomes a systems thinker. Roles melt into capabilities.
This has profound psychological consequences. Our sense of identity at work has long been anchored in production, in productivity. In the tangible outputs we create. As AI begins to share in that production, what we’re left with is the distinctly human work of curation and judgment. We move from “I make” to “I decide what matters.” That’s both liberating and unsettling.
It also changes what leadership must provide. In the canal era of industry, leaders created structure and scale. In the railroad era, they created networks and standards. In the rewiring era, they must create meaning. The organisation itself becomes a cognitive environment. A deliberately designed space where people and machines collaborate on decisions. Leaders become system designers for trust, clarity, and psychological safety.
The best of them already act this way. They treat well-being not as a perk but as part of the operating model, because they understand that attention, energy, and trust are shared resources. They know that speed without coherence burns people out and degrades decisions. They design for flow: the right rhythm of focus and recovery, of autonomy and alignment.
If you watch the pattern across industries, you can see the new circuitry glowing faintly in the dark:
Shared semantic models that let teams speak a common data language.
Decision systems that recommend next actions instead of issuing reports.
Learning velocity as the metric that replaces output.
Micro-crews with distributed P&L authority.
AI systems that suggest, but still leave the final call to human judgment.
Each light on that map is a neuron in a larger organisational brain. Together, they form the early outline of an economy built on prediction, context, and flow.
And yet, as with every great rewiring, progress comes unevenly. Many firms are still operating like canal companies in a railroad age, with heavy process (justified by old-style leaders, slow to pivot, obsessed with throughput rather than outcome. They mistake automation for transformation. They deploy AI as a faster horse, not a new track.
The uncomfortable truth is that technology rarely fails us; we fail to redesign the human systems around it. That’s why only four per cent of enterprises, according to BCG, have achieved real, scaled AI value. The rest are still dragging the canal barge.
So what does it mean to design for the new rails?
It starts with mapping where decisions actually happen — not where the org chart says they do. Follow the data, follow the friction, follow the emails that circle endlessly before landing somewhere safe. That’s your current wiring diagram. Then ask: which of these circuits are truly creating value, and which are just producing heat?
Next, choose one process and rebuild it around intent. Clarify what you’re trying to achieve, the assumptions you’re making, and the constraints that define success. Encode that intent in a simple, testable way, then let data and AI support it. Don’t automate noise; automate clarity.
Finally, reimagine one team as a micro-network. Give them a complete slice of the value chain, access to data, and the autonomy to act. Hold them accountable for learning velocity, how quickly they can sense, decide and adapt rather than for fixed outputs. Treat them as the prototype for your future organisation.
Do that, and you’ll feel the new gravity start to take hold. Decisions will accelerate. Ownership will deepen. Meaning will start to reform around purpose instead of process.
Because ultimately, The Great Rewiring isn’t about machines. It’s about meaning. It’s about how we, as humans, learn to live inside systems that think with us. It’s about preserving agency and empathy in a world that moves faster than comprehension.
The horse, the canal, and the railroad were all ways of moving goods and people. This rewiring moves ideas, decisions and value. And just like before, it will redraw our maps: of organisations, of industries, of lives.
When we look back a decade from now, we’ll see that the real winners weren’t those who bought the most AI, but those who redesigned their human systems first — who built the cultural and cognitive rails for the next century of work.
So, as you step into your work next week, ask yourself: Where in your world is the wiring old? Which loops keep you circling, and where could a cleaner circuit release energy? Start there. The future doesn’t arrive all at once; it arrives as a better connection.
We are all, willingly or not, engineers in this new network. And the tracks we lay now will decide how far, and how humanely, we travel.