In 1881, the first factory switched from steam to electricity. You would expect a productivity revolution. It did not happen. For nearly 50 years, factories replaced the central steam engine with a central electric motor and kept everything else the same. Same belt-and-shaft layout. Same floor plan. Same workflow. Electricity was faster steam.
The productivity breakthrough came a generation later, when factory owners realized they no longer needed to organize the entire building around a single power source. Electric motors could be distributed. Every machine could have its own. The constraint had moved, but the layout had not.
This is exactly how most organizations think about AI. Linear. One process in, one faster process out. Input, acceleration, output. The mental model is a straight line.
But AI does not work in straight lines. It works like WD-40. It gets into every joint of an organization. Customer data connects to supply chain decisions connects to pricing connects to product development. The value is not in speeding up one link. It is in what happens when the links start talking to each other in ways they never could before.
Donella Meadows mapped this decades ago in her work on systems thinking. She identified pressure points, places where a small shift produces disproportionate system change. Most AI deployments target the surface. Speed, cost, volume. The deep influence sits elsewhere. In mental models. In how decisions get made. In the boundaries between departments, and eventually between companies.
Amazon did not use AI to make demand forecasting faster. They connected forecasting, inventory, logistics, and pricing into a system that optimizes itself across dimensions no human team could coordinate simultaneously. The value is not in any single prediction being better. It is in the network effect across predictions.
The human brain is wired against this. Research on exponential growth bias shows that even educated populations systematically underestimate nonlinear patterns. Early-stage exponential curves look indistinguishable from linear ones. So executives look at AI, see a 20% improvement in one process, and conclude they understand the impact. They are standing in an electrified factory, staring at the same belt-and-shaft layout, wondering why nothing really changed.
Three things to consider:
- First, map where AI touches your organization today. If it is a list of isolated use cases, you are thinking linearly. Second, ask what happens when those use cases connect.
- The second-order effects are where the real value sits.
- Third, check whether your org chart reflects the old power source or the new one. If your AI team reports to IT, you are still organizing around the steam engine.
The constraint moved. Has your layout?
Sources:
- Factory electrification history: Citrix (2025), “To Understand AI’s Future Impact, Check Out This Playbook from 150 Years Ago”
- Donella Meadows, systems leverage points framework
- Exponential growth bias research: Big Think, “Your Brain Is Wired for Linear Thinking”
- Amazon AI integration: Stanford Digital Economy Lab
