Electricity gives us a useful way to think about AI governance. Power is experienced locally. People care where the plant is built, how much the bill costs, who gets service restored first, and what risks their community absorbs. But electricity also depends on a grid that stretches beyond any one town or state. Local choices matter, yet no community can pretend the system ends at its border.
AI is beginning to take on that same shape. A school board may want one set of rules for student chatbots. A hospital network may need another for diagnostic tools. A state may want strict limits on automated hiring or child-facing AI companions. Those decisions are local in the sense that the harms are felt locally. But the systems underneath are rarely local. The same foundation models, cloud providers, data brokers, software vendors, and security standards may sit behind thousands of separate uses.
That creates a governance problem that neither side can solve cleanly. If every state or city writes its own AI rules, communities keep the power to respond to what they actually fear. They are not forced to accept a distant standard written for someone else’s politics, industries, or risk tolerance. But a patchwork can also make the system harder to inspect, harder to secure, and harder to trust. An AI tool used across hospitals, schools, banks, and employers may end up governed by dozens of overlapping rulebooks while the technical system underneath remains the same.
A single national framework has the opposite appeal. It could make audits clearer, liability easier, security stronger, and compliance less chaotic. But it could also erase the places where disagreement matters. Communities do not all face the same risks from AI, and they do not all define harm the same way. A clean grid can become a quiet transfer of power away from the people who live with the consequences.
The Conundrum:
As AI becomes more like infrastructure, should governance stay close to the communities that experience its harms, allowing different places to write different rules around schools, hospitals, policing, hiring, energy use, and children?
Or should AI be governed more like a national grid, with shared standards strong enough to keep a deeply connected system reliable, auditable, and secure, even when that means local communities lose some control over the systems shaping their lives?
When AI is experienced locally but built and operated through shared infrastructure, what deserves more weight: the legitimacy of local rulemaking, or the reliability of one common system?
Colombia