some thoughts on a post agi world and economy

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the economy is mostly atoms + energy + instructions.

atoms are what you want (house, a meal, a road, a phone). energy is what moves atoms. instructions are what tell energy how to move atoms.

for the last ~200 years, instructions were scarce.

coordination was hard. planning was expensive. management was a tax.

so we built companies to bundle cognition + capital + labor.

agi flips that.

when intelligence becomes abundant, two things happen at once:

1/ planning/coordination collapses in cost
schedule, route, negotiate, monitor, procure, forecast, train, qa — all become software.

2/ physical reality stays stubborn
gravity, distance, tool constraints, safety, permits, supply chains, breakage, fraud, weather, and “someone has to show up” or “be responsible” don’t go away.

so the bottleneck moves from “can we think of the plan?” to “can we reliably execute in the real world?”

in an agi world, physical labor allocation becomes a giant real-time optimization problem:

• what is the task (desired end-state)
• where is it (location + access)
• when does it need to happen (deadline + dependencies)
• with what (tools, parts, permissions, energy)
• who/what can do it (human, robot, fleet, contractor)
• how do we know it’s done (verification)
• what happens when it fails (fallback + accountability)

today we approximate this with managers, contractors, call centers, marketplaces, and paperwork. it’s slow because the instruction layer is slow, and the management layer is often bureaucratic.

with agi, the natural shape is a superintelligent labor os:

• every job is a spec: goal + constraints + acceptance test
• every worker/robot becomes an api: capabilities + availability + reliability score
• every city becomes a graph: travel time, risk, rules, access
• pricing becomes a scheduling signal: urgency, difficulty, scarcity
• verification becomes the trust layer: sensors, photos, receipts, audits, reputation
• failures become training data: real-world evals → better dispatch policies

robots will eat the predictable middle.

humans will concentrate in the edges: ambiguity, care, judgment, negotiation, supervision, taste.

one of the durable advantages is a trust graph that makes strangers executable.

agi makes cognition cheap. we will turn cheap cognition into reliable atoms moved on time.

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