The AI CEO Will Route a Million Documents and Close Zero Decisions

The case for the AI CEO

In a recent episode of Moonshots with Peter Diamandis, a panel of technologists discussed the inevitability of the AI CEO. Their observations are precise: CEOs spend 90% of their time routing information. Documents come in. Decisions go out. The core work is "documents in, documents out."

AI can do this better. It can scan millions of documents in real time, track every employee at granular detail, eliminate the cascade problem where information gets diluted through layers of hierarchy. The thesis is sound. AI should be better at information routing than humans.

But it solves for the wrong bottleneck.

Visibility is not coordination

The AI CEO they describe is optimized for visibility: what is everyone doing? That is a real problem. Senior leaders operate in an information vacuum. The instinct to fix this with better instrumentation is correct.

But visibility — knowing what people are doing — is not the same as coordination. Coordination answers a different question: are decisions closing?

A team can be shipping features on schedule and still be drifting. The scope can be creeping without anyone naming it. Ownership can be implicit. Objections can stay silent. Decisions can reopen three times before anyone notices the pattern.

The AI CEO they envision will route information brilliantly. It will produce dashboards showing who worked on what, when, and for how long. What it will not show is whether those decisions held, whether they closed with clear owners and timelines, or whether the organization is accumulating coordination entropy faster than it can correct.

Information dilation is not decision quality

There is an economic pattern that applies here. In 1865, the economist William Stanley Jevons noticed something counterintuitive about steam engines: as they became more efficient at burning coal, total coal consumption went up, not down. The cheaper a resource is to use, the more of it gets consumed. Efficiency does not reduce demand. It unlocks it.

AI is doing this to cognitive work right now. When AI makes execution cheap, organizations do not do less. They do dramatically more. And every new output generates a decision that did not exist before.

Their solution to "more decisions" is faster information routing. Time dilation, as one panelist put it: course corrections accelerating from decades to years to months to weeks to minutes. AI can assimilate the volume of information required to make those corrections. Humans cannot.

But faster information routing does not reduce decision load. It increases it.

When AI can scan millions of documents and flag every anomaly, every misalignment, every potential issue, the human CEO does not get fewer decisions. They get more. And those decisions surface faster than the organization's coordination structures can absorb them.

Coordination has a natural tendency toward disorder. Agreements go unrestated. Ownership stays implicit. Objections remain silent. Context shifts between conversations and no one marks the change. I call this coordination entropy: the drift from clarity to ambiguity in collective decision-making.

No one decides to create confusion. It accumulates.

And when execution volume is high and decision velocity is fast, coordination entropy scales faster than it ever could before. A vague decision that might have affected one project now affects twelve. Small ambiguity compounds before anyone can correct it.

The AI CEO routing a million documents at light speed will not prevent this. It will accelerate it.

What type of information would actually work

The instrumentation they describe tracks activity: who worked on what, for how long, with what output. This confirms that work is happening. It says nothing about whether that work is producing durable coordination.

The AI CEO would be far more effective if the information being routed was about closure quality, not activity.

Not: "This team shipped 15 endpoints this week." But: "This team's last three decisions closed with clear owners and timelines. The decision before that reopened twice before holding."

Not: "Sarah worked 40 hours." But: "Sarah owns three open decisions. Two are past their deadline. One is blocked waiting for cross-team alignment that has not been explicitly committed to."

Not: "The project is 80% complete." But: "The project has four unresolved scope questions. No one has explicitly decided to defer them or address them. They are drifting."

This is the type of information that would make the human CEO more effective. Not because they can see what people are doing, but because they can see whether decisions are closing.

Why this distinction matters

The panelists are right: the CEO's remaining role is to hold purpose and set strategy. But strategy requires knowing whether the organization can execute on decisions, not just whether people are executing tasks.

With visibility data, the human CEO knows the team is busy. With closure quality data, the human CEO knows whether strategic decisions are actually closing at the team level — or drifting into ambiguity.

Structural signals, not social signals. The human CEO can distinguish between a team that is on track and a team that is drifting. Not because they can see effort, but because they can see whether decisions hold.

Coordination entropy becomes visible. When decisions drift, when scope creeps without acknowledgment, when ownership stays implicit — those patterns surface before they compound. The AI can route that signal in real time. But only if the signal exists.

The locus of control becomes measurable. When the AI CEO makes "course corrections in real time," who owns those decisions? Is the AI recommending, or is it deciding? Without instrumentation that tracks closure and ownership, that distinction collapses.

Strategy becomes more effective. If the AI CEO is routing perfect visibility data while coordination entropy scales invisibly, the human's 10% is being spent on a foundation that is unraveling.

What the AI CEO needs to actually work

The AI CEO is inevitable. But the AI CEO fed with activity data will produce perfect dashboards while coordination quality decays.

The missing layer is instrumentation that shows whether decisions are closing. Not just whether work is happening. But whether the coordination layer is producing durable outcomes — decisions with owners, timelines, and stakeholder alignment that survive contact with reality.

This is why we built Growth Wise — to instrument the coordination layer so organizations can see whether decisions close, whether they hold, and whether coordination is producing durable outcomes or accumulating entropy. The AI CEO will need this type of information to actually work.

The panelists are describing a future where the AI scans everything, tracks everything, routes everything. That future is coming. But if the "everything" being tracked is activity rather than closure, the AI CEO will be solving for the wrong problem — faster than any human could stop it.