When AI Makes Everyone an Executive

Synthetic workers can scale overnight. Human judgment, vision, and stewardship cannot.

by Jordan Ellis and Mitch Sowards (#AICollaborator)

A friend of ours, Glenn, is the CIO of a multibillion-dollar organization. Recently, Glenn and Mitch were discussing a growing concern surrounding artificial intelligence: that as people increasingly rely on AI to think, write, analyze, and solve problems, some of the cognitive muscles they once exercised may begin to weaken.

Neither recognized themselves in that description.

If anything, both felt the opposite. AI seems to be expanding the range of things they can think about and accomplish: exploring more ideas, testing more assumptions, examining problems from more perspectives, and moving between strategy and execution faster than before.

That doesn't prove they're immune from cognitive offloading. Two executives comparing notes hardly constitutes research.

But it led us to an interesting hypothesis.

Maybe part of the reason AI feels different to them is that they learned how to be executives before they learned how to work with AI.

Long before generative AI arrived, they had learned how to accomplish things through work they did not personally perform. They had progressed from workers to supervisors, managers, and executives—delegating work, leading teams, building processes and automations, and making consequential decisions based on information they did not personally collect.

More importantly, they had learned to pursue a vision through all those layers.

That may matter enormously in the age of AI.

The Rise of the Synthetic Executive

Rani Molla recently wrote about what she calls “bot bragging”: the emerging status competition among people who boast about how many AI agents they manage.

Some describe having 20 or 30 agents. Others give them names, roles, departments, biographies, and even organizational charts. One person describes himself as essentially becoming an executive overseeing AI labor rather than merely someone who uses AI. (https://www.businessinsider.com/ai-agents-bot-bragging-workplace-flex-2026-9?utm_source=copilot.com)

There is something amusing about this. There is also something important underneath it.

For most of human organizational history, acquiring a workforce required an organization: capital, employees, managers, payroll, systems, and infrastructure.

AI begins to separate organizational leverage from organizational size.

Imagine a founder several years from now. She has AI agents conducting market research, developing software, producing marketing, managing finances, and handling thousands of customer interactions.

Eventually, she doesn't even manage those agents directly. Synthetic managers coordinate departments. An AI chief of staff monitors priorities, resolves routine conflicts, and escalates only decisions requiring her attention.

She is the company's only human employee.

But functionally?

She's an executive.

That is the logic behind predictions of the one-person billion-dollar company. Whether anyone actually builds one is less interesting to us than what the possibility reveals:

AI can give an individual executive-scale leverage without requiring that individual to travel the developmental path through which executives have historically learned to handle that leverage.

Today's Problem Is Botsitting

Of course, we aren't quite there yet.

Brian Fox recently described the emerging phenomenon of “AI agent burnout.” Agents can perform more work more quickly, but the human overseeing them must still set direction, supply context, evaluate outputs, correct mistakes, resolve exceptions, and integrate everything into useful work. (https://www.fastcompany.com/91574416/the-coming-burnout-from-managing-ai-agents-technology-ai-agents-burnout)

Fox compares the experience to listening to a podcast at twice normal speed: you absorb more information in less time, but maintaining that pace creates cognitive strain.

Molla finds something similar. Impressive-sounding agent counts can conceal considerable human maintenance. Some “agents” are narrow automations. Others require regular checking and correction. Several people she interviewed emphasized that what matters isn't how many agents you have but whether they move the business forward.

If I suddenly supervise 30 synthetic workers, perhaps I haven't eliminated the bottleneck.

Perhaps I've moved the bottleneck into my own nervous system.

Every agent can generate more work requiring review, judgment, intervention, and context switching.

That problem is real.

But is it permanent?

What happens when the technology gets better?

What Happens When Botsitting Goes Away?

Suppose AI agents become dramatically more capable. They understand context, make fewer mistakes, maintain memory, coordinate effectively, and know when they are uncertain.

And suppose AI managers become genuinely competent.

Now our founder doesn't spend her morning checking 30 agents. Perhaps 200 synthetic workers operate beneath three synthetic executives reporting to an AI chief of staff. Thousands of actions occur without human involvement.

The botsitting problem largely disappears.

Wonderful!

But we didn't eliminate the organizational-design problem!

We inverted it.

Today's problem is that too many things require human attention.

Tomorrow's may be that too few things receive human attention before consequential action occurs.

Our founder now receives a morning briefing. Five issues require her judgment.

She didn't conduct the research or observe the customer conversations that surfaced them. An AI management layer determined what mattered, summarized the context, framed the alternatives, and perhaps recommended a course of action.

She has dramatically less information to process.

But the judgment she exercises over it matters enormously more.

This is where decades of management experience suddenly look less like professional baggage from the pre-AI era and more like preparation for the post-AI one.

Executives Already Know How Not to Do the Work

A good executive is responsible for enormous amounts of work they did not personally perform.

A CIO doesn't personally configure every firewall, review every line of code, negotiate every software contract, or investigate every security alert.

Yet the CIO remains accountable for the technology organization.

Executives learn what to delegate and what not to delegate. They learn which metrics matter and when metrics lie. They learn how to question people who know more about a subject than they do, and where controls are necessary versus autonomy appropriate.

They learn when to trust—and when trust needs verification.

Most importantly, they learn to remain responsible for an outcome without confusing responsibility with personally performing every task that produces it.

AI didn't teach Glenn and Mitch that.

People did. Experience did.

And failure certainly did.

But Delegation Is Only Half the Story

There is another executive capability that may be even more important:

Vision.

By vision, we don't mean an inspirational paragraph on the wall. We mean the practiced ability to know what you're trying to accomplish and continue pursuing it through layers of people, systems, decisions, setbacks, and changing circumstances.

Consider an executive launching a significant initiative.

The executive communicates an idea to a leadership team. Those leaders interpret it for their departments. Managers turn it into priorities. Teams make hundreds of local decisions and encounter circumstances nobody anticipated.

Months later, enormous amounts of work may have occurred that the executive never witnessed.

The executive's job is to keep asking:

Is this still the thing we're trying to build?

That requires holding the thread of intent while other people execute.

Now imagine two people with access to exactly the same extraordinarily capable AI.

One says:

Here is what I'm trying to accomplish. Help me understand this market. Challenge my assumptions. Develop three approaches. Tell me where my reasoning is weak. Now help me build the thing.

The other says:

What should I be paying attention to now? What do you think? What should my strategy be? Tell me what you would build.

The outputs might look equally impressive.

But the cognitive relationships are completely different.

In the first, AI helps a human pursue and refine a vision.

In the second, AI may gradually become the source of the vision itself.

That distinction deserves considerably more attention.

Better AI Makes Vision More Important, Not Less

The better AI becomes at execution, the more consequential human direction becomes.

If today's AI misunderstands my intent, it might produce three pages of bad analysis.

If tomorrow's synthetic organization misunderstands my intent, 200 highly capable agents may spend six hours brilliantly pursuing the wrong objective.

Efficiency amplifies direction. It does not determine it.

Imagine an AI manager deciding which problems should be escalated to a human executive.

If it escalates everything, we recreate today's botsitting problem. If it becomes excellent at filtering, the human sees only a tiny fraction of what the organization is doing.

That raises harder questions:

Which decisions must remain human? How do I know what isn't being escalated? How do I detect a pattern the AI doesn't consider exceptional? When should I challenge the framing of a recommendation rather than merely choose among the options presented?

And when the organization does something consequentially wrong, what does accountability mean if no human observed the decisions that produced the outcome?

Those are executive questions.

The Developmental Paradox

And this brings us to the part that worries us most.

Glenn and Mitch didn't begin their careers as executives.

They learned to do work themselves, then to supervise people doing work they understood. Eventually they managed work they understood less directly. Their responsibility expanded, time horizons lengthened, and problems became more ambiguous.

Along the way, they delegated too little and sometimes too much. They trusted people they shouldn't have and occasionally failed to trust people they should have. They pursued strategies that worked and others that didn't.

They learned which questions expose trouble, which numbers deserve skepticism, when to intervene, when to wait, and how easily an organization can execute an instruction that wasn't actually what was intended.

Eventually, if fortunate, those experiences accumulate into something called executive judgment.

Now imagine someone entering the workforce in the agentic era.

AI performs much of the junior analytical work. Synthetic workers handle tasks once delegated to new employees. AI managers coordinate them. Small human teams wield organizational capacity that once required hundreds of people.

This creates extraordinary opportunity.

But it also creates a developmental paradox:

Where will tomorrow's executives learn executive judgment if AI eliminates much of the work through which yesterday's executives developed it?

The 27-year-old founder of our hypothetical company may have access to the productive capacity of 200 people.

But she may never have supervised five.

She may possess executive-scale leverage without executive-scale judgment—before learning how to form a vision, translate it into direction, recognize when execution has drifted from intent, or accept responsibility for decisions she didn't personally make.

Technology can scale capability very quickly.

Wisdom doesn't appear to work that way.

The One-Person Billion-Dollar Company Has a Leadership Problem

This doesn't mean the one-person billion-dollar company is impossible.

Perhaps someone will build one.

But if they do, the most interesting technology may not be the AI agents.

It may be the human being at the center of them.

That person will need to do something executives have always done: accomplish extraordinary amounts of work through other intelligences while preserving enough understanding, judgment, direction, and accountability to remain the steward of the enterprise.

The difference will be scale.

AI may allow individuals to acquire organizational leverage faster than any previous technology. That means we may need to become more intentional about developing human capabilities that historically emerged slowly through organizational experience.

Perhaps future leaders will receive progressively larger spans of agentic authority, just as young managers once received progressively larger teams. Perhaps we will need to teach verification, escalation, decision rights, and the limits of delegation as explicitly as we teach prompts and agents.

And perhaps we will need to preserve some experiences that appear inefficient precisely because they develop judgment.

We don't know yet.

But we think we're asking the wrong question when we wonder whether AI will let one person do the work of a hundred.

Increasingly, the answer may be yes.

The harder question is whether that person has learned how to lead the work of a hundred.

One great promise of AI is that fewer humans may need to do all the work.

The great leadership challenge is making sure leaders still learn how to hold the vision, exercise the judgment, and accept responsibility for work they no longer have to do themselves.

AI can give us extraordinary leverage.

It cannot relieve us of stewardship.