Why Should Authority Belong to a Title?

There are two people in the meeting room.

One sits down the long table.

She has been on this problem for three months.

Where it will break. Why it will break. Where the customer is actually stuck. Which plan looks safe, and will still fail.

She may be the clearest person in the room.

The other person sits at the top.

He has sign-off. He is busy. On this problem, he may have read one page of summary.

Then the problem starts moving up.

First to the manager. The manager translates technology into risk. Further up, risk is translated into resources. Further up, resources are translated into: do we do this quarter or not?

Two weeks later, the meeting finally decides. They approve a safer version.

And that version is the one she argued against two weeks earlier.

Nobody is trying to sabotage anyone. Nobody in the room has to be a fool.

Something very ordinary has happened in the organization:

The person closest to the problem has no decision rights.

And the person who actually has them is farthest from the problem.

The scene is so ordinary we barely notice it as strange. Companies have always run this way.

You join. You get a role. The role becomes a title. Behind the title sits a fairly fixed basket of authority.

You become a manager, and you can approve some things. You become a director, and you can approve more. You become a VP, and more and more problems land on your desk — including many you have never actually done.

We rarely stop to ask:

Why should a company's authority belong, for the long term, to a title?

Notice. I am not asking whether managers should be deleted. Whether the CEO is useless. Whether the company should make everyone equal. Those questions are too simple.

The interesting question is why a person usually has to get a title first, then a basket of decision rights, and only then the power to handle the problems.

Title → Authority → Decision

Why is that sequence treated as obvious?

In the last Future Lab essay, we looked at a strange fact.

AI has made a lot of doing very fast. Writing code. Doing research. Sorting material. Writing reports. Analyzing a problem. Generating a plan. Work that used to take days can now appear in hours, sometimes minutes.

The company did not speed up with it.

The plan is ready by afternoon. The decision may arrive two weeks later. The code is written, and it is still waiting for review. The analysis is done, and it is still waiting for alignment.

The fastest part is over. The slowest part has only just begun.

So the faster AI gets, the more visible a once-quiet problem becomes:

What is expensive inside a company may no longer be execution.

It is: who can decide. Who dares to nod. When it breaks, who owns it.

If decision is becoming the new bottleneck, we have to keep asking:

Is the way we allocate decision rights today still reasonable?

Do not rush to curse hierarchy. Hierarchy was not designed by idiots. The opposite: it was once a very effective piece of organizational technology.

In 1937, Ronald Coase asked a question that later became classic: if markets let people trade freely, why do we still need firms?

One important answer: because recontracting every time is expensive.

Finding who will do the work is expensive. Negotiating terms is expensive. Moving information is expensive. Monitoring is expensive. And after something breaks, reopening the question of who is in charge this time is more expensive still.

So the company did something clever. It packed a lot of authority into roles in advance. What a manager can decide. What a director can decide. What a VP can decide. Written ahead of time.

When a problem appears, nobody has to invent the power structure again.

Seen this way, the org chart is also a permission table written in advance. It gives up some flexibility. It buys stability. For a long time, that trade was a bargain.

That is also why “delete management” sounds sexy and is often hard to do.

Early Google ran a version of the experiment. In 2002, Larry Page and Sergey Brin once removed engineering managers. They disliked traditional management. They wanted engineers to just do the work. It sounded very Google.

Then something very practical happened. Expenses. Conflicts. Priorities. Resource fights. All the small, sticky problems started piling onto Larry Page.

In the end, the managers came back.

Google’s own Project Oxygen later reached a conclusion that was not revolutionary at all: good managers really do have value.

So the question was never: can we delete the manager?

Delete the manager, and the problems do not leave with him. Often they just restack on someone else’s desk.

Hierarchy is clumsy. It has one huge virtue: it is stable. When something breaks, at least everyone knows where to send it.

AI is starting to change some of those premises. Not all of them. Only some.

In the past, even knowing who actually understood a problem was expensive. A person’s experience might live only in their head. Why a judgment was right might leave no record. Ten people hitting different problems at once, and a manager could not see in real time what was happening to each of them.

So the organization used a rough method. If we cannot rejudge “who knows most” every time, let the title decide “who is in charge.”

It was a compression. Imperfect. Cheap.

AI is making some of the reasons for that compression disappear. Looking things up got cheaper. Summarizing got cheaper. Sorting information got cheaper. Translating across departments got cheaper. What the organization has already done is easier to retrieve. Even a person’s history of judgment can, in theory, be recorded.

In other words: we are starting to be able to know who is actually close to a problem.

The strange part is that after we know, the decision rights often still do not move.

Organization theory noticed this crack a long time ago.

In 1997, Philippe Aghion and Jean Tirole made a distinction that is still useful. They split organizational power into two kinds.

Formal Authority. The nominal power. On the contract, on the title, on the org chart: who may decide.

And Real Authority. The power that actually operates. Who holds the information. Who truly understands the problem. Who in fact shapes the decision.

These two things do not always sit in the same person.

Look back at that meeting room. The person down the table may have the information Real Authority needs. The person at the top has the title Formal Authority needs. One knows what is happening. The other has the right to stop it.

A lot of corporate slowness happens between these two kinds of power.

The problem has to climb from the person who understands it to the person who can authorize it. Then it has to be explained, translated, and repackaged the whole way up.

We have called a large part of that work message-passing work.

AI can make the passing faster. It has not solved the last question: who can make the problem stop?

Then a tempting idea appears.

What if the future company did not always send problems climbing the hierarchy? What if, sometimes, authority could move a little toward where the problem is?

Notice. A little.

Not abolishing the CEO. Not putting everything to a vote. Not replacing older people with younger ones. Not letting experts always win. And not letting AI give every employee an “authority score.”

Just a narrower question: can a person’s influence on a concrete problem come more from their real judgment on that problem, and less from their box on the org chart?

In the past: Problem follows the hierarchy. The problem follows the layers.

Could more of the future look like: Authority follows the problem. Authority moves a little with the problem.

This is not a daydream with no ground under it.

Hospital emergency teams have been doing something like it for a long time. Organization research has a name for it: dynamic delegation.

In high-pressure, fast-changing settings, the formal hierarchy does not disappear. A hospital is still a hospital. Senior doctors are still senior doctors. The accountability structure does not suddenly vanish.

But in a given moment, whoever is closest to the problem, whoever holds the most relevant information, can temporarily take the operational lead. The situation changes, and the lead moves again.

That matters. It shows that a stable structure of responsibility and a moving operational authority do not have to cancel each other.

A company does not have to choose between two extremes: bureaucracy, or everyone equal. There may be something in the middle.

Other experiments throw cold water on the idea.

Some companies tried internal prediction markets. Many employees bet, forecast, and put their judgment on the table. The result was interesting. The crowd’s aggregated call was sometimes more accurate than the official forecast.

And then? The org chart did not disappear. The people who predicted better did not automatically receive decision rights.

Why? Because seeing clearly and being able to own the bill are not the same thing.

You can predict, very accurately, that a project will probably fail. Whether to kill it, cut the team, write off the money already spent, and take the hit to the customer relationship — that is another matter.

A company is not a forecasting contest. It is not trying to find who guessed best. In the end someone has to say: all right. We do it this way. And then catch the consequences.

So three things have to be pulled apart. In the industrial age they were often tied into one title. They are not the same thing.

Who knows most. Who can decide. Who is finally accountable.

Understanding a problem does not mean you should hold the final decision. Shaping the whole room does not mean the bill should land on you when it breaks. Having created enormous value for the company in the past does not mean every new problem today should be yours to call.

The industrial solution was simple: pack all of this into the role. Director. VP. CEO. The higher the title, the more you were assumed to know, decide, and own.

The design was crude. For a long time it was good enough. AI is making that crudeness harder to ignore.

Especially in fields that are very new. AI, for example.

A twenty-five-year-old may be using the newest agents every day. Taking workflows apart. Trying new models. Failing. Rebuilding. A fifty-five-year-old VP may have ten times the management experience, and have seen far more organizational trouble.

On this concrete problem, the twenty-five-year-old may still be closer to reality.

Then a very specific question appears: why does their judgment have to wait another ten years before it is allowed to weigh more in the room?

If someone’s judgment is verified by reality again and again, should their influence on that problem rise faster?

I lean toward yes. But it is not that simple.

AI has not automatically given young people more power. It may have done the opposite.

Some 2025 studies using large U.S. occupational records found that in firms adopting generative AI, the relative employment of junior roles declined. An important reason was not that companies were wildly firing the young. It was that they started hiring fewer of them.

Stanford work based on payroll data saw a similar early signal: in some occupations more exposed to AI, young workers were hit first.

That points to an ironic possibility. Young people may hold some of the newest capability. The doorway through which they enter the organization, accumulate experience, and build judgment credit may be getting narrower.

So this is not an essay that says: AI is here, older people should step aside.

Reality is more tangled. New capability still enters the decision layer slowly. At the same time, the path for people who have that capability to enter the company may also be narrowing.

The ladder is still there. The first rung is starting to go missing.

The reverse is also true.

If someone is older, or is no longer the person who knows most in a new field, does that mean their value has vanished? Of course not.

That they should not call a particular new problem today, and how much value they have created for this community in the past, are two different accounts.

They may not be the right person to decide whether we should ship this agent system. They may still be the person most worth asking: the last time we hit a similar organizational change, where did it die?

The failures they have seen. The holes they have stepped in. Their reading of people. Their instinct for risk. Those things are real value too.

Which opens another problem the future will have to solve: Current Authority ≠ Historical Contribution.

How much decision influence someone should have today, and how much value they created in the past, are not the same bill.

Traditional companies like to pay both with a title. Promotion means: you did well. It also means: from now on you have more power. When technology moved slowly, the two could stay tied. As change speeds up, they start to separate.

On one side, people with new judgment have to wait in line. On the other, people with historical contribution fear being declared expired.

We will leave that here. It deserves its own essay.

Now we have to attack our own idea.

Suppose we really say: in the future, authority should follow “real value” more often. It sounds good. The problem arrives immediately.

Who defines real value?

The CEO? The board? Employees? Customers? Or AI?

If the CEO defines it, you still have the old hierarchy. If employees vote, the company may turn into a parliament. If customers define it, people start performing the customer. If an algorithm defines it, it sounds most objective — and may be the most dangerous.

Because once a metric starts deciding power, people start farming the metric. That is the classic problem behind Goodhart’s Law: when a measure becomes a target, it begins to distort.

Suppose the company starts recording that Jason was right seven times out of his last ten judgments, and someone else was right nine out of ten. The second person gets a higher judgment weight. It sounds scientific.

Then what do people do? They pick problems that are easy to win. They avoid new problems with no history. They fight for credit. They log every time they were right. They soften every time they were wrong. Speaking in a meeting is no longer only about the problem. It becomes: farming your authority as an asset.

People used to get ahead by being seen. In the future they may get ahead by farming a score.

We have discussed visibility over value before. A future upgrade is entirely possible. The old version was: let my boss see me. The new version: let the algorithm think I am reliable.

That path leads to a very Black Mirror version.

The company gives everyone an authority score. AI watches your history of judgment, your hit rate, your project results, peer reviews, forecast accuracy, network influence — and computes, in real time, how heavy your words should be today.

On the surface, this is exactly what we wanted. Title no longer matters. Capability decides power. It sounds fair.

Look closer, and it may only have upgraded corporate bureaucracy into algorithmic bureaucracy.

The boss did not disappear. The boss became the model.

The danger is not entirely fictional.

Research on algorithmic management has already found that when algorithms start assigning tasks, scoring performance, and setting the pace of work, the organization can look more automatic and flatter. People on the front line do not necessarily get more judgment. They may get less.

Before, you could at least ask the boss: why? Later the system only tells you: this is the optimal result.

Worse, an authority score can feed itself. People who were right in the past get more power. People with more power get more resources. People with more resources find it easier to produce another “correct” result.

The sociologist Robert Merton studied a similar pattern long ago: the Matthew effect. People already recognized keep being recognized.

What about newcomers? People with no record? Minority views? Problems that have never happened before? The system is most likely to underrate them.

And the most important problems of the AI era are often exactly those: problems that have never happened before.

So “let AI allocate authority” is not necessarily more advanced than a title. It may only be another bureaucracy. Even more dangerous.

In the past, when something broke, you could at least find a name. Director. VP. CEO.

The worst version of algorithmic bureaucracy is everyone telling you: that is how the system calculated it. And no one is actually accountable.

That is more dangerous than slowness. Slowness, at least, still has a person attached.

More interesting still: the future may run the other way entirely.

We have been asking whether AI will pull authority closer to the problem. Brynjolfsson and Hitzig, in 2025, offered another possibility worth taking seriously.

In the past, a lot of authority had to be pushed down for a simple reason: headquarters did not know what was happening on the ground. Much of the knowledge was local, tacit, hard to say. Only the people on site knew.

But if AI gets better at collecting, sorting, summarizing, modeling, and sending that information up in real time? The information advantage of the front line may fall.

The result may not be authority moving down. It may be authority moving further up.

That is a counterintuitive future. The company does get flatter. Fewer managers. A thinner middle. Fewer people passing messages. And the CEO sees more than any CEO before.

The middle gets thinner. The top gets stronger. You are looking at a flatter company. It is not necessarily a more democratic one. It may be the opposite.

Below, they execute. Above, they sign.

After AI pulls out the middle, power plugs straight into the top. That road is just as possible.

So we cannot announce that authority in the future company will necessarily become dynamic. We cannot announce that dynamic authority is the answer. We do not even know yet which way AI will finally push power.

It may pull authority closer to the problem. It may concentrate it further at the top. More likely: different kinds of power start moving in different directions.

Professional judgment may become more dispersed. Information may become more transparent. Execution may become more automatic. Final accountability may become more concentrated.

That is the part I think is actually worth studying.

For the person sitting down the table, this essay is not asking you to revolt. It is not telling you your boss is an idiot, so you should take power.

The real question is: have you built a kind of judgment credit that does not depend on a title?

What you said. What you judged. Why you judged it that way. What happened after.

If those things can keep being verified by reality, then even if your title does not change, your real influence should start to.

That may be one of the things actually worth accumulating in the AI era.

For the person sitting at the top, this essay is not asking you to step aside.

You can take more money. Hold a larger title. Keep final sign-off. None of that is the problem.

Those things mean something else: you carry more responsibility.

Formal authority does not automatically make you understand every problem. Your real job may less and less be proving you always know most. It may be: knowing when to trust the person who understands more than you.

Then, after their judgment, making the last decision. If it is wrong, the bill is yours.

That does not weaken the CEO. In a sense, it lets the CEO become a CEO again.

In the industrial age, a title answered three questions in a very cheap way:

Who knows most. Who can decide. Who is finally accountable.

We pretended those three people should be the same person. Because that used to be the least trouble.

AI is pulling them apart. Perhaps in the future, the person who knows most, the person who can decide, and the person finally accountable were never supposed to be the same person forever.

Then the real question arrives. If those three people can be different, how should the future company reallocate authority?

Whose judgment should weigh more? Who should hold the last decision right? Who should own failure? How do we count past contribution? How do we count new capability? How do we count a young person’s chance? How do we count an older person’s experience? How far should AI be involved? And where should it stop?

This essay does not answer those questions. I do not know the answers either.

But I am more and more sure of this: