Here’s the thing people say when they learn these masterpieces hang in Kansas City and not in China: they were taken from us. Looted cheap, the argument goes, while the country was too poor and weak to hold onto them.

There’s something to that. But it’s the wrong lesson, and mostly the wrong facts. On paper, these sales were legal. That’s exactly why getting such works back across borders is so hard today. If they’d been stolen, the law would be simple. The trouble is that most of what left China left through what was, at the time, a sale.

Set the paintings aside for a moment. Two business situations.

A pharma company comes out of a bruising PR scandal. Its reputation is damaged, good people won’t join, and to keep hiring it has to pay well above market. Fair deal? Now a tech stock drops hard for days, an investor can’t take the loss, and he sells at the bottom. Days later it roars back and he’s sick about it. Was his sale fair?

Both were fair. And so, in all likelihood, were those Song paintings.

Fairness has one test, and only one

We tend to think “fair” means the price was reasonable — that it did justice to what the thing was really worth. Good instinct, wrong answer. Fairness has exactly one test: did both sides, sitting in their actual circumstances at that actual moment, agree on the price?

The wounded company paid up because it was weak. The investor sold because, right then, cash beat the odds of holding. Nobody held a gun to anyone. The price was just what two people settled on from inside their own situations. That’s fair.

Now the painting. A collector selling a scroll cheap during a war is hard to read about, yes. But the cash he got might have been the only thing standing between his family and starvation that week. To a man whose life is on the line, life-saving money is worth more than anything. And these were not naïve sellers. A family that owned a Song masterpiece knew exactly what it was worth. They knew the price was low. In front of survival, knowing it changed nothing.

Desperation, fear, the raw need for cash — none of that breaks fairness. It is the price. How badly you need money is part of what money is worth to you. The market just reports that back to you, coldly and accurately.

“He got ripped off” is something we say afterward, holding up a different clock. Judging a sale at the bottom by the rebound that came later is exactly that mistake. The price struck back then was an honest readout of back then.

Before you call a deal unfair, name the fair price

Anyone who calls a deal unfair owes you one answer first: so what was the fair price?

Underpriced compared to what? A modern auction record set centuries later, in a completely different market? Irrelevant. The “going rate” at the time? In a war there were too few sales for any average to mean a thing, and a different method gives you a number that’s off by an order of magnitude.

That’s the whole problem. To call something unfair, you need a “right price” to measure it against — and there is no way to fix that number objectively. Whatever assumptions you use to build it fall apart under a hard look.

So “unfair” turns out to be a claim you can neither define nor disprove. “Both sides agreed” is the only standard that needs no assumptions and rests on a plain fact: the deal happened, so it was fair.

This happens in your company every day

I’m not telling this to relitigate history. I’ve spent seventeen years as an organizational consultant, and what I care about is how this exact logic shows up at work — and how badly most people misread it.

A strong candidate wants a lot of money. Simple reason: he moves around, and every move gets him repriced by the market. A team wants him. HR throws the flag: his number breaks our pay band, and making an exception wouldn’t be fair to the people already at that level.

So the company loses him.

Look closely at that word, unfair. It’s the same empty word as “the painting was taken unfairly.”

“Unfair to existing employees” assumes there’s a fair salary for the role. So what is it? The pay band? A band is just a rule the company wrote at some point, on some logic of its own. It might capture an internal baseline for a while, but it can’t price supply and demand in real time.

A long-timer’s low salary is also a fair deal

So have the underpaid long-timers been wronged? No. Their pay is a fair deal too.

They’ve climbed slowly because they’ve stayed inside and never had the market re-price them. The outside hire looks expensive because every job change re-tagged him. The one who moves gets a premium; the one who stays takes a discount. That’s not injustice — it’s just two different ways of pricing the same person.

If a long-timer really felt underpaid, the door out is always open. Why doesn’t he walk? Because leaving means a new environment, the risk of not fitting in, the cost of proving himself from scratch — and in his own math, dodging all that is worth more than the raise he’d get. So staying for less is his best move, all things weighed. He’s spending a salary discount to buy the safety of the familiar.

The door is open and he stays anyway. That’s fair.

Back to the candidate you blocked

So “paying the new hire well is unfair to the team” is a false problem, top to bottom.

His high pay is a fair deal he struck with the company. Their lower pay is a fair deal each of them struck with the company. The two have nothing to do with each other. When HR uses a made-up “internally fair price” to kill a deal that has real agreement behind it, it’s fighting something real with something imaginary — and the cost is a person who’d have created value, walking away.

The question a manager should be asking was never “is this unfair to the people we already have.” It’s “can this person create value worth what he costs.” If yes, hire him. If no, don’t. That’s a business call about value, and it has nothing to do with fairness.

What the strong own, they don’t sell

In a fair deal, nobody owes anybody. Strong position, you get a premium. Weak position, you take the discount. The market is evenly cold to everyone: no refunds, no dignity preserved. It just prices your situation at the moment you sign.

That Song painting didn’t sell for a price we’d find acceptable today. But the price was never the real problem.

Picture it now. If A Solitary Temple Amid Clearing Peaks were sitting in a museum in China and a foreign buyer offered ten billion dollars, would anyone sell? No. Not at any number. To an owner with real strength — a museum, a nation, anyone — it simply isn’t for sale. Put differently: its price has gone to infinity, so high that it enters no deal at all.

That’s the part of the history that actually stings. The tragedy wasn’t selling cheap. It was that in a poor, weak age, things that should never have been for sale got pushed onto the market at all. Weakness didn’t make you take a low price. Weakness took away your right to refuse the deal in the first place.

It’s hard, emotionally, that these treasures ended up abroad. But a century on, the takeaway isn’t the grievance of having been fleeced. It’s the resolve to get strong enough that it never happens again. Charging it to “unfair dealing” is the cowardly read. Charging it to “we weren’t strong enough” is the clear-eyed one, and the useful one.

What’s true for nations is true for people. The gap between a top performer with real leverage and someone backed into a corner is far more than salary. The real line is this: the strong have things that aren’t for sale. Some terms they won’t take no matter how broke they are; some lines no offer will move them past, and they’ll walk. The person with nowhere left to go will put everything on the table, because survival comes first.

And this is exactly where managers get it backwards. The person you actually want is usually the one who names a high number, says no to bad terms, and walks when it doesn’t work — because he has standing, options elsewhere, things not for sale. The one who takes whatever you offer, however hard you push, however the terms move? Look one layer deeper: is he eager for the job, or is he out of options and betting everything on it?

So when HR blocks a high-asking candidate for “breaking the band” and “internal fairness,” the real thing to judge isn’t whether his price is fair to the people already there. It’s whether the person willing to name his price is worth it. Asking the right question beats guarding a made-up yardstick of fair.

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This year, as it approaches, Doubao, Yuanbao, Kimi, DeepSeek, and other major AI platforms announce that during exam hours they will temporarily disable photo-based question solving, problem analysis, and essay generation. Everyday chat and general queries are unaffected.

The public applauds, reading it as corporate responsibility, a gesture in defense of the exam’s fairness.

I want to offer a contrarian view. As a gesture, the move is flawless. As an anti-cheating measure, it is close to meaningless. And what the platforms are so visibly guarding against is not student cheating at all.

They closed an entrance that does not physically exist

Start with what the move actually prevents.

The gaokao examination room has long been a sealed physical environment. Candidates may not bring in any electronic device. Even the proctors’ phones are collected and held. The rooms have full radio-signal jamming. Which means that during the exam, in that time and that space, a phone cannot get in, and even if it did, it would have no signal.

So what real obstruction does disabling an AI’s photo-solving function add, inside that room? None. Because the entrance of “using AI to photograph and solve a question” does not exist there in the first place. For a tool that cannot be brought in and cannot connect, whether or not you switch off one of its functions makes zero difference to cheating inside the room.

Some will object: the truly high-tech cheating is not a student pulling out a phone in the room, but using a concealed micro-device to transmit the paper outside, where an off-site team feeds it to an AI, gets an instant answer, and relays it back. The chain sounds sophisticated, but it has one physical precondition it cannot get past: the questions must first get out of the room. And the room’s signal jamming cuts off not just cellular networks but all wireless transmission in that band. Micro-cameras and hidden earpieces depend on exactly that wireless signal. If the questions cannot get out, the AI outside, however powerful, has nothing to work with.

Suppose further that some ring really had the technology to break through state-grade jamming. That would mean a large criminal organization with professional equipment and serious counter-surveillance capability. Against cheating at that level, switching off the AI function is the worst possible move. Because during the gaokao, if someone uploads real exam questions to a platform for answers, the real-name data, timestamps, activity logs, and question images left in the back end are ready-made hard evidence. Following that data trail to make arrests is far more precise than scanning a sea of candidates by eye. To actually counter this kind of cheating, what a platform should do is retain the data and assist the investigation, not sever, with its own hands, a line that would have left perfect evidence.

In either case, what actually secures the exam room is physical isolation and entry screening, not a temporary blackout of every AI on the network.

A textbook case of Security Theater

The security expert Bruce Schneier coined a precise term for this: security theater.

It refers to measures that appear to improve security, that let the public visibly feel someone is in charge, but that add nothing to actual safety. Their real output is not security but a feeling of security, and a posture of “I have done my duty.” Certain elaborate airport-screening steps that stop no real threat are the classic example.

AI platforms going quiet during the gaokao is a textbook case of security theater. It gives the public a response, and a sense that the platforms are responsible and the exam is safe. But what it shuts off is a useless channel that no cheater would, or could, actually rely on.

What the platforms guard against is not cheating, but a PR crisis

If it is useless, why do the big firms execute it in lockstep?

Because what they are doing is, in essence, defensive self-protection. In the current regulatory climate, the absence of an explicit legal obligation does not mean the absence of real exposure. If the law actually required platforms to prevent cheating, it would be a written, hard compliance rule, not a soft press release carrying the words “voluntary industry action.”

If, during the gaokao, it came out that some ring had used a particular company’s AI to generate a top-scoring essay, then even if the cheaters bore full legal liability, that platform would be drowned in public outrage and could face severe regulatory correction. So when a platform disables a function, what it is guarding against is not cheating in the exam room, but its own entanglement in a cheating scandal. What it is isolating is its own commercial risk, not the exam’s risk of fraud.

This motive shows most clearly in the platforms’ selective action.

If a platform were truly committed to exam fairness, then cheating is cheating wherever it happens. The civil service exams, the provincial exams, the postgraduate entrance exam, the various professional qualification exams all decide countless people’s fates, and all face the same potential motive to cheat. Why do the platforms loudly suspend functions only during the gaokao, while ignoring the thousands of other exams across the year?

Because the gaokao is where society’s spotlight falls. It is the most sensitive in public opinion, the one whose blowback is fiercest if something goes wrong. That the platforms perform only for the gaokao shows that what they are optimizing is not the scenario with the highest risk of cheating, but the scenario with the highest risk of blame to themselves.

They never had the ability to be the gatekeeper

The deeper reason is that, technically and structurally, platforms simply lack the conditions to be a gatekeeper of exam fairness.

In time, they cannot know the full map of exams. Across a year there are thousands of exams nationwide, differing in schedule and subject. A platform cannot, and has no obligation to, track the precise timing of each one. In content, they cannot discern a question’s intent. Feed a hard physics problem into the chat box, and the platform has no way to tell whether the screen on the other side belongs to a student revising at home or a cheating ring inside an exam room.

Put these two blind spots together and you get a knot that cannot be untied. For a platform to truly carry out “preventing cheating” across all exams, it would have to do both impossible things at once.

Follow the logic to its end: if a platform had to answer for every exam, and it can distinguish neither the timing nor the intent, then its one foolproof option would be to disable the answering function permanently. To destroy the tool itself in order to keep it from being misused, this reductio’s endpoint exposes the fatal feature of security theater. It does not scale. A real line of defense, like signal jamming, can cover every exam room indiscriminately. Security theater can only be performed under the brightest spotlight, the gaokao, for everyone to see.

A useless move that blurred a clear allocation of accountability

The original chain of accountability was perfectly clear.

Cheating in the gaokao has long been a criminal offense. Deterrence comes from criminal law. Securing the exam room comes from the exam organizers, through screening and physical jamming. The consequences of cheating, and of organizing it, are borne by those involved.

In this chain, every actor is defined and capable of bearing its accountability. And the platform’s only sensible role here is not the gatekeeper before the fact, but the evidence source after it. Deter with criminal law, isolate with physical means, trace and obtain evidence with the platform’s data. That is each part in its proper place.

When a platform disables its answering function, it looks like doing a bit extra. In fact it uses a seemingly accountable move to package a self-interested act of risk avoidance. The cost of this move lies not only in its uselessness, but more in the fact that it muddied water that had been clear. It gives the public the illusion that preventing cheating has become the tech companies’ accountability, quietly diluting an allocation of accountability that was already clear. If nothing goes wrong, everyone is happy and the firms collect a round of applause for social responsibility. If something does, this blackout still stops nothing. What it guards against was never cheating, only the platform’s own risk of being blamed. And that risk should never have been carried by an AI platform that has neither the obligation nor the ability to stand guard.

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But for a multilateral forum like this, scale and spectacle are the least important measures of success.

Hosting an international forum is not hard. You fund it, send the invitations, set a respectable agenda, and countries will send people. The group photos, the closing communiqué, the corridor pleasantries — none of this amounts to success. It only proves the meeting took place.

The real test is a single one: did the major powers the forum exists to bring together each send their actual decision-makers, at a level that matched?

By that test, this year’s Dialogue fell short. China’s defense minister was absent for the second year running. The delegation was led by a professor from the National Defense University and made up largely of military scholars, and the customary China session was cancelled. For a venue whose central draw is the interaction between the US and China, once the Chinese decision-making level is missing, then no matter how striking the American remarks, how active the ASEAN mediation, or how complete the closing consensus, on the one thing that mattered — the US and China meeting substantively in this room — it had already come up empty.

The major powers never needed this venue

Why is “whether the major powers send matching decision-makers” an absolute threshold for judging the forum’s success?

Because the major powers already have dedicated, direct channels between them. They do not need a forum like this to broker the connection.

The clearest evidence took place in the very same window. The Dialogue ran in Singapore from May 29 to 31. And on May 28 and 29, the US and Chinese militaries held a separate meeting in Hawaii: the 2026 working-group meeting of the China-US Military Maritime Consultative Agreement. Under this standing bilateral mechanism, the two militaries assessed how the code for safe air and maritime encounters had been implemented, and discussed concrete measures to improve frontline safety. The substance, in other words, was real working business.

Set the two meetings side by side and the logic is plain. In the same week that defense ministers were taking turns at the podium in Singapore, making public statements, the actual professional issues between the US and Chinese militaries had already been handled through the bilateral channel in Hawaii. One was a public stage facing an audience. The other was a working session behind closed doors. The substance always happens in the latter.

The same holds at the highest level. The strategic communication between the US and China was completed in Beijing two weeks earlier, where the two heads of state reached consensus on building a constructive and stable strategic relationship. What Hegseth said at the Dialogue about the US-China relationship was, in essence, a restatement of the tone set in Beijing.

The point is evident. For the two parties that matter, the US and China, substantive communication happens through direct channels — at the head-of-state level, and at the professional military level. Singapore’s multilateral stage is an optional stop, one you may attend or skip. The major powers come as a courtesy to the host and as a show of support for multilateralism, not out of necessity.

A connector’s value is always conferred. It rests on each party’s provisional judgment that “routing this through you is smoother, more dignified,” and that judgment can be withdrawn at any time. The connector itself holds no leverage to demand that the major powers extend it.

This is exactly the PMO inside a large company

Anyone who has run a project inside a large company will recognize this situation. It is the PMO, the project management office.

The PMO owns no business unit. It controls no P&L. It cannot decide anyone’s bonus or anyone’s exit. It has no native base of power. Its entire value lies in coordinating the business units and support functions to push a cross-departmental project to completion.

There is an easily overlooked premise here: the business units, and the business and support functions, are not natural adversaries who cannot work together. Given enough willingness, they have every channel and every ability to communicate directly. The PMO’s role is never to make impossible collaboration possible. It is to make what could already be done go more smoothly, more efficiently.

In other words, the PMO is organizational lubricant, not an indispensable bridge.

A connector like Singapore has value for the same reason: letting the major powers meet on neutral, multilateral ground carries more political dignity and a more multilateral look than meeting privately. What it offers is not the meeting itself, but the dignified dressing around the meeting. It too is lubricant, not a necessity.

It takes everyone to succeed, and only one to fail

Whether I was a consultant on the vendor side in my early years, or later a PMO on the client side, at every project’s closing celebration I would make a point of thanking each department for its support. This was not hollow courtesy. Because I knew one thing well: for a project to succeed, every power-holding party has to lift it together; for it to fail, only one of them has to decide not to cooperate.

This is a structural asymmetry. Success is a sufficient condition that must be met in full — you have to assemble the genuine investment of every power-holder, with none missing. Failure is a necessary condition that needs only a single point to hold — let any one power-holding department decide to let go, and the project collapses. And they need not oppose it openly, need not commit any error. They need only happen to have a higher priority at the critical moment, and delay or disengage in a fully compliant way when their cooperation is required. The coordinator, holding no power, is left helpless, with no one even to hold accountable, because the other side has done nothing procedurally wrong.

Map this logic onto the Dialogue and it is accurate to the point of cruelty. For a forum like this to succeed, all the major powers must want to attend, send real representatives, and take it seriously, all at once — an exacting condition. To discount its value, only one power of sufficient weight has to treat it coolly. China did not need to boycott it openly. It only had to send a delegation of scholars, with the defense minister absent, and the forum’s core value was already shaken, the grand affair already reduced to form. Singapore could do nothing about it.

Neutrality is the foundation; believing yourself indispensable is the dead end

The PMO’s greatest asset is staying neutral. The moment one business unit judges it to be partial, leaning toward another side, that unit will put up its guard and move to sideline the PMO.

The PMO’s most dangerous illusion is mistaking the dignity others extend to it for evidence that it is indispensable. Once it begins to overstep, convinced that without it the business cannot run, the parties respond in the simplest, most direct way. After all, they could always deal with each other directly.

The same goes for a multilateral hub. When a hub is judged by a major power to be no longer neutral, the power answers with action: lower the level of attendance, stay away, or set up its own table elsewhere. The hub has no counter to this.

Accurate self-knowledge is a virtue

So why can some connectors not master this balance? Because it is a demanding art: to win the genuine acceptance of the power-holders, and at the same time hold fast to neutrality, captured by no one. Whether you can master it depends on the complexity of the environment in which you were trained.

Position can be conferred from outside. Geography gave Singapore its position on the strait; an organizational appointment gives a person the PMO title. But the capability to wield that position will never arrive automatically with the title. It can only be forged through repeated rounds of contest, in an environment of high complexity where many interests are fiercely entangled.

Here one must choose the right benchmark. Singapore’s complexity should not be compared sideways against other hub economies — that is a contest among equals. It should be measured against the very objects it is trying to balance: against China, against the United States. Is Singapore complex?

A city-state of just over five million, with no hinterland and no strategic depth, an economy heavily dependent on entrepôt trade and finance, an internal landscape of interests that is relatively simple. Against China: 1.4 billion people, the weighing of interests across dozens of provinces, a complete industrial system, the sediment of several thousand years of civilization. Against the United States: fifty states, the tension between federal and state power, the contest of two parties, a web of interests spanning the globe. In internal complexity, Singapore and these two giants are simply not of the same order.

And yet what it must balance and navigate are precisely these two players, whose internal complexity exceeds its own by orders of magnitude. The major powers possess a training ground the small state cannot provide. This is no fault of Singapore’s. It is a fate determined by size. A city-state of five million was never meant to, and never could, possess the depth of governing complexity of a great power.

Look around the world and the other connectors face the same situation. Every small economy that took a hub position by virtue of geography and timing is in the same place: the position came from circumstance, but the capability to wield it can only be cultivated in great-power-scale complexity, and they are, by nature, short of that scale of training. The mismatch between position and capability is their shared and deep structural fragility.

Needed, not indispensable

So the PMO, the multilateral connector, and every middle coordinator without real power, all walk the same line between life and death: are they needed, or do they believe themselves indispensable?

Their value is real, but it is conferred from outside, an addition rather than a foundation. Every success they have comes not because they conquered anyone, but because every power-holder capable of sinking them chose, this time, to hold back. The moment such a coordinator misreads this collective goodwill as “it has to be me” and begins to overstep, it is destined to be bypassed, sidelined, and finally shown to be dispensable.

The mature coordinator stays clear-eyed about its own place.

In February, I wrote that a company’s regional headquarters is, in essence, a transfer station, one that the AI era will eventually route around with an end-to-end direct flight. The Dialogue now shows the same logic projected onto the level of nations, onto geopolitics. Only this time, what routes around the hub is not AI, but the direct channels the major powers already hold.

A coordinator’s fragility is the fate written into the position itself: its existence always depends on whether those with real power are willing to need it. A connector, whether a PMO or a nation, faces its most dangerous moment not when it comes under attack, but when it begins to believe the world cannot do without it.

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What is worth discussing is a different and very large group of participants: corporate executives.

The number of executives in my network doing HYROX has grown large enough to be a phenomenon. Some of them genuinely love the sport. But for another group, participating in HYROX has little to do with the sport itself.

I can say this with some confidence, because years ago, when marathons were at their peak, more than one executive told me privately: I don’t actually want to run, but I have to. I have to present myself as someone bursting with energy, or it undermines how I lead my team day to day. And so executives flocked to marathons. The medals went up one by one, the captions without exception about perseverance, about pushing past limits.

There is another motif with a long lineage: sleeping little. “I only sleep four hours a night.” “The city at 4 a.m.” This template of executive narrative has survived to this day, and some add one more line: I have more energy after training, I work more efficiently.

Today’s HYROX wave is almost identical to the marathon era. The form changes, the core does not: using the body’s extreme performance to vouch for one’s leadership.

After much thought, here is what I want to say to those executives running and training against their own will: you do not need to project total energy and physical prowess to look like an executive. The standard for what an “executive” looks like is something you invented in your own head, not something the world imposed on you.

What is the fatal flaw in the logic of this executive narrative?

Start with sleep. Chronically sleeping four hours a night damages cognition, judgment, and emotional stability. The science on this is solid and not up for debate. The “short-sleep elite” is an act of self-mythologizing, not a capability worth emulating. A leader who needs “I sleep less than you” to establish authority is not revealing his energy. He is revealing a liability in his judgment.

Now the “more energy after training” claim. This one needs to be taken apart. Regular moderate exercise does improve energy, mood, and sleep quality. But the version executives tell is different. Their version says: I just finished a high-intensity session and went straight back to full-throttle work, with no recovery needed. What this violates is not “whether exercise works.” It violates a basic fact: after high-intensity training the body needs to recover, and performance drops during recovery.

Treating “I can run nonstop, I don’t need recovery” as a badge of honor is not strength. It is the denial of a physiological fact. Genuinely high-level athletes understand the importance of recovery. Only people performing exercise insist they don’t need it.

Why has this narrative template gone global?

Why do so many executives, in China and abroad alike, need a race, a medal, and a caption about four hours of sleep?

My read is this: because leadership reveals itself too slowly, and in ways that are not direct enough.

Whether an executive is any good comes down to the quality of their judgment, the outcomes of their decisions, whether the organization got better in their hands, whether talent stayed. These signals share one trait: they are slow, slow enough to take months, quarters, even years to surface. And they are indirect. You cannot turn them into a photo grid, cannot attach a caption and post tonight to collect likes by morning.

People need immediate feedback to affirm themselves, and executives are no exception. He too tends to want constant, ideally daily, confirmation that “I deserve this seat.” But the real evidence of leadership cannot supply that immediate feedback, so physical performance gets recruited as a substitute metric. It is fast, it is visible, it is quantifiable.

HYROX’s product design fits this psychological need seamlessly: a standardized course, a global leaderboard, split times to the second, a finisher’s badge. What it sells was never just a race. It sells a credential that is immediate, displayable, and comparable.

A distorted form of KPI thinking

Go one layer deeper and this stops being as simple as “finding a substitute.”

An executive who spends years managing an organization through quantifiable metrics will, over time, unconsciously apply the same logic to himself. Organizational achievement is a slow variable that offers no immediate confirmation, so he needs a metric that produces a score right now to confirm he is still in control. Physical performance is exactly such a metric: it has numbers, it has rankings, it has deadlines.

This is the distortion of KPI thinking. It turns from a tool for managing an organization into a way for the manager to affirm his own worth. When the real evidence of leadership refuses to settle, distorted KPI thinking drives him to find something in his life that can settle, and to treat it as proof that “I’ve still got it.”

So flocking to HYROX is not executive vanity. A formidable physical performance is, at bottom, a counterfeit currency of leadership: same face value, but it cannot be redeemed.

Closing

If reading this far makes you uncomfortable, I suggest a question to check yourself: if this race had no finisher’s certificate, no ranking, and no one would ever see your result, would you still enter?

That question helps you separate out your original motive.

Leadership that is genuinely secure does not need the body’s extreme performance to vouch for it. It reveals itself slowly, perhaps invisibly, but it is the real thing. A person who needs a flashy performance of physical markers to demonstrate his “leadership” is not lacking fitness. He is lacking confidence in that slow, even invisible, leadership of his own.

The performance of physical markers grows more popular not because executives grow more vain, but because in the age of KPIs we have less and less patience to wait for the real evidence of leadership.

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Most coverage read this as “the Oscars are pushing back against AI.” I’m not going to judge whether the rule is good or bad for film as an art form. But as someone who has spent seventeen years in organizational design and run a fair number of layoff projects, I see something in this rule I have seen countless times inside companies: an organization takes on a accountability it has no capacity to fulfill, then uses that unenforceable rule to accomplish something entirely different from what it claims in public.

An Organization Should Clear Three Tests Before Taking On an Accountability

Before any organization writes a rule or takes on an accountability, it should answer three questions. Can you define the accountability clearly? Do you have the tools to enforce it? And if you can neither define it nor enforce it, what does this rule become as it runs?

The first two questions decide whether a rule can be fulfilled. The third decides what an unfulfillable rule does to the organization. The Oscar AI rule fails all three.

Test One: It Can’t Even Define “Human-Authored”

The screenplay award requires scripts to be “human-authored.” But the rule never defines what human-authored means.

I generate a first draft with AI, then rewrite it line by line. Does that count as human-authored? I write a detailed outline and character bios, then have AI fill in the dialogue. Does that count? I have AI produce twenty versions, pick one, and revise it. Does that count? The rule gives no answer.

What makes it worse is that the rule’s stance on AI is self-contradictory. On one hand, it assumes AI cannot be an author, which is why a fully AI-generated script is ineligible. On the other hand, it assumes that the moment a person uses AI, the script’s “human author” status becomes suspect and subject to review. These two positions undercut each other. If AI is just a tool, then the person using it is the full author, and the rule has no grounds to doubt him, just as no one doubts a script’s authorship because it was typed on a computer. If using AI dilutes “authorship,” then the Academy is conceding that AI in fact carried part of the creative work, which makes AI an author in functional terms, so on what basis does the Academy say it can’t be counted as one?

The Academy has to call AI “not an author” and “an author” at the same time for this rule to hold. A standard whose core concept contradicts itself cannot, in principle, be fulfilled. Test one: the rule fails on definition.

Test Two: Its Only Tool Is “I Reserve the Right to Ask”

Even if the definition problem could be solved, enforcement is a non-starter.

The only tool the Academy has given itself is that general clause: the right to request more information. This is not investigative power. It is not a verification mechanism. It is a “tool” that depends entirely on the honesty of the person filing. A screenwriter says, “AI just helped me organize some research, the script is mine,” and the Academy has no way to disprove him. A script is not like a performance. With a performance you can at least see whether what moves on screen is a human. A script is pure text. Its creation process is invisible, untraceable, unverifiable.

The Academy knows this. The phrase “reserves the right to request more information” is itself an admission: I have no capacity to verify on my own initiative, I can only ask a question after the fact. Test two: the rule fails on tooling.

Test Three: A Rule That Controls Nothing Is More Dangerous Than No Rule

Undefined and unenforceable, what does this rule become as it runs?

It becomes a rule for which no one can be held accountable for the outcome. Suppose a film wins Best Original Screenplay, and it later comes out that AI was used extensively. What happens next? The screenwriter says the core conception was his, AI only provided negligible assistance (”negligible,” of course, being a thoroughly subjective notion). The Academy says its rules required AI use to be disclosed, and he didn’t disclose it honestly. The judges say they only assess whether the script is good, not whether AI was used. No party can be held accountable.

But the real danger is not that no accountable party can be found. The real danger is that this rule controls nothing while making everyone believe everything is under control.

In organizational design terms, this is worse than having no rule at all. With no rule, people at least know no one is handling this, so they stay alert and look for their own way out. A rule that spins in place while looking authoritative makes everyone involved stop staying alert, stop finding their own way out, because they believe someone is already standing in front of them. Test three: the rule fails on the false sense of security it creates.

The Rule Has Two Real Functions, One External, One Internal

By this point the rule’s true function is clear. It was never meant to keep AI scripts from winning, because it can’t. But it does have two real functions.

Externally, it deflects liability. As long as the rule exists, the Academy can claim “we protected human creativity.” If something goes wrong, the Academy can push the accountability onto “the person who didn’t disclose honestly.” Take the credit when nothing goes wrong, pass the blame when it does.

Internally, it pacifies. This is the rule’s real engine. A large group of people in the film industry are afraid of being replaced by AI. They need a response, they need to see “the organization taking action.” This rule is that response. It does not need to be enforceable. It only needs to make that anxious group believe “the Academy is holding AI back for us.”

These two functions are two sides of the same thing. Precisely because it can deflect liability externally, it can promise protection internally, because it never intended, and never had the capacity, to deliver that protection, and it can still pass the blame if something goes wrong. A rule can both shift costs outward and hand out a sense of security inward, on one condition only: that it was never meant to be actually enforced.

So Who Ultimately Pays the Cost of This Failed Rule?

The cost doesn’t disappear just because no accountable party can be found. It is merely transferred.

The first to pay are the top creators. The truly irreplaceable actors and screenwriters were never afraid of AI in the first place. Their irreplaceability comes from within, the way Roberto Bolle still stands at the center of the stage at fifty, unafraid of robots, because a robot cannot imitate the unspeakable individual choices in every one of his muscles. These people should be winning on the strength of good work. Instead they now have to first prove they are “human enough,” and that proof can’t be adjudicated. The rule does not protect them. It drags them into a process they don’t need and that works against them.

The second to pay is the audience. In the theater the audience only sees the finished work. Whether a story moves them, whether a performance makes them hold their breath, has nothing to do with whether AI was used on the script. But this rule may keep a good script and a good performance out of contention because of “impure origins,” and may even affect whether they get financed and made at all. What the audience loses is the good work it could have seen.

The third to pay is the entire market, and the timing could not be worse. Global box office has stalled for two consecutive years. North American box office in 2025 was more than twenty percent below 2019. More tellingly, if you strip out ticket price increases and count actual admissions, the figure is down nearly forty percent from 2019. And the fact that this decline began before Covid shows that the audience leaving is not cyclical but structural, a long-term erosion in the appeal of the content itself. Mid-budget original films are disappearing in large numbers. The market increasingly leans on a handful of big-IP sequels. Audiences grew tired of formulaic storytelling long ago. At precisely this moment, a rule that narrows the sources of creation rather than widening them is, indeed, “ill-timed.”

And this is exactly what exposes the motive. If the Academy and the film industry were truly acting against the downturn, for the health of the market, what would the rational choice be? To embrace the gimmick. In today’s market, a film loudly marketed as “screenplay entirely written by AI” is itself a marketing flashpoint, and might well outsell the hundredth formula picture made under the rule’s protection. An industry genuinely driven by commerce, facing a downturn like this, would not ban the gimmick. It would use it.

They chose to ban it rather than use it. That tells you the original purpose of the rule was never the market and never creativity. It was the job-security anxiety of one internal group.

I have run many layoff projects. I have seen this maneuver many times, and the features are almost always the same. Facing an irreversible structural change, an internal group does not look for a way out. It manufactures a set of rules that look like “protection,” cannot actually be enforced, and shift the cost onto someone else. Its function is never to solve the problem. It is to delay the problem, to make the people staying put feel “the organization is protecting me.” Drop this AI rule into the cases I’ve handled, and nothing about it stands out.

“Delay” Never Saved Anyone’s Job

I am not against a transition period. An industry needs time to adapt to AI. That is entirely normal, and it should have it. What I am against is turning “transition” into “resistance,” and turning “adaptation” into “delay.”

In the layoff projects I’ve handled, this maneuver ends the same way almost every time. It never actually saves anyone’s job. It only delays. And the real cost of delay is spending the time that should have gone toward finding a Career Option B on a position that was never going to hold.

This Oscar rule transferred a cost it could never bear onto top creators, the audience, and the market, while keeping for itself a position from which it can both take credit and pass blame. But what it really takes away is from the practitioners who should have used this transition period to find their next path. What the rule hands them is not protection. It is the illusion that “we’re holding AI back for you,” and the greatest effect of that illusion is to make them stay put, at ease, and not go looking for a Career Option B.

When an organization speaks to its own people through a rule it cannot even enforce, what it is protecting is never the thing it claims to protect. At most, it buys the people unwilling to face reality a little more time to stay where they are.

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Many CEOs, when faced with middle-management failure, instinctively reach for the same fix — they replace the person. They try to find a more reliable middle manager. But Miranda’s response is not just about replacing people. She relies on a different approach altogether: structure plus intervention.

Why Middle-Management Failure Is Inevitable

Middle-management failure is something virtually every organization of scale runs into. It has three structural causes.

First, distorted hiring incentives. The reward system for middle managers runs naturally counter to “hire someone better than yourself.” Hiring a strong performer destabilizes the manager’s own position. So middle managers naturally gravitate toward candidates who are “just good enough, but unlikely to surpass them.”

Second, information monopolization. A middle manager’s position in the org chart is, by design, the chokepoint where information flows up and down. So the manager has a built-in incentive to filter what travels upward (reporting only what reflects well on them) and to filter what travels downward (passing only their own interpreted version).

Third, resource hoarding. Middle managers tend to keep resources within their own span of control, rather than letting them flow to where they would create the most value. The reason is that they evaluate their own performance by “how many people I manage” and “how much budget I control.”

None of these are moral problems. They are structural ones. Replacing the person doesn’t solve them. Any middle manager who stays in that position long enough will develop similar tendencies.

Senior Leaders Have Always Known to Counter with Structure

Mature CEOs know that “hoping middle managers will self-regulate” is not a sustainable plan. The answer has to be institutional design.

I’ve written before, in a piece called Skip-Level Reporting: Management Taboo, Organizational Necessity, about how CEOs use institutionalized skip-level mechanisms to counter middle-layer distortion. Matrix dotted lines, the PMO, talent development programs — all of these are bypass designs that allow senior leaders to reach real signals from the front line, around the middle manager’s monopoly.

The dual-assistant system in The Devil Wears Prada is one expression of this same logic, applied at the closest-in role to the boss.

It splits access to the boss into two dimensions and divides them between two assistants. The first assistant handles the work world: office matters, screening visitors, managing the calendar. The second assistant, in addition to supporting the first assistant, handles the life world: delivering The Book to the boss’s home every night, processing requests that cross between work and personal life.

The two assistants therefore form an asymmetric picture of the boss. The first assistant sees Miranda’s work face. The second assistant, to some degree, sees Miranda’s life face. Neither can assemble a complete picture of Miranda, and neither can monopolize the boss’s decision-making information.

The design intent here is not “the boss is so important she needs two people.” It is to break the monopoly. Even if Emily wanted to be the sole information node, she could not block Andrea from entering Miranda’s home every night, because that is, by design, part of Andrea’s job.

But Even the Best Structure Cannot Fully Eliminate Failure

Organizations are ultimately run by people. No matter how well-designed the structure, it cannot offset the downside of human nature.

The film captures this beautifully. In the opening, Miranda tells Emily explicitly that the previous two second-assistant candidates Emily sent over were inadequate. This time, Miranda insists on personally interviewing the candidate that HR sends.

My middle-school chemistry teacher, a nationally distinguished teacher, once said: one test may not tell you anything, but two is enough. The same principle applies in management.

A first failed recommendation can be a coincidence. Emily may not yet have fully understood Miranda’s hiring preferences (even though Emily herself was promoted up from the second-assistant role). But a second failure is no longer a coincidence. It is a pattern. Either Emily’s judgment of people isn’t strong enough (a capability problem), or she is engaging in threat-averse hiring (a motivational problem).

Whichever it is, the conclusion is the same: Emily can no longer be allowed to recommend candidates, because she is likely caught in middle-management failure. So Miranda switches channels, routing the search through HR directly. That is how Andrea entered Miranda’s field of view.

The CEO Must Retain the Ability to Intervene

This is the part of the film that’s worth real study.

Many CEOs, once their company reaches a certain scale, gradually let go of micro-level intervention. The reasoning sounds entirely sensible: let the middle managers do their jobs, give your reports room to operate. But this is a trap. Once a CEO fully abandons the ability to intervene, middle-management failure becomes much harder to detect. The CEO also loses their own acuity for reading micro-signals, so even if they later want to step in, their judgment will be off. Retaining the ability to intervene is therefore non-negotiable.

The screenwriters use the most economical strokes to show the two layers at which Miranda retains this ability.

The first layer is diagnosis-triggered intervention. Emily recommends two unacceptable candidates, and Miranda switches channels right after the second failure. This kind of intervention is temporary, diagnosis-triggered, and has a clear threshold (two is enough). The CEO doesn’t need to step in for every decision, but the option to step in must remain alive.

The second layer is more subtle, and more elegant: the second assistant delivers The Book to Miranda’s home every night.

Most people don’t realize that this daily, unremarkable task is, in essence, a structural backdoor. Unlike “mandated skip-level reporting,” it carries no adversarial edge. It is just a passage left in the structure that looks completely innocuous, framed as part of the second assistant’s job. Emily has no grounds to oppose it.

But every time Andrea delivers The Book, she enters Miranda’s personal space. Once two people are designed into the same private space, anything is possible. Miranda can observe Andrea’s real behavior, unfiltered by Emily. Andrea can also bypass Emily and pick up Miranda’s actual concerns directly.

This design has a direct equivalent in modern corporations: the one-on-one mentorship between senior executives and high-potential employees inside a talent development program. The stated purpose is dignified — “senior leaders supporting the growth of high-potentials.” The middle manager has no grounds to object, because this isn’t directed against them. It is called “corporate talent strategy.” But every one-on-one conversation puts the high-potential into the senior leader’s field of view. And every such encounter creates a window for the senior leader to access information that hasn’t been pre-filtered by the middle manager.

That is the real essence of a structural backdoor. It is not a public “going around the middle manager.” It is a passage left in the structure that looks unremarkable, and that allows the possibility of contact to exist naturally.

The mark of a mature CEO is here: at the macro level, the CEO can let go, but the intervention channel always stays open. Retaining the ability to intervene is not the same as intervening in everything. Miranda does not manage Andrea day-to-day, but she always has direct access to Andrea, through the line of The Book delivery.

Variants of the Dual-Assistant System

Because this design has proven effective, it has been passed down as a piece of management wisdom and has evolved into structurally similar variants in many serious enterprises. Job rotation pairs, for example: putting two people on the same role in alternating shifts, which both maintains continuity and creates internal competition. Or a higher-level version: co-CEOs.

These structures take different forms, but their internal logic is identical. They are built from structural design to ensure that no middle manager can monopolize information, personnel, or resources in any of the three dimensions.

But This Is Decidedly Not Dual-Headed Management

Middle managers instinctively resent these designs because they reduce the manager’s irreplaceability. Their objections usually sound very professional: high coordination cost, unclear division of labor, prone to internal friction, decision-making is slow.

These reasonable-sounding objections, without exception, are middle managers defending their own irreplaceability.

But the CEO also needs to hold a clear line: the dual-assistant system is not dual-headed management.

As I noted in Skip-Level Reporting, true dual-headed management is when two or more peer middle managers each hold final decision-making authority. That kind of structure destroys the front-line supervisor’s authority and drags the organization into political chaos.

The dual-assistant system is different. Decision-making authority and resource-allocation authority sit, from start to finish, with Miranda alone. Emily and Andrea are two execution nodes. Miranda is the single decision node. The two assistants also carry an informal seniority order: first assistant versus second assistant.

The dual-assistant design is: distributed execution nodes + a single concentrated decision node + asymmetric distribution of contact surface. What separates it from dual-headed management is where final decision-making authority sits — concentrated, versus dispersed. This is where the two are often confused, and getting it wrong turns an elegant design into an organizational mess.

Closing

The real method for CEOs facing middle-management failure has three pieces, and all three must be present.

Structurally, contain the damage that failure can cause.

Diagnostically, use sampling judgment to identify the specific nature of the failure.

Through intervention, make targeted corrections within what the structure allows.

Middle-management failure cannot be eliminated. Any CEO who believes “I can find a middle manager who will never fail” underestimates human nature. Any CEO who believes “if I design the right structure, everything will run itself” overestimates structure.

Next time, we’ll look at the hardest-to-detect form of organizational crisis: pseudo-optimization. When a company starts loudly preaching cost reduction and efficiency, the real intent may have nothing to do with making the company better.

Defense Secretary Pete Hegseth has framed the move with a clear slogan: “AI-first fighting force.” The official announcement contains two key verbs: “augment warfighter decision-making” and “maintain decision superiority across all domains of warfare.”

This means AI is no longer hovering at the edge of the military system. It has entered the core of the decision chain.

What This Means in Organizational Language

“Augment warfighter decision-making” is a phrase that carries far more weight than it appears. When AI enters the decision-augmentation layer, what actually happens is this:

First, the information layer is filtered by AI. Warfighters no longer see raw intelligence. They see what AI has already synthesized.

Second, the option set is narrowed by AI. The choices on the table during a decision are the ones AI recommends.

Third, the time window is compressed by AI. AI decides far faster than humans. Humans either keep up, or they get bypassed.

Fourth, the human role becomes Confirm. A system recommendation pops up on screen, and a person presses confirm. In legal terms, that act is “human decision-making.” In organizational reality, it’s “approval of an AI decision.”

Decision augmentation is not decision authority preservation. It is decision authority migration. The people accountable for decisions move from being the agents of judgment to being the people who stamp the algorithm’s judgment.

The second verb is more direct. “Maintain decision superiority across all domains of warfare” translates to: we want AI to enter every domain of warfare and every link in the decision chain. Not selected links. Not high-risk links. All links.

Read together, these two phrases describe what happened on May 1. The U.S. military, openly, lawfully, and on its own initiative, has inserted AI into the entire chain that runs from battlefield situational awareness to command-level decision-making.

My March Concerns, Now Accelerated

In March, I wrote a piece titled AI Runs Entire Kill Chains in War. In Business, It Can’t Even Run A Supply Chain. In that piece, I introduced a matrix: decision architecture (Point Decisions vs. Autonomous Chains) on one axis, application domain (Civilian vs. Military) on the other. The most dangerous quadrant I called the Unleashed Chain: Autonomous Chains + Military.

Let me re-quote one passage from that piece:

In civilian domains, humans are forced to take the blame for AI’s mistakes. But in military autonomous chains, AI becomes humanity’s perfect scapegoat. Plausible Deniability.

I named this state the Reverse Moral Crumple Zone.

What I worried about then was this: this kind of buffer zone would gradually let military AI deployment slip out of control. Once “AI malfunction” becomes a legitimate excuse, any side can probe its opponents under the cover of AI, deflect accountability with “system failure,” and stall accountability with “AI being patched.”

The DoD’s announcement deploys at a speed and scale that far exceeds what I imagined two months ago. And it’s all public, lawful, and written into an official announcement. The Reverse Moral Crumple Zone is no longer merely a theoretical risk. It now has an official institutional carrier.

Testing Against Sun’s Decision Authority Matrix

In that earlier piece, I laid out three conditions for identifying an Unleashed Chain.

Condition One: Who has the authority to halt?

In other words, when an AI decision goes wrong, do humans actually have the ability to stop it?

IL6 and IL7 themselves are data security tiers, not weapon firing chains. But they mean AI is moving into higher-classification, more combat-proximate decision environments. Once AI capabilities get embedded into target identification, situational assessment, electronic warfare, or command-and-control workflows, the human “suspension right” still exists in a physical sense (you can choose not to press confirm), but at battlefield tempo, it cannot be exercised. Whether you press confirm or not, the front line is already moving.

This “suspension right” only exists in theory across the decision chain. From situational awareness AI, to target identification AI, to command decision AI, to weapon system AI. The number of links is too high. The “suspension right” at any single node has been compressed by time and information until it is barely more than formality.

Condition Two: Can the cost of halting be absorbed?

Even if you can suspend, is the cost of doing so acceptable?

In military scenarios, this condition is almost never satisfied. To suspend an AI recommendation means giving up a few seconds of response window. In modern combat, a few seconds is the difference between life and death. So even when the system is imperfect, you tend to press confirm anyway, because the cost of not confirming is enormous and immediately visible.

Condition Three: If something goes wrong, who is on the hook?

This is the heart of the Reverse Moral Crumple Zone.

Every link in the chain can deflect, and every deflection sounds reasonable. In the end, no specific person has to bear ultimate accountability for any specific failure.

Three conditions. Military AI satisfies none of them. This is exactly what makes the Unleashed Chain so dangerous.

The Development of Military AI Cannot Be Stopped

International commentary tends to treat peace as the default and war as the aberration. Most pieces discussing AI militarization assume all players are passively swept into it, and that no one actively wants this to happen.

That’s not necessarily true.

Any tool that offers strategic advantage plus deniability will attract some, if not all, players to actively use it. AI in military decision-making offers both: advantage (decision speed, information integration, target identification) and deniability (algorithmic black boxes, distributed accountability, “system failure”). For any rational military force, this is too good to refuse.

So the question is not “why would anyone let AI into the military decision chain.” The question is “how could no one.”

Once a single major player begins to develop military AI, the prisoner’s dilemma sets in. Every player’s optimal strategy becomes “I will also push hard on military AI,” even if everyone knows the collective optimum is “no one develops it.”

That doesn’t mean every international effort to constrain military AI should be ignored. In November 2024, at the APEC summit in Lima, the U.S. and China agreed that “humans must maintain control over the decision to use nuclear weapons.”

But the same explicit language is absent for conventional military AI. The two countries used a much softer phrase for that: “develop AI technology in the military field in a prudent and responsible manner.” What that phrase means in diplomatic language requires no elaboration.

A single nuclear misfire is a civilizational-level outcome that no one can absorb. Both sides have the incentive to legislate. A conventional misstrike, however severe, is local, negotiable, and absorbable. “AI malfunction” in the conventional space is a digestible risk.

Second, the scope of conventional weapons is too broad for any agreement to define. Conventional military AI has no clear boundary. Target identification, ballistic prediction, command decisions, logistics, electronic warfare, intelligence analysis. Every one of these involves AI, and the line cannot be drawn.

On top of that, the technology stack for military AI and civilian AI is the same. You can’t ban military use, because the same algorithms can be repackaged as civilian. Any verification mechanism will fail.

So neither country has signed a conventional military AI agreement. Not because no one thought of it, but because such an agreement is structurally impossible. An agreement that cannot be defined, cannot be verified, and cannot be enforced is meaningless.

Conventional Military AI Is More Controllable, and Therefore More Dangerous

Here is the judgment I cannot avoid: World War III is just a matter of time.

Not because someone presses the nuclear button. Under an AI-first fighting force framework, there will be no button-press moment. World War III will more likely emerge from a short-term cascade of conventional military “AI malfunctions.”

Country A discovers “system failure” is a useful excuse and probes once. Country B watches A succeed and copies the approach. The two enter an “AI malfunction race”: you strike, I strike, all of it is “system issues”, without yet counting the possibility of Country C’s AI exploiting the chaos. One probe crosses the other side’s red line. One “system patch” comes too late. One response cannot be contained. Then escalation.

Military AI is an irreversible historical current. Avoiding World War III is wishful thinking. The only question is: which AI will be the Archduke Franz Ferdinand?

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There’s a lot to cover, so I’ve structured this as a three-part series. Each piece tackles an independent organizational design or talent management issue. They’re standalone, so you can read them in any order.

Part 1: Commitment Is the Rarest Form of Leadership.

Part 2: Middle-Management Failure Isn’t Solved by People. It’s Solved by Structure.

Part 3: Pseudo-Optimization, the Hardest Form of Organizational Crisis to Spot.

Onto Part 1.

Plenty of executives publicly declare they’re “all in” for their company. I don’t doubt their sincerity. But what they usually mean is endless overtime, packed travel schedules, sacrificed family time, canceled personal plans. These are real investments, and they’re visible. But they’re behavioral performance. They don’t touch what’s actually at the core of an executive at the top.

The Commitment I’m talking about isn’t the usual sense of dedication, engagement, or loyalty. It’s a form of internal stability specific to those at the top. It has two layers: I cannot have any other position, which is the exclusivity of desire; this position cannot have anyone else, which is the legitimacy of identity. The first means a person isn’t tempted by other positions. The second means a person doesn’t depend on external validation to maintain authority. Both must hold simultaneously. Together, they form the kind of extraordinarily stable, and extraordinarily dangerous, high-position core that Miranda embodies.

Many people see this kind of conviction as arrogance. The reality is different. People who genuinely make it to the top almost always carry this near-offensive level of self-certainty, even when they verbally deny it. It’s not a personality flaw. It’s the entry ticket to high-level positions. Top organizational ranks systematically filter out anyone who thinks “I might not be the best fit, but I’ll give it a try.” Those who reach Miranda’s level have, almost without exception, passed this filter.

This piece is about how high-position Commitment forms internal stability, and how it externalizes into three observable behaviors across different organizational scenarios: pruning in trade-off scenarios, showing your hand in communication scenarios, and reason stacking in announcement scenarios.

Before I get into the cases, one clarification. Analyzing Miranda is not the same as praising her. The job of an OD consultant isn’t to defend powerful people. It’s to see clearly why they function, what they function on, and what organizational costs that mode of operation leaves behind. This piece dissects a mechanism. It doesn’t endorse the mechanism or the person.

Also, Miranda is an American executive in the film, but her playbook is universal. At the strategic layer of multinational corporations, the underlying logic is the same everywhere. “Cultural difference” is mostly an excuse. The real difference is the calibre of the individual. I’ll come back to this in the second half of the piece.

Case 1: The Trade-off Scenario, Pruning

In the first film, Miranda’s authority at Runway is under threat. Jacqueline, editor-in-chief of Runway France, has been informally selected by the board as Miranda’s replacement. Through external maneuvering, Miranda arranges for Jacqueline to receive a job offer with compensation far exceeding market rate. Jacqueline can’t resist. She willingly steps out of the succession race. Miranda holds her position without firing a shot.

The brilliance of this move isn’t in “removing the rival.” It’s in “making the rival want to leave.”

The standard playbook for removing a rival is sidelining, isolating, sniping, hoarding resources, making them miserable. The flaw in these tactics is that they trigger violent retaliation. Even if you succeed, the rival leaves bitter. In a small industry, that grievance becomes a future boomerang. Miranda does the opposite. She makes Jacqueline feel like the winner. After all, being poached at premium compensation is, on paper, a major career upgrade.

The cost of this move is also clear. The lucrative position she gave Jacqueline could have gone to Nigel, Miranda’s most loyal lieutenant of many years. Miranda knows exactly how much Nigel wants this opportunity. She moves anyway.

In this decision, Miranda pruned away the legitimate feeling that “Jacqueline doesn’t deserve to make that much.” She pruned away the legitimate obligation that “Nigel deserves a reward for years of loyalty.” She even pruned away the legitimate concern that “the people around me will see through this and judge me.”

Miranda pruned all of this because each of these things conflicts with the single core objective: holding the editor-in-chief position.

The truly mature high-position leader isn’t someone who doesn’t know what they’re sacrificing. It’s someone who knows, and still brings down the knife.

Case 2: The Communication Scenario, Showing Your Hand

In the popular Chinese workplace reading, Miranda is the archetypal “gaslighting boss.” But she actually isn’t.

The signature of gaslighting is ambiguity and drift. Standards stay unclear, leaving you perpetually uncertain whether you’re good enough. Standards shift, so what was acceptable yesterday isn’t today. Criticism is non-falsifiable, and any pushback gets reframed as “I’m telling you this for your own good.” Ultimately, the manager controls through guilt, self-doubt, and fear.

Miranda is not this. She speaks bluntly, sometimes offensively, but every statement is a falsifiable fact. That said, not being a gaslighter doesn’t make her ideal management. High-transparency, high-standard, high-pressure environments can still hurt people, and may not be sustainable. I’m only distinguishing between two different management techniques here.

When Andrea fails to book Miranda a flight back to New York during a hurricane, Miranda coldly tells her, “I’m disappointed in you.” She means it literally. She missed her daughter’s Rachmaninoff recital, and she truly is disappointed. What she’s saying to Andrea is performance feedback, not a personal attack.

Another scene is even more direct. Miranda gives Andrea a few hours to retrieve the unpublished next Harry Potter manuscript, as a chance to redeem herself after a previous mishap. She makes it explicit: if you can’t get the manuscript on time, don’t bother coming back to the office. This is a do-or-die test, with completely defined standards and consequences. Andrea has zero ambiguity to navigate as she heads out. She doesn’t have to guess what Miranda is thinking, because Miranda has put everything on the table.

The second film has a similar moment. Twenty years later, Andrea is brought back to Runway via a skip-level CEO hire. Miranda’s first move is to tell her openly: “I’m going to make you fail.” Her communication logic hasn’t shifted in twenty years. Say what you mean. Put the standard on the table. Write down the consequence.

This is the core showing up in the communication scenario. A leader who genuinely believes “I’m the right person for this position” is paradoxically the one most willing to speak the truth, because the truth doesn’t threaten her authority. Her authority comes from the core, not from her subordinates’ guesswork.

Gaslighting is the tool of low-tier managers. When a manager isn’t sure whether he’s the right fit, his only option is ambiguity, drift, and emotional manipulation to maintain authority. He can’t let his subordinates see clearly, because once they see clearly, they might realize he can’t actually hold the position. Ambiguity and drift are his shield. Miranda-style managers obviously don’t need this shield.

Case 3: The Announcement Scenario, Reason Stacking

In the back half of the first film, Miranda abruptly decides to bring second assistant Andrea to Paris instead of first assistant Emily, who was originally slated to go.

The film cleverly packages this swap as a chain of plausible coincidences. Emily catches a serious cold, blanks on a key visitor’s name at an important charity gala, and Andrea steps in to cover. So Miranda decides to switch.

But viewed through the lens of a high-stakes personnel decision, the gala incident was just the last straw. Miranda’s internal valuation of the two assistants had likely already shifted some time ago. Emily is a known, stable executor, while Andrea is a still-rising variable. Emily’s slip didn’t create the judgment. It just gave a pre-existing judgment a publicly executable trigger.

This is a universally applicable management move. I call it Reason Stacking. The actual decision is made quietly before any triggering event. The so-called triggering event simply provides a legitimate attribution. The displaced person and observers can only protest or grieve at the surface attribution, and the protest goes nowhere, because the real decision logic was never at that layer.

One important note: showing your hand and reason stacking are not contradictory. Miranda’s transparency happens at the execution layer, where the task, the standard, and the consequence are all on the table. But that doesn’t mean she discloses every strategic judgment. A high-position leader can be highly transparent on execution while remaining selectively transparent on the attribution of decisions.

Emily’s real tragedy is that she fully bought into this softened attribution. She broke down emotionally, fixating on the idea that if she hadn’t fallen sick, hadn’t slipped up, she wouldn’t have been replaced. That’s exactly where this mechanism functions most successfully.

Of the three cases, reason stacking is the most counterintuitive and the hardest to see through, because it operates inside the decision-maker’s mind. Pruning is observable from outside. You can see who Miranda removed, who she sacrificed. Showing your hand is observable from outside. You can hear how Miranda speaks, watch how she assigns tasks. But reason stacking is invisible. It’s the inner activity of the decision-maker.

And the reason this attribution-packaging exists is precisely because Miranda has a core. A leader who genuinely believes “I’m the right person for this position” makes every decision from the judgment of the core, not as a reaction to triggering events. But she can’t lay this bare to the organization at every moment. So she waits for a surface attribution to arrive, and packages the core’s judgment as “there’s a reason for it.”

People without a core can’t do Miranda-style reason stacking. They’ll find reasons and do packaging too, of course, but it’s defensive. It covers hesitation, fear, or blame avoidance. Their decisions were always pushed along by triggering events anyway. They react when a subordinate makes a mistake, decide when a crisis emerges, act when external pressure mounts. Miranda-style reason stacking is different. It’s not “I have no judgment, so I’m scrambling for a reason.” It’s “the judgment was already complete, and I’m waiting for an attribution the organization can accept.”

Three Cases, One Core

Pruning, showing your hand, reason stacking. These three behaviors look like they belong to different scenarios, but they all share a single core.

She prunes because the core is clear enough that secondary objectives don’t tempt her. She shows her hand because the core is stable enough that she doesn’t need ambiguity to maintain authority. She does reason stacking because the core is independent enough from triggering events that she has to wait for an attribution the organization can accept.

Many managers try to copy Miranda’s moves and end up imitating her badly, because the core was never built. A manager who doesn’t genuinely believe “I’m the right person for this position, and this position is the only one I want” will see every move distort in execution.

There Is No Cultural Difference. Only Tier Difference.

A common reaction, especially from Chinese readers, when reading these cases is: “This is American workplace stuff. It’s different here.”

That reaction is wrong.

At the strategic layer of multinational corporations, organizational and personnel maneuvering is universal. Anyone well-educated who has reached that level, whether in New York, London, Shanghai, or Singapore, understands this language. Pruning is pruning. Reason stacking is reason stacking. Across cultures, they look almost identical.

“Cultural difference” is just a convenient excuse. When a Chinese MNC employee says “this isn’t how we do it here,” it usually means one of two things. Either they haven’t seen that the high-position people around them are using the exact same tools, just packaged differently. Or they’re at a layer that hasn’t yet been exposed to genuinely strategic organizational and personnel decisions.

I’ll add a sentence that may make some readers uncomfortable. At the execution layer of MNCs in China, people often don’t reach Miranda’s tier. But this isn’t a cultural problem. It’s a tier-filtering problem. MNCs in China have a limited ceiling, decision-making power sits remotely at headquarters, and the room for individual maneuvering is insufficient.

The Chinese individuals with the most strategic vision, the deepest Commitment, and the sharpest grasp of objective exclusivity and reason stacking are mostly not at MNCs. They use the exact same playbook as Miranda, just with different cultural packaging.

Cultural difference is the excuse. Tier difference is the truth.

Closing

What’s hardest to replicate about a Miranda-style manager isn’t vision, charisma, or decisiveness. It’s a high-position Commitment that goes beyond mere belief: I’m the right person for this position, and this position is the only one I want.

This high-position Commitment makes trade-offs feel natural.

This high-position Commitment makes communication stable.

This high-position Commitment makes decisions independent.

Commitment can’t be learned. It’s something you either have or you don’t.

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This May Day, a pun has been stuck in my head: LAIbor (Labor + AI).

When knowledge labor is being irreversibly displaced by artificial intelligence, how exactly are we, the white-collar workers who spend our days shuttling between slide decks, workflows, and meetings, supposed to celebrate a holiday that belongs to laborers?

Let us turn the clock back exactly 140 years.

The Perfect Gambit of 1886: Trading “Time” for “Leverage”

On May 1st, 1886, workers in multiple American industrial cities moved simultaneously. Around eighty thousand workers took to the streets in Chicago alone, with hundreds of thousands more across the country joining the eight-hour workday movement. What they were fighting for became one of the most quoted slogans in labor history: “Eight hours for work, eight hours for rest, eight hours for what you will.”

Days later, the Haymarket affair exacted a bloody price. Strictly speaking, 1886 did not immediately deliver the eight-hour workday as we know it today. The real institutional victory was the cumulative result of decades of labor movements, industry negotiations, and legal reforms that followed.

But as an organization design consultant, once you see through the labor supply-and-demand structure of the time, you realize this was not merely a fight for more rest. It was an extraordinarily shrewd macroeconomic play.

America was in the midst of the Gilded Age industrial expansion. Mechanization and the factory system had lowered the skill barriers for traditional craftsmen across many production processes. Workshops no longer needed artisans who had fully mastered a trade. They needed cheap labor capable of completing partial tasks along a production line. Outside the factory gates stood an army of unemployed workers ready to step in at any moment. In the face of absolute labor surplus, individual workers had virtually no bargaining power.

So the pioneers found their breakthrough: time.

If the hours each worker could be deployed were compressed, and the production system still had to keep running, employers would have no choice but to hire more people, absorbing a significant portion of the surplus labor force back into the system. As unemployment pressure dropped, workers as a collective would have a chance to reclaim their leverage at the negotiating table.

They astutely recognized the powerful alignment between individual interest and collective interest, and used “shutting down the physical production line” as their ultimate weapon to force industrial capitalism to answer a fundamental question:

Are people laborers, or are they fuel?

No Copying the Playbook: Why Knowledge Workers Cannot Replicate This Today

140 years later, history has come full circle.

We face the same “skill downgrade.” Only this time, it is knowledge work being downgraded. AI is flattening the barriers to writing code, drafting copy, building reports, and even data analysis. Roles that once required three years of experience to fill competently can now be handled, at least passably, by a junior employee who knows their way around a prompt.

Knowledge workers are facing our own “labor surplus” crisis.

So can we, like our predecessors, form alliances and refuse to cede our positions to AI in order to force a compromise?

The answer is brutal: extremely difficult. This path is no longer as directly effective as it was in the Gilded Age.

The strikes of 1886 created enormous pressure because the assembly line stopped when workers stopped. But in the LAIbor era, if you decide to stop typing, your employer does not even need to wait for another person to sit down at your desk. They just need to reroute part of the workflow to large language models and automation tools.

The “strike vacuum” you use as a threat is precisely the void AI is best at filling.

What is more, knowledge workers’ interests are no longer unified. A senior consultant whose efficiency has doubled thanks to AI and a junior consultant who has lost entry-level opportunities because of AI are unlikely to sit in the same trench. AI has even, in the short term, significantly reduced the competitive threat that junior consultants pose to senior ones. When our collective interest base has been thoroughly fragmented, the traditional “workers’ alliance” loses much of its real-world foundation.

The New Guild of the LAIbor Era: Building Moats for the Elite

If traditional alliances are no longer effective, are knowledge workers truly at the mercy of algorithms?

No. Alliances still exist. They are simply undergoing a brutally Darwinian upgrade.

The alliance of the future is no longer “the weak huddling together for warmth” to protect low-level repetitive work. It is more likely to become “the collective lock-in of critical resources.” It increasingly resembles a new kind of guild.

The “digital guild” of copyright and data: consider the 2023 Hollywood writers’ and actors’ strike. Its lesson was not that “humans refuse to write or perform.” It was that they converted strike pressure into new rights boundaries: AI cannot simply devour credits, compensation, likeness, voice, and control over historical works. The real leverage was not “I quit,” but “you cannot bypass me to define my labor, replicate my labor, or train my replacement.”

The “gatekeeper network” of accountability and ethics: AI cannot go to prison. AI does not understand human nuance. In high-stakes domains like finance, healthcare, strategic decision-making, and organization design, the top knowledge workers of the future will form alliances based on professional integrity and the authority of final interpretation. AI can generate countless proposals, but the person who signs off on the layoff list, the financial report, or the medical diagnosis must still be a trained, certified human who is willing to bear the consequences.

In the industrial age, manual laborers fought for working hours.

In the AI age, knowledge workers must fight for Decision Rights: Who has the authority to decide whether AI can be used? Who has the authority to decide whether data can be used for training? Who has the authority to review, veto, interpret, and sign? Who bears accountability when things go wrong?

These questions are the new eight-hour workday of the LAIbor era.

Closing: Become the Arranger of Meaning

On this LAIbor Day, we must stay clear-eyed: competing with AI on efficiency is futile, and attempting to simply replicate the working-hours struggle of the industrial age to save our jobs is fighting the last war.

The only thing we can do is proactively move toward the domains where “AI cannot bear the consequences” and where “human friction” is irreplaceable, and build new barriers there. Your bargaining power no longer depends on how fast you execute. It depends on how deeply you judge, how clearly you explain, and how much accountability you are willing to bear.

To the pioneers of 1886, who proved that people are not fuel.

And to all of us in 2026. May we prove, in the jungle of AI, that people are more than code.

Happy LAIbor Day!

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The result was beyond absurd. This was not a case of “lost in translation.” It was a complete inversion. A blunt curse had been transformed into a warm blessing. Staring at the same comment on the same screen, Chinese and English readers were being split into two diametrically opposed information realities. And there was not a single indication anywhere on the page to warn the English reader: what you are seeing is not what the original says.

There Is a Mole in Your Information Chain

I introduced a concept in a previous article that I call the “AI Mole.” The most covert way AI embeds itself is not by conspicuously replacing your job. It is by slipping undetected into the middle of your information chain, making a critical judgment call on your behalf without you ever knowing it happened.

In this case, that innocuous “Translate” button is a perfectly disguised AI Mole.

You assumed it was a neutral tool, a faithful dictionary that gives you what you ask for. But it has long ceased to be a dictionary. It has become a decision node. It inserted itself between you and the original information, made an unauthorized conversion, and got it spectacularly wrong. The most dangerous part is that you have no way of realizing it is wrong, because the English output reads flawlessly: grammatically correct, logically coherent, perfectly natural. It just happens to mean the exact opposite of the original.

This is what should send a chill down your spine: AI errors are no longer obvious gibberish on a screen. They are lies wrapped in flawless packaging.

This Is Not Censorship. This Is Far Worse.

Some might shrug: platforms processing content is standard practice, is it not?

But censorship and manipulation are two fundamentally different things.

Censorship says: “I find this inappropriate, so I am removing it.” When you see a notice saying “this content has been removed” or your search returns nothing, you at least know something was taken away. You retain a basic defense mechanism: you are aware that information is missing. In other words, you know that you do not know.

Manipulation says: “I have altered this content, and I will make you believe you are reading the original.” You have no idea anything has been changed. You do not know that what you received is not the author’s intent. The thought “I might be misled” never even crosses your mind. You are trapped in the blind spot of not knowing that you do not know.

Censorship strips you of information. Manipulation strips you of judgment.

Which is more dangerous needs no elaboration.

The Inability to Determine the Cause Is Precisely the Problem

How was this bizarre translation produced? A fundamental technical defect? A systematic bias in how the AI model handles certain contexts? Or did someone define a strategy behind the scenes, with AI faithfully executing it?

We do not know. And there is almost no way to verify.

But that is precisely the core of the problem.

When AI becomes the “black-box executor” in an information chain, whoever is behind it gains a perfect cloak of invisibility. If this was a deliberate strategy, discovery can always be met with a casual deflection to the algorithm: “This was auto-generated by AI. We will continue optimizing the model.” And if it truly was a system glitch, the platform may not have sufficient motivation to fix it.

I call this structure the “Reverse Moral Crumple Zone.”

I discussed the concept of Moral Crumple Zone in a previous article: when a system fails, the human takes the blame. The classic scenario is an autonomous vehicle crash where accountability lands on the safety operator. In that scenario, AI is the actual decision-maker. The human is the scapegoat.

The Reverse Moral Crumple Zone works in exactly the opposite direction: humans set the rules, and the system takes the blame. Decision-makers define the strategy behind the curtain. AI executes it on the front stage. When caught, the excuses are always ready: “systematic bias,” “algorithmic limitations,” or “we are working on optimization.” Here, the human is the one pulling the strings. AI is the perfect scapegoat.

Two seemingly opposite directions, but the underlying structure is identical: accountability bounces endlessly between humans and systems, never landing on the party that actually made the decision.

Human-in-the-Loop? First You Need to Know There Is a Loop.

Every mainstream AI governance framework today, whether it flies the banner of “Responsible AI” or takes the form of internal corporate AI compliance policies, is built squarely on one core assumption: humans can oversee AI output.

The Human-in-the-loop model argues that humans approve AI output at every step before it is released.

The Human-on-the-loop model takes a step back: humans do not need to approve every step, but they monitor from the side and intervene when anomalies appear.

The “Translate button” case shatters both models simultaneously.

Human-in-the-loop fails because no approval step exists. You tap “Translate,” and AI feeds you the result directly. No human verifies whether the translation is accurate.

Human-on-the-loop fails equally because you cannot even detect the anomaly. You do not read Chinese. The English in front of you is grammatically flawless, logically coherent, and reads naturally. It just happens to mean the opposite of the original. You receive no signal whatsoever that human intervention is needed.

The real blind spot is not whether humans are in the loop. It is whether humans even know there is a loop that requires their presence.

When AI’s acts of manipulation are themselves invisible, when AI’s erroneous output looks identical to correct output, every governance framework built on the assumption that “humans can oversee AI” is reduced to an exercise on paper.

How Many “Translate Buttons” Are Hiding in Your Organization?

You think a Translate button flipping a social media comment is harmless? Transfer that same underlying mechanism into the “AI-powered automated processes” your enterprise is actively pursuing, and think again.

Suppose your company deploys an AI meeting system. It listens in automatically, generates minutes, extracts action items, and emails them to all relevant parties. During a critical review meeting to decide whether a major project should go live, the technical lead issues an explicit warning: “This underlying architecture has a fatal flaw. Launching it will most likely cause a collapse.”

A brutally blunt dissenting opinion. Just like that “curse” in the comment section. But the AI, while capturing and distilling this statement, silently “polishes” it into a mild action item: “The technical team recommends continued optimization of architecture performance prior to launch.”

No human review. The AI follows its preset workflow and distributes the sanitized minutes directly to every executive. Leadership sees nothing but green lights, approves the launch, and disaster follows.

A project worth hundreds of millions fails. Strategy derails. None of it reversible. Yet every party can deflect accountability perfectly. This is precisely the core argument of what I call “Sun’s Decision Authority Matrix,” introduced in a previous article: once an AI-driven autonomous chain is running, accountability dilutes along the chain until every party can say, “I am not the one accountable.”

A single, unremarkable Translate button turned a curse into a blessing in broad daylight, and you had no idea it happened. If that same structure is now embedded in your enterprise, are you truly ready to let it run unsupervised?

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Whether or not this was the sole trigger for his removal, the episode raises a question that every large organization eventually confronts: is skip-level communication a breach of order, or a repair mechanism for when order fails?

In conventional corporate thinking, unauthorized skip-level contact is treated as a cardinal sin. But as an organization design consultant, I have found the opposite to be equally true: large organizations cannot function without some form of institutionalized skip-level mechanism to correct the information distortion and talent burial that hierarchical reporting inevitably produces.

Clearing the Concept: From Emotional Overreach to Institutional Bypass

Before we go further, a boundary needs to be drawn. This article is not an endorsement of employees going over their manager’s head to grab resources, nor of emotionally charged escalations.

In practice, “skip-level” covers several very different behaviors:

• Unauthorized overreach: bypassing your boss to get a senior leader to approve routine operational decisions.

• Institutionalized skip-level communication: scheduled, bounded conversations between senior leaders and frontline staff.

• Matrix governance: dotted-line reporting within matrix structures.

• Talent sponsorship: formal links between high-potential employees and senior executives.

This article is about the latter three. When formal hierarchy cannot transmit information accurately, cannot identify talent fairly, and cannot counterbalance distortions in middle-management power, how does an organization build the necessary bypass through deliberate design?

Piercing the Information Funnel: Why Large Organizations Need a Bypass

Every time information climbs one level up the reporting ladder, it goes through a round of filtering and polishing. This is not because middle managers are dishonest. It is because performance pressure, risk aversion, and limited line of sight make it inevitable. You could simply call it human nature. If senior leaders rely solely on a single vertical reporting line, what they end up hearing is very likely a version of reality that bears little resemblance to the facts.

An institutionalized skip-level mechanism is, at its core, a cross-verification channel designed to counter distortion, allowing top decision-makers to bypass the filters and sense what is actually happening on the ground.

The Hidden Logic of the Matrix: What Dotted Lines Really Do

Zoom out to multinational corporations, and skip-level communication evolves into a system-level check-and-balance design.

To guard against systemic risk, large multinationals typically adopt matrix structures: regional functional teams report with a solid line to local business leaders, while maintaining a dotted-line report to their corresponding function at global headquarters. Even within a single regional department, dedicated teams may maintain direct links with their counterparts at HQ. In HR, for example, regional Centers of Excellence (CoE) frequently coordinate with the global HR CoE.

Most people interpret the dotted line as a “collaboration relationship.” From an organization design perspective, it is actually headquarters’ bypass sensor line. The value of the matrix lies not only in aligning resources, but in countering information monopoly, preventing regional fiefdoms, and checking single-line distortion. Introducing a dotted-line report adds a parallel verification channel outside the local manager’s evaluation system, trading micro-level structural tension for macro-level architectural stability.

Beyond the matrix dotted line, senior leadership frequently deploys another, more covert information bypass: the PMO (Project Management Office). On the surface, the PMO’s job is to drive one cross-functional project after another to completion. But from an information governance perspective, every project is a legitimate, bounded exercise in cross-level information collection. Projects require cross-departmental collaboration, regular progress reports to senior leadership, and the surfacing of resource bottlenecks and execution roadblocks. In this process, what senior leaders receive is far more than project status. It is an organizational situation map that bypasses the normal reporting line. Many PMO practitioners may not even realize that one of the most critical functions behind their never-ending stream of projects is ensuring that senior leadership’s information pipeline is not monopolized by middle management. This also means that even when a project fails to deliver its stated objectives, it may still be considered a success in the eyes of senior leadership.

The Hidden Talent Shield: Counterbalancing Evaluation Bias

At the micro level of manager-subordinate dynamics, institutionalized skip-level mechanisms serve yet another critical function: talent retention.

Under performance pressure and scarce promotion opportunities, the interests of a manager and their direct reports are not always naturally aligned. When a line manager holds outsized control over span, evaluation authority, and resource allocation, talent identification becomes highly susceptible to distortion by personal insecurity or preference. Within a single reporting line, a high-potential employee who runs afoul of their direct manager’s defensiveness often has no option but to resign.

It is precisely to preempt this that organization design consultants typically go one step beyond finalizing the org structure, proactively proposing talent development programs that build in structured one-on-one conversations between high-potential employees and senior executives as a core component. Translated into the language of organizational politics, this is not merely a development mechanism. It is a legitimately embedded Trojan horse: a talent protection mechanism.

It gives talent visibility. More importantly, it creates an invisible verification deterrent: when line managers know that senior leaders have a legitimate channel to engage directly with their subordinates, they are compelled to be fairer and more transparent in day-to-day management and performance evaluations.

Guardrails: Where Skip-Level Design Must Stop

Undesigned skip-level activity destroys order. Designed skip-level activity can restore it. To prevent a “bypass” from becoming a “short circuit,” effective skip-level mechanisms require strict boundary guardrails:

• Purpose boundary: skip-level mechanisms may only be used for macro-level information verification, major risk early warning, and talent calibration. They must never become a shortcut for subordinates to bypass their manager for routine resource negotiations or approvals.

• Process boundary: there must be clearly defined contexts and frequency constraints. Quarterly closed-door reviews, formal skip-level interviews, standing committee briefings. Not ad-hoc boundary-crossing at will.

• Feedback boundary (the most critical of all): after obtaining information through a skip-level channel, senior leaders must never issue operational directives directly to subordinates over the head of the line manager. If senior leaders start giving orders through the bypass, the organization descends into chaotic dual command, and frontline managers’ authority is destroyed.

Closing: Embracing Structured “Controlled Disorder”

Corporate management has never been a black-and-white blueprint exercise. The value of good organization design is not in trying to eliminate the complexity of human nature, but in using precise structural arrangements to constrain it.

Skip-level reporting is not a cure-all. But when “going over someone’s head” is stripped of its emotional charge and institutionalized through matrix dotted lines, talent programs, and PMO structures, it is no longer a disruption to managerial order. It is how large organizations find the real balance between discipline and agility.

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The concept reportedly came from South Korea. Koreans consume nearly 80 servings of instant noodles per person per year, and 24-hour unmanned noodle shops are a familiar sight on the streets of Seoul. The format landed in Shanghai in late 2024, sparking a city-wide wave of curiosity. Social media was flooded with tags like “K-drama late-night canteen” and “Seoul street food experience.”

I visited one of these shops in Shanghai recently, specifically to inspect the food preparation area.

The toppings sat in the refrigerator, dish by dish, each with a lid that any customer could lift at any time. Shared tongs were stuck in beside them. Anyone could grab them. The shop had three security cameras, but only one was angled toward the food area. I estimated that if two or three people stood in front of the refrigerator at the same time, they would create a perfect physical obstruction. The camera would capture essentially nothing of what was happening at the food station.

This is not a management failure at one particular shop. This is a structural defect baked into the business model itself.

One Bowl of Noodles, Four Suspects

Suppose you eat a bowl of noodles loaded with toppings at one of these shops, and you get food poisoning afterward.

Was the cheese not fresh? Did you not cook it long enough? Did the previous customer contaminate the shared tongs without cleaning them? Or were the unsealed disposable bowls and chopsticks already compromised?

Four possible causes, each pointing to a different party in the process: the supplier, you, another customer, or the equipment operator. Once something goes wrong, there is virtually no way to determine which link in the chain failed.

In a traditional staffed restaurant, this ambiguity does not exist. The entire process, from raw ingredients to kitchen to your plate, falls within the restaurant’s control. If something goes wrong, the restaurant is the accountable party, full stop. Staffed restaurants certainly have food safety problems too. Filthy kitchens are hardly rare. But the critical difference is this: when something goes wrong in a staffed system, it can be traced, accountability can be assigned, and the system can improve.

Whether an accountability chain exists and is traceable is a completely different question from whether the people on that chain are doing their jobs properly. The first is a system architecture problem. The second is a management problem. The fatal flaw of the unmanned noodle shop lies in the first: its accountability chain is physically severed at the point of service.

Food delivery at least has tamper-evident seals. An intact seal means the problem originated at the restaurant. A broken seal means something happened during delivery. It is imperfect, but it provides a physical demarcation point that can partition accountability. The unmanned noodle shop has no such demarcation. None at all. From refrigerator to tongs to cooking machine to your mouth, the entire process is a continuous, unmonitored, multi-user open flow. Because no one is watching the stage where real-time judgment and correction matter most, even after-the-fact investigation is nearly impossible.

I have written previously about what I call the “Sun’s Decision Authority Matrix,” one of whose core arguments is this: when an AI-driven autonomous chain is running, accountability dilutes along the chain until every party can say “it’s not my problem.” The accountability structure of the unmanned noodle shop is perfectly isomorphic. When something goes wrong, every party can point the finger at someone else. On-site, no single party’s accountability covers the complete chain from ingredient to ingestion.

And this is before we even consider the low-probability but catastrophic risk of deliberate tampering. What should truly alarm us is not that such incidents happen every day, but that the system was designed from the outset without any front-end intervention mechanism.

The Bar, the Delivery Rider, and the Vending Machine

Consider this scenario: you order a drink at a bar, step away to use the restroom, and come back. Do you drink it?

Anyone with basic safety awareness would not. At a bar, “never leave your drink unattended” is practically common sense. You have no way of knowing what might have been added during the minutes you were gone.

Now think about the topping station at an unmanned noodle shop. The loose cheese, vegetables, and eggs sit exposed from opening to closing, and many of these shops never close at all. Any customer, or even a non-paying passerby, could sneeze over the toppings, handle the shared tongs with unwashed hands, or do anything else while no one is watching.

Why are we so vigilant at bars but so trusting at noodle shops? On what basis do we assume that strangers in an unmanned noodle shop are inherently more trustworthy than strangers at a bar?

Then recall the recent controversy over delivery riders bringing food orders into restrooms. A rider, desperate to use the bathroom, carried the sealed food package inside with him, and customers found it revolting. But if we compare the two situations calmly, the food safety risk in that scenario is actually far lower than in an unmanned noodle shop. The food was sealed, stayed within the rider’s line of sight, and accountability could be clearly traced to a person. Even if the rider left the package outside the restroom door to spare the customer’s feelings, creating a brief gap in the accountability chain, the exposure was short, the packaging intact, and the customer could at least take some comfort in that. At the unmanned noodle shop, the uncovered food is fully and continuously exposed to anyone’s reach, for hours on end.

Finally, the vending machine. I know someone will argue: vending machines are unmanned too. Why are they fine?

Because a vending machine’s products are factory-sealed and physically isolated from the point of manufacture to the point of purchase. Before the product drops into the pickup slot, no external party can physically interfere with its quality. When something goes wrong, the accountability chain is crystal clear.

Line up these scenarios on a spectrum: vending machine (fully sealed, no human judgment required) → food delivery (tamper-evident seals, a rider watching, but with brief blind spots) → bar (the customer knows to stay alert) → unmanned noodle shop.

The noodle shop sits at the worst end of this spectrum, worse even than the bar. Because at a bar, at least you know to stay vigilant. The noodle shop wraps itself in the warm aesthetic of “K-drama vibes” and “cozy late-night canteen,” lulling you into dropping your guard entirely.

Someone might push back: if a staff member were standing in the shop, would they really be watching the food station? Maybe they would just be scrolling their phone. But that is beside the point. A person on-site, even one who is not paying close attention, provides three things that no unmanned system can: visual deterrence against customer misconduct, real-time detection of anomalies, and the ability to intervene within seconds of spotting a problem.

They do not need to watch like a hawk. They just need to be there. A security camera cannot deliver any of these three.

The Price of Convenience

I will not deny it: unmanned noodle shops tap into a genuinely distinctive urban need. A full wall of novelty choices, the absolute “introvert-friendly” absence of staff watching you, and the light participatory thrill of adding your own toppings and cooking your own bowl. This specific combination of emotional payoffs is something a FamilyMart, a Lawson, or a late-night street-side fried rice stall simply cannot offer.

It addresses a real niche. It is a real business.

But as an organization design observer, I need to look through the surface of this transaction. You think you are spending twenty-something yuan for late-night convenience and a taste of K-drama atmosphere. But what the system has quietly priced in is this: you are simultaneously waiving the establishment’s obligation to maintain on-site quality control.

That is the most hidden trade-off in this entire business model. “On-site accountability” has been swapped for “Absolute introvert-friendliness.”

In exchange for the convenience of not being bothered by staff, you have transferred food safety accountability from a system where someone is physically present and watching, to a blind zone with no physical safeguards where the only recourse is after-the-fact accountability. And as we have already seen, in an open environment where evidence is virtually impossible to preserve, after-the-fact accountability is a dead end in practice.

This is not a fair trade. And this hidden cost: does the consumer truly understand it at the moment they push open the door?

The Bigger Picture

The unmanned noodle shop is not an isolated phenomenon. Just a few years ago, the dominant narrative in global business innovation was the “sharing economy.” Assets are scarce, labor is abundant, so put idle assets to work serving more people.

Now the wind has shifted abruptly. Unmanned noodle shops, unmanned game rooms, unmanned dessert bars are springing up across city streets. The underlying logic has flipped to: “People are too expensive. Remove them.” And we have already seen what happens to the accountability chain when you rip people out of it.

I am not against automation itself. Autonomous driving has genuine safety and strategic value. Unmanned ports address high-risk, high-intensity, round-the-clock operational demands. Smart agriculture tackles labor shortages and food security. These forms of automation replace work that humans are unwilling to do, physically unable to do, or unable to do well enough. But the unmanned noodle shop replaces a shop attendant who earns a few thousand yuan a month, a role plenty of people are willing to fill. This kind of consumer-sector “save on labor” play should not be conflating itself with the hard-core innovations that genuinely matter for national infrastructure and public welfare.

(I will write a separate piece on the capital narrative patterns behind the unmanned economy.)

Back to That Bowl of Noodles

I am not against technological progress, nor against business model innovation. But when the core logic of a so-called “innovation” is simply removing a human from a process, and after removing them, the on-site judgment and real-time correction they provided is not replaced by any equivalent systemic safeguard, that is not innovation. That is operating without a safety net.

A vending machine can run unmanned because its process never required human judgment in the first place. Products stay physically sealed from start to finish. That is clean automation.

The process inside an unmanned noodle shop is full of moments that demand judgment: Is the ingredient fresh? Is there cross-contamination? Has the food been cooked long enough? Is anything abnormal happening on-site? But these judgment calls have not been replaced by better mechanisms. They have simply been eliminated. This is pseudo-automation that offloads safety costs onto consumers and statistical probability.

A healthy business system, staffed or unstaffed, should be able to answer one question clearly at every critical node: “If something goes wrong here, who is on-site to catch it?”

The unmanned noodle shop cannot answer that question. And this unanswerable gap in the system is being gift-wrapped in the cozy aesthetics of “late-night canteen” and “K-drama vibes,” and sold at a premium to every customer who walks through the door.

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A Record-Breaking Fine and a Disturbing Detail

On April 17, 2026, China’s State Administration for Market Regulation (SAMR) imposed administrative penalties on seven major e-commerce platforms in the “ghost takeout” case, ordering rectification, suspending new cake-shop listings for three to nine months, and levying a combined RMB 3.597 billion (approximately $497 million) in fines and confiscations — the largest penalty since the Food Safety Law took effect. Among the seven, Pinduoduo — the Chinese e-commerce giant whose international arm, Temu, has rapidly expanded across the US and Europe — received the largest penalty at RMB 1.52193 billion ($210 million) through its domestic operating entity, Shanghai Xunmeng Information Technology.

Regulators said they uncovered more than 3.6 million transferred cake orders and 67,604 “ghost shops.” In Pinduoduo’s case, regulators found 9,463 decorated-cake merchants whose qualifications had not been properly vetted: 4,522 had not uploaded food business permits, while 4,941 had permits whose scope did not cover decorated cakes. Authorities also said that, during the investigation, platform employees used “violent” and “soft confrontation” tactics to obstruct enforcement; a later People’s Daily report said one enforcement officer suffered a fractured left index finger and a right-ankle soft-tissue injury after an employee deliberately slammed a door.

As someone who has spent years working in organizational development — designing structures, advising on layoffs, and sitting across the table from employees at the worst moments of their careers — I didn’t see this as just another corporate scandal. I saw a textbook case of organizational design failure.

The question isn’t “what kind of employees would do this?” The question is: what kind of organization produces this behavior systematically?

Three Mechanisms That Turn Employees Into Liability Shields

This wasn’t a rogue employee having a bad day. When frontline workers physically assault government regulators to protect their employer’s data, it almost always reflects three overlapping organizational mechanics:

First, the organization elevates targets above boundaries. When a company’s operating logic is “results at all costs,” compliance stops being a floor and starts becoming a process. Processes can be delayed. And once they become delays, they become obstacles to override. This is how legal and ethical lines gradually fade from red to gray to invisible.

Second, the organization codes obedience as loyalty and dissent as disloyalty. Many companies claim to have open-door policies. But the actual signal transmitted through promotions, assignments, and who gets protected is: whoever doesn’t charge forward in a critical moment isn’t a team player. Whoever pushes back is a liability. Over time, employees stop learning to judge right from wrong. They learn to judge which way the wind is blowing.

Third, the organization leverages individuals before the crisis and discards them after. The system stays invisible; the individual absorbs the blast. During normal operations, it’s “we’re a family.” After an incident, it becomes “this was the action of specific individuals.” According to Chinese media reports, PDD dismissed several employees from its government relations team after the confrontation became public — the very people who had been on the front line.

If you’ve studied the Theranos case, you’ll recognize the pattern. Elizabeth Holmes built a culture where questioning the technology was treated as insufficient belief in the mission. Employees who raised safety concerns were isolated, sidelined, or terminated. The organization rewarded blind commitment and punished professional judgment — until the whole thing collapsed, and the frontline employees who had carried out questionable directives were left holding the bag.

But Individuals Still Have a Choice

Having said all that, I have no sympathy for the employees who chose violence.

There’s a line in the James Bond franchise that I think about often: “A license to kill is also a license not to kill.”

The same logic applies in any workplace. An organization can pressure you. A boss can imply what they want. Peers can create momentum that feels impossible to resist. But you are still an adult. You still have a choice.

You might fear losing your job. You might fear being labeled as disloyal. You might make a poor judgment call in the heat of the moment. But the moment you cross a legal line — physically blocking regulators, destroying evidence, assaulting an officer — that stops being “just following orders” and becomes your decision.

Mature organizational design doesn’t mean training employees to obey more efficiently. It means building a protected path for refusing illegal directives — an escalation mechanism where saying “no” is safe, not career suicide. If the only way to prove loyalty is to charge blindly forward, and holding the line means bearing consequences, then the company’s most dangerous feature isn’t its hustle culture. It’s that it has turned legal risk into a loyalty test for its most vulnerable employees.

The Mission Illusion: Demanding Religious Devotion on a Commercial Contract

Many private companies’ deepest problem isn’t that they pursue profit. Profit is the nature of commercial enterprise. There’s nothing to apologize for.

The problem is that many companies insist on wrapping profit-seeking in a quasi-sacred narrative: talk of mission, talk of family, talk of “changing the world together” — and then, under the cover of that rhetoric, demand from employees a level of commitment that exceeds their contract, exceeds their compensation, and sometimes exceeds the law.

This is a very specific organizational pathology: using a commercial compensation structure to extract devotion that belongs in a mission-driven institution.

Mission-driven organizations — research labs, public health agencies, conservation groups — can legitimately ask for extraordinary commitment. But that works because their selection mechanisms, reward structures, professional identity systems, and risk-sharing frameworks are all calibrated to support it. People accept lower pay at a research institution because they receive professional recognition, intellectual freedom, career identity, and societal respect in return.

A private company can absolutely articulate a vision. But the prerequisite is acknowledging that you are, first and foremost, a commercial entity. You cannot offer baseline compensation, fragile job security, and opaque promotion rules while simultaneously demanding that employees adopt a “co-founder mindset” toward overtime, sacrifice, blame-absorption, and boundary-crossing.

That’s not a grand narrative. That’s a psychological contract violation.

The most common version of this in Chinese tech is the phrase: “The company trained you.” But training was never charity — it was investment. Development programs exist to increase output per employee. A company can certainly invest in you, and you can certainly appreciate a good boss, a good platform, and good opportunities. But none of that changes a fundamental fact: you signed a labor contract, not an indenture.

Culture Isn’t Free: Stop Performing Gratitude, Start Distributing Value

From an OD perspective, culture matters enormously. But it matters because it drives collaboration, trust, decision speed, and organizational resilience — not because it makes the founder feel good about themselves.

The problem is that when many companies talk about “building culture,” what they actually want is low-cost compliance control.

The rewards an organization provides to employees roughly fall into three tiers:

Symbolic rewards — praise, vision statements, identity, rituals, the feeling of being seen and valued.

Economic rewards — salary, bonuses, equity, profit-sharing, and compensation genuinely tied to performance.

Structural rewards — clear promotion pathways, real decision-making authority, information transparency, and fair, predictable rules.

A healthy organization layers all three. Symbolic rewards create cohesion. Economic rewards provide consideration. Structural rewards give people a reason to believe in the future and trust the system.

Too many companies only invest in the first tier. They use the cheapest possible symbolic gestures to purchase the most expensive forms of emotional labor and organizational loyalty: vision without distribution, inspiration without equity, gratitude without rules.

This produces absurd management theater. Some Chinese companies have developed a peculiar obsession with Thanksgiving — an American holiday — turning it into an annual ritual where employees write thank-you cards, film testimonial videos, and publicly express gratitude to the company “for providing a platform.” The founder weeps on stage. The audience sits stone-faced. Not because employees are incapable of gratitude, but because adults can tell the difference between genuine appreciation and performance.

If you want employees to treat the company like their home, don’t ask them to write gratitude cards or memorize corporate values or repost the CEO’s speeches on social media. Give them a real stake — in financial distribution, in governance, in upward mobility.

Culture is not free. Any culture that demands extra commitment, extra obedience, or extra sacrifice must have a corresponding value-distribution mechanism as its foundation. If a company offers only symbolic rewards while being radically stingy on economic and structural ones, it isn’t building culture. It’s purchasing loyalty at the lowest possible cost.

That’s not sophisticated management. That’s a scam.

The Ultimate Clarity: Work to the Highest Standard, but Only for Yourself

Seeing through the transactional nature of private employment doesn’t mean becoming cynical, and it certainly doesn’t mean coasting. Quite the opposite — the most clear-eyed professionals tend to hold themselves to higher standards than anyone around them.

Because they’ve finally understood something: I’m not working hard to repay the company. I’m working hard to honor my own craft.

Why still deliver excellent work? Not to demonstrate loyalty, but for three reasons:

First, your market value. Your deliverables, your judgment, your reliability — these become your pricing basis in the talent market. Everything you produce today becomes a bargaining chip for tomorrow.

Second, your professional self-respect. Some people produce excellent work even in mediocre organizations. Not because the company deserves it, but because they have standards. High standards aren’t inherently noble, but they do protect you from being dragged down by your environment.

Third, your transferable capabilities. Companies pivot. Business lines collapse. Bosses get replaced. Platforms turn on you. But the skills you’ve built through repetition and refinement — judgment, project management, conflict resolution — are assets that nobody can take away.

So here are the two statements I despise most in the corporate world.

The first is what companies say when they lay people off: “We invested in you.” The second is what employees say when they’re let go: “I gave you my best years.”

Stop narrating an employment relationship as if it were a blood oath. What exists between employer and employee is a contract, a set of responsibilities, compensation, deliverables, and a partnership that either side can reassess at any time. You can be deeply professional, deeply committed, and deeply reliable within that relationship. But you don’t need to stake your moral identity, your life’s meaning, and your sense of self on a single private company.

You can take responsibility for your craft. You can uphold industry standards. You can invest in long-term capability. But you should never charge into legal jeopardy for a private company’s bottom line.

Hustle culture itself isn’t dangerous. What’s dangerous is when an organization systematically pushes its own legal, reputational, and ethical risks down onto its most vulnerable frontline employees — and then rebrands that downward transfer as “shared vision,” “all hands on deck,” and “showing up when it counts.”

That is the real danger in private enterprise. Not that it pursues profit, but that it disguises one-way risk transfer as shared values — offering commercial-contract compensation while demanding beyond-contract sacrifice and obedience.

Hold the line of your contract. Hold onto your leverage. Work to the highest standard, but only on your own behalf. That isn’t cold-blooded. It’s the scarcest form of clarity an adult can maintain inside the organizational world.

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Pre-made meals are almost perfect from a business standpoint: fast, low labor cost, consistent quality. The output isn’t stunning, but it’s edible. The cost is equally obvious: an entire generation of chefs has been stripped of the environment they need to grow.

In the past, a chef built integrated skill by cycling through dozens of dishes every day—vinegar cabbage, mapo tofu, red-braised pork, sweet-and-sour ribs. No one was deliberately training him. He was training himself through the breadth of dishes he had to cook. Culinary progress is, at its core, built through that breadth, one tiny iteration at a time: pull the heat back two seconds sooner here, let the vinegar infuse a little deeper there. A chef’s skill isn’t something you “learn” and then hold steady. It’s something you sharpen every single meal.

Pre-made meals skip this entire process. They also dismantle the training ground where young cooks were supposed to level up. When an entire industry accepts “good enough” and stops pursuing excellence, the ceiling of what the industry can produce stalls—or collapses. Because a ceiling is not a static target. It is the dynamic result of hundreds of tiny “let me improve this one more time” acts every day.

The restaurant industry is no exception. The AI-driven layoff decisions your company is making are structurally the same story: capabilities and skills that were once carried along by the system as a natural byproduct are being stripped away, one efficiency optimization at a time.

The Ceiling Blind Spot

When companies run any efficiency optimization—whether adopting AI or switching to pre-made meals—the benchmark is always in the present tense: How much does this save today? How much faster does it make us today?

The core driver behind this benchmark is: speed.

Because customers demand speed and bosses demand speed, anything that clears the minimum bar of “usable” gets accepted. One or two such compromises don’t hurt. But a years-long obsession with speed means nobody ever benchmarks against “potential and future ceiling.”

What will top-tier capability look like in ten years? Where will the real moat be in the second half of the game? Since these questions can’t be quantified in this quarter’s report—and certainly can’t be converted into a year-end bonus—everyone naturally uses the present as their reference system and silently assumes it will remain valid forever. This is what I call the Ceiling Blind Spot.

Where Does the Ceiling Come From?

A sharp pushback here: even if we keep doing everything by hand, we don’t know where the ceiling is either. If nobody can see it, what’s the point of saying AI is blinding us to it?

To answer that, we need to ask a more basic question: where does a ceiling actually come from?

A ceiling is never a static endpoint. It’s a dynamic process, pushed forward by two forces working in tandem.

The first is leaping: a breakthrough brought by new technology. One-shot, discontinuous, impossible to predict with precision.

The second is grinding: the more common, more everyday kind. Through long-term competition and polishing, pushing just slightly beyond what was possible yesterday. Today’s marketing profession looks nothing like the one fifty years ago—not because of some sudden miracle, but because over those fifty years, countless marketers spent every day thinking about how the last campaign could have been better. That’s what accumulated into today.

These two forces don’t operate independently. They are interlocked.

A leaping breakthrough can only take root if it lands on soil that has been cultivated by years of grinding. The first iPhone in 2007 didn’t descend from an alien civilization. It was decades of incremental progress in screens, batteries, and wireless technology finally hitting a critical threshold. Without those decades of grunt work, no one could have conjured that device.

Daily grinding is not the opposite of technological leaping. It is the only precondition that makes leaping possible.

We’ve Turned AI Into a Pre-Made Meal

AI is very fast, and its ability to synthesize is extraordinary. What it synthesizes is not one or two people’s experience but the distilled output of countless human works absorbed through training data. So some will argue that AI output should exceed what any individual human can produce—especially when a small number of exceptionally talented people push AI to its limits, feeding in high-quality input that should raise AI’s ceiling for everyone.

But the training mechanism of current AI models systematically suppresses minority preferences. In academic circles, this is called preference collapse. When the vast majority of users accept “good enough,” even if a tiny minority pushes for excellence, the model optimizes in the direction of “satisfy the vast majority.” The input from the few who pursue the highest standards gets structurally erased in training.

So, beyond speed, AI has not delivered anything that exceeded my imagination. It typically gets me to about 90% of what I had in mind. In rare cases it hits 100% or 105%. But it has never handed me something that made me gasp and say “I had no idea it could be done this way.”

AI is not incapable of breaking through human imagination. AlphaGo’s “Move 37” in its match against Lee Sedol stunned the world’s top Go players. AlphaFold’s protein structures exceeded what biologists had built up over decades of intuition. These are genuine moments where AI broke past human imagination. But those moments came after humans spent years designing extremely sophisticated training environments with clear rules and measurable outcomes, forcing the AI to grow something new on its own. In everyday work, our use of AI is far more straightforward: open the dialog box, type a prompt, glance at the output, think “eh, not perfect, but usable,” and ship it.

AI’s built-in mechanism for suppressing minority preferences, combined with our refusal to provide the depth and friction of real collaboration, has together turned AI into a pre-made meal.

We could have used AI as a sous-chef—in rounds of scrutiny and tearing up the draft, pushing out that last millimeter beyond the ceiling. But we couldn’t be bothered. We wanted speed. We accepted “good enough.” And so the moment that would have made you pound the table—along with that one millimeter of human progress—quietly evaporated without a trace.

AI-Era Layoffs

Today, there’s no need to debate whether to embrace AI. Almost every business leader has already answered “yes.” AI has handed them two especially tempting bullets.

The first bullet is direct task replacement. Especially for jobs that once sounded prestigious—”senior analysis, synthesis, integration”—writing research reports, doing competitive analysis, pulling data. These were the core work of knowledge workers. Now AI does it faster, more consistently, and without complaining or calling in sick.

The second bullet is far more subtle: the evaporation of human coordination. Research from Asana shows knowledge workers spend about 60% of their time on coordination—group chats, alignment, status-chasing, conflict resolution. A large chunk of that coordination was organized around the very analysis tasks AI is now taking over. When the task is gone, the coordination around it has no reason to exist.

So executives see a perfect picture on their dashboard: fewer people, lower coordination costs, output still holding. A supremely satisfying Double Kill.

But hidden inside the second bullet is a lethal cognitive trap.

What AI actually reduces is coordination around codifiable tasks. The uncodifiable coordination—reading nuance in ambiguous situations, managing cross-functional trust, aligning judgment across parties with conflicting interests—has not decreased. In fact, it may have increased, because someone now has to verify AI output at critical checkpoints, resolve logical conflicts between multiple AI-generated artifacts, and clean up failures AI cannot be held accountable for.

When business leaders see “expected total coordination time is down” and use that as the basis for another round of cuts, the people most likely to be collaterally damaged are precisely the ones doing the uncodifiable work.

The Trap of Cross-Level Layoffs

The most extreme version of AI-triggered layoffs is wholesale delayering: eliminating entire junior or middle tiers, and having a handful of senior employees plus AI deliver the output directly.

On current financial statements, this decision looks spectacular. One senior employee plus AI can replace an entire team. Costs drop, speed rises, quality is arguably acceptable.

But the cost is this: once a tier is erased from the organization wholesale, the capabilities it carried vanish with it. And that disappearance is irreversible.

Five years later, when you discover a critical piece is missing and try to hire it back from the outside market, you’ll find you can’t. The candidate pool at that tier has shrunk across the entire market. When every company makes the same “clever” decision, when no company is paying to cultivate that tier, the industry’s talent reservoir dries up.

That’s the moment you realize: the junior roles you replaced with AI weren’t just “cheap labor.” They were the training ground for your organization’s future senior employees.

Without junior roles doing the grunt work, without newcomers climbing the ladder step by step, without young employees developing business instinct through countless small mistakes—when you eventually need a seasoned expert who can operate independently, the market simply cannot grow one.

This is the same mechanism by which pre-made meals have broken the integrated training environment that produces skilled chefs.

The Downward Spiral

By this point, two lines of degradation in the AI era have already begun to interlock.

One is on the output side: AI’s built-in preference collapse, combined with our unwillingness to push back, keeps it locked at “90%.” The other is on the input side: cross-level layoffs eliminate junior and mid-level roles, ensuring the next generation of people who could push AI to 95% or higher will never emerge.

The two lines accelerate each other. The more AI behaves like a pre-made meal, the more reason companies have to cut people. The deeper they cut, the fewer people will exist in the future who could push beyond pre-made-meal-level output. This is not two pieces of bad news running in parallel. This is a self-reinforcing downward spiral.

Keep at Least One Person on Every Layer

After seventeen years in organization design, my operating principle is simple: You can lay people off. But keep at least one person on every team, at every layer.

The first pushback I always hear: “What if that layer was already bloat? I’d identified the middle tier as overgrown. AI arrives, I take the chance to trim it—why keep one?”

It’s a sharp objection and deserves a real answer.

The middle tier in most companies is actually two different things:

Type A: pure headcount bloat. Tiny spans of control, absurdly long reporting lines. Cutting the whole tier is correct. It should have been cut before AI even arrived.

Type B: a load-bearing wall in the chain that passes capabilities down through the organization. The value they carry is simply hard to quantify. They may be mentoring newcomers, doing uncodifiable cross-functional coordination, or serving as the translation layer between execution and strategy.

The fatal problem: Type A and Type B look identical on a financial statement. You can’t tell which is fat and which is bone.

What I oppose is not cutting bloat. Cut it! What I oppose is this: business leaders who can’t distinguish Type A from Type B, seduced by AI’s promise of efficiency, default to treating everything as Type A and uproot Type B along with the rest.

“Keep at least one person per layer” is not about protecting bloat. It is a defensive mechanism—a very cheap insurance policy on your own survival. Real unknown risk—the kind that doesn’t yet have a name or a job description—cannot be addressed with a dedicated project. It can only be absorbed by structural redundancy within the organization. Think of a competitive bodybuilder’s body fat: the lines look perfect at 5%, but a single cold can collapse the system. Moderate redundancy is not inefficiency. It is reserve.

But the one you keep can’t be just anyone. Not the most obedient, the cheapest, or the best at polishing status reports. It has to be the node—the person who can mentor newcomers, make judgment calls in ambiguous situations, and hold a conversation with both the executive and the intern. They are the ember of that layer, not its leftover.

If a team can’t even identify such a person, then its problem isn’t “can AI replace us.” It’s something much deeper: organizational tissue death. And AI cannot cure that.

There’s an even more radical objection: AI will eventually deliver full cross-functional automation. Organizational layers will disappear entirely. Why keep anyone? In a previous article, I used Sun’s Decision Authority Matrix to demonstrate that the “Civilian × Autonomous Process” domain—fully autonomous AI execution across departments—is structurally unimplementable in commercial settings for the foreseeable future, because halting authority cannot be exercised, the cost of halting is unbearable, and accountability cannot be assigned. (The full argument is in “AI Runs Entire Kill Chains in War. In Business, It Can’t Even Run a Supply Chain.“) Here, I’ll state only the conclusion: the chains that actually run in the real world are still held up by organizational structure and people. “Keep one per layer” is not an obsolete recommendation. It is the only executable defense against error.

This Isn’t a Benefit for Employees. It’s an Insurance Premium for Shareholders.

Perhaps you’ll say I’m arguing on behalf of employees. Why should shareholders pay for unknown risk? Isn’t keeping these people just dead weight?

Exactly the opposite.

What shareholders buy is not just this year’s financial performance. It is the long-term viability of the company. Organizational redundancy is not a gift to employees. It is a premium shareholders are paying on their own future. A company trimmed too “clean” may see short-term share price appreciation, but its resilience and evolutionary capacity have been severely overdrawn.

There is a classic agency problem buried here. Management has a bounded tenure and a powerful incentive to convert long-term corporate risk into a beautiful short-term financial result during their time in the seat. In this cross-level-layoff frenzy, shareholders are ultimately the victims. Shareholders who actually care whether the company will be alive in five or ten years should not permit management to drain the life-saving cushion as if it were just fat.

The Organization Is More Reliable Than Any Individual

My bottom line: the organization is always more reliable than any individual.

People quit, get sick, have bad days. Organizational structures don’t. A well-designed organization keeps its capabilities even when a specific seat changes hands—because those capabilities are embedded in the workflow, the layer-by-layer mentorship, the cross-function collaboration. They don’t live inside any single person’s head.

But cross-level layoffs in the AI era are doing the exact opposite.

On the surface, the organization is “slimming down.” What’s actually happening: capabilities that should be held and preserved by organizational structure are being concentrated into the hands of a few surviving senior employees. At the cost of long-term organizational stability, the personal value and indispensability of a few individuals are being pushed to a level that brings the organization itself no benefit.

That isn’t slimming down. That is privatizing the organization’s capabilities into the heads of a handful of people.

When a top hotel’s star concierge leaves, the hotel doesn’t just lose an employee. It loses his high-end network, his sharp judgment, the trust he built with clients over years. Because the hotel never designed a mechanism to retain these things, assets that should have belonged to the company walked out the door with him.

Cross-level AI layoffs are replicating this process across the entire white-collar world. The organization thinks it’s using AI to streamline. It’s actually mortgaging its core capabilities to the handful of people still standing.

Years later, when business stalls, key people defect, or a major incident hits, no one will trace it back to that “AI-driven cost optimization” decision. Just as no one today traces bad vinegar cabbage back to that first bag of pre-made meals. Pre-made meals aren’t perfect, but they’re edible and fast, so the boss went with them. Once the habit of “aiming at passing” sets in, the capabilities that can only grow through slow repetition—like a chef’s muscle memory for heat—quietly evaporate, one at a time.

I Don’t Care If AI Reduces Jobs

My motivation in giving these warnings to companies is not an attempt to save any particular employee’s job.

Companies are, at their core, the machine that keeps the supply of capability flowing through society. If that machine starts cannibalizing itself in the name of efficiency, then I—as the machine’s ultimate end user, as someone who has to eat every day—will eventually pay the price.

So here’s where I actually stand:

I don’t particularly care whether AI reduces job opportunities. Technology has always moved forward. Jobs have always been created and destroyed. That is not a new problem.

What I care about is this: walking into a restaurant and being unable to get a plate of vinegar cabbage that actually has flavor. Buying a bottle of shower gel online and not being able to find a single bottle with decent packaging. Calling any company’s customer service and reaching someone who has lost the ability to solve any problem that falls outside the SOP.

Will AI replace jobs? That’s a problem for business leaders and macroeconomists.

But will the baseline of commercial civilization turn to sand? Will the quality of everyday life for all of us slide? Those are questions for every single one of us.

This is the final installment of a three-part series on AI-era layoffs. The first two are “GenAI Is Cutting People It Can’t Replace” and “AI Didn’t Kill the Best Concierges. It Killed the Path to Becoming One.”

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“Der Hölle Rache” is widely regarded as one of the most technically demanding arias ever written. Two octaves of coloratura fury, rapid-fire staccato passages at the extreme upper limit of the human voice. The number of sopranos alive who can perform it at the level it demands is vanishingly small.

The Queen was sung by Tetiana Zhuravel, a Ukrainian coloratura soprano. This role is essentially her calling card. She has sung it at major opera houses across Europe. She keeps getting hired because she can do what very few sopranos alive can do.

Here’s the thing: she is not the lead. The leads are Prince Tamino and Princess Pamina. They carry the narrative and they’re on stage for most of the evening. The Queen has roughly ten minutes of stage time in a three-hour opera. But if you cast the wrong singer in this role, the opera fails.

She is not the most important role. She is the most irreplaceable one.

This distinction matters far beyond the opera house. In organizational design, we call it a critical position.

What “critical position” actually means

“Critical position” is not a rigorous academic concept with a single universally accepted definition, which is precisely why it gets misused so often. But in my practice as an organizational design consultant, the most useful definition is this: a critical position is a role that, if left vacant or filled by the wrong person, would cause disproportionate damage to the organization.

The key word is disproportionate. Every position matters. If it didn’t, the position shouldn’t exist. But not every vacancy creates the same level of disruption. Some gaps can be covered by redistributing work, bringing in a contractor, or promoting from within on a reasonable timeline. A critical position is one where none of these remedies are adequate.

The defining characteristics are straightforward.

• The role has a high and direct impact on outcomes because of what the position connects to structurally: a regulatory requirement, a process bottleneck, a technical dependency.

• The talent pool is scarce, meaning you cannot post the job and expect qualified candidates within a normal hiring cycle.

• And the ramp-up time is long, because the role requires depth that takes months or years to build.

One thing that is not a reliable indicator: rank. In fact, the primary purpose of identifying critical positions is to surface roles that are easily overlooked precisely because they don’t sit at the top of the org chart. Everyone already pays attention to the C-suite. The exercise exists to find the positions that few people are watching but that would cause a crisis if they went vacant tomorrow.

The CEO is Tamino

A great Tamino elevates the entire opera. A mediocre one is noticeable but survivable. But the talent pool for Tamino is large. There are many tenors who can sing this role competently. A casting director does not lose sleep over finding one.

The Queen of the Night is a different matter. The talent pool is tiny. The requirements are extreme. A casting director who cannot fill this role has a genuine crisis.

Can a CEO be a critical position? Sometimes. A founder-CEO whose personal vision is the company’s entire strategy may genuinely be irreplaceable in the short term. But whether or not the CEO qualifies is beside the point. The CEO is already the most watched, most succession-planned role in any organization.

Now compare that to the compliance officer in a heavily regulated industry whose specific certification takes three years to obtain and whose departure could put the company’s operating license at risk. Or the systems architect whose role sits at the intersection of three business processes that no one else fully understands, not because of individual brilliance, but because the organizational structure concentrated that dependency in a single node. These are the positions this exercise is designed to find.

This is organizational design, not talent management

Most companies get the sequence backwards. They treat critical position identification as a talent management exercise: HR runs a workshop, gathers input from business leaders, produces a list, hands it to the succession planning team.

But critical positions are a function of organizational architecture. The correct sequence is: design the structure first, then identify which positions in that structure are critical, then build the talent strategy around them. The identification step requires understanding the strategic logic of the architecture, the flow of decisions and dependencies, the places where the structure creates concentration risk.

When you redesign an organization, you simultaneously redraw the map of where those concentrations live. A restructuring can eliminate a critical position that existed before, create one that didn’t, or consolidate three non-critical roles into one that becomes critical. This is why, after every organizational redesign I deliver, one of the first questions I ask the leadership team is: in this new structure, which positions are critical, and has that changed from before? If the answer is “we haven’t thought about it,” the redesign is incomplete. You’ve drawn the boxes and connected the lines, but you haven’t stress-tested the architecture for single points of failure.

For HR professionals who have always treated this as part of their domain: identifying which positions are critical is an architectural question. What you do with the people in those positions (succession, retention, development) is absolutely talent management and absolutely your domain. Starting with people instead of structure is how high performers get confused with critical positions.

Mozart didn’t just write beautiful music. He designed a structure and knew exactly which position carried the highest risk if the casting went wrong. That’s organizational design.

What identifying a critical position is supposed to trigger

Once a position is identified as critical, it demands sustained, concrete attention: targeted succession planning for that specific role, active retention efforts for the current occupant, and contingency planning that answers the question “if this person is gone tomorrow, what do we do on day one, day thirty, and day ninety?” Every one of these actions costs real time from line managers and HR. This is not a box-checking exercise. It is ongoing, resource-intensive work, which is exactly why the list must be short.

Two ways companies destroy the exercise

Mistake one: equating critical positions with the top two layers of the org chart.

This is the most common error. When asked to identify critical positions, most leadership teams default to listing their most senior roles. But those roles end up on the list because of their rank, not because of a rigorous assessment of replaceability. Your C-suite is, by definition, the most watched, most succession-planned part of your organization. Putting them on the list wastes attention that should be directed elsewhere.

Mistake two: letting the list grow until it loses meaning.

In some organizations, the critical positions list covers twenty percent or more of all roles. At that point, the concept has been destroyed. If your list has fifty names on it, your line managers and HR team cannot realistically give each one the sustained attention the exercise demands. The whole point is differentiation. Spread that attention across a fifth of your workforce, and you’ve accomplished nothing.

Why does the list grow? Almost always because of organizational politics. When department heads see that other departments have positions on the list and theirs don’t, they feel their team’s significance is being questioned. This is a fundamental misunderstanding. Criticality and importance are different dimensions. But the emotional reaction is real, and HR, caught between analytical rigor and organizational harmony, often compromises. A few more positions get added. Then a few more. The list expands, and the exercise becomes theater.

The position belongs to the organization, not the person

Here is where most people’s intuition leads them astray, and where Mozart provides the sharpest counterargument.

Mozart wrote the Queen of the Night for his sister-in-law, Josepha Hofer, tailoring the role to her extraordinary upper register and agile coloratura. You might argue this proves that critical positions follow the person. After all, Mozart literally built the role around Josepha.

But The Magic Flute has been performed continuously for 235 years. Josepha sang her last Queen of the Night in 1801. Every production since has cast this role by evaluating candidates against the requirements of the position itself: hit F6, sustain the staccato passages, project the dramatic weight the music demands. Those requirements have never changed. The woman who inspired the role has been dead for two centuries. The role endures.

This is exactly how critical positions work. A long-tenured specialist may have become so identified with a function that people can’t imagine anyone else doing it. But criticality must be assessed based on the role’s structural characteristics: what it connects to, what qualifications it requires, what happens when it’s vacant. When that person leaves, the position remains. And if the organization defined it clearly enough, it knows exactly what to look for next.

A note on permanence

A position’s criticality generally persists as long as the organizational structure that created it remains in place. The Queen of the Night has been a critical position for 235 years because the structure of the opera hasn’t changed.

But organizations do change. During a digital transformation, a legacy systems architect may temporarily become the most critical role in the company. Once the migration is complete, that position may no longer exist at all. The test remains the same: would a vacancy cause disproportionate damage right now? If yes, it’s critical, even if temporarily.

Back to the opera house

Tetiana Zhuravel took her bow in Shanghai to a standing ovation. She had been on stage for a fraction of the evening, sang two arias, and had been, for the duration of those arias, the single most important person in the building.

The leads carried the story. The orchestra carried the music. But when the Queen of the Night opened her mouth, every person in the audience understood instinctively what organizational designers spend careers trying to articulate: not all roles are created equal. Some are important. Some are irreplaceable. Knowing the difference is the most fundamental act of organizational clarity there is.

The next time you look at your org chart, ask yourself: where is your Queen of the Night? Not your CEO, not your highest-paid executive, but the role that appears modest on paper, that no one is actively watching, yet if the wrong person fills it, or if no one fills it at all, brings down the house.

Better yet, find out if your organization has a critical positions list. If it has fifty names on it, or if it mirrors the top two layers of your org chart, it hasn’t been stress-tested.

If you can’t find the list at all, your organizational design isn’t finished.

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The vehicles did not stop because of an accident. Police preliminarily determined the cause to be a system failure. But the vehicles’ stopping in live traffic lanes caused secondary collisions and severe congestion. Other drivers, not expecting stationary vehicles in the middle of a highway, collided with them.

On April 1, Baidu’s customer service told multiple media outlets that all robotaxi operations across Wuhan had been suspended, with no timeline for resumption. The timing was not an April Fools’ joke. The passengers stranded on elevated highways the night before can confirm.

I read this news and felt something I don’t often feel: vindication.

Two months ago, I wrote about this exact scenario

In February, I published an article about my experience riding a WeRide autonomous vehicle in Abu Dhabi through Uber. That article introduced several concepts I developed from what I observed in the back seat.

The first was the “accountability fuse.” The car had a human in the driver’s seat. Uber called him a “Vehicle Specialist,” not a driver. He wasn’t driving. But he was there. I argued that his real function was not technical backup but accountability absorption. If something went wrong, he was the person who could be pointed at and asked: “Why didn’t you intervene?” Like a fuse in an electrical circuit, he existed to burn out and protect the system behind him.

The second concept was the “accountability vacuum.” When AI holds decision-making authority but no one in the organization has been clearly designated as accountable for the AI’s decisions, accountability doesn’t become unclear. It evaporates. Every party in the chain has a reasonable explanation for why it’s not their fault.

That article ended with an image. In Abu Dhabi, the Uber app showed an “unlock door” button that was greyed out. The human specialist opened the door from inside. I wrote: “Next time, there won’t be a Saif. Only that button. You press it, and it turns grey. That greyed-out button doesn’t just lock you inside the car. It locks accountability outside.”

Wuhan proved this wasn’t a theoretical exercise. The button didn’t just turn grey. It stopped working entirely.

With a human driver, accountability is clear

In Abu Dhabi, with Saif sitting in the driver’s seat, the accountability for my safety as a passenger was unambiguous. It sat with Saif, and through Saif, with the company that employed him. How Saif and his employer divided that accountability between themselves was their business, not mine. As a passenger, I didn’t need to think about my own safety. That was someone else’s job.

This is no different from a traditional taxi. You get in, you say where you’re going, you arrive. If something goes wrong during the ride, the accountability falls on the driver and the company behind the driver. The passenger is never asked to make decisions about their own safety during the trip. That’s the deal. That’s what you’re paying for.

The key point is not just who is accountable. It is that the accountability is exercised in real time. The driver is there throughout the journey, continuously making judgments, continuously ready to act. If the car breaks down on a highway, the driver calls for help. The driver decides whether it’s safe to exit. The driver opens the door. The passenger doesn’t have to make any of these decisions. Accountability isn’t a document filed somewhere. It is a living function, performed by a human, every second of the trip.

Without a human driver, that accountability cannot be replicated

Remove the human from the vehicle, and this real-time accountability chain breaks the moment the system fails. This is not a criticism of any particular company’s execution. It is a structural observation. A machine can be shut down, recalled, or updated. It cannot be held accountable. Accountability requires a human who can answer the question: why did you let this happen?

This structural gap cannot be closed by remote support, by corporate promises, or by better technology. Here is why.

Remote support is not a human in the vehicle. Every major robotaxi operator maintains remote human support. Waymo has its Fleet Response team. Baidu has remote operators who can take direct control of vehicles. But remote support is conditional. It depends on the network being up, the system being online, enough staff being available, the connection being fast enough, and the operator being able to accurately assess a situation through a camera feed. In Wuhan, when the system failed, passenger accounts indicate that every one of these support channels failed with it. Saif’s presence in the driver’s seat is unconditional. He is there regardless of network status, server capacity, or staffing levels. You can add redundancy to remote support, add backup channels, add more staff. But you can never turn conditional presence into unconditional presence.

A corporate promise of “full accountability” is not real-time protection. A company can declare that it accepts full accountability for anything that goes wrong. But the purpose of clear accountability is not to determine who pays compensation after someone is hurt. The purpose is to ensure that someone is actively protecting the passenger throughout the journey. “We accept full accountability” is a promise that can only be redeemed after something has already gone wrong. During the two hours a passenger is trapped on an elevated highway, that promise cannot open the door, cannot assess whether exiting is safe, cannot call for help through a functioning channel. In any context involving human life, accountability that cannot be exercised in real time is not accountability. It is a liability settlement waiting to happen.

Even foreseeable risks were not addressed. What makes the Wuhan incident particularly hard to defend is that none of it was unforeseeable. System failures happen. Fleet-wide failures are possible when vehicles share a single network architecture. Passengers in a stopped vehicle on an elevated highway will need to communicate with someone. Emergency communication should function independently of the system it backstops. Every one of these risks could have been written on a whiteboard. And for every one of them, the question is the same: who is accountable for the passenger’s safety in this scenario, and how will that accountability be exercised in real time? Wuhan showed these questions had not been answered. If the foreseeable scenarios don’t have clear, executable accountability solutions, how can anyone be confident that the unforeseeable ones will?

Developing truly autonomous ride-hailing means accepting this accountability vacuum as a structural cost, not a transitional inconvenience. The SOS button failing along with the driving system is a design flaw, and it’s fixable. The lack of a physical emergency stop button is an engineering gap that Zoox has already solved voluntarily. The customer service collapse is an operational failure. All fixable. But the absence of a human in the vehicle is not a flaw. It is the product. And with it comes an accountability gap that no amount of engineering can close.

Private vehicles don’t have this problem. Robotaxis do. Here’s why.

If you own an L4 or L5 private vehicle, the accountability structure is comparatively straightforward. You purchased the car. You chose to activate the autonomous driving function. If something goes wrong, the question is between you and the manufacturer, a product liability framework with established legal precedent. You accepted the technology’s risk when you bought the vehicle.

There is also no structural dependency on remote support. The SAE J3016 standard, which defines the L0 through L5 levels of driving automation adopted worldwide, does not include remote support as a component of any level. The standard defines who performs the driving task, who monitors the environment, and who serves as the fallback. It is entirely about the relationship between the human in the vehicle and the automated system. In fact, L4 and L5 vehicles are required by the standard to be capable of reaching a minimal risk condition, a safe stop, independently, without any remote assistance. A private autonomous vehicle that loses network connectivity must still be able to protect its occupants on its own. The accountability framework is self-contained: you, the car, and the manufacturer.

Robotaxis cannot operate within this clean framework. There is no human driver in the vehicle, so when the system fails, someone must be reachable from outside the vehicle to take accountability for the passenger’s safety. Remote support is not optional for robotaxis. It is the only human link in the chain.

But here is the structural problem: the moment remote support becomes essential, it introduces an accountability layer that the SAE standard never defined and that no regulatory framework has fully addressed. Who operates the remote support? If the remote operator makes a wrong judgment call, who is accountable? If network failure makes the remote operator unreachable, who is accountable then? If remote support is overwhelmed by simultaneous incidents across a fleet, who is accountable for the passengers left waiting?

Private vehicles live inside a clean, self-contained accountability framework. Robotaxis are forced to depend on a layer that sits outside any standardized framework, that is conditional on infrastructure beyond the vehicle’s control, and that has already been shown to fail at scale.

In a robotaxi, the passenger owns nothing, chose nothing about the underlying system, cannot take over driving, and has no direct relationship with the technology provider. They hailed a ride. Their reasonable expectation is identical to hailing any other taxi: I get in, I arrive safely, the driving is someone else’s job. The accountability vacuum lives precisely in the use case that is being commercialized most aggressively.

The stakes are uniquely high

Because autonomous ride-hailing directly involves human lives, there is no such thing as a minor incident. Cruise had one pedestrian incident in San Francisco in October 2023. Its California permit was revoked. Operations were suspended nationwide. The CEO resigned. GM ultimately shut down the entire robotaxi business after investing over $10 billion. That incident triggered a regulatory, governance, and credibility crisis from which Cruise never recovered.

The risk profile is also categorically different from individual vehicles. When a private car breaks down, one car stops. When a robotaxi fleet suffers a system failure, roughly a hundred vehicles can stop simultaneously across an entire city. The economic cost, delayed freight, missed flights, diverted ambulances, gridlocked commerce, radiated outward from every stopped vehicle. And if a system failure can happen accidentally, it can happen deliberately. A single network intrusion, timed to rush hour, could reproduce the same scenario across any city where a robotaxi fleet operates.

The benefits of autonomous driving in military and emergency applications are clear, and the accountability challenges are minimal. Military vehicles carry trained personnel who accepted operational risk. Emergency and medical transport often carries no passengers at all. Civilian ride-hailing is the application that places untrained, uninformed, ordinary civilians into an accountability vacuum they never agreed to enter.

A valuable lesson, at the right time

In fairness, this incident may ultimately prove valuable for the entire industry. One hundred passenger vehicles stopping is better than one hundred vehicles losing control. Small robotaxis stopping is better than a fleet of autonomous freight trucks stopping on a highway. And a system-wide failure occurring now, during the early stages of deployment with no fatalities, is far better than the same failure occurring after thousands of vehicles are operating across dozens of cities.

The Wuhan shutdown is an opportunity to confront the question that matters most. Not just the technical questions about network redundancy and emergency system design, which are important but solvable. The deeper question: how should accountability be structured between the service provider and the passenger when there is no human driver in the vehicle? This is a question that engineering cannot answer. It requires a deliberate decision by regulators, operators, and society.

So what does society gain?

After everything discussed above, it is fair to ask: what does society gain from autonomous ride-hailing that justifies these costs?

The safety potential is real. Waymo’s published data, collected in real-world mixed traffic, shows about a 92% reduction in serious-injury crashes across over 170 million driverless miles. But the industry’s larger safety promise, virtually eliminating the 1.19 million annual global road deaths (WHO), rests on a condition that does not currently exist: a road environment with high autonomous vehicle penetration. Some simulation-based research suggests that optimal safety may not fully materialize until penetration rates are much higher than today, with one study finding an optimum around 70% in its modeled environment. In the current reality, autonomous vehicles and human drivers share the same road operating on fundamentally different logics, and many collisions that still involve robotaxis are initiated by human drivers, especially in rear-end scenarios. The full safety benefit is real but conditional, and the conditions may take decades to arrive.

Robotaxi fleets also generate real-world driving data that feeds back into algorithm improvement and adjacent applications.

But the most immediate, tangible economic benefit of removing the human driver is straightforward: it eliminates labor costs. Driver compensation accounts for about 70% of traditional ride-hailing operating costs. This is the primary commercial incentive driving the industry forward. A secondary argument is that autonomous ride-hailing releases drivers into other industries where labor is more urgently needed.

The accountability vacuum is the cost. It is borne by the passenger. The labor savings are the benefit. They are captured by the company. The question is whether the social justification for this transfer holds up.

The world currently has 186 million unemployed people (ILO, Employment and Social Trends 2026). The global “jobs gap,” people who want paid work but cannot access it, stands at 408 million (ILO, Employment and Social Trends 2026).

Are other industries short of workers?

Do we lack drivers?

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Meta’s public messaging has been more measured. A spokesperson emphasized that the focus is on the impact AI creates, not just how often it’s used. But when your performance review is tied to quantified AI adoption targets, the distinction between “encouraged” and “required” gets very thin on the ground.

I’ve spent 17 years designing performance and accountability systems for organizations. When I read this, my first reaction wasn’t outrage. It was recognition. I’ve seen this pattern before. Not the AI part. The organizational behavior underneath it.

I understand the desperation

Let’s be fair about where Meta is coming from.

The company has accumulated roughly $80 billion in operating losses through its Reality Labs division since 2020, chasing a Metaverse that attracted almost no users and became a global punchline. Layoffs have hit the company in waves: approximately 21,000 jobs cut across 2022 and 2023, 600 from the AI unit in October 2025, around 1,000 from Reality Labs in January 2026, and another 700 in March 2026. And despite aggressive moves in the AI space, Meta has yet to produce a consumer-facing AI product that competes head-to-head with ChatGPT or Claude. The open-source Llama models have earned respect in the developer community, but in the race for mass-market adoption, Meta is not yet in the leading pack.

Capital markets are watching. After the Metaverse debacle, investors need to see that Meta has a credible next act. Embedding AI into performance reviews sends a clear signal: this company is all in on AI. Every employee, every workflow, every output.

I get it. The signal needed to be sent. But desperate people make desperate moves, and desperate moves are rarely attractive.

The KPI itself is a textbook mistake

Evaluating employees on whether they use a specific tool is process-oriented management. It measures compliance, not performance. It tells you that someone opened an application. It tells you nothing about whether the work got better.

Every manager has faced a version of this choice. Your team is behind schedule. You can say “everybody stay late tonight.” Or you can say “I need this on my desk by 9am tomorrow.” The first controls the process. The second sets the outcome. They might lead to the same result, but they are fundamentally different management approaches. The first tells people how to work. The second tells people what to deliver, and trusts them to figure out the how.

“Use AI” is the equivalent of “stay late.” It prescribes a method instead of raising a standard.

If you want a presentation built in one hour instead of four, set that as the expectation. If someone hits it with AI, great. If someone hits it without AI, equally great. You got what you asked for. But under Meta’s system, the person who used AI and took four hours scores higher on their review than the person who delivered in one hour without it. You’re not rewarding performance. You’re rewarding obedience.

And it gets worse. When you incentivize tool usage, employees start forcing AI into workflows where it adds no value, or actively degrades quality, just to check a box. The organization doesn’t get more productive. It gets more performative. People optimize for the metric, not for the outcome. This is Goodhart’s Law playing out in real time: when a measure becomes a target, it ceases to be a good measure.

Even if all-in is the right strategy, this is the wrong execution

Let’s set aside whether the KPI is smart or stupid. Even if Meta’s leadership genuinely believes the entire company needs to adopt AI, the way they’re going about it is a change management failure.

You don’t open a company-wide transformation with the most drastic move available. A sweeping KPI overhaul that affects nearly 79,000 employees, rolled out before you’ve demonstrated a flagship product that justifies the shift, is enormous organizational risk. You’re asking the entire company to reorganize how they work around a technology whose internal value proposition hasn’t been fully proven yet.

Effective change management sequences the pressure. You start with pilots. You let early adopters demonstrate value. You build internal case studies. You create pull, not push. What Meta did is all push, no pull. “Use this tool or your review suffers” is coercion, not transformation.

The irony is hard to miss. If you wanted to signal all-in commitment to a new technology direction, there are gentler ways to do it. You could rebrand the entire company to show your conviction. Wait. They already tried that with the Metaverse.

The broader pattern is more alarming than the KPI

This decision doesn’t exist in isolation. Over the past year, Meta has made a series of moves that, viewed together, should concern anyone who thinks about organizational health for a living.

In June 2025, Meta appointed 27-year-old Alexander Wang as its first-ever Chief AI Officer, leading the newly formed Superintelligence Labs. Wang is a genuinely accomplished founder who built Scale AI into a $29 billion company. But the appointment was not without friction. Yann LeCun, widely known as one of the “godfathers of AI” and a long-time leader of Meta’s AI research, left the company in November 2025. In a Financial Times interview, he called Wang “young” and “inexperienced,” noting a lack of background in how research is actually practiced. Within months of Superintelligence Labs launching, at least eight employees departed, including a twelve-year veteran who joined Anthropic and researchers recruited from OpenAI who returned after less than a month.

The organization has been flattened aggressively, with reports of manager-to-engineer ratios reaching 1:50 in some AI teams. Internal tensions have emerged as newly hired AI talent reportedly commands compensation packages that dwarf those of existing staff, with some long-tenured employees threatening to leave.

None of these moves is inherently wrong in isolation. Promoting younger talent injects energy. Flattening hierarchies can accelerate decisions. Paying market rates for scarce AI talent is rational. But doing all of them simultaneously, while also overhauling performance evaluation criteria and continuing to execute wave after wave of layoffs, dramatically shrinks the margin for error.

When you restructure your leadership, flatten your organization, revamp your evaluation system, and cut headcount all at the same time, you are making one very specific bet: that the new configuration will deliver transformative results fast enough to justify the upheaval. In Meta’s case, that means producing a flagship AI product that wins the mass market. Not an internal tool. Not a research paper. A product that ordinary people choose to use every day.

I’ve written before that every serious player in the international AI arena needs a competitive LLM. That logic applies at the enterprise level. If you’re going to restructure your entire organization around AI, you need something to show for it. As of today, Meta doesn’t have that. And without it, all this organizational disruption is just disruption.

The question that concerns me most

Here’s where my OD instincts kick in.

I don’t believe that nobody inside a 79,000-person organization realized that process-oriented KPIs are a bad idea. This is not an obscure insight. It’s covered in any introductory management course. Any competent HR professional would flag it. Any experienced people manager would feel the wrongness intuitively.

So why did it happen?

There are only a few possible explanations. People raised objections and were overruled. Or nobody dared to speak up. Or the decision was made unilaterally, without meaningful consultation.

Any of these is far more concerning than the KPI itself.

If this were a client engagement, a single bad KPI wouldn’t keep me up at night. Bad metrics get fixed. But the fact that a decision this obviously flawed passed through an organization of that size without being stopped? That’s the signal that makes me want to start examining the power structure. Who holds decision rights? Who has veto power? Is there a functioning feedback loop between frontline managers and executive leadership? Or has the organization reached a point where the CEO’s conviction overrides every institutional check?

A bad KPI can be fixed in a week. A broken power structure takes years to repair, if it gets repaired at all.

The cow is alive. But the conditions matter.

Meta is a cash cow. That’s not in dispute. The advertising business generated over $200 billion in revenue in 2025. The company has resources that most organizations can only dream of.

But cash reserves don’t make an organization immune to structural decay. They just extend the timeline. You can make bad decisions for longer before the consequences become visible. The Metaverse proved this: it took years and $80 billion before the market forced a course correction.

There’s an ancient Chinese military proverb from the Zuo Zhuan, written over 2,500 years ago: “The first drumbeat brings full morale. The second, it fades. The third, it’s gone.” The Metaverse was Meta’s first drumbeat. It was bold, it was loud, and it failed. The AI pivot is the second drumbeat. The organization is already fatigued, the talent pool is churning, and the market is skeptical. If this one doesn’t land, there won’t be enough morale left for a third.

Each cycle erodes something that money can’t easily rebuild: the trust of talented people who have to decide whether this is a company worth committing their careers to.

A cash cow is still a living organism. If the conditions aren’t right, the cow dies.

What I’d tell any CEO considering the same move

Raise your output standards. Shorten your deadlines. Demand higher quality. Then let your people figure out how to get there.

Some will use AI. Some won’t. Some will use it for certain tasks and skip it for others. That’s not a problem. That’s professional judgment. And professional judgment is exactly what you should be measuring, rewarding, and protecting.

The moment you start evaluating people on which tools they used instead of what they delivered, you’ve stopped managing performance and started managing compliance. AI doesn’t change the fundamental principles of good management. It just gives people one more way to get the job done.

Measure the outcome. That’s it.

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In meetings, I untangle accountability. During layoffs, I untangle accountability. Eventually, it started bleeding into my personal life. At a restaurant, I’d look at the menu and wonder whether the pricing authority sits with the head chef or the owner.

So when a friend asked me, “How come you never go to a temple to pray?”, my answer was simple:

“The accountability is unclear.”

Let me unpack that. Those words carry at least a few layers of meaning.

When it works, who gets the credit? When it doesn’t, who takes the blame?

You go to a temple and make a wish. There are only two possible outcomes.

The wish comes true. Congratulations, divine blessing. But did you really do nothing during that time? Doesn’t your own effort count? If you got that year-end bonus, was it because you worked overtime every day, or because you visited Lingyin Temple back in January?

The wish didn’t come true. Then clearly, your heart wasn’t sincere enough.

The phrase “sincerity brings results” is the underlying architecture of this entire system. Its brilliance lies in one elegant design: all success is attributed to the system; all failure is attributed to the user.

An organizational design professional encountering this structure will feel physically uncomfortable. My daily job is helping companies sort out exactly this question: who gets credit for success, and who bears accountability for failure. If you told me your company runs a performance system where good results are credited to leadership’s wisdom and bad results are blamed on employee execution, I would make you tear the whole thing up and start over.

And yet this same logic has been running in temples for thousands of years, with remarkably high customer satisfaction.

Some prayers are perfectly measurable. Most are not.

To be fair, not all prayers are hopeless. Some are actually highly compliant with the SMART framework from management theory.

In China, before heading out to sea, fishermen pray to Mazu: protect us and bring us back safely with a full catch. Run this prayer through the SMART criteria one by one, and it’s nearly perfect.

• Specific: come back alive, boat full of fish.

• Measurable: did the person return, how much fish, crystal clear.

• Achievable: a reasonable expectation, nobody’s asking to haul in a boatload of bluefin tuna.

• Relevant: fishing is their livelihood, so the catch is directly relevant.

• Time-bound: this one voyage. And they pray to Mazu alone, so attribution is clean.

Praying for pregnancy works too. Either you’re pregnant or you’re not. Binary outcome, relatively clear time window.

These prayers have high “success rates” precisely because they are assessable, especially on the Achievable dimension.

But the vast majority of prayers in everyday life are nothing like this. You solemnly pray: “Let my stock portfolio double this year.” The question immediately arises: is doubling achievable? What about a 50% gain? 10%? If you prayed for a 1% return, you wouldn’t need divine help. The market will probably get you there on its own. But nobody makes a special trip to a temple to pray for 1% returns. The things truly worth praying for are the hard things. And where exactly is the line between “hard” and “unrealistic”? Who defines it? The divine won’t tell you, and you can’t tell yourself.

Pray too conservatively, and you don’t need the blessing. Pray too ambitiously, and when it doesn’t happen, you can’t say the prayer failed. The A in SMART (Achievable) is simply undefinable in the context of prayer.

Even the divine would need to conduct an impact assessment

Religion, as a sacred institution, is fundamentally oriented toward goodness. So it naturally needs to screen every prayer for moral standing.

Some prayers obviously should not be granted. If someone walks into a temple and prays “please let my Ponzi scheme survive another year,” I believe any deity would reject that filing outright. A request this clearly in violation of basic moral standards cannot pass the initial screening.

But a huge number of prayers are not black and white.

“Let me crush my competitor this year.” Good or bad? You might be winning the market with a better product, entirely legitimate. But your competitor might have hundreds of families depending on those paychecks. You win, they lose their jobs.

“Let the construction project I’m leading move forward smoothly.” But this project might involve building a factory in a residential neighborhood, with fierce opposition from local residents. Your “smooth progress” is their “disaster.”

None of these prayers are as obviously wrong as the Ponzi scheme. The person praying genuinely believes they are doing the right thing. But the consequences of each prayer involve invisible chains of interests, and whether the outcome is good or bad simply cannot be determined at the moment of praying. Even an omniscient, omnipotent deity would need to conduct a complex, systemic evaluation before deciding whether to grant the wish. This is no longer a moral judgment. This is an impact assessment.

Two temples, one wish. Who gets the credit?

Some people say: after praying at one temple, you shouldn’t visit another temple for a while.

Why? Because if you visit two temples in quick succession and make the same wish, and the wish comes true, which temple gets the credit? Don’t forget, after a wish is fulfilled, there’s still the crucial step of “returning to give thanks.” Which temple do you go back to?

Attribution cannot be split.

So people say: just stick to one temple for a period of time. Fair enough. But how long is “a period of time”? Three months? Six months? A year? The people offering these definitions obviously can’t cite any religious policy document or standard operating procedure as evidence.

If you wanted to compare which temple is more effective, you couldn’t run a controlled experiment either. Visit two temples at the same time for the same wish, and if it works, you can’t split the credit. Visit at different times for the same wish, or different times for different wishes, and you have no basis for comparison at all.

From any scientific perspective, the question “which temple is more effective” is fundamentally unanswerable.

So I don’t go

It’s not just about burning incense and praying at temples. Christianity, Islam, Judaism, Hinduism, and other religions all follow the same pattern. What believers pray for comes down to the same few things: peace, health, wealth, guidance. We are all human. Regardless of skin color, language, or faith, our needs are remarkably similar. It’s just that the vast majority of everyday prayers are inherently vague and unassessable. And precisely because they are unassessable, belief systems can never be disproven.

My position remains the same. If your company’s performance system looked like this: success attributed to leadership’s vision, failure blamed on employees’ poor execution, evaluation criteria vague and shifting, achievability of targets left entirely to subjective judgment, reporting lines tangled with no clear ownership of results, I would make you redesign the whole thing.

The temple’s system, I can’t change, and I don’t want to. But at least I know why I don’t use it.

The accountability is unclear.

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You know this is a nearly impossible ask. Masa is the kind of place you can’t get into even with months’ notice. The concierge looks at you and says, “Let me see what I can do,” then picks up the phone.

Fifteen minutes later, he’s back. “Done. Tomorrow, 8:30pm, two guests.”

This isn’t magic. This is a relationship network built over a decade. The restaurant’s executive chef knows him because he’s been sending guests there every week for ten years. The maître d’ owes him a favor because last year, when the maître d’s family visited the city, he arranged a private tour. He can get you that “impossible reservation” not because he’s better at using a phone than you are, but because he holds something you don’t: a web woven through ten years of daily work, with every thread carrying trust.

But here’s the question: does that daily work still exist?

A pyramid being hollowed from the bottom

Think of a concierge’s capabilities as a four-layer pyramid.

The base: information.

Knowing what’s in the city, how to get there, when things open, which neighborhoods are worth exploring. When guests arrive in an unfamiliar city, this is the first thing they need. This was the concierge’s most fundamental daily function.

Layer two: basic execution.

The guest tells you what they want, you make it happen. “Book me Le Cinq tonight.” “Get me two tickets to Rigoletto at the Royal Opera House tomorrow.” “Arrange a car to the airport.” The guest has already made the decision. You carry it out.

Layer three: personalized judgment and holistic planning.

The guest hasn’t figured out what they want. You figure it out for them. “I have two free days. Put together an itinerary for me.” Now you’re weighing the guest’s fitness level, interests, and pace preferences. You’re deciding between a national park outside the city or an art museum downtown. You’re choosing which Michelin restaurant fits the evening and what style of bar works afterward. You’re factoring in weather, distances, crowd levels, and timing. You produce a plan the guest couldn’t have come up with on their own, but that feels “just right” when they experience it. This used to be the concierge’s core professional moat.

The top: relationship capital and “making the impossible happen.”

Getting a guest into a restaurant no one can get into. Securing tickets to a show that sold out months ago. Arranging a private experience that doesn’t exist on any website. This isn’t about information, execution, or judgment. It’s about a decade of accumulated favors, trust, and reputation.

This is a classic capability development path. Without doing large volumes of basic execution, there’s no opportunity to develop judgment. Without years of sending guests to restaurants and buying tickets, building up credibility and goodwill along the way, there’s no way to weave that golden network. The pyramid held together because the base supported everything above it.

This used to be a matter of course.

Then technology started pulling out the layers from below.

Three waves, each removing one layer

The first wave was the internet (roughly 1995-2007).

Before this, guests arriving in an unfamiliar city had almost no other source of information. The concierge was their only efficient interface with the city. After the internet, guests could research attractions, restaurant rankings, and transportation routes before they even packed. By the time they reached the hotel, most basic questions no longer needed asking. The base layer was gone: the concierge’s information monopoly was broken.

The second wave was the smartphone and apps (2007-2015).

Google Maps replaced hand-drawn directions on paper maps. OpenTable replaced phone calls to book restaurants. StubHub replaced ticket brokers. Uber replaced arranging cars for guests. Hotels themselves launched digital platforms, bundling dining, transportation, and ticketing into mobile apps. Guests could not only find information on their own but act on it themselves. As one industry insider put it bluntly: “I’ve got a portable concierge in my pocket. And I don’t have to tip.” The second layer was gone: basic execution no longer required the concierge.

The third wave is AI (2015-present).

This wave attacks from both sides. From the outside, guests open ChatGPT and type: “I have two free days in New York, I like nature and good food, plan something for me.” The output is already quite good. From the inside, hotels have been deploying AI chatbots to standardize service workflows, turning personalized recommendations and itinerary planning into replicable products. AI chatbots now handle 80% of routine guest inquiries at many properties. One hotel industry report showed a 78% drop in concierge call volume during peak periods at a resort. The third layer is being stripped away: personalized judgment and holistic planning are being squeezed from both the organizational inside and the guest-facing outside simultaneously.

After three waves (the third still underway), only the top of the pyramid remains: relationship capital, the ability to get things done that no one else can. This layer, AI cannot yet reach.

A castle in the air

This isn’t a phenomenon unique to the hotel industry. Across nearly every service industry, both employers and individuals are racing to adopt AI. Customer service chatbots are standard. Standardized workflows are being automated at every level. But the work that sits at the very top of any profession, the work that depends on human relationships, contextual judgment, and the ability to navigate ambiguity through trust, remains extremely difficult to replicate at scale. A chatbot can answer a guest’s question, but it can’t make a phone call that trades on a personal favor. An LLM can draft a consulting framework, but it can’t originate one that reshapes how a client thinks about their business. The more thoroughly the lower layers get automated, the more visibly irreplaceable the top becomes.

But top concierges are more valuable not simply because they’re scarcer. It’s because the pipeline that produced them is closing. Rising prices may not fully reflect improving capability. More likely, they reflect the market’s expectation that future supply is about to collapse.

The relationship networks those top concierges hold were built over ten to fifteen years of working through the three layers below. Every restaurant reservation was a deposit into a relationship. Every ticket purchased was another connection formed. Every itinerary planned was another layer of judgment deposited. The “impossible tasks” they accomplish today are the compound interest on 3,650 “ordinary days” before them.

Now a new hire joins the concierge desk, and the situation is stark. The information work at the base has been absorbed by the internet. The execution work at layer two has been absorbed by smartphone apps. The judgment and planning work at layer three is being squeezed by AI from both ends. There is almost nothing left that would allow them to organically accumulate the relationships and judgment needed to reach the top.

And it’s not just the work itself that disappeared. The leverage disappeared with it. A concierge used to be valuable to a restaurant because they represented a steady stream of guests from the hotel. That stream now flows through OpenTable and Google. A new hire sitting quietly at the concierge desk has far less to offer partners than their predecessor did fifteen years ago. Not because they’re less capable or less motivated, but because the structural currency they could trade on has been steadily absorbed by technology.

This is what a castle in the air looks like: the top still stands, but the structure beneath it has been hollowed out, one layer at a time.

The path is broken. But who benefits?

There’s a layer of incentive structure here that most people haven’t noticed.

AI is leverage for senior concierges. They already have judgment, a relationship network, and trust deposits at every high-end restaurant and venue in the city. Plug in AI, and they can respond to guests faster, serve more requests simultaneously, cover a wider range. Their output per unit of time is being amplified.

AI is a crutch for junior concierges. It can help them generate a decent-looking itinerary faster, but it can’t earn them the trust of a restaurant’s executive chef, can’t build them a decade of personal connections, can’t teach them what it means in a real situation when “this guest won’t like that kind of arrangement.”

So the result isn’t everyone getting stronger together. It’s this: the veterans use AI to thicken their moat, while the newcomers lose the very work that used to build their capabilities.

From an individual perspective, senior employees embracing AI is an entirely rational decision. It means widening their advantage over successors at the same time that the path for successors is narrowing. But what does this mean for the organization?

Hotels think they own this capability. They’re just renting it.

For many luxury hotels, the concierge has never been just an add-on service. It’s a core part of the hotel’s competitive advantage. Guests are willing to pay more for a hotel not just because the rooms are bigger, the location is better, or the decor is nicer, but because they believe: at this hotel, things that can’t be done elsewhere can be done here.

The problem is that this capability often doesn’t truly belong to the hotel. It belongs to a specific senior concierge as an individual. Who he knows, who takes his calls, who holds a table for him, who treats him as someone worth doing favors for. None of this lives in the brand manual, in standard operating procedures, or in any app. The hotel sells this capability as its own differentiator, but if it hasn’t been replicated, passed down, or institutionalized, the hotel doesn’t actually own it. It’s merely renting one person’s relationship network, judgment, and reputation.

The moment that person retires or leaves, what disappears isn’t just an employee. It’s a piece of the hotel’s competitive advantage that genuinely existed.

This is the problem AI creates that runs deeper than “the career path is broken.” AI lets senior employees amplify their personal leverage through technology while simultaneously dismantling the task structures that new hires need to develop capability. The result isn’t a stronger organization. It’s an organization whose core capabilities are increasingly bound to a handful of individuals. Skills and capabilities should be preserved within the organization, not tied to specific people. But if the way AI gets used only makes veterans stronger and newcomers hollower, the organization is effectively re-privatizing capabilities that should be organizational assets, handing them to a few incumbents.

For the senior employees, this is a smart deal. For the organization, it’s dangerous.

This isn’t just a hotel story

If you swap “concierge” for “consultant, lawyer, analyst, engineer,” and swap “booking restaurants for guests” for “writing reports, doing research, building slide decks,” you get an almost identical structure.

These professions share the same capability formation mechanism: newcomers start with high-frequency, low-risk, repeatable tasks, develop judgment through repetition, then gradually take on high-risk, relationship-intensive work, eventually forming the scarce capabilities that only a few possess. AI is cutting exactly the first two stages of that pipeline. The result isn’t “we don’t need experts anymore.” It’s “the mechanism that produces experts is broken.”

The concierge profession is worth paying attention to not because it matters in itself, but because it has already completed this cycle in full. The base has been stripped away layer by layer, the top has become more valuable and more unreachable at the same time, and capability has flowed from the organization to the individual. What consulting, law, finance, and engineering are experiencing with GenAI right now is the same road. They’re just in the first half.

A few days ago, I wrote about how GenAI is cutting the people it can’t replace: organizations are eliminating the roles that carry the talent development function while retaining the roles whose work is actually being compressed by AI. The concierge profession is what that road looks like at the finish line.

Top players still have a seat at the table. They may even eat better than before. But “how to become a top player” is a question this profession can no longer answer.

And an industry that can only sustain its peak but cannot produce the next generation to reach it isn’t becoming more elite. It’s becoming more fragile. Because today’s top players all walked up from yesterday’s bottom, one step at a time. Cut off the path from the bottom, and the top stops renewing itself.

This is the deepest impact that the technological revolution, led by GenAI, has on a profession. Not replacing it, but stripping it of the ability to reproduce and elevate itself.

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I know what this means, because I walked that exact path myself. I’m bypassing the very road that made me who I am.

And behind this lies a problem far more serious than “fewer entry-level jobs.” GenAI isn’t compressing low-end labor itself. It’s compressing the work that used to carry the talent development function. And organizations are clearing headcount from the bottom, where it’s cheapest. Efficiency gains and capability gaps are happening simultaneously.

I previously wrote a piece analyzing why layoffs always start with junior employees. Today’s article goes one layer deeper, into the structural problem beneath that pattern.

“Who gets cut” and “whose work gets replaced” are not the same thing

Statistically, GenAI adoption is hitting junior employees harder: more attrition, more early-career disruption. This easily creates a misreading impression: that junior work is more replaceable by GenAI and mid-level employees are safer.

I believe the opposite is true.

What GenAI is best at absorbing isn’t ground-level legwork. It’s the work that mid-level employees do in volume: research synthesis, document consolidation, preliminary analysis, standardized summaries, drafting, review. These “presentable work products,” the kind that used to require years of solid professional training to produce reliably, are precisely why mid-level roles exist. And they’re precisely what LLMs compress best.

Yet when organizations make cuts, juniors go first. Not because their tasks are more replaceable, but because cutting them is cheaper, easier, and draws the least resistance.

There’s a mismatch here that almost no one has named explicitly. I use two dimensions to illustrate it:

• Task Replaceability: To what extent can GenAI handle the day-to-day work of this role?

• Headcount Reducibility: How low is the cost and friction of eliminating someone at this level?

Overlay these two dimensions, and a counterintuitive picture emerges:

Mid-level employees sit in the “high task replaceability, low headcount reducibility” quadrant. GenAI can handle most of their daily output, but the economic cost of cutting them directly is steep: larger severance packages, longer notice periods, more complex legal processes. Letting go of a mid-level employee with 12 years of tenure and a $7,000 monthly salary might cost $70,000 in severance (N+3). That same budget can eliminate ten junior employees with 2 years of tenure and $2,000 monthly salaries. When the CFO is staring at a fixed severance budget and a headcount reduction target, the math makes the decision.

Junior employees sit in the “low-to-medium task replaceability, high headcount reducibility” quadrant. Much of what they do (on-site coordination, unstructured communication, handling ambiguous information) isn’t that easy for GenAI to replace. But cutting them is the cheapest and least painful option. They’re not cut because they’re underperforming. They’re cut because they’re affordable.

Organizations follow the vertical axis (who’s cheapest to cut), not the horizontal axis (whose work GenAI can actually do). The work GenAI actually compresses sits at the mid-level, but the cost clearing happens at the bottom of the pyramid.

This mismatch is dangerous because it creates a false narrative: it looks like organizations are using GenAI to replace “low-end work,” when in reality they’re reducing headcount in the cheapest way possible. After the juniors leave, their work floats up to mid-level. Mid-level employees are now integrating AI output while picking up basic tasks they haven’t touched in years. Those who can’t sustain it leave on their own. Voluntary resignation means zero severance. The organization cuts from the bottom and indirectly forces out the middle. One round of severance payments, two layers of people gone. In the short term, it looks like a smart deal. But in the process, something far more critical is silently disappearing.

The output remains. The development doesn’t.

Many entry-level roles used to carry a dual function. On one hand, they were part of the delivery process: immediate output. On the other, they were mechanisms for training, observation, and selection: talent development.

When a junior employee first joins an organization, most of what they do isn’t glamorous. But whether someone has structural thinking, problem awareness, resilience under pressure, the ability to turn ambiguous problems into clear outputs, none of that shows up in a one-hour interview. It’s built layer by layer through foundational work, and it’s observed the same way.

When GenAI takes over the “immediate output” function of these tasks, it simultaneously strips away their “development” function. But the organization’s dashboards only register the former: efficiency up. The latter, the quiet erosion of the talent pipeline, never appears on any report.

This is what I call false prosperity. Faster delivery, lower labor costs, better margins. But the prosperity is borrowed. Organizations aren’t just saving on low-value labor costs. They’re also eliminating the cost that used to generate the next generation of professional talent. You won’t immediately see “top talent disappearing.” You’ll see reports coming out on time, deliverables arriving even faster. But three to five years later, you’ll notice that the people capable of high-level judgment haven’t emerged at the rate they used to.

This is, in essence, a hidden liability. And like all hidden liabilities, its most dangerous feature is that everything looks fine right up until it doesn’t.

“But can’t AI also help juniors learn faster?”

This is the most common rebuttal, and the most comforting optimistic narrative. But it conflates two things.

AI can accelerate learning. It cannot create learning environments.

A junior employee who uses AI to produce a polished report hasn’t necessarily understood why it’s written that way. What they’ve skipped is precisely the most valuable part of the training: groping for structure in ambiguity, making trade-offs under pressure, getting sent back six times by a senior before finally grasping what “good enough” actually means. Developing judgment isn’t an information acquisition problem. It’s a process of repeated trial, error, and correction. That process requires real delivery environments, with real pressure, real consequences, and real feedback.

Courses can be accelerated with AI. But judgment isn’t taught in courses. It’s forged in real work.

I’m not saying all entry-level work must be preserved, nor am I denying that AI can improve learning efficiency. The real problem is that organizations have no incentive to deliberately rebuild the judgment-formation mechanisms that were once embedded in real delivery work. AI can increase the speed of learning. But what’s actually missing isn’t speed. It’s the environment.

The market won’t self-correct

Many people enjoy the easy narrative: AI frees humans from repetitive labor so we can all do higher-order, more creative work.

Freed to do what, exactly? Who bears the cost of training? Who absorbs the inefficiency of people still learning? Who pays the tuition for the next generation’s talent pipeline?

Companies won’t do this voluntarily. Companies naturally optimize for lower cost, higher efficiency, and more immediate returns. You can’t acknowledge that GenAI delivers significant efficiency gains while also expecting most companies to voluntarily maintain a low-efficiency, high-development talent pathway.

There’s also a classic collective action problem at work: every company has an incentive to free-ride on others’ development investment. If Company A spends resources developing junior employees, Company B can simply poach the mid-level talent that Company A produced. As GenAI makes the “skip development, hire ready-made” strategy increasingly viable, fewer and fewer companies will choose to bear the cost of developing talent.

This isn’t a moral failing of any single company. It’s a structural market failure.

The global policy toolkit isn’t addressing the right problem

Governments haven’t been idle on AI’s workforce impact. Training levies, tax credits, mandatory AI literacy programs. There’s no shortage of initiatives. But they all share a fundamental blind spot: they all assume “development” is an activity that can be separated from work, independently subsidized, and independently assessed.

That assumption is disconnected from reality. The reason a junior employee can become a reliable mid-level professional in three to five years isn’t because someone put them through a training program. It’s because they did massive amounts of real work on real projects. Development doesn’t happen alongside work. Development is the work itself.

Once AI takes over the tasks that carried the development function, you can’t compensate by building a separate “training program” on the side. No matter how much you subsidize, if junior employees don’t get the chance to do real things in real delivery environments, development simply won’t happen.

The question isn’t “who pays for training.” It’s “who preserves the environment where development happens.”

The real solution is straightforward and right in front of us

If development is embedded in work, then protecting the development function means preserving the space within work where development can occur.

To put it bluntly: layoffs are fine, but organizations must deliberately retain opportunities for a portion of their people to do real work on real projects, even when AI is perfectly capable of replacing them. This isn’t about efficiency. It’s about ensuring that when models fail, when tasks exceed model boundaries, or when the regulatory environment shifts, the organization still has people with the judgment to take over.

This logic isn’t new. The defense industry has been doing it for decades.

Fighter jet manufacturers don’t disband their design teams during years without new contracts. Shipyards take on civilian orders between naval commissions. Not because civilian products are more profitable, but because once you scatter the team and shut down the line, the cost of rehiring, retraining, and rebuilding cohesion for the next contract far exceeds the cost of keeping them on through the gap. What they practice is capacity preservation: maintaining the capability base so it’s ready when needed.

Defense and knowledge work aren’t the same industry, of course. But they face the same organizational choice: whether to sacrifice, in the name of short-term efficiency, a capability that looks redundant in peacetime but becomes decisive under stress.

Knowledge-intensive organizations now face that exact choice. You can let AI handle most delivery. But you need to retain enough people doing real work on real projects. On-the-job training, not classroom courses, not simulations, but sustained practice under real delivery pressure. These people and the skills they develop aren’t “redundancy.” They’re your capability reserve.

This is a risk management decision, not a charity decision.

A company’s Plan B is also a nation’s bottom line

From a company’s perspective, capacity preservation is Plan B. AI isn’t infallible. Models get updated and interrupted, networks go down, policies change. An organization that outsources all cognitive labor to AI without retaining sufficient internal human capability is as fragile as a manufacturer that depends on a single supplier for every component. Maintaining a capability reserve isn’t a waste of resources. It’s an insurance policy.

But if this choice is left entirely to individual companies, the result will mirror the collective action problem described above: some companies will always choose not to retain, not to develop, not to invest, and then scramble to poach talent from the market when things go wrong. When everyone thinks this way, there’s no one left to poach.

So ultimately, this is a national-level problem.

Just as the defense sector can’t hand all manufacturing capacity to peacetime’s most efficient bidder, the knowledge economy can’t hand all cognitive capacity to the AI era’s most efficient solution. By the time you realize the capacity isn’t in your hands, it’s already too late. The ability to develop talent isn’t just a corporate pipeline. It’s an economy’s industrial base.

The specific policy tools can be designed over time. Perhaps some form of mandatory training retention ratios. Perhaps changes to how on-the-job training is treated for tax purposes. Perhaps industry-level requirements modeled on aviation’s mandatory manual flying hours, a minimum threshold for human-performed work. These need deeper discussion.

But the direction should be clear: not building a separate training system outside of work, but preserving the space for human participation in real work, within work itself.

The moment an organization stops treating real work as a development mechanism by design, it begins, behind the appearance of rising efficiency, to overdraw on its future supply of judgment.

An organization unwilling to develop people may look more efficient in the short term. A society unable to sustain the development of people will inevitably pay a far greater price.

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