Inbound & Agile

Tag: The Unfolding Thought Podcast

  • When the Framework Becomes the Problem

    When the Framework Becomes the Problem

    Geoffrey Moore built one of business’s most durable ideas by noticing what a familiar chart left out.

    The Technology Adoption Life Cycle drew its categories from Everett Rogers’s research on the diffusion of innovations. Innovators tried a new technology first. Early adopters followed them, then the early majority, late majority, and laggards. High-tech marketers turned those categories into a smooth market-development story: win over one group, use it as a reference for the next, and keep moving from left to right.

    Moore says that story was abstracted largely from flagship successes. He and other Silicon Valley operators had also lived through ventures that disappeared or left their shares worthless. Those failures did not look like a smooth market-development story. When Moore recast the curve, he drew gaps between the groups and made one much larger than the others.

    That gap became the chasm.

    Two Technology Adoption Life Cycle bell curves compare a familiar continuous model with Geoffrey Moore's revision: innovators, early adopters, early majority, late majority, and laggards are separated by gaps, with a wide chasm between early adopters and the early majority.
    The familiar curve implies a smooth handoff. Moore’s redraw makes the discontinuities visible, especially the chasm between early adopters and the early majority. Original visualization by Inbound & Agile, based on Geoffrey A. Moore’s Crossing the Chasm.
    Cover of Crossing the Chasm by Geoffrey A. Moore.
    Crossing the Chasm.

    The familiar framework in Crossing the Chasm did not begin with Moore defending a model. It began with him deciding that an accepted model had become too elegant to describe what companies were actually experiencing.

    After my conversation with Geoffrey Moore for The Unfolding Thought Podcast, I kept returning to the same thing: he built the chasm by distrusting a framework that looked more orderly than the world it was supposed to explain. We talked about chasms, tornados, and staircases. But the larger question is what happens when people stop treating those images as aids to thought and begin treating them as reality.

    The chasm exists as an idea because Geoffrey Moore was willing to break a framework that no longer fit the evidence.

    Moore calls these root metaphors, borrowing and broadening an idea from Stephen C. Pepper’s World Hypotheses. Pepper used root metaphors to describe the organizing images beneath entire systems of thought. Moore applies the idea more practically to frameworks such as the chasm, the tornado, and the staircase. The point is not literary. A metaphor gives people a shared picture of a complicated situation, directs attention, and helps them act before they have an ironclad case. It makes some relationships obvious, some actions sensible, and other possibilities harder to see.

    This is the old distinction between the map and the territory. The map is useful precisely because it leaves most of the territory out. It gives us something small enough to carry, share, and use. But the thing that makes it useful is also what makes it dangerous. If we forget what was left out, we start treating lines someone drew on a map as if they were features of the landscape itself.

    Comparison of a simplified map with a straight route and topographical terrain with a winding route, illustrating that a model can guide without containing reality.
    A framework relates to reality the way a map relates to territory. Its usefulness depends on simplifying. Its danger begins when we forget what it left out. Original visualization by Inbound & Agile.

    Not every framework puts its metaphor in the title, but every framework simplifies. I am using framework broadly here. A metaphor, a model, and a dominant logic are not the same thing. What they share is that each selects which relationships matter, which facts deserve attention, and which actions seem reasonable.

    Eventually, people stop saying, “This situation resembles a chasm.” They say, “We are in the chasm.” The comparison has become geography.

    That is when a framework can become the problem, especially after its assumptions have been built into the budgets, metrics, roles, and routines through which the organization operates.

    We cannot lead without simplifying

    Management frameworks are reductive. That is their purpose.

    No leader can absorb every relevant fact, understand every relationship, anticipate every response, and calculate every possible result before making a decision. Herbert Simon’s work on bounded rationality begins with this constraint. Karl Weick, Kathleen Sutcliffe, and David Obstfeld describe sensemaking as turning unclear circumstances into a situation we can understand in words and use as a springboard into action. We simplify because we have to. A good framework makes the simplification usable.

    In an author’s note in Crossing the Chasm, Moore writes that experienced technology executives often told him the book had not really taught them anything they did not already know. It had gathered their “scattered intuitions and rueful learnings” into a coherent framework. They passed the book to colleagues partly to spread the vocabulary. Some companies made it required reading simply so everyone could discuss the market from a shared starting point.

    That is an enormous organizational advantage. Before the framework, six people may be carrying six partial understandings that take an hour to explain and still do not quite connect. After the framework, one person can say “the chasm,” and the group can retrieve an entire pattern of customers, risks, and strategic choices. The word compresses experience.

    Moore called this “metaphor-market fit” in our conversation. The metaphor feels intuitive enough that people can use it without stopping to reconstruct the argument every time.

    This becomes especially valuable when a company faces something new. In Crossing the Chasm, Moore describes the choice of a first mainstream market as a “high-risk, low-data” decision. The company must make a consequential commitment with little useful hard information and no direct experience from which to predict what will happen.

    Waiting for certainty can paralyze a company. Pretending certainty exists gives it false confidence. A framework gives people a provisional way to act, investigate their assumptions, and revise the description as reality supplies information.

    AI is forcing leaders to choose a metaphor

    The evidence is incomplete, the capabilities are changing, and businesses still have to make decisions. So leaders reach for a comparison.

    Calling AI a tool leads toward training people to use it. Calling it a coworker leads toward questions about roles, supervision, and responsibility. Calling it an employee leads quickly toward headcount and replacement. Calling it infrastructure suggests that the company itself needs to be redesigned around it.

    Four ways of describing AI, as a tool, coworker, employee, or infrastructure, lead to different organizational responses and show that metaphors shape leadership decisions.
    Calling AI a tool, coworker, employee, or infrastructure makes different decisions feel reasonable. Original visualization by Inbound & Agile.

    All four comparisons can be useful. The problem begins when a company chooses one, builds the budget and operating plan around it, and then treats evidence that does not fit as resistance or confusion rather than a reason to revisit the original description.

    We need the map. We also need to remember that we drew it.

    The description has already started prescribing

    Moore told me that strategy first describes a situation and then prescribes what to do. If the description is wrong, a coherent and competently executed strategy can make the wrong prescription look rational.

    I think this happens more often than leaders admit. Teams can spend hours debating tactics without noticing that the metaphor supplied a questionable account of the problem before the meeting even began.

    Consider the language of Crossing the Chasm. A chasm is dangerous terrain. You have to get across it. Moore adds a D-Day metaphor, a beachhead, concentrated force, invasion, and adjacent territory. Once that description is accepted, many of the prescriptions begin to feel self-evident. Pick one narrow market. Concentrate resources. Establish a defensible position. Expand from there.

    Change the metaphor, and different actions begin to look reasonable.

    Ecosystem

    If the new market were described as an ecosystem, leaders might notice mutual dependence and adaptation.

    Garden

    If it were a garden, they might pay more attention to cultivation, timing, and conditions they cannot control.

    Conversation

    If it were a conversation, they might emphasize listening and reciprocal change.

    One metaphor is not always better than another. Each draws attention to a different part of the situation and leaves another part harder to see. The cognitive scientist Dedre Gentner’s structure-mapping theory helps explain why. An analogy transfers relationships from something familiar into something less familiar. The metaphor brings a pattern of inference with it. Once the market is a chasm, the logic of crossing comes too.

    The metaphor does not stay inside one person’s head. William Ocasio’s attention-based view of the firm argues that what decision-makers do depends on which issues and possible answers receive their attention. The company directs that attention through its rules, resources, relationships, and procedures.

    Once a framework is built into planning templates, budgets, and meeting agendas, it has become part of the organization’s attention system. It helps determine which facts are easy to see, which questions sound intelligent, and which possibilities never make it into the room.

    A framework has no agency of its own. The danger comes when people encode its assumptions in budgets, metrics, roles, and decision routines. At that point, challenging the framework also means challenging the organization built around it.

    I see this in marketing. We draw a funnel to describe one possible path toward a purchase, then build the reporting system around it. Before long, behavior the funnel cannot explain gets treated as a tracking problem, rather than evidence that customers were not actually moving through the world in the shape of our diagram. By then, questioning the framework also means questioning the system built around it.

    THE FRAMEWORK SERVES REALITY

    Contradictory evidence causes the description to change.

    REALITY SERVES THE FRAMEWORK

    Contradictory evidence is filtered, renamed, or dismissed.

    A framework becomes a problem when it starts protecting itself from the world it was built to explain.

    Every metaphor has a boundary

    Moore said something during our conversation that I think matters more than it gets credit for:

    All metaphors have an efficient frontier.

    Within some boundary, a metaphor clarifies more than it distorts. Its efficient frontier is the point at which that balance reverses. Past it, the same metaphor begins creating more confusion than insight.

    Moore is unusually direct about the limits of his best-known model. Chasm crossing is a particular transition in the adoption life cycle, not a permanent operating method. Microsoft did not follow his niche strategy, and its inherited market position made it a terrible precedent for the ordinary challenger. The book is also explicit that crossing the chasm is a B2B model.

    Digital consumer services often spread differently. Rather than forcing them into the chasm model, the book adds a separate Four Gears framework for acquisition, engagement, monetization, and enlistment. Moore marked the boundary and built another tool instead of asking a famous model to explain a market it could not.

    Organizations have good reasons to ignore those boundaries. A successful framework gives them vocabulary and confidence. People know it, leaders know how to present it, and teams know how to operate within it. The more familiar the model becomes, the harder it is to revisit when conditions change.

    When a framework is right at the wrong level

    A framework can fail in at least two ways. It can be stretched beyond the conditions it was built to explain. It can also be used to answer a question that exists at a different level.

    Cover of The Infinite Staircase by Geoffrey A. Moore.
    The Infinite Staircase.

    Moore develops the second problem through the staircase metaphor in The Infinite Staircase. Physics, chemistry, and biology occupy the lower stairs. Desire, consciousness, values, and culture emerge above them. Language, narrative, analytics, and theory appear higher still.

    The precise number of stairs is not Moore’s point. He told me he could have chosen ten or twelve rather than eleven. Higher does not mean better or more important. It means that each level depends on the levels below without being reducible to them. An explanation that works on one stair does not automatically explain another.

    Businesses make this mistake when they use language and theory to manufacture something that exists through shared experience. A company writes a values statement and assumes it has created values. It publishes a culture deck and assumes it has created a culture. It teaches a leadership framework and assumes it has created judgment.

    Language alone can name, examine, and reinforce what people experience together. Employees learn what an organization values from what leaders notice, reward, tolerate, and do when the stated values become expensive. A statement can remain perfectly coherent while the culture teaches the opposite lesson every day.

    A workshop on collaboration will not overcome a compensation system that rewards individual wins. Saying people come first will not rebuild trust after employees watch leaders treat them as expendable. Those are not communication problems. They are evidence that the framework and the lived reality do not match.

    Polaroid could build the future but could not recognize the business

    The most common story about failed innovation is that leaders could not see the new technology coming. Polaroid is a more interesting case because it could see digital imaging very clearly.

    Mary Tripsas and Giovanni Gavetti’s historical study of Polaroid’s response to digital photography drew on company archives and interviews. Polaroid invested in digital technology early. By 1989, it had leading work in image sensors and lossless compression. It had a functioning high-resolution digital camera prototype by 1992.

    Polaroid had leading-edge digital-imaging research capability. It failed to develop several of the manufacturing, product-development, marketing, and distribution capabilities needed to turn that research into the right business.

    Polaroid’s success had been built on the economics of instant photography. The company could sell cameras relatively cheaply and earn recurring revenue from film. Its leaders understood imaging through that relationship between hardware and consumables. A standalone digital camera that did not create continuing film sales looked unattractive inside the model that had made Polaroid successful.

    Comparison of the simplified and documented Polaroid stories: Polaroid saw digital early, built leading technology, and still could not recognize the business.
    The simplified story treats Polaroid as a case of blindness. The documented history shows a company that saw digital early, built leading technology, and still could not recognize the business. Original visualization by Inbound & Agile, based on Tripsas and Gavetti’s historical study of Polaroid’s response to digital photography.

    Management kept interpreting digital products through analog economics. It favored products that preserved a printing or consumables component while the company underinvested in low-cost electronics manufacturing, rapid product development, and new distribution channels. Despite having a working prototype in 1992, Polaroid did not announce its PDC-2000 megapixel camera until 1996, by which point more than 40 other firms were already selling digital cameras.

    That created a reinforcing loop. The old business model directed investment away from the capabilities a standalone digital business required. The absence of those capabilities then made the new business look even less viable from inside Polaroid. The framework shaped the company’s capabilities, and the missing capabilities appeared to confirm the framework.

    C.K. Prahalad and Richard Bettis called this kind of governing worldview a dominant logic. Experience in a successful core business creates mental maps for allocating resources. Over time, the company’s planning, compensation, staffing, and structure can reinforce them. Decades of success had given Polaroid’s framework evidence, believers, vocabulary, and an organization designed to make it true again.

    The most dangerous framework may be the one that once explained the business brilliantly.

    Even our cautionary stories become frameworks

    Even the Kodak story we use to warn against old frameworks has been flattened into one. Kodak did not simply invent digital photography and ignore it. Natalya Vinokurova and Rahul Kapoor’s archival study of Kodak’s attempts at strategic renewal documents decades of investment in digital imaging and other attempts at renewal. Kodak led the United States digital-camera market in 2004 and 2005.

    It still failed, but the management problem was more complicated than blindness. Kodak had to find a viable path from an extraordinarily profitable legacy business into a market with uncertain timing and worse economics.

    “Do not be Kodak” teaches leaders to look for denial. It may leave them unprepared for the harder case: a company can see the disruption, invest heavily, and still fail to find a new business capable of sustaining the enterprise. Seeing the transition did not guarantee that a business with film-like economics existed on the other side.

    The framework should create questions, not end them

    Moore described three ways of testing a framework and said we should use all three at some point in the process.

    TEST 01

    Does it correspond with the facts?

    What does the evidence actually show? Which observations support the framework? Which do not? Are we taking failed ventures as seriously as flagship successes, the way Moore did when he redrew the adoption curve?

    The question sounds obvious. It becomes difficult once the framework determines which data the organization collects and what people are willing to recognize as evidence.

    TEST 02

    Does it cohere with what else we know?

    An explanation should fit with the broader body of credible knowledge around it. A sales model that works only if we ignore how customers buy, a culture model that contradicts what incentives reward, or an AI strategy that assumes capabilities the technology does not possess has a coherence problem even before the results arrive.

    Coherence is not proof. A completely wrong worldview can be internally consistent. It is one test.

    TEST 03

    Does it work?

    The framework should improve our ability to act. Does it help people make better decisions? Does it predict anything useful? Are the results durable, or do they look good only inside the measurement system the framework created?

    The three tests still leave one question unanswered. A framework can correspond with the facts, cohere with what else we know, and produce results while serving a bad purpose. Moore kept returning to another question near the end of our conversation:

    What is it most important for you to be in service to?

    Accuracy and usefulness do not tell us whether a framework’s purpose is worth serving. A model can work for one department while moving costs onto everyone else, or produce growth while damaging customers or employees. Leaders still have to ask who benefits, who pays, and whether the framework is making consequences disappear because they fall outside the map.

    When the map stops serving us

    Those questions are useful only if an organization can tolerate their answers. A framework has to remain answerable to the world and to the purpose it is supposed to serve, even when the evidence threatens a plan, an executive’s judgment, or expertise built around the model.

    Start by stating what the framework leads us to expect and what evidence would show that expectation is wrong. Then give someone both permission and protection to bring that evidence into the room.

    The statistician George Box warned that a person “must not be like Pygmalion and fall in love with his model.”

    The discrepancy between the model and the world is where learning begins.

    That is why intellectual humility becomes an operating requirement. It cannot remain a private virtue or a vague reminder to keep an open mind. The organization needs ways to surface evidence that does not fit and revise plans before defending the framework becomes more important than understanding what is happening. Otherwise, the people with the most authority can explain away each discrepancy until reality makes the correction for them.

    That is how the chasm entered Moore’s original map. The smooth adoption curve could not explain why promising ventures kept failing between early enthusiasts and mainstream customers. Moore did not dismiss those failures as noise or somebody else’s poor execution. He treated them as evidence that the accepted model could not see something important.

    AI will keep changing faster than any one of our current metaphors. When its behavior no longer fits the category a company chose, leaders should treat that mismatch as information, not as a reason to defend the budget, organization, or strategy built around the old description.

    We need the map, and we need the metaphor. But when the world stops matching either one, our job is not to explain away what does not fit, blame somebody else’s execution, or hide consequences that fall outside the frame. Reality may not be refusing to cooperate. The framework may no longer be serving us. The discipline is to remain more committed to the world than to the idea that once helped us see it.

    Geoffrey Moore and Eric Pratum in the episode artwork for The Unfolding Thought Podcast.
    Geoffrey Moore and Eric Pratum for The Unfolding Thought Podcast.

    Sources and further reading

  • Deep Work Is a Social Contract

    Deep Work Is a Social Contract

    Years ago, I tried working the way all the productivity books told me to work.

    I configured Outlook to check my email only twice a day. I opened Slack only a few times. Everyone I worked with knew that if something was genuinely urgent, they could call or text me.

    Almost no one did.

    The system worked. I could concentrate. I was able to stay with difficult work for longer than a few minutes at a time.

    My boss told me explicitly that I seemed aloof.

    And I was punished for it.

    I was ostracized. People spoke badly about me when I was not around. I was put on a performance improvement plan despite delivering exceptional results. I was responsible for more revenue and more revenue growth than either of the other two people with my job title, yet I was paid 25 percent less.

    The organization benefited from the results while penalizing the way of working that helped produce them. The habits that made me more productive also made me less integrated into the team. I eventually hated working there. I spent two and a half years trying to make it work. The day they put me on the PIP, I quit. That was one step too far.

    At the time, I thought I had solved the problem: control the notifications and protect the calendar. I had solved the mechanical part. I had not accounted for what my availability meant to everyone else.

    That is why so many people can follow all the productivity advice and still be unable to do meaningful work. Focus is governed by the people around us.

    You can block your calendar and turn off every notification. If the people around you treat unavailability as a problem, you still do not have permission to focus.

    We usually treat concentration as a matter of personal discipline. Turn off notifications. Block your calendar. Put your phone in another room. Learn to say no. Wake up earlier. Find the right application, routine, or method.

    Those practices can help. They do not change what happens when your manager changes your priorities, a colleague treats every message as urgent, a client expects an immediate answer, or your team interprets delayed responsiveness as a lack of commitment.

    We experience distraction individually, so we assume we should fix it individually.

    In most workplaces, though, the people around us help create distraction and enforce the expectations that keep it going.

    Productivity is social

    Shawn Vanderhoven came to a similar conclusion through a three-year study of leaders and teams. In my conversation with him for The Unfolding Thought Podcast, he explained that he and his collaborators began by asking a straightforward question: what prevents people from doing their best work?

    A consistent answer was that people could not get deep. They were asked to switch tasks continually. Priorities kept changing. The people around them, often without intending to, made sustained attention nearly impossible.

    Vanderhoven and his team then studied leaders from 150 companies who appeared to create the opposite conditions. In Pause: How to Lead Brilliant Work in a Busy World, he reports that teams led by people who deliberately pause spend three times as much time on their largest projects.

    Pause: How to Lead Brilliant Work in a Busy World by Shawn Vanderhoven with Anne Vanderhoven
    Pause: How to Lead Brilliant Work in a Busy World, by Shawn Vanderhoven with Anne Vanderhoven.

    The important word is led.

    Vanderhoven is not saying that individuals have no responsibility for their attention. In our conversation, he acknowledged that unusually disciplined people can create a substantial amount of focus even in distracting environments. The problem is that they often pay for it socially. They are described as detached, unresponsive, inflexible, or difficult.

    That describes what happened to me. My system protected my concentration, but it did nothing to protect my standing with the group.

    Work depends on other people. We need information, decisions, cooperation, and trust. We also want our managers and colleagues to see that we are contributing. Once a group treats immediate availability as evidence of cooperation, focus starts to look like withdrawal. That becomes especially problematic in a remote environment, where managers cannot see you sitting at your desk, attending meetings, having conversations, or look over your shoulder to see what you are working on. They might read your apparent disconnection as you being out mowing the lawn or getting groceries while they are paying you to work.

    The problem is not imaginary. Sophie Leroy’s research on attention residue found that when people switch away from unfinished work, part of their attention remains with the original task. They might have moved to the next meeting or message physically, but not cognitively. Research on collaboration overload has also shown how structures intended to increase cooperation can slow decisions, overwork employees, and place especially heavy demands on high performers.

    Microsoft’s 2025 examination of workplace activity describes what this looks like at scale. Its telemetry showed a workday crowded with email, messages, meetings, and app switching. In the accompanying survey of 31,000 knowledge workers across 31 markets, 48 percent of employees and 52 percent of leaders said their work felt chaotic and fragmented. The widely repeated figure of 275 daily interruptions applies to the most heavily pinged 20 percent of users, so it should not be mistaken for the experience of every employee. Even with that qualification, the larger pattern is hard to miss: many companies have made near-continuous access to coworkers feel normal. Microsoft calls it the infinite workday.

    The problem is not personal

    If distraction were primarily an individual failure, personal productivity advice would be enough. We could teach people to manage their notifications and calendars, then hold them accountable for concentrating.

    But a notification is only the final delivery mechanism. Someone decided to send the message. Someone created the expectation that it should receive a quick response. Someone scheduled the meeting, added another priority, or delegated a task without discussing what it would replace.

    Vanderhoven calls the trail of distraction a leader leaves behind a digital wake. A manager might be highly focused on an important outcome while remaining unaware that each new request is fragmenting the attention of several other people. The manager experiences momentum. The team experiences interruption.

    This can happen even when every request is reasonable. In fact, that might be the harder problem we have to solve.

    Stephen Covey’s familiar illustration of priorities used a jar, large rocks, and pebbles. Put the large rocks in first, then allow the pebbles to fill the remaining space. Vanderhoven argues that the modern workplace has outgrown the illustration. The problem is no longer too many pebbles. We have more large rocks than the jar can hold.

    Most of the opportunities in front of us might be genuinely worthwhile. The proposed campaign could work. The product improvement could matter. The client request is legitimate. The new AI initiative might eventually be essential. We still cannot do all of them well at the same time.

    Once the jar contains more large rocks than it can hold, calling all of them priorities stops being useful. Someone has to decide what will not get done.

    That decision belongs to leadership because employees cannot resolve conflicting organizational commitments on their own. If a manager announces three priorities while leaving ten existing obligations untouched, the team does not have three priorities. It has thirteen pieces of work and an additional burden: deciding which promises it is safe to break.

    One of the practices Vanderhoven describes is exposing this hidden workload. After leaders identify the work that supposedly matters most, they ask people to put everything else they are still expected to do on the table. Only then can the group decide what to stop, shrink, postpone, or reassign.

    Until the hidden work is visible, a new priority is simply another demand. Putting everything on the table reveals what the organization is actually committed to doing.

    Deep work is not isolation

    If distraction is social, it can be tempting to solve it by reducing the number of people, conversations, and interruptions around us. I understand the impulse. It is essentially what I tried years ago. The problem is that too much isolation carries its own cost.

    In my earlier episode of The Unfolding Thought Podcast with Neel Doshi, he told me about an engineer who realized he had not spoken with anyone at work in four days. That might sound like an extraordinary opportunity for deep work. It could also produce loneliness, detachment, and the sense that the work is no longer connected to anything larger than the task itself.

    Shawn made a useful distinction in our conversation: struggle can come from too much connection or too little. A person in meetings all day might be unable to think. A person working almost entirely alone might spend just as much cognitive energy wondering whether anyone sees the work, whether it matters, and whether they still belong.

    A randomized experiment by Ethan Bernstein, Jesse Shore, and David Lazer helps explain why neither extreme works. The researchers compared groups with constant interaction, no interaction, and intermittent interaction. Constant interaction raised the average quality of solutions but made it less likely that a group would discover the best one. Isolation encouraged exploration but produced less consistent performance. Groups that interacted intermittently got the benefits of both. They could learn from one another without losing the independent exploration that led to better solutions.

    What I take from that study is that good work needs a rhythm. We need time when nobody else can get at the problem through us, and we need regular points at which other people can challenge what we are seeing. Either extreme makes failure more likely.

    A workable social contract defines when access is actually necessary and when concentration will be protected. It does not require everyone to disappear.

    AI creates capacity

    Imagine two similar companies adopting the same AI tools. One uses them to remove administrative work and return time to research, judgment, and thought. The other turns every saved hour into more assignments and expects every employee to cover more ground. Both can report a productivity gain, but they are building very different kinds of companies.

    Shawn described this. AI makes it possible for him to take on work outside his normal expertise. The output might look good enough, particularly when he does not know the field well enough to recognize what is missing. When AI works in an area he understands deeply, however, he can see the distance between a plausible draft and excellent work.

    AI can produce an answer. The harder question is whether the user knows enough to judge what it produced, another thing Neel Doshi and I touched on in our podcast discussion.

    The research so far gives us good reason to take that problem seriously. A field experiment involving 758 Boston Consulting Group consultants found substantial improvements in speed and quality for tasks within the capabilities of the AI system used in the study. For a task outside that capability frontier, however, consultants using AI performed worse. The researchers call this the jagged technological frontier: two tasks that appear similarly difficult to a person might fall on opposite sides of what AI can do reliably.

    This does not mean the productivity gains are imaginary. In a study of 5,179 customer-support agents, access to a generative AI assistant increased issues resolved per hour by about 14 percent on average. The largest improvements went to less experienced and lower-performing workers, while the most experienced workers saw little benefit. The results suggest that AI can spread effective practices, but they also show why it is misleading to speak of a single universal productivity effect.

    A 2025 study of 319 knowledge workers adds another caution. Higher confidence in generative AI was associated with less self-reported critical-thinking effort, while greater confidence in one’s own ability was associated with more. This was a survey, not a causal experiment, so it does not prove that AI weakens thought. It does suggest that the way people understand their own responsibility changes when a confident machine enters the workflow.

    Leaders can easily misread all of this. If AI reduces the time needed to produce an artifact, they might conclude that people need less time for the work. But production is only one part of knowledge work. Someone still has to choose the problem, define what good looks like, examine the result, notice what is absent, understand the consequences, and take responsibility for the decision.

    Better tools can increase the need for thought because they give us many more possibilities to evaluate. A cheap draft is useful only if someone still knows how to decide whether it is good.

    When AI saves five hours, the important management decision comes next: will those hours improve the work, or will they simply be filled with more work?

    Who owns the time AI saves?

    Suppose an AI tool saves an employee five hours each week.

    What happens next?

    Does the employee spend some of that time investigating a harder question, improving the work, learning something new, or talking with the people affected by it? Or does the organization immediately add five hours of additional assignments?

    I think most companies will not answer explicitly. The calendar will answer for them. Once people can produce drafts and analyses faster, managers will ask for more of them. A person who can monitor three agents will be asked to monitor ten. The saved time disappears because there is always another worthwhile thing the company could do. It gets replaced by more volume, not more quality.

    You can see the sensitivity to that tradeoff in the reaction to apparent “AI slop.” Someone comments on a social media post and gets told it must have been written by AI, even when it was not. This has happened to me. The person is reacting to the feeling that you are pushing quantity ahead of quality.

    This is how AI could accelerate a company into having almost no deep work at all. It will not need to distract people in the familiar way. It can remove individual tasks while increasing the number of active projects, decisions, reviews, and conversations competing for attention.

    Microsoft’s infinite-workday report makes the same danger unusually explicit: AI might offer a way out, but without a redesigned rhythm of work, companies risk using it to accelerate a broken system.

    We should use the capacity, but we also need to decide what we want it for.

    An AI-generated meeting summary should let some people skip the meeting. A first draft should leave more time to challenge its assumptions, not automatically create demand for three more drafts. Faster research should make room for a better decision about which evidence matters. Automated administrative work should give some time back to the work that still requires expertise.

    None of those outcomes will happen because the tool makes them inevitable. They happen when leaders choose not to consume every minute the tool saves.

    A social contract for focus

    Leaders do not need to redesign the entire organization before they can improve the conditions for deep work. They do need to stop treating concentration as a private preference that employees should defend by themselves.

    Make priorities include the work that will stop

    If a new priority does not remove or shrink anything already on someone’s plate, it is another assignment. Put the existing work next to the new request and decide what stops.

    Turn delegation into a discussion of tradeoffs

    Before assigning new work, ask what the person is currently doing, what accepting the new task would displace, and what help would make the change possible. The question is not simply whether the new request matters. It is whether it matters more than the work it will replace.

    Establish shared rules for access

    Teams need to know what deserves an immediate response, which channels carry urgent requests, when delayed responses are acceptable, and how protected concentration will be communicated. A leader can use personal credibility to make focus socially safe. Employees should not have to appear uncooperative in order to think.

    Design a rhythm between solitude and collaboration

    Do not rely on constant availability to create connection, and do not rely on isolation to create focus. Give people predictable stretches to work independently and purposeful opportunities to compare, question, and reconnect.

    Decide what AI-generated capacity is for

    Do not measure the change only by counting output. Look at whether decisions improve, people learn faster, or the most important work receives more attention. If every efficiency gain creates another commitment, AI has not protected attention. It has increased throughput.

    A useful place to begin is with the question Shawn recommends that leaders ask their teams:

    What is distracting us right now, and what can we do together to win that attention back?

    The work AI cannot do for us

    Individuals still have responsibility for how they use their attention. No organizational policy can make someone care about excellent work. No manager can remove every interruption, and no team can protect unlimited periods of concentration.

    But people cannot productivity-hack their way out of a culture that continually places belonging, responsiveness, and focus in conflict.

    AI is making this conflict harder to ignore. We are able to create more, answer faster, and take on work that once seemed beyond our capacity. None of that tells us which opportunity deserves attention, when a plausible answer needs another hour of thought, or how to protect the relationships that make difficult work worthwhile.

    Those are leadership decisions.

    If a company consumes every minute AI saves, it might become faster while also becoming busier and shallower. Reserving some of the time for judgment, learning, and concentration would be a deliberate choice. It will not happen on its own.

    AI will give many of us more time to think. Whether anyone is allowed to use it that way will depend on the people around them.

    Shawn Vanderhoven and Eric Pratum for The Unfolding Thought Podcast.
    Shawn Vanderhoven with Eric Pratum for The Unfolding Thought Podcast.

    Listen to my full conversation with Shawn Vanderhoven on The Unfolding Thought Podcast.

    Shawn’s book, Pause: How to Lead Brilliant Work in a Busy World, written with Anne Vanderhoven, is available from Amazon and other booksellers.

  • When an Institution Stops Defining Its Own Values

    When an Institution Stops Defining Its Own Values

    An institution does not have to collapse to lose itself. It can keep its name, buildings, leaders, members, and mission statement. It can even become more visible and powerful.

    The loss occurs when its people stop looking to the institution to decide what its own values require. A church can preach forgiveness while a political tribe defines who deserves it. A company can celebrate quality while its incentives teach that speed is what matters. A university can defend inquiry while status rewards only safe conclusions.

    When the stated value conflicts with the system that supplies attention, belonging, reward, and threat, the mission statement is rarely the deciding force.

    That is the leadership problem I kept returning to after my conversation with Jonathan Rauch for The Unfolding Thought Podcast. Rauch’s recent book, Cross Purposes: Christianity’s Broken Bargain with Democracy, is explicitly about Christianity and American democracy. It is also a case study in how an institution can remain recognizable after an outside system has begun defining what its values mean.

    The institution still has formal authority. Something else now has formative authority.

    A lifelong atheist reverses course

    Rauch is not a Christian arguing that Americans should return to church. He is a gay, Jewish atheist who once expected declining religious influence to reduce dogmatism and social conflict.

    In 2003, he wrote approvingly about what he called “apatheism,” a growing indifference toward religion. He expected less religious conviction to produce less dogmatism.

    He now calls that one of the dumbest things he ever wrote.

    The need for identity, meaning, moral certainty, community, and transcendence did not disappear as institutional religion weakened. Rauch believes much of that energy migrated into politics. To understand why that migration is so consequential, we have to distinguish the levels at which religion and politics ordinarily operate.

    Politics is primarily an effort to order the world we inhabit. It concerns laws, institutions, resources, power, and choices whose consequences we can observe, debate, and experience. Religion is concerned first with questions that cannot be settled with certainty through ordinary evidence: why we are here, what ultimately matters, what makes a life good, what we owe one another, and whether reality contains purposes beyond us. Those answers have to be held, at least partly, through faith.

    In that sense, religion sits beneath politics as a foundation, while politics operates above it as an application. “Higher” does not mean more important. It means closer to visible collective action. Our answers to ultimate questions constrain which political goals feel permissible, which compromises feel moral, and which uses of power feel legitimate.

    Religion does not need laws or police power to exert that influence. If a person believes that “thou shalt not kill” expresses an ultimate moral obligation, the belief itself constrains behavior. Families, congregations, and cultures can reinforce the standard long before the state becomes involved. When enough people share such a belief, politics often translates it into law, establishing an expectation that applies even to people who do not share the faith.

    This does not mean every religious rule is good, correct, or worthy of becoming law. It means belief systems shape the boundaries within which politics operates. A legislature can govern a religious institution’s conduct. It cannot make someone believe, and it cannot vote ultimate meaning into existence.

    Religion exerts that influence through story, ritual, repetition, family, community, prohibitions, and ideals. A religious tradition embeds moral meaning beneath conscious argument. It teaches people what feels sacred, shameful, natural, and right, even when they cannot explain every step in the reasoning. That cultural and social learning, accumulated over generations, is difficult to reproduce when the formal belief system is removed.

    When religion weakens, the needs it served remain. For many people, politics begins doing two jobs: ordering common life while also supplying identity, belonging, transcendence, and moral certainty. But politics is a product of the world it is trying to govern. It can make rules, distribute resources, and allocate power. It cannot adequately answer questions about what lies beyond that world.

    Contingent political judgments then begin hardening into articles of faith. Disagreement feels like a battle over reality. Compromise becomes moral surrender. Opponents become enemies of the good rather than citizens with a different judgment.

    Rauch’s reversal is that politics does not simply become more important after religion weakens. It begins filling the role of religion without necessarily possessing the practices longstanding religious traditions developed to restrain fear, pride, vengeance, and tribal certainty. It can inherit religious intensity without inheriting humility, forgiveness, self-examination, and coexistence.

    What remains can still look religious even as politics increasingly supplies its meaning. Rauch’s framework helps explain how.

    In Cross Purposes, Rauch describes three broad forms of Christianity. “Thin” Christianity has become too indistinct to inspire or form people. “Sharp” Christianity replaces spiritual distinctiveness with fear, partisan identity, and political combat. “Thick” Christianity remains demanding and recognizable while developing enough confidence in itself to coexist with people who believe differently.

    Rauch applies that framework to several Christian traditions. He criticizes portions of mainline Christianity for becoming difficult to distinguish from secular progressivism, and portions of white evangelical Christianity for becoming difficult to distinguish from Republican politics. His point is not simply that one party corrupted a church. It is that institutions can import an outside politics from either direction and then mistake that politics for their own mission.

    Do the standards move?

    None of this means a religious institution has been captured whenever its members enter politics, support a party, or vote for a flawed candidate. Politics forces choices among imperfect options. People can make policy tradeoffs without adopting a politician’s character or allowing a party to define their faith.

    A vote is an outcome, not proof of formation. Institutional formation becomes visible when the standards used to interpret the vote begin moving with the coalition. The question is not simply whom people support. It is whether the outside system starts deciding what their own tradition means.

    Support for Donald Trump among white evangelical Protestants illustrates why the distinction matters. In a 2020 Pew Research Center survey, 80 percent said he fought for what they believed in, yet only 15 percent said “morally upstanding” described him very well and just 31 percent liked how he conducted himself apart from his positions. A 2016 study by political scientist Michele Margolis found that white evangelical Republicans who affirmed more core evangelical beliefs were less likely to prefer Trump during the Republican primary but more likely to support him once he became the nominee. Both findings are consistent with political tradeoffs. Neither, by itself, proves institutional capture.

    The more revealing evidence concerns moral standards. In 2011, 30 percent of white evangelical Protestants told the Public Religion Research Institute that an elected official who behaves immorally in private can still behave ethically in public office. In 2016, the figure reached 72 percent. But PRRI’s comparisons over time also show Republicans, white mainline Protestants, white Catholics, and Democrats shifting with political circumstances. The pattern is not unique to one faith or party. People across groups can revise standards around a favored coalition.

    Identity can move with the coalition too. A Pew panel study followed the same respondents from 2016 to 2020. Among white adults who did not initially identify as born-again or evangelical, 16 percent of those expressing a warm view of Trump adopted the evangelical label by 2020, compared with 1 percent of those with consistently cold or neutral views. That does not prove insincerity. It shows political and religious identity moving closely enough together that we should ask which was defining the other.

    That question also complicates what surveys mean by “Christian.” Identity, belief, practice, and participation are not the same. A 2021 study published in Sociological Forum found that Christian nationalism was significantly associated with support for Trump among voters who did not attend religious services, but not among churchgoing voters. The study does not establish that nonattenders are insincere or churchgoers more virtuous. It shows that Christian-nationalist ideas can exert political force even when detached from the institution and practices that might otherwise shape what the identity means.

    This is where Rauch’s distinction between Thin and Thick Christianity becomes especially useful. If we say “Christian” when we mean anyone who accepts the label, we may be combining people formed by regular worship, communal accountability, and demanding moral commitments with people for whom Christianity functions mainly as a cultural or political identity. Christianity can become thinner even while the number of people claiming the identity remains substantial.

    Taken together, these studies do not prove that politics replaced religion. They show what leaders should examine. When moral standards and group labels begin tracking an external alliance, which institution is defining which? My reading of Rauch is that this does not arise from an inherent flaw in Christianity. It arises when people who claim a faith become less bound by its central moral precepts than by political identity. For some people claiming a white evangelical identity, religion can supply the label while politics exerts more force over judgment and behavior. The identity survives after the formative discipline weakens.

    Why the outside system wins

    But why does an outside system gain formative authority in the first place?

    Because the systems competing to form us do not receive equal time or reach us with equal frequency. The institutions and media that occupy most of our time, repeat their interpretations most often, and tie those interpretations to belonging and status will exert the greatest influence. A value heard for an hour or two each week has little chance against a worldview reinforced every day.

    Some of the most important passages in Cross Purposes come from pastors who believed they had lost authority inside their own churches.

    One estimated that a church might have a congregant’s attention for two hours each week while political television had the same person for twelve. Others told Rauch that members arrived already formed by political media and then evaluated sermons, pastors, and other Christians according to that political worldview.

    By the time people arrived at church, they were not waiting for a pastor to explain how to see the world. Many arrived with a political worldview already in place and used it to judge the church.

    Experiments with partisan news support this mechanism. In one randomized experiment, regular Fox News viewers who were paid to watch CNN for a month changed what they knew and considered important on subjects CNN covered. Many of those effects receded after they returned to their normal viewing. Another field experiment found that exposure to partisan news changed consumption and immediate knowledge even when most policy positions and partisan attitudes remained stable.

    Repetition does not instantly erase every prior belief. It decides which facts and interpretations are most available, most familiar, and most likely to frame the next judgment.

    Organizations often behave as though communicating a value is the same thing as forming people around it. Write the value on a wall. Put it in onboarding. Ask the CEO to mention it at the annual meeting. Assume the culture will follow.

    Culture does not work that way.

    Research on organizational climate and culture describes culture as the assumptions and values that guide organizational life, while climate consists of the meanings people attach to their repeated experiences. People learn what matters by watching decisions, exceptions, rewards, punishments, promotions, and the behavior that produces status.

    Steven Kerr made the same point more bluntly in his classic 1975 paper, “On the Folly of Rewarding A, While Hoping for B”. Organizations routinely say they want one behavior while rewarding another. People notice what actually pays off.

    A company says quality comes first but pays bonuses based only on volume. It gets volume.

    A leadership team says it wants candor but punishes the person who raises an inconvenient concern. It gets silence.

    A business says customers come first but protects a high-performing salesperson who repeatedly harms customer relationships. It teaches everyone that revenue comes first and customers come second.

    Rauch’s pastors describe the same conflict in church life: a church teaches humility, forgiveness, and love of enemies while partisan media can reward certainty, humiliation, fear, and victory. If the second system receives more attention, creates more belonging, and confers more status, repeating the stated values more loudly may have very little effect.

    This is not primarily a communication problem. It is a competition between formative systems.

    The question is not whether people know your values. It is whether following them makes sense inside the system you actually run.

    Leaders can see which system is winning by following attention, reward, threat, and the exceptions made when a stated value becomes costly.

    The goal is not to isolate people from outside influence or give leaders control over private belief. Institutions need outside ideas and criticism, and they do not deserve deference merely because they are institutions. The danger is unrecognized capture. The test is whether an institution’s actual decisions give members a coherent basis for evaluating the competing systems trying to shape them.

    When an institution wants to know which system is actually forming its members, it should look at the moments when following its stated values imposes a cost. That is where the ability to lose becomes decisive.

    Why the ability to lose matters

    Politics becomes especially dangerous when it begins doing the work of religion. In ordinary democratic politics, elections are part of an ebb and flow of shared power. One coalition governs for a time. Another loses, remains legitimate, and has a chance to persuade voters and govern later. Defeat can be consequential without becoming an answer to ultimate questions.

    Once politics is asked to supply identity, meaning, transcendence, and moral certainty, an election no longer feels like a temporary decision about who governs. It feels like a battle for the country’s soul. If one side represents salvation and the other evil, perhaps even Jesus and Satan, accepting defeat does not feel like restraint or power sharing. It feels like allowing evil to triumph.

    The structure is the same regardless of which party casts itself as the defender of good. Opponents stop being citizens who judge policies differently and become evidence of corruption. Political affiliation becomes a proxy for moral worth. Friendship across political lines gets harder because the disagreement appears to reach all the way down to what kind of person someone is.

    Values are easiest to honor when they cost nothing. The revealing moment comes when honesty threatens the sale, quality threatens the schedule, fairness threatens an ally, or restraint threatens a political victory. If the same value repeatedly disappears under pressure, the organization may not have a value. It may have a preference that survives only when the surrounding system permits it.

    If losing means annihilation, domination begins to look like self-defense.

    Political scientists use the phrase “losers’ consent” to describe the willingness of people who lose an election to continue accepting the legitimacy of the democratic system. It is not an instruction to accept a fraudulent or unfair result. Research suggests that consent is sustainable when the route back to power remains genuinely open. James Fearon’s model of self-enforcing democracy makes that logic explicit; studies have found that alternations of power can narrow the legitimacy gap between winners and losers and that people are more likely to accept outcomes when elections are free and fair. This also places obligations on winners. Research on mutual forbearance describes why democratic competitors sometimes refrain from exploiting every legal advantage when doing so would make future competition less credible. Repeated losses can reduce trust, while affective polarization can deepen the divide between winners and losers. Across these findings, the practical requirement is the same: procedures must be fair and the possibility of participating meaningfully tomorrow must remain believable.

    The same principle applies inside an organization. People can accept a rejected proposal, a missed promotion, or a strategic decision they opposed if they believe the process was fair, dissent has not destroyed their future, and the next decision has not already been rigged against them. Healthy institutions do not prevent people from losing. They preserve a path by which people can lose, remain legitimate participants, and try again.

    Rauch argues that the Christian teachings he summarizes as “do not be afraid,” respect the equal dignity of every person, and forgive rather than destroy opponents can help make political loss survivable. A reader does not need to accept the theology to see the institutional function. Groups that can tolerate uncertainty, preserve the standing of dissenters, and remain bound by their commitments when they lose are less likely to treat every decision as an existential war.

    A tradition that teaches people how to lose without abandoning conviction can keep politics from becoming a religion. When that formative work weakens, politics absorbs religious intensity and every contest begins to feel ultimate.

    Conviction without domination

    Can an institution retain strong convictions, accept limits on its power, and coexist with people who reject its beliefs?

    Rauch’s constructive counterexample comes from the Church of Jesus Christ of Latter-day Saints and its negotiations with LGBTQ advocates.

    In 2015, church representatives, Republican and Democratic legislators, Equality Utah, the ACLU of Utah, and other faith communities supported Utah Senate Bill 296. It extended employment and housing protections to LGBTQ Utahns while defining protections for religious institutions. The participants did not resolve their disagreement about marriage or sexuality. They identified protections they could enact together while continuing to disagree. The compromise was not a universal template: it omitted public-accommodations protections, and the ACLU criticized its unusually broad exemptions.

    The relationships built through that process supported later collaboration. Rauch treats the federal Respect for Marriage Act as another application of the same approach: protect same-sex marriages while preserving specified religious-liberty protections. Both the ACLU and the U.S. Conference of Catholic Bishops argued that important protections remained incomplete, though for different reasons. Compromise did not require anyone to pretend the disagreement was unimportant.

    Rauch connects the church’s approach to its theology of moral agency. His interpretation is that protecting another person’s meaningful ability to choose can be a religious obligation even when the person makes a choice the church considers wrong. That gives compromise a foundation inside the institution’s own identity instead of treating it as reluctant surrender to secular pressure.

    Compromise is not automatically virtuous. Some proposed compromises preserve injustice, ask only the vulnerable party to concede, or weaken rights that should not depend on negotiation. The example supports something narrower and more useful:

    An institution can maintain a strong identity without trying to dominate everyone outside it.

    Confidence in an identity may make negotiation easier. A brittle institution experiences disagreement as erasure. A more secure institution can distinguish changing a law, losing a vote, or accommodating a neighbor from surrendering its reason for existing. The pattern leaders should look for is not weak identity, but strong identity capable of surviving constraint, disagreement, compromise, and loss.

    What leaders should examine

    The leadership lesson from Rauch’s case is not that organizations should become less political, more religious, or more ideologically neutral. Organizations have different purposes. A civil-rights group should advocate. A church should teach a faith. A political party should seek political power. A business should make choices about customers, employees, investment, and risk.

    Every institution should understand what is actually shaping its members’ judgment about the mission.

    Leaders can begin with five questions.

    1. Who receives enough attention to define reality?

      Your annual values presentation is competing with compensation plans, customer demands, professional networks, social media, managers, coworkers, and daily experience. Which source has enough repetition and credibility to define what people believe is really happening?


    2. What behavior produces status and reward?

      Ignore what the organization says it celebrates. Who gets promoted, protected, invited into important meetings, forgiven for mistakes, and trusted with resources? Those decisions teach the culture what success means.


    3. Which commitments disappear under pressure?

      Every institution makes exceptions. Look for the direction of the exceptions. If quality is always sacrificed to speed, candor to harmony, or fairness to a high performer’s results, the exception may be the operating value.


    4. Can someone disagree and still belong?

      Strong cultures need boundaries. They also need a way to distinguish disagreement from betrayal. When every dissenting idea becomes evidence that someone is not truly part of the group, leaders lose access to corrective information and members learn to conceal what they see.


    5. Can the institution lose without abandoning itself?

      Can a leader lose an argument and continue supporting the team? Can a business lose an account without breaking its standards? Can a movement lose a fair vote and continue using legitimate procedures to contest the next one? Commitments that survive loss are more credible than commitments that appear only in victory speeches.


    These questions do not prevent outside influence. They make the influence visible enough to evaluate.

    The mission statement can survive after the mission is gone

    It would be easy to dismiss Cross Purposes as an argument about Trump, white evangelicals, or the proper relationship between Christianity and democracy if you have not read it.

    Rauch is examining what happens when an institution no longer gives its members a strong enough account of its own purpose to resist being redefined by another system.

    The sequence matters. As the institution’s formative force weakens, another system begins defining reality for its members. Its standards start moving with that outside system. Loss begins to feel existential, domination becomes defensible, and obedience to the outside system is mistaken for fidelity to the mission.

    That can happen from the political right or left. It can happen through donors, customers, algorithms, compensation plans, professional status, or fear. It can happen without anyone consciously deciding that the mission should change.

    By the time leaders notice, the institution may still look much as it always did. The name remains. The meetings continue. The values are repeated. The organization may even be winning.

    The test is not whether members can recite the institution’s values. It is whether those values still govern when another system offers more belonging, status, power, or protection from loss.

    An institution does not lose itself only when it changes its mission statement. It can lose itself while repeating the statement more loudly than ever.

    Jonathan Rauch and Eric Pratum during their conversation on The Unfolding Thought Podcast
    Jonathan Rauch and Eric Pratum on The Unfolding Thought Podcast.

    This essay draws on my conversation with Jonathan Rauch for The Unfolding Thought Podcast and his book, Cross Purposes: Christianity’s Broken Bargain with Democracy.

  • If Integrity Requires Heroism, the System Is Already Broken

    If Integrity Requires Heroism, the System Is Already Broken

    Robert Coram’s biographies praise people who chose principle over career. They also expose a failure that should scare all of us:

    Too many institutions need exceptional courage to hear ordinary truth.

    In 1967, Lieutenant General Victor “Brute” Krulak went to the White House with an argument President Lyndon Johnson did not want to hear.

    The United States was prosecuting the Vietnam War through search-and-destroy operations and attrition. Krulak believed the decisive contest was for the security and allegiance of the people living in South Vietnam’s villages. The Marines could and should fight large enemy formations, he argued, but destroying units and counting bodies would not by itself defeat an insurgency.

    Robert Coram reconstructs what happened next in Brute, his biography of Krulak. According to that account, Krulak told Johnson that American strategy was producing unnecessary casualties and that responsibility reached the top of the government, “including you, Mr. President.” If the president did not change course, Krulak warned, he would lose the war and the next election.

    Johnson rose, placed a hand on Krulak’s shoulder, and ushered him out without another word.

    Lieutenant General Victor “Brute” Krulak in uniform against a cream, charcoal, and brass editorial background.
    Lt. Gen. Victor H. Krulak.
    Cover of Brute by Robert Coram.
    Brute by Robert Coram.

    Coram argues that the confrontation helped cost Krulak a fourth star and the job he wanted most: Commandant of the Marine Corps. Johnson later selected another Marine. Krulak retired as a three-star in 1968.

    Krulak’s confrontation with Johnson is not a neat parable about a truth-teller and a villain. Coram does not write neat parables. His biographies preserve achievement, ambition, contradiction, collateral damage, and uncertainty in the same frame.

    A president responsible for a war needed the information Krulak carried. Admiring Krulak’s courage is not enough. Why did delivering necessary information have to resemble a career-ending act of heroism?

    The fork in the road tests more than character

    When I interviewed Coram for The Unfolding Thought Podcast, he explained what he had looked for in the military figures he chose to write about. He wanted people who reached a fork in the road, did what they believed was right, and paid a price.

    Robert Coram in a dark suit and round glasses against a cream, charcoal, and brass editorial background.
    Robert Coram.

    A moral fork tells us what a person does when duty, self-interest, loyalty, and fear point in different directions. It reveals more than a résumé, values statement, or performance review ever could. Coram found versions of that test in Krulak; in fighter pilot and military theorist John Boyd; and, at an almost unimaginable physical extreme, in Medal of Honor recipient and Vietnam prisoner of war George E. “Bud” Day.

    The fork also tells us something about the road builder, not just the traveler.

    When people must repeatedly choose between doing good work and preserving a viable career, the institution is not merely discovering their character. It is manufacturing the conflict. It is making personal courage compensate for defects in how information travels, how authority responds, and how careers are governed.

    Some moral choices will remain costly in any organization worth serving. No process can make courage unnecessary. But when routine truth-telling predictably requires career-threatening heroism, the price is no longer just evidence of the employee’s virtue. It is a leadership metric.

    A heroic culture can still be a silent culture

    Organizations often talk about candor as if it were a personality trait:

    • Good employees speak up.
    • Courageous leaders welcome bad news.
    • Weak people stay quiet.

    Decades of organizational research describe a more relational problem. New York University professor Elizabeth Morrison defines upward voice as employees voluntarily communicating suggestions, concerns, or information about problems to people higher in the hierarchy.

    Silence is not the absence of information. It is the withholding of information that may already exist somewhere lower in the system.

    That means a quiet meeting is ambiguous. Everyone may agree. Or the people who disagree may have decided that the cost of speaking is not worth paying.

    In a study of 3,149 employees and 223 managers, James Detert and Ethan Burris found that managerial openness was more consistently related to improvement-oriented voice than transformational leadership was. Employees’ sense of psychological safety helped explain the relationship. The effect of leader behavior was especially strong among high performers, the very people an organization can least afford to train into silence.

    Psychological safety does not require every leader to be gentle or every employee to feel comfortable. Amy Edmondson originally defined it as a shared belief that a team is safe for interpersonal risk-taking. That makes it easier to ask for help, report mistakes, and challenge an assumption. It does not make every assertion correct, remove performance standards, or exempt anyone from the consequences of deliberate misconduct.

    Coram’s account of Krulak contains a small scene that makes the larger problem visible. During a visit to Vietnam, Krulak asked Marines what they thought. They gave him answers he did not expect and did not like. Afterward, he asked his son, Navy chaplain Victor Krulak, what was wrong with the battalion.

    His son suggested that people elsewhere might have been telling the general what he wanted to hear.

    Coram writes that the possibility appeared not to have occurred to Krulak, even though Krulak himself knew that subordinates often manage what senior officers see.

    Krulak’s blind spot makes his confrontation with Johnson more instructive. The same person can be unusually willing to carry unwelcome truth upward and still make it difficult for truth to travel upward to him.

    Candor is not secured by placing one brave person in the hierarchy. It has to survive every level of the hierarchy.

    “To be or to do” is both a challenge and an indictment

    John Boyd made the decision point explicit.

    Coram’s Boyd follows the fighter pilot from the cockpit to the Aerial Attack Study, energy-maneuverability theory, the F-15 and F-16 development battles, and the body of work later associated with the OODA loop. Boyd’s accomplishments depended on extraordinary concentration, technical insight, a network of allies, and an appetite for conflict that often made him nearly impossible to ignore and just as difficult to manage.

    John Boyd in flight gear against a cream, charcoal, and brass editorial background.
    Col. John Boyd.
    Cover of Boyd by Robert Coram.
    Boyd by Robert Coram.

    He divided officers into people who wanted “to be” somebody and people who wanted “to do” something.

    Coram shows Boyd applying that test to Air Force officer James Burton. Burton had refused an order to alter a chart comparing aircraft performance because the alteration would create a lie. He was removed from his Pentagon job and passed over for promotion. Boyd told him he had reached a fork: he could pursue the promotion and its trappings, or continue the work he believed was right. He could not, Boyd said, have a normal career and do the good work.

    As a moral challenge, “to be or to do” draws a clear line. As an organizational condition, it should be alarming.

    Why should a normal career and good work be incompatible?

    What information will never reach a decision-maker if the price is obvious before anyone speaks?

    How many capable employees will make the sensible choice, protect their families and futures, and leave the unaltered chart in a drawer?

    Hero stories can mislead managers. We admire the outlier who accepts the sacrifice and then quietly build systems that assume the next person will do the same. When the next person does not, we call it a character failure rather than asking what the incentives were designed to produce.

    Boyd also shows why an institution cannot simply reward combativeness. He could be brilliant, abrasive, domineering, and wrong. His crusades imposed costs on colleagues and family members who did not choose them.

    An organization that depends on finding a hero is fragile. A stronger one preserves the function Boyd and men like him serve: challenging assumptions, demanding evidence, and testing doctrine, without requiring every reformer to become him.

    Courage does not make a claim true

    Coram told me that one of his four military subjects did not fit his moral-fork rule: World War II fighter pilot Robert Lee Scott Jr.

    Brigadier General Robert Lee Scott Jr. in Air Force dress uniform against a cream, charcoal, and brass editorial background.
    Brig. Gen. Robert Lee Scott Jr.
    Cover of Double Ace by Robert Coram.
    Double Ace by Robert Coram.

    Scott was a real combat pilot and a double ace. He flew guest missions with pilots of the American Volunteer Group, the original Flying Tigers, and later commanded the regular Army Air Forces’ 23rd Fighter Group. His 1943 memoir, God Is My Co-Pilot, became a bestseller and then a film. He also told stories so fluently and so often that the border between experience, performance, and memory could become difficult to locate.

    Cover of Robert Lee Scott Jr.'s 1943 memoir God Is My Co-Pilot.
    Scott’s 1943 memoir, God Is My Co-Pilot.

    In one late version of the book’s origin story, Japanese rounds struck Scott’s aircraft from behind and drove rivets from the armor behind his seat into his back. After landing, he was supposedly operated on without anesthesia in a candlelit cave, where the title appeared to him on the cave wall. Coram found that the cave revelation was absent from the original book, that he could find no injury record, and that Scott had not received a Purple Heart. Those absences do not prove that every element was invented, but they leave the later account without the evidence that should support it.

    Scott matters here because moral courage is not a method for determining truth. Neither sincerity, confidence, sacrifice, nor opposition to authority proves a claim. Institutions need people to raise unwelcome information, and they need disciplined ways to test it.

    A useful truth-telling system has to protect both candor and evidence.

    If only safe claims can be voiced,
    evidence is filtered before examination.

    If every contrarian claim is celebrated as brave, dissent becomes theater.

    A serious truth-telling system protects the messenger long enough to evaluate the message.

    What ordinary people usually do at the fork

    The most revealing moral fork in Coram’s body of work may not belong to a pilot or general.

    In his memoir Ink, Coram recalls working in 1959 as an untrained attendant at Georgia’s state mental hospital in Milledgeville. By then, it was less a hospital in the ordinary sense than a vast custodial city. The institution averaged more than 11,800 patients in the late 1950s. It sprawled across roughly 200 buildings and 2,000 acres and, by the 1960s, was described as the largest mental hospital in the world.

    That scale did not produce more care. The New Georgia Encyclopedia records overcrowding, “conscious neglect,” and an institution often able to meet only basic daily needs rather than provide appropriate treatment. In a place that large, abuse could disappear into wards, bureaucracy, and sheer numbers.

    That helps explain the importance of Jack Nelson’s investigation for the Atlanta Constitution. His 1959 reporting documented experimental drugs given without patient or family consent, major surgery performed by a nurse without supervision, and staff and doctors drunk on duty. The series would win the 1960 Pulitzer Prize.

    Cover of Ink by Robert Coram.
    Ink by Robert Coram.

    Coram admired Nelson. He understood that this was what a newspaper could do: enter a closed institution and force powerful people to answer for what happened inside.

    Then, Coram writes, a doctor ordered him to dispose of boxes of medicine to hide them from Nelson’s reporting. Coram believed the labels, dates, and drugs might matter to the investigation. He flushed the pills down a patient toilet, carried the packaging to a dump, and said nothing when the reporter returned.

    He believed the material might be evidence. He recognized the choice. He obeyed the institution anyway.

    “I wanted to help him,” Coram writes. “But I did not.”

    I do not read that sentence as a verdict on the young man. I read it as a warning to anyone who designs work for other people.

    Most employees are not Bud Day. They should not have to be.

    They have mortgages, health insurance, reputations, visas, commissions, pensions, children, and an accurate understanding of what authority can do to them. They watch what happens to the first person who raises a problem. They notice whether the concern is investigated, whether the leader becomes curious or offended, and whether the messenger’s next assignment quietly disappears.

    The employee who remains silent may be making a moral mistake. The organization that makes silence rational is making a management mistake.

    The price rarely stops with the person who speaks

    Coram’s biographies also complicate the idea of individual sacrifice by showing who else pays.

    Bud Day endured five years, seven months, and thirteen days as a prisoner of war, including periods of prolonged torture. Dorie carried that captivity into public life, organized with other POW families, preserved information, kept their household functioning, and later lived beside the physical injuries and recurring nightmares that came home with him. Mary Boyd and her children experienced a different version of cost: absence, volatility, financial strain, and the work required to preserve the ideas for which John Boyd received public credit.

    George E. “Bud” Day in uniform against a cream, charcoal, and brass editorial background.
    Col. George E. “Bud” Day.
    Cover of American Patriot by Robert Coram.
    American Patriot by Robert Coram.

    Those family accounts do not erase Day’s courage or Boyd’s achievements. They complete the ledger.

    We recognize, celebrate, and talk about a few heroes, but the cost of their heroism is paid not only by them. It is also paid by the people around them.

    Organizations usually record the visible price: the stalled promotion, the resignation, the lost command. The cost that migrates into a home, a colleague’s workload, a customer’s risk, or a caregiver’s life often disappears from the scorecard. Calling the employee heroic can become a way of praising a sacrifice the institution has no intention of accounting for.

    Build for candor before you need courage

    Values statements are useful only if the operating system makes them credible. Here are six ways to make candor less dependent on individual courage.

    1. Give unwelcome information a route around the hierarchy

    An open-door policy still requires an employee to walk through the boss’s door.

    NASA’s Aviation Safety Reporting System was designed around a different insight. Aviation workers can voluntarily report safety incidents to an independent program that keeps identities confidential, removes identifying details, and offers limited protection from penalties for qualifying unintentional violations. Deliberate and criminal acts are not sheltered. The design does not confuse learning with impunity; it reduces the personal risk that prevents useful safety information from entering the system.

    Most organizations do not need a miniature NASA. They do need a channel whose independence, confidentiality, response time, and escalation rules are real rather than decorative.

    2. Make dissent part of the work, not a personality contest

    Do not wait for a natural contrarian to challenge the plan. Assign the function.

    Before commitment, ask one group to assume the decision has failed and construct plausible reasons why. Gary Klein’s premortem works because it gives knowledgeable skeptics permission to voice reservations before failure converts them into hindsight. Rotate the dissent role so that disagreement does not become one person’s identity or one person’s career risk.

    Require the decision owner to state what evidence would change the decision. “Convince me” is not a standard; it is an invitation to contest status. A falsifiable threshold is a standard.

    3. Make the most powerful person speak last

    If the senior leader announces a preference first, every answer that follows has been contaminated by information about what the hierarchy wants.

    Collect judgments independently before discussion when possible. Ask for disconfirming evidence, not just concerns. Replace “Does anyone disagree?” with questions that require content: What assumption is carrying the most risk? What would we expect to see if we are wrong? Who has information that does not fit the current story?

    Then wait long enough for an answer.

    4. Keep a decision record that can survive memory

    Record the decision, material assumptions, competing interpretations, predictions, unresolved concerns, owner, and review date. The purpose is not bureaucratic self-protection. It is to keep the organization from rewriting what it once believed after the outcome is known.

    Scott’s stories show how repetition can harden a satisfying account. A contemporaneous record gives later reviewers something other than confidence and status to examine.

    5. Review outcomes without defending rank

    In my conversation with former Royal Australian Air Force fighter pilot and Afterburner CEO Christian “Boo” Boucousis on The Unfolding Thought Podcast, the debrief was the central subject. Boo described an operating rhythm used after every mission, successful or not: compare the objective with the actual result, identify the cause of any gap, and choose a concrete action for the next mission. His organization applies the same discipline to business teams. The purpose is to convert experience into usable intelligence while keeping rank and ego from controlling the account.

    The simplicity is deceptive. A useful debrief has to remain a professional inquiry rather than a trial, a victory lap, or a briefing in which subordinates discover what the leader wants them to say. Preserve competing accounts. Separate facts from inference. Assign changes and return to see whether they occurred.

    6. Audit what happens to the messenger

    Nonretaliation cannot be measured by counting how many people were formally fired for speaking up. Career penalties are often quieter: a missed invitation, a lower-visibility assignment, exclusion from information, an unexplained performance downgrade, or the conclusion that advancement now lies elsewhere.

    Track whether people who raise consequential concerns remain, advance, and continue to contribute. Ask them what happened after they spoke. Review whether concerns were acknowledged, investigated, and closed. Hold leaders accountable for retaliation and for teaching teams through visible behavior that candor is futile.

    The Government Accountability Office’s recent review of federal disclosure systems reaches a similar practical conclusion: accessible confidential channels, credible protection against retaliation, consistent accountability, and visible leadership commitment all influence whether people trust a speak-up system.

    What leaders owe the truth-teller

    Coram is right to admire Krulak’s willingness to carry an unwelcome judgment to Johnson. The choice revealed the general’s sense of duty and the relative value he placed on career and country. We need people who will act that way when a consequential truth has no safe route to power.

    Admiration can stop the inquiry too early.

    The leader’s question is not only, Will someone be brave enough to tell me? It is also, What expectations have I created about what happens to people who tell me something I do not want to hear?

    A sound institution cannot remove every moral fork. It can keep routine evidence, doubt, error, and disagreement from becoming tests of personal martyrdom. It can make candor ordinary enough that exceptional courage is reserved for exceptional circumstances.

    When doing the work and keeping a viable career repeatedly point down different roads, the organization has confused a character test with a management system.

    Moral courage is a virtue. It should not be an operating system.

    The episode and sources

  • AI Is Not the Next Printing Press. It May Be the Next Reformation.

    AI Is Not the Next Printing Press. It May Be the Next Reformation.

    AI is not just changing what machines can do. It is changing how we understand our own value, our place in the world, and what it means to be human.

    On Christmas Day in 1521, a communion wafer fell onto the floor of a church in Wittenberg.

    This was not an ordinary Mass. Andreas Karlstadt had come dressed in the clothing of an ordinary person, not the vestments of a priest. He gave laypeople both the bread and the wine and invited them to take Communion even if they had not confessed. Many had not observed the required fast. Some were even said to have drunk brandy. About a thousand people reportedly came to participate in a service that openly broke with the religious practice they had known all their lives.

    Unless you understand the world these people inhabited, those details can sound like minor violations of church etiquette.

    They were not.

    For a Christian in early sixteenth-century Western Europe, the Mass was the moment when a priest did something no ordinary person could do. He consecrated bread and wine. In the inherited theology of transubstantiation, the substance of the bread became the body of Christ while the appearances of bread remained. The wafer still looked and tasted like bread, but worshippers understood themselves to be encountering the real presence of Christ.

    The priest’s vestments marked him as someone set apart for that responsibility. Confession prepared a person to receive the sacrament without approaching God while burdened by unforgiven sin. Fasting prepared the body for the encounter. Laypeople usually received Communion infrequently, often only at Easter. When they did receive it, they ordinarily received only the bread, placed in the mouth by a priest. These practices were not random rules layered around a symbolic meal. They expressed an entire understanding of the relationship between God, the Church, the priest, the body, sin, and salvation.

    Karlstadt challenged almost all of it at once. His ordinary clothing questioned the spiritual difference between a priest and everyone else. Communion without confession rejected the requirement that communicants first seek forgiveness through priestly confession. Eating and drinking beforehand treated the required bodily discipline as unnecessary. Giving the wine to laypeople challenged a practice that had separated priest from congregation for centuries.

    He was not simply asking them to treat the wafer as ordinary bread. At that point, Karlstadt still affirmed Christ’s real presence in Communion. He was asking them to inhabit a new account of who could approach and handle the sacred.

    Then two consecrated wafers fell, one onto a man’s coat and another onto the floor. Karlstadt told the people to pick them up.

    They would not.

    These were not defenders of the old order. They had come because they believed reform was necessary. They were willing to defy the canons of All Saints, inherited ritual, and their own political ruler. Yet touching the Host meant touching what they had spent their lives believing was the body of Christ. That was a boundary their bodies would not let them cross. Karlstadt had to retrieve the wafers himself.1

    Worshippers in a sixteenth-century church hesitate as a communion wafer lies on the floor
    The Christmas Day 1521 service in Wittenberg.

    The people in that church had changed their minds before they had changed their instincts. They were ready to reject the old rules, but not yet ready to touch what those rules had taught them was sacred.

    That is what a real change in worldview looks like. An old system can lose its intellectual authority before it loses its hold on the body.

    This is not another technology

    This is the part of artificial intelligence I think we are missing.

    We keep comparing AI to the printing press, electricity, radio, the internet, and social media. Those technologies did astonishing things. They changed what people could do, how quickly they could do it, how far an idea could travel, who could speak, and how people behaved.

    The crucial word is how. Those technologies also affected identity and meaning, sometimes profoundly. But their central effect was to change how human beings acted in the world. They changed the conditions of life without generally making people across almost every level of society wonder whether human contribution itself was becoming unnecessary.

    AI is becoming a different kind of event because people believe it will not merely change their work. They believe it may eventually do their work better than they can and remove the need for them to do it at all.

    Once that premise takes hold, the questions escalate quickly.

    If AI can perform enough of my work to make my role unnecessary today, or will be able to soon, what is my place in the world? What is my purpose? What am I worth? If work is one of the main ways I contribute to other people, what happens when that contribution no longer requires me? What is a human life for then? Do human beings matter anymore? Do I matter? Will my children?

    Those are not forecasts about what AI will actually accomplish. They are questions people can begin asking as soon as they believe replacement is coming. A life can be reorganized by an expectation before the prediction comes true.

    Machines have threatened jobs before, sometimes on a vast scale. What is distinctive now is that generative AI has carried replacement anxiety into an unusually broad range of occupations built around language, interpretation, judgment, creativity, and leadership. The question can reach a manager, doctor, lawyer, teacher, artist, writer, programmer, executive, or public official in essentially the same form: if the ability that made me useful can be reproduced without me, where does my value now reside?

    AI does not need to replace all of those people for the worldview to begin changing. It only has to make replacement feel plausible.

    That is why I think the Protestant Reformation gives us a better comparison than the printing press.

    I am not saying AI is a religion. I am not saying the twenty-first century will repeat the sixteenth. I am saying both events force people to reconsider a world that had made sense. They destabilize the institutions that explained what was true, who could know it, what made a life valuable, and where an ordinary person fit within the whole.

    The printing press was an extraordinary technology that helped the Reformation spread. The Reformation was the crisis of meaning.

    AI is the technology. The crisis gathering around it is about how we make meaning.

    A world that made sense

    To understand why the Reformation changed so much of western and central Europe so profoundly, we have to begin by taking the world before it seriously.

    Many people in late-medieval Latin Christendom did not merely hold a list of Catholic beliefs. They inhabited a world in which visible and invisible reality were joined.

    The church building made that world physical. A parishioner could look from the ordinary people crowded into the nave, to saints painted on a screen, to the crucified Christ above it, to images of the Last Judgment, heaven, hell, and purgatory. Beyond the screen, at the altar, the priest brought Christ into the church in the consecrated bread. Diarmaid MacCulloch describes this as a floor-to-ceiling account of the Christian story. Creation, sin, suffering, death, judgment, and eternal life were not ideas discussed somewhere else. They surrounded a person while that person stood in church.2

    The same system organized life outside the building. Church bells marked the day. Fasts, feasts, Advent, Lent, Easter, and Christmas gave the year its shape. Baptism brought an infant into the Christian community. Confession and penance addressed sin. Church law helped govern marriage, sex, and inheritance. Guilds, monasteries, and confraternities joined work, charity, status, and prayer.

    Purgatory offered an account of what happened to the saved who still needed to be purified after death. Masses, prayers, alms, pilgrimages, relics, and indulgences gave the living something meaningful to do for parents, spouses, children, and friends who had died. A family paying for a Mass was not simply purchasing a service from a church. Within that world, the family was still caring for someone it loved.

    This was why the Church mattered. It did not merely tell people what to believe on Sunday. It explained where the dead had gone, why suffering existed, how guilt could be forgiven, which work was holy, who had authority, how time should be lived, and what a human life was for.

    That system was neither uniform nor peaceful. Clergy could be corrupt. Popes pursued money and power. Local practices varied, and reform movements long predated Luther. Still, the late-medieval Church was not an empty shell waiting for one brave man to expose it. It met real spiritual needs. Even its abuses drew power from needs people genuinely felt.

    That was true of indulgences. An indulgence addressed punishment for sins already forgiven. Within the larger account of confession, penance, and purgatory, it made sense. An indulgence campaign connected fear for the dead with papal authority, local politics, banking, and the financing of St. Peter’s Basilica in Rome.3

    When Luther challenged the indulgence campaign in 1517, he pulled on a thread connected to almost everything.

    At first, he was arguing about repentance, grace, punishment, and pastoral care. He was not yet presenting a complete new church. But the disagreement could not remain narrow because his opponents eventually made authority the issue. If the pope and the accumulated teaching of the Church supported the practice, what right did a provincial friar have to contradict them?

    That question forced another: what if the pope was wrong?

    Then another: what if a church council could be wrong?

    Then another: what if the practices through which Christians had sought forgiveness, cared for the dead, distinguished holy lives, and understood salvation were not merely imperfect, but based on a fundamental error?

    The controversy stopped being about the proper use of indulgences. It became an argument about who could say how reality worked.

    Luther’s answer altered the structure of the Christian life. He argued that a person was justified by God’s grace through faith, not by accumulating merit. Scripture, not the pope or accumulated tradition, was the final authority against which doctrine had to be judged. Baptism and the shared priesthood of believers gave ordinary Christians a spiritual standing that did not depend on membership in a separate religious caste. Monastic vows did not create a higher class of Christian. Ordinary work performed in faith could be a vocation.

    Each claim answered a theological problem. Together, they changed where a person stood in the universe.

    Within Lutheran and other Protestant accounts, a monk or nun was not necessarily closer to God than a parent, farmer, merchant, or servant. Priests remained important, but no longer belonged to a spiritually superior estate. Clergy could marry. Care for the dead changed when purgatory and Masses for souls were rejected. Images that had mediated sacred presence could be condemned as idols.

    A monk who had understood his vows as a path to holiness could discover that the path itself was a mistake. A nun who had been given to a convent by her family could be told that leaving, marrying, and raising children might be more faithful than remaining. A priest could become a husband and father. A family that had endowed Masses for its dead could learn that those Masses accomplished nothing. A craftsperson could hear that ordinary labor was a calling from God, while a student preparing for an ecclesiastical career could watch that entire career structure collapse.

    This was not liberation in one clean direction. It was disorientation.

    In Wittenberg, the changes moved so quickly that some students questioned whether their education had any point. The attack on private Masses threatened clerical livelihoods. When monasteries closed, land changed hands, men and women had to find new places in households and local economies, and poor relief had to be reorganized. When reformers removed images, they did not merely redecorate churches. They stripped away objects through which generations had understood sacred history and encountered the holy.45

    Western and central Europe did not move together from one worldview to another. It fractured.

    Catholics, Lutherans, Reformed Protestants, Anabaptists, and others offered incompatible accounts of the Eucharist, baptism, salvation, church government, and political obedience. Families divided. Refugees crossed borders. Rulers and churches built rival settlements through catechisms, schools, law, discipline, and force. Religious fracture became entangled with political revolt, state formation, persecution, and war. Catholic reformers also clarified doctrine, built institutions, and renewed their own account of authority.5

    The world had not become meaningless. It had become a place in which the old sources of meaning no longer settled the most important questions.

    The printing press helped claims move faster and farther. It made controversy cheaper to reproduce and harder to contain. But the press was not the thing that caused a priest to marry, a family to stop paying for Masses for the dead, a ruler to seize a monastery, or a worshipper to stare at an empty space where a saint had stood.

    The Reformation did those things because it changed what the world meant.

    The world AI found

    The modern world is not united by one church, one theology, or one account of salvation. We disagree about religion, morality, politics, human nature, and almost everything else.

    Yet our institutions still depend on a mostly unspoken account of the person.

    We use human performance to distribute income, status, opportunity, and recognition. Schools reward writing, reasoning, memory, and problem solving. Professions issue credentials meant to prove knowledge and judgment. Employers pay more for abilities that are difficult to acquire and scarce in the labor market. People build identities around being able to do something that someone else needs.

    Ask someone what they do, and the answer will usually be a job.

    That is not an accident. For many people, work provides income, but it also provides structure, community, status, competence, and a way to explain one’s contribution. Becoming good at difficult work changes a person. The failures, repetitions, corrections, mentors, and unusual cases become part of how that person sees the world.

    This account of human value has always been morally incomplete. A person is not worth more because that person writes better, calculates faster, remembers more, or earns a higher salary. Still, our schools, firms, professions, and labor markets regularly behave as though useful performance is evidence of both economic and personal value.

    Then a machine begins producing the performance.

    That is the disruption.

    The most important thing about generative AI is not that it can make a document, image, plan, diagnosis, or recommendation more quickly. It is that those outputs often functioned as evidence of a human capacity. A finished essay implied that a student had read and synthesized. A legal analysis implied that a lawyer had interpreted. A diagnosis implied that a clinician had reasoned. A strategy implied that a leader had decided. A painting implied that an artist had imagined and made.

    Now the artifact can appear without proving what we thought it proved.

    That is destabilizing enough. But the fear goes further. If a machine can produce every economically valuable performance better than the people who once produced it, what are the people for? What is their purpose? Where do they find the recognition that tells them they matter? What is the meaning of a life that appears to be needed by no one?

    AI did not invent the weakness in our assumptions. It exposed how much meaning we had asked work and performance to carry.

    It does not matter yet whether the fear is right

    AI cannot actually do all the things people fear it will do. It makes elementary mistakes. It invents facts. It often needs a human to define the problem, gather the right information, recognize an error, and take responsibility for the result. A strong performance on a benchmark does not mean a system can replace a person in the messy conditions of a real job.

    All of that is true, but it does not answer the argument I am making.

    I am not arguing that AI will eliminate every profession or that a machine is already more valuable than a person. I am arguing that a large number of people now fear that their future usefulness is being placed in competition with a machine. Because our institutions have taught people to locate identity, security, and recognition in useful work, I think that fear can become a question about personal value.

    This is not a fringe anxiety. In 2025, Pew Research Center found that 52 percent of American workers were worried about AI’s future workplace impact, and 32 percent expected it to leave them with fewer opportunities. In a separate survey, 64 percent of American adults expected AI to mean fewer jobs over the next 20 years. Across 31 countries, Microsoft and LinkedIn found that 45 percent of knowledge workers worried AI would replace their job.7910

    The fear reaches the next generation. In a 2026 study, Common Sense Media found that 57 percent of parents with children under 18 believed AI would make it harder for their children to find jobs. Nearly half of the young people surveyed worried about AI’s effect on their economic future, 49 percent thought it would make finding a job harder for them personally, and 59 percent thought people like them would have a harder time.11

    Those surveys do not show that AI will cause mass unemployment. They do not show that workers believe their lives are literally worth less. They establish something important here: people already believe the ground is moving beneath them.

    The anticipation becomes part of the event.

    So, what happens when you believe there may be no job for you to get? What do you study? Do you borrow money for a degree? Do you spend years mastering a profession that may not want beginners by the time you arrive? Do you save for a future you cannot picture? Do you have children when you are not sure how they will support themselves or where they will find a place to contribute?

    These questions can change decisions before any forecast is proved. In Deloitte’s 2025 survey of more than 23,000 Gen Z and millennial respondents across 44 countries, more than six in ten of the respondents who used generative AI worried that it would eliminate jobs and said the concern was motivating them to seek work they considered safer from disruption.12

    The International Labour Organization offers an important counterweight. It estimates that one in four jobs worldwide has some exposure to generative AI, but says transformation is more likely than full replacement. Exposure is not disappearance. A task is not a job, and a benchmark is not a workplace.6

    Still, the distinction between transformation and replacement does not settle the question of meaning. An OECD survey found that most workers using AI thought it complemented their abilities, while about half of the users in finance and almost half in manufacturing also believed it had made some of their own skills less valuable.8

    Think about that. A person can become more productive and feel less valuable at the same time.

    The fear is already entangled with people’s sense that they matter. In an American Psychological Association survey, workers worried that AI would make their duties obsolete were substantially more likely than other workers to say they did not matter to their employer or coworkers. The survey cannot prove that AI anxiety caused those feelings. It does show that fear of obsolescence and felt insignificance are arriving together.13

    This is how a worldview begins to shift. The new account does not have to be true, complete, or stable. It only has to make the old account stop feeling certain.

    When work stops explaining our place

    Now imagine believing that the work through which you expected to contribute may no longer need you.

    You do not have to lose your job for that belief to change you. A student can look at the field that once promised a future and wonder whether it is foolish to enter. A midcareer professional can look at years of accumulated skill and wonder whether the market will still recognize it. A parent can look at a child and wonder what preparation could possibly be enough. Someone choosing whether to start a family can wonder whether the next generation will have any economically useful place at all.

    The actual labor market may eventually prove some of those fears wrong. It may produce new roles, new industries, and forms of work we cannot yet name. But a worldview is not only a description of what is objectively true. It is the set of assumptions through which people decide what is worth doing.

    If I believe no employer will need my skill, the belief changes whether I develop it. If I believe entry-level work will vanish, the belief changes whether I pursue the profession. If I believe my children will enter a world in which machines outperform them at every economically valuable task, the belief changes how I imagine their future.

    For many people, a job has been the most ordinary proof that there is a place for them. Someone needs what I can do. Someone will exchange part of the world’s resources for my contribution. Other people can describe me through the role, and I can describe myself through it.

    When that proof becomes uncertain, the question is larger than, “How will I earn money?”

    It becomes, “Where am I needed?”

    The Reformation created a similarly profound disruption in vocation. Before it, monks, nuns, and priests occupied a religious estate set apart from ordinary household and economic life. Protestant reformers attacked that hierarchy. A monastery was not necessarily a place of higher holiness. Marriage, parenting, civic office, farming, craft, and household labor could all be callings in which a person served God and neighbor.

    That change supplied one influential inheritance for the modern idea that ordinary work can express a calling. The later history is long and complicated, and our current habit of identifying ourselves with jobs cannot be credited to the Reformation alone. But the connection between work, identity, contribution, and purpose remains powerful. We want work to mean something because we want our lives to mean something.

    AI presses directly on that connection.

    The important issue here is not whether AI will actually eliminate a particular job. It is what happens when a teacher, programmer, designer, accountant, lawyer, doctor, manager, or writer begins to believe the activity through which that person contributed will no longer require a person.

    The question changes from, “How will I do my work?” to, “Why would anyone need me to do it?”

    That is the difference between a technological adjustment and a worldview-changing event. A new tool changes the method. The belief that human contribution itself is becoming unnecessary changes the person who imagined having a place in the world.

    It is not only what AI is capable of doing that matters. It is what people think its capabilities mean about them.

    What is a person worth when performance is no longer unique?

    The Reformation forced people to ask how grace, faith, sacraments, penance, merit, and the Church’s mediation fit together in the relationship between a human being and God. What made one life holy and another ordinary? Who could speak with spiritual authority? What did freedom mean, and what obligations remained?

    AI presses a different question, but one at a similar depth: why does a person matter?

    For a long time, we have answered partly by pointing to abilities. Humans reason. Humans use language. Humans create art. Humans solve problems. Humans understand one another. Humans exercise judgment.

    But even if a person had no grand theory of human uniqueness, there was usually a simpler answer available: I can do something that other people value.

    For many people, some role, task, trade, service, form of care, or exercise of judgment offered a way to contribute. The work might not be glamorous or deeply fulfilling. It might even be miserable. Still, being needed offered a basic form of recognition. There was a place for me because there was something I could do.

    Now a system can produce behavior associated with many of those abilities.

    We can respond by moving the boundary. Perhaps humans reason better in unusual situations. Perhaps humans possess consciousness, embodiment, emotion, moral responsibility, relationships, or a lived history that a machine lacks. Those differences may be real and essential.

    But notice what happens if our defense of human value remains a performance contest.

    If people matter because they write better, then an improvement in machine writing weakens the argument. If people matter because they diagnose more accurately, the argument becomes vulnerable to the next medical benchmark. If people matter because they create beautiful images, every advance in image generation appears to subtract from human uniqueness.

    That should make us question the argument, not the people.

    A child does not have to outperform a model to possess dignity. A person with dementia does not lose value as memory fails. A disabled person is not less human because a machine can perform an activity that person cannot. A worker’s worth cannot be identical to the market scarcity of a skill.

    AI is not making human life less valuable. It is exposing how often we confused value with superiority, dignity with productivity, and meaning with economic usefulness.

    That could be a healthy correction.

    It could also be brutally destabilizing in societies that distribute income, status, health, housing, and opportunity according to market value. Telling people that their dignity is unconditional does not pay a mortgage when an employer has decided their skill is no longer scarce.

    The economic and existential questions therefore cannot be separated.

    If work is one of the main places people experience contribution, and if our institutions treat productive output as the basis for security and respect, AI can create a crisis of meaning even while increasing total wealth. A society can become more productive while many of its members feel less necessary.

    The problem is not simply that a machine might become more like a person.

    The problem is that we built a world in which people were valued as imperfect machines.

    AI makes that world harder to defend.

    If a machine can eventually do all economically valuable work better than all of us, the fear is not merely that we will lose our salaries. The fear is that we will lose the most ordinary evidence that somebody needs us. What is my value then? What is my purpose? What is a life for when its contribution appears unnecessary? Those questions may rest on a false account of human worth, but that does not make them less real to the person asking.

    A worldview is not replaced by proving that the old one has a flaw. It is replaced when people and institutions learn to live inside another account of who they are.

    Where the comparison stops

    The Reformation was not a technology. It was a collection of religious, political, economic, and social movements within Latin Christendom. It unfolded across generations through churches, rulers, cities, families, wars, markets, and competing doctrines. AI is a technology being deployed around the world in societies with very different histories and accounts of human meaning.

    I am not arguing that a model company is a new church, a programmer is a new Luther, or a chatbot is a religion.

    Nor am I arguing that radio, electricity, industrialization, the internet, or social media left human meaning untouched. They had enormous effects on human life and identity. But they primarily transformed our how: how we moved, worked, communicated, produced, consumed, and organized daily life.

    AI can press more directly on our why. Why am I needed? Why does my work matter? Why should I develop an ability a machine may soon perform better? What gives a person purpose when useful performance is no longer distinctly human? The other technologies changed the conditions in which we pursued meaning. AI is beginning to make people question some of the sources from which they drew meaning in the first place.

    The analogy is not between a model and Luther. It is between two periods in which an inherited account of a person’s place in the world stopped settling the most important questions.

    The work of making meaning

    Before we decide how to use AI, we need to understand what kind of change it is.

    Treat AI as another technology and familiar questions follow. How do I use it? How do I work faster? Which skills should I learn? Which jobs will grow? Which rules should govern it?

    Those questions matter, but they are about how.

    They do not answer the question AI has placed underneath them: what really is my worth?

    The Reformation matters as an analogy because it shows what happens when a civilization’s inherited answer to a question like that stops feeling certain. People do not simply update a belief and continue living as before. They reinterpret their past. They reconsider their obligations. They make different decisions about work, family, authority, education, community, and the future. The disruption travels outward from an altered account of who a person is and what a life is for.

    The parent wondering whether a child will ever find work is not merely planning for a new labor market. The parent is trying to imagine whether the child will have a recognized place in the world. The professional afraid that years of expertise will become worthless is not merely calculating future income. The professional is asking whether the life spent developing that expertise still adds up to something. The young person looking at a machine that can write, code, draw, diagnose, and advise is not only choosing a major. That person is trying to identify a form of contribution that will still prove there was a reason to develop into someone.

    We should not dismiss those fears by insisting that AI will create new jobs. It may. We should not intensify them by pretending we know that human labor will disappear. We do not.

    The deeper problem is that both answers leave the same assumption untouched: a person’s worth depends on remaining economically useful.

    As long as that is the answer, every improvement in AI will feel like a reduction in human value. Every new benchmark will move the boundary behind which we try to protect our importance. We will say that machines can write but cannot reason, then that they can reason but cannot judge, then that they can judge but cannot care. If our worth always rests on the next task a machine cannot perform, we have made human value contingent on the machine’s limitations.

    That is not a stable place to stand.

    We have to put the more difficult question at the forefront: what makes a human life valuable even when that life is not the most efficient way to produce an output?

    Answering it will require more than reassuring slogans. We can say that every person has dignity, that relationships matter, that care matters, that experience matters, and that responsibility matters. But we also have to ask whether we believe those things deeply enough to live by them when productivity points in another direction.

    This is hard work because the old account is built into us.

    Return one last time to Wittenberg.

    The people at Karlstadt’s service had accepted enough of the new theology to break rules that had ordered Christian practice for generations. Yet when the wafer fell, the old world was still present in their hesitation. Their explicit beliefs had changed before their embodied understanding of reality had changed.

    We can see the same divide in ourselves.

    Most of us would reject the claim that a person’s worth equals a salary, a title, or the scarcity of a skill. We know that a child matters before the child can work. We know that an older person does not become worthless upon retirement. We know that illness, disability, unemployment, or dependence does not erase someone’s humanity.

    Yet when a machine performs a valued skill better or faster, many of us feel the threat personally. We do not experience it merely as a new method. We experience it as evidence that we may be worth less.

    Our stated belief is that human value exceeds useful performance. Our reaction reveals how much of the old worldview remains in the body.

    Saying that the old account of our worth was flawed is not enough. We have to learn to live inside another account of who we are.

    If we can answer the question of human worth in a way that survives being outperformed, we do not have to treat every advance in AI as evidence of our own disappearance. We may still have serious reasons to worry about jobs, income, power, and inequality. But we will not have to confuse the loss of a task with the loss of a self.

    If we cannot answer it, we will keep defending our value through whatever work machines have not yet mastered. That territory may continue to shrink. Long before every job is actually replaced, we may experience ourselves as replaceable altogether.

    And then the question, “What is the point of life?” will no longer sound rhetorical.

    The printing press, radio, electricity, the internet, and social media changed what people could do and how they could do it. AI is changing how people understand their own purpose, value, and place in the world.

    AI is changing the why.

    Notes and sources

    1. Lyndal Roper, Martin Luther: Renegade and Prophet (Random House, 2017), chapter 10, especially pp. 218–224 and 235. Roper describes Karlstadt’s Christmas 1521 service, its theological and political context, the dropped wafers, and the communicants’ refusal to touch them. Roper also notes that the brandy story came through hostile reporting and was later repeated by Luther.
    2. Diarmaid MacCulloch, The Reformation: A History (Penguin, 2005), introduction and chapter 1, especially “Seeing Salvation in Church” and “The First Pillar: The Mass and Purgatory.” MacCulloch reconstructs the late-medieval Church as a vigorous system in which the physical and spiritual, the living and dead, and everyday and eternal life were joined.
    3. Roper, Martin Luther, introduction, especially pp. 1–6, on the social, sacramental, political, and financial system surrounding the indulgence campaign. See also MacCulloch, The Reformation, chapter 3.
    4. Roper, Martin Luther, chapters 9–10, especially pp. 218–230, on the Wittenberg reforms, the collapse in university enrollment, threats to clerical careers, changes to poor relief, iconoclasm, and the struggle over secular authority.
    5. MacCulloch, The Reformation, introduction and chapters 4, 14, and 16, on uncontrolled early reform, the Peasants’ War, confessionalization, institutions, family, vocation, and everyday life. See also Roper, Martin Luther, chapters 12–13 and 16.
    6. International Labour Organization and NASK, “Generative AI and Jobs: A Refined Global Index of Occupational Exposure” (2025). The report estimates that one in four jobs worldwide has some exposure to generative AI while emphasizing that transformation is more likely than full replacement.
    7. Luona Lin and Kim Parker, “U.S. Workers Are More Worried Than Hopeful About Future AI Use in the Workplace,” Pew Research Center, February 25, 2025.
    8. OECD, “The Impact of AI on the Workplace: Main Findings from the OECD AI Surveys of Employers and Workers” (2023). The surveys covered more than 5,000 workers and 2,000 employers in finance and manufacturing across seven countries.
    9. Pew Research Center, “How the U.S. Public and AI Experts View Artificial Intelligence” (April 3, 2025). The survey included 5,410 U.S. adults and a separate sample of 1,013 AI experts.
    10. Microsoft and LinkedIn, “AI at Work Is Here. Now Comes the Hard Part,” Work Trend Index Annual Report (May 8, 2024). The findings draw on a survey of 31,000 knowledge workers across 31 countries.
    11. Common Sense Media, “Generation AI: What Kids and Families Think About AI” (March 2026). The national U.S. study surveyed parents and young people ages 12 to 17 about AI, education, employment, and the future.
    12. Deloitte, “2025 Gen Z and Millennial Survey” (May 14, 2025). The survey included 23,482 respondents across 44 countries.
    13. American Psychological Association, “2023 Work in America Survey: Artificial Intelligence, Monitoring Technology, and Psychological Well-Being” (2023). The association between obsolescence worry and feeling insignificant at work is correlational.
  • AI Cannot Decide What Your Business Is For

    AI Cannot Decide What Your Business Is For

    Every AI deployment turns vague values into executable instructions. If leaders do not define purpose, knowledge, and reality, the system will borrow its philosophy from metrics, training data, and vendors.

    Imagine a leadership team announcing its AI strategy in one sentence: We will achieve the same growth with 15 percent fewer people.

    That may be a target. It may even be a responsible response to difficult economics. But it is not a compelling account of what the organization hopes to become. It tells employees which cost should fall, not which capability should rise. It describes subtraction as though subtraction were a future.

    Michael Schrage raised a version of this problem in our recent conversation. The trouble with treating AI chiefly as a source of efficiency is not only that the ambition is small. It is that the ambition supplies the system with a purpose. The tools are then asked to optimize labor cost, cycle time, or output without anyone having to say what the work is ultimately for.

    AI does not make philosophy irrelevant. It makes philosophy operational.

    Once a model can recommend, rank, route, generate, negotiate, or act, words such as value, quality, loyalty, productivity, and risk can no longer remain inspiring abstractions. They have to become instructions. Someone, somewhere, must decide what counts as evidence, which categories matter, which tradeoffs are acceptable, and whose outcomes deserve priority.

    If leadership does not make those choices deliberately, the choices do not disappear. They are inherited from the data, the metric, the workflow, or the vendor.

    The danger is not only that AI will fail. It is that AI will faithfully optimize a purpose no one examined.

    Philosophy is already in production

    In his MIT Sloan Management Review essay, “Philosophy Eats AI,” Schrage argues that AI systems inevitably embody positions on three old philosophical questions.

    Teleology asks what the system is for. Is a customer-service agent meant to close tickets, reduce expense, restore confidence, preserve a relationship, or help a customer make a better decision? These purposes overlap until they do not. At the moment of conflict, the system needs a priority.

    Epistemology asks what counts as knowledge. Which evidence may the system trust? Does a recorded click outweigh a customer’s explanation? Is a prediction sufficient grounds for action? How should the system treat uncertainty, disagreement, or information that does not fit its prior patterns?

    Ontology asks what kinds of things exist in the system’s world. Is a person a customer, a user, an account, a risk score, a member of a household, or a participant in a community? Which relationships are visible? Which are erased because the database has no field for them?

    These questions can sound academic until a system denies a loan, selects a job candidate, prioritizes a patient, assigns a sales lead, or decides which employee appears unproductive. Then ontology becomes a data model, epistemology becomes an evidence rule, and teleology becomes an objective function.

    Philosophy has entered production.

    The practical mistake is to imagine that a technically neutral system waits for human values to be added later. There is no neutral ranking, neutral threshold, or neutral definition of success. Every deployment encodes a view of what matters, even when that view arrives disguised as a default setting.

    When metrics become metaphysics

    Organizations need metrics. The problem begins when a proxy stops being treated as a partial representation and becomes the reality the system is permitted to see.

    Suppose a company defines loyalty as repeat purchases. That definition is measurable and useful. It is also incomplete. A customer might repurchase because switching is difficult, because a contract traps them, or because every alternative is worse. The metric registers all three as loyalty. An AI trained to increase the number may improve retention while degrading the relationship that retention is meant to represent.

    The same problem appears when knowledge work is reduced to visible activity. Messages sent, documents produced, tickets closed, or hours online are traces of work. They are not the work itself. Judgment often creates value by preventing unnecessary activity, reframing the question, or recognizing that the requested output should not exist.

    AI makes this confusion more consequential because it can optimize the proxy continuously and at scale. A weak metric in a monthly report can mislead a meeting. A weak metric embedded in an autonomous workflow can reorganize behavior across the company.

    When a metric becomes the target, it can become the organization’s working definition of reality.

    This is why the familiar warning that “the map is not the territory” becomes a governance issue. The model will travel the map it is given. Leaders remain responsible for asking what the map excludes, who drew its borders, and whether arriving at the marked destination would actually count as progress.

    The accumulation of philosophical debt

    Technical debt accumulates when short-term engineering choices make future systems harder to change. Organizations now face a related problem: philosophical debt.

    Philosophical debt accumulates when fundamental questions are left unresolved but systems must still be built. The team does not agree on what a qualified lead is, so it automates the current scoring rule. Leaders have not settled what good service means, so the agent optimizes handle time. No one can define productive collaboration, so the dashboard counts individual output.

    Each choice is defensible as a temporary convenience. Together, they harden into an operating philosophy no one remembers choosing.

    The debt is easy to miss because the system can perform well by its own measures. It can become faster, cheaper, and more consistent. But consistency is not the same as correctness. A system can scale a category error with extraordinary reliability.

    This is one reason vendor selection alone cannot be an AI strategy. Vendors can provide capable models, safeguards, and implementation patterns. They cannot decide what your customers should become, what obligations you have to employees, or which forms of value your business should protect. When leaders leave those questions blank, product defaults fill the space.

    Agency raises the philosophical stakes

    The stakes rise as AI moves from answering questions to taking action.

    A conversational model can produce a flawed recommendation that a person ignores. An agentic system can convert the same flaw into a sequence of actions before anyone notices the premise. It can segment customers, change offers, move money, contact suppliers, or alter a workflow. Greater autonomy shortens the distance between an assumption and its consequences.

    This does not mean every decision needs executive review. That would defeat much of the value of automation. It means the boundary between machine discretion and human judgment must reflect the moral and strategic weight of the decision, not merely the model’s confidence.

    A model may be highly confident that a customer belongs in a low-value segment. Confidence does not answer whether the category is fair, whether the treatment will become self-fulfilling, or whether the company wants to build a relationship in which past value sets the ceiling on future attention.

    “Human in the loop” is therefore too vague to be a governance principle. Which human? At what point? With what information? Empowered to question which premise? Accountable to whom?

    Expert judgment should not be a ceremonial inspection step attached to the end of an automated process. It must help define the categories, evidence, exceptions, and purposes that shape the process from the beginning.

    Critical thinking is not a soft skill

    Schrage asks a bracing question in the episode: How do you contribute meaningful value when you are no longer the smartest creature in the room?

    One answer is that intelligence and judgment are not interchangeable.

    A model may generate more options than a team, retrieve more information than an expert, and expose patterns no individual could detect. It still cannot relieve leaders of the obligation to decide what deserves to be optimized. In fact, greater machine capability makes that obligation harder to avoid.

    Critical thinking in an AI-enabled organization is not simply the ability to spot hallucinations. It is the ability to examine the frame:

    • What problem have we decided this is?
    • What must be true for this recommendation to make sense?
    • Which values have been converted into variables?
    • What does the system make invisible?
    • Which outcome would falsify our theory of value?
    • Who benefits if the model is right, and who absorbs the cost if it is wrong?

    These questions are sometimes dismissed as slowing innovation. Used well, they do the opposite. They prevent a company from scaling a misunderstanding.

    Promptathons as organizational inquiry

    One of Schrage’s most exciting practices is the promptathon, a collaborative setting in which people use generative AI to explore problems, alternatives, and opportunities together. The important word is collaborative.

    Most workplace AI adoption begins as private experimentation. Individuals learn to write faster, summarize more, or automate pieces of their jobs. That can produce real gains, but it also keeps the organization’s assumptions hidden inside personal workflows.

    A well-designed promptathon can surface those assumptions. Different functions can ask the same model to describe a valuable customer, a successful project, or an acceptable tradeoff. The disagreement among the answers is not noise. It is evidence that the organization has been using shared words without a shared meaning.

    That makes the promptathon more than a prompt-engineering exercise. It can become a form of institutional philosophy conducted through prototypes. Teams can test how a definition behaves before embedding it in a system. They can compare what sales, service, finance, operations, and customers each mean by “value.” They can discover which disagreements require leadership decisions and which can remain productively plural.

    The goal is not consensus on every abstraction. It is enough clarity to know which purpose governs a particular system, which evidence it may use, and when competing values require escalation.

    A philosophy brief for every consequential AI system

    AI governance often begins with inventories, risk levels, privacy reviews, and technical controls. Those are necessary. They do not answer the prior question of what the system should be trying to accomplish.

    Before a consequential AI system enters production, its owners should be able to produce a short philosophy brief. It need not quote philosophers. It should answer seven practical questions:

    1. What is this system for? State the human or organizational outcome, not merely the task it performs.
    2. What evidence counts? Identify the data the system may trust, the uncertainty it must expose, and the claims it may not infer.
    3. What world does it recognize? Name the people, relationships, categories, and time horizons represented in the model, along with the important realities that remain outside it.
    4. Which tradeoffs require a person? Specify the conflicts the system may resolve and those that need accountable human judgment.
    5. Who should become more capable? Describe how customers, employees, or partners gain agency rather than merely becoming easier to manage.
    6. What counter-metric could reveal a false victory? Pair the primary objective with evidence that would show the system is winning the measure while losing the purpose.
    7. Who bears the downside? Make distribution visible. An average benefit can conceal concentrated harm.

    The brief will not eliminate disagreement. Its value is that it makes disagreement inspectable before software turns it into routine.

    The strongest AI strategy is not “What can this model do?” It is “What should this system make possible, for whom, and at what cost?”

    From cost effective to cost affective

    Near the end of our conversation, Schrage plays with the difference between effect and affect. Organizations want cost-effective AI, systems that achieve an outcome with fewer resources. He argues that they should also want “cost affective” AI, systems designed with human feeling, motivation, and identity in view.

    The phrase is more than wordplay. Technology changes not only what people produce, but how they experience themselves while producing it.

    An AI system can make an employee more capable, curious, and willing to exercise judgment. It can also make the same employee passive, monitored, and reluctant to think beyond the recommendation. A customer agent can help someone understand a difficult choice, or it can perfect the art of moving them through a funnel. Both systems may reduce cost. Only one enlarges the person.

    This suggests a more ambitious way to measure return on AI. Alongside time saved, errors reduced, and revenue created, ask whether the system improves the human capacity around it. Do people make better judgments after using it? Do teams ask better questions? Can customers act with greater understanding? Does expertise become more transferable, or merely more concentrated in the machine?

    Schrage has long argued that companies should ask not only how to create more valuable innovation, but how innovation can create more valuable people. That shift changes the unit of analysis. The person is no longer a cost adjacent to the technology. Human capability becomes one of the technology’s intended outputs.

    The advantage is clarity

    AI capability will continue to spread. Competitors will gain access to similar models, interfaces, and agents. The durable advantage will not be possession of the tool alone.

    It will be the clarity to give powerful tools a purpose worth pursuing, the discipline to distinguish evidence from convenience, and the imagination to define value as more than whatever happens to be measurable.

    Leaders sometimes hope AI will help them avoid hard choices by revealing the objectively best answer. More often, it reveals how much judgment was concealed inside the question. It forces a company to say what a good customer relationship is, what productive work looks like, which risks are acceptable, and what kind of organization it intends to become.

    Those are philosophical questions, but they are not luxuries. They are design requirements.

    AI cannot decide what your business is for. It can only make your answer more consequential.

    Michael Schrage, MIT researcher, innovation expert, and author.
    Michael Schrage, MIT researcher, innovation expert, and author

    This essay grows out of Eric Pratum’s conversation with Michael Schrage on The Unfolding Thought Podcast. Watch or listen to “Why Philosophy Will Matter More Than AI.”

  • AI Doesn’t Transform Work. Management Systems Do.

    AI Doesn’t Transform Work. Management Systems Do.

    AI can turn knowledge work from craft into process. Whether that produces a golden age of work or an AI slop factory depends on what leaders redesign around it.

    About an hour into my conversation with Neel Doshi, he described a chief technology officer who had a puzzle.

    The company’s engineers were coding faster with AI. The expected business improvement never appeared.

    Neel asked the question that should have come before the rollout: If accelerating engineering did not accelerate the company, what made anyone think engineering was the bottleneck?

    This is question about AI at work separates output from performance.

    AI can help a person write more code, produce more slides, generate more campaign concepts, summarize more meetings, and answer more email. Every one of those activities can increase while the organization stays exactly where it was or gets worse. The code waits for a decision. The slides create another review cycle. The campaign concepts converge on the same familiar ideas. The meeting summary gives ten people ten more things to read.

    The problem is not that the tool failed. The problem is that we accelerated one part of a system and called it transformation.

    Faster work is not necessarily a faster organization

    The early evidence on AI productivity is real, but it is not uniform.

    In a study of 5,179 customer-support agents, researchers found that a generative AI assistant increased issues resolved per hour by 14 percent on average and 34 percent among novice and lower-skilled workers. The tool appears to have helped less-experienced workers apply practices that stronger workers had already learned.

    A separate experiment with 758 consultants found similarly large gains on tasks that sat within AI’s capabilities. People with AI completed 12.2 percent more tasks, worked 25.1 percent faster, and produced higher-quality results. But on a task outside that capability frontier, the people using AI were 19 percent less likely to reach the correct answer.

    Then, there is the uncomfortable counterexample. In a randomized trial involving experienced open-source developers working in codebases they knew well, AI use made them 19 percent slower. Before the work, they expected AI to make them 24 percent faster. Afterward, they still believed it had made them 20 percent faster. The tools have improved since that 2025 study, and METR’s 2026 follow-up says newer systems probably produce more benefit, but selection effects made the size of that benefit impossible to estimate reliably.

    These findings do not cancel one another out. Together, they tell us something more important: “Does AI improve productivity?” is an underspecified question.

    Whose productivity? On what task? At what level of experience? Measured at what point in the workflow? Over what period? At what cost to the next person in the system?

    The practical unit of analysis cannot be the prompt or even the worker. It has to be the system that turns work into an outcome.

    If AI helps a copywriter produce twice as many drafts but the client still approves one campaign a month, draft production was not the constraint. If an engineer produces code faster but product decisions, security reviews, and customer adoption do not move, coding was not the constraint. If managers use AI to generate more feedback but employees need more time to interpret generic advice, the organization has transferred effort rather than eliminated it.

    Activity is easy to accelerate. Throughput is harder.

    From craft to process: why that phrase is dangerous

    Neel’s most provocative claim was this:

    “What AI can do for knowledge work is what mechanized factories did for manufacturing.”

    Manufacturing moved from craft toward process. Neel believes AI can do the same for knowledge work.

    It is an illuminating analogy, but only if we resist the easiest interpretation of it. Process does not mean that knowledge work becomes perfectly repeatable. It means we become more explicit about how work moves, where variation enters, who evaluates it, and how the system learns.

    Modern manufacturing was never as deterministic as it looks from a distance. Materials vary. Machines drift. Conditions change. People discover better methods. Systems such as the Toyota Production System and Six Sigma were built to notice and manage variation, not to pretend it had disappeared.

    Generative AI makes the variation more obvious. The same instruction can produce different answers. A model can perform brilliantly on one task and fail on another that looks almost identical. Its capabilities change. The surrounding data change. The meaning of “good” often depends on context that exists only in the heads of customers, operators, and subject-matter experts.

    That means expert judgment is not a final inspection step tacked onto an AI process. Judgment is part of the production system.

    This is where the factory analogy can mislead us. The worst version of industrialization reduces the person to a monitor of the machine: wait for output, check a box, fix the occasional defect. That would turn a great deal of knowledge work into the cognitive equivalent of watching a conveyor belt.

    The better analogy is a production system in which the people closest to the work can stop the line, diagnose a problem, change the method, and teach the improvement to everyone else.

    AI can make knowledge work more process-like without making people more machine-like. But only if leaders distribute the authority to question and improve the process. Otherwise, we will not get continuous improvement. We will get continuous generation.

    Four modes of AI augmentation, organized by individual versus collective use and unstructured versus structured use.

    Neel described four broad modes of augmentation. Most organizations begin in the lower-left corner and mistake that starting point for the whole field.

    The performance dip is real. It is not a blank check.

    Neel offered a useful definition of transformational change: performance gets worse before it gets better.

    People need to learn new tools, but that is the smaller part of the problem. They also have to learn how to delegate to a system that does not understand responsibility, how to evaluate fluent output that may be subtly wrong, how to break work into parts, how to give useful feedback, and how to decide when a human conversation is the shortest route.

    In the middle of that learning curve, an experienced person can reasonably think, “I could have done this faster myself.” Sometimes they are correct.

    Economists have described a related pattern as the Productivity J-Curve. General-purpose technologies often require investments that ordinary productivity measures do not capture well: redesigned processes, new skills, new organizational structures, and new business models. During the investment period, measured productivity can stagnate or decline. The gains arrive later, if the complementary changes actually work.

    That gives leaders a reason to tolerate a dip. It does not give them permission to romanticize failure.

    Not every valley leads somewhere. A team can get worse because it is learning, or because the use case is poor, the tool is unreliable, the work is badly designed, or the underlying problem was never worth solving. Calling every disappointment “transformation” is how a temporary experiment becomes a permanent drain.

    A responsible performance dip has boundaries:

    • A specific capability the team is trying to develop.
    • A real work outcome, not an adoption target.
    • A baseline against which the change can be judged.
    • A protected period for practice.
    • Clear risks that require human review.
    • A date when the team will decide whether to continue, change course, or stop.

    “Use AI more” satisfies none of these conditions. It is not a strategy. It is not even a complete instruction.

    Individual acceleration can create a collective tax

    One of the people Neel spoke with during an AI transformation was an engineer who had not talked to another person in four days. She was producing work, but the way the company had introduced AI was removing the collaborative part of the job, the part she enjoyed.

    It is tempting to dismiss this as a preference. Some people like quiet, independent work. Not every task needs a meeting, and collaboration can become its own form of bureaucracy.

    But knowledge work depends on more than the sum of individual outputs. Teams combine perspectives, expose assumptions, carry tacit context, notice weak signals, and decide what is worth doing. Those functions can be slow. They are also where much of the organization’s judgment lives.

    Recent research makes the tradeoff clear.

    In one experiment, access to AI ideas helped individuals write stories that evaluators found more creative and enjoyable. Across the group, however, the stories became more similar to one another. Everyone could improve locally while the collective pool of ideas narrowed.

    Another set of experiments found that human-AI collaboration improved immediate task performance but was followed by lower intrinsic motivation and more boredom when people returned to solo work. That does not prove AI will make work demotivating; the studies used bounded online tasks. It does warn us that the experience of performing better and the experience of developing capability are not the same thing.

    The collaboration evidence is not uniformly negative. A field experiment with 791 Procter & Gamble professionals found that individuals working with AI matched the performance of two-person teams without it. AI also helped technical and commercial professionals produce more balanced solutions, crossing some of the functional boundaries that usually shaped their ideas.

    That is a meaningful benefit. It also raises a management question: If AI can reproduce some benefits of a team for one task, what human capabilities still need a team to develop over time?

    The answer will differ by work. The point is to ask before collaboration disappears by accident.

    “AI native and people first” has to be testable

    Neel recommends that leaders describe the goal as “AI native and people first.” It is better language than “AI first” because it refuses to rank the tool above the people, customers, and purpose of the organization.

    But a phrase this appealing can become decoration. To mean anything, it has to change decisions.

    I would look for six signs.

    1. The organization measures the outcome of the system

    Do not lead with prompts written, licenses activated, hours “saved,” drafts produced, or agents built. Measure cycle time from need to usable outcome, downstream rework, error rates, customer response, decision quality, revenue, cost, or whatever the system exists to accomplish.

    The closer the metric is to clicking the AI button, the easier it is to mistake motion for value.

    2. Expertise is inside the loop

    Neel noted that he can detect weak AI writing about his own research but may not recognize errors in an explanation of quantum physics. That should determine where AI is used and who reviews it.

    The person who can judge the output needs enough context, time, and authority to change the process, not merely approve what the system hands them.

    3. The process preserves divergence before it converges

    When everyone begins with the same model, the model can become a hidden agenda setter. It defines the first set of possibilities, and people refine from there.

    For work that depends on novelty, teams should sometimes think independently before using AI, deliberately seek disconfirming views, and compare genuinely different approaches. Faster convergence is valuable only after the search space is rich enough.

    4. Learning is funded as real work

    If people are expected to learn only after their normal work is finished, the organization is choosing nights and weekends as its AI strategy.

    Protected practice time, real tasks, feedback from experts, and permission to discard a weak use case are investments in capability. A one-hour prompt-training session followed by an adoption quota is not.

    5. Leaders redesign their own work

    Leaders cannot remain at the level of saying, “Build an agent for that.” They need direct experience with the tool’s strengths, failure modes, and demands on judgment.

    More importantly, they need to change the calendars, approvals, meeting habits, decision rights, and incentives that keep everyone else working in the old way. Technology added on top of an unchanged management system usually becomes one more layer of work.

    6. The organization protects contribution, not just employment

    Neel’s hopeful scenario is a “golden age of work” in which AI helps more people solve problems and improve the systems around them. That requires more than promising not to eliminate jobs.

    People need consequential choices to make, skills to build, colleagues to learn with, and evidence that their contribution changed the outcome. A job can survive while everything motivating about it is removed.

    The scarce resource is no longer production

    The cost of producing a plausible draft has collapsed. The cost of deciding what deserves attention has not.

    As generation becomes abundant, several things become scarcer: trustworthy judgment, distinctive ideas, shared context, willingness to revise, and the motivation to take ownership of an outcome. Those are not soft concerns around the edge of AI adoption. They are the constraints that determine whether faster generation becomes value or noise.

    Neel is right that AI creates an unusual opening. Organizations can use it to give more people access to context, coaching, and the ability to improve work that once felt fixed. They can also use it to increase output, centralize control, weaken apprenticeship, and fill every channel with artifacts no one needed.

    The technology does not choose between those futures. The management system does.

    Eric Pratum and Neel Doshi, guest on The Unfolding Thought Podcast.
    Eric Pratum with Neel Doshi for The Unfolding Thought Podcast.

    Listen and read further

  • The Debrief Is Where Experience Becomes Intelligence

    The Debrief Is Where Experience Becomes Intelligence

    Something Christian “Boo” Boucousis said during our recent conversation on The Unfolding Thought Podcast has been sticking with me: fighter pilots debrief everything.

    They debrief good missions. They debrief bad missions. They debrief missions interrupted by weather or equipment problems. They do not wait for something dramatic to go wrong, and they do not assume that doing the work again means they learned from doing it the first time.

    Most businesses do.

    Think about a project that did not go as planned. Maybe it was a marketing campaign that did not produce enough qualified leads. Maybe a product launch was late. Maybe a good employee left, a client relationship deteriorated, or a strategy everyone liked never produced the expected result.

    What normally happens next?

    People explain. The audience was wrong. Sales did not follow up. The client changed the brief. Someone missed a handoff. The market shifted. We did not have enough time. The technology did something strange.

    Any one of those explanations might be true. Several might be true. But, more often than I think most leaders would like to admit, the explanation is accepted because it is plausible, not because anyone really tested it. The meeting ends, everyone goes back to work, and a few months later the organization encounters a suspiciously similar problem wearing different clothes.

    That is experience. It is not necessarily learning.

    Experience is only evidence. It becomes learning when it changes what you do next.

    Most companies repeat more than they iterate

    Businesses like to say they are iterative. Agile companies work in sprints. Marketing teams run experiments. Product teams release versions. Leaders collect metrics and talk about continuous improvement.

    But repetition and iteration are not the same thing.

    If you run the next sprint with the same assumptions, incentives, decision rights, and behaviors, you did not iterate. You just started over. If you collect customer feedback, put it in a report, and then proceed with the plan everyone already preferred, you did not close a feedback loop. You documented feedback.

    Boucousis makes a useful distinction between speed and velocity. Speed is movement. Velocity includes direction. A business can answer emails faster, create more content, ship more features, and hold more meetings while moving very quickly in the wrong direction.

    This is one reason a good debrief actually begins before the work. You need to know what you are trying to achieve well enough for reality to disagree with you.

    “Launch the campaign” is not a useful objective. That is an activity. “Create 40 qualified sales conversations from this audience without taking acquisition cost above the level we agreed upon” is an objective. It gives you a result to compare with an intention.

    The same is true of meetings, hires, technology implementations, and nearly everything else. “Install the CRM” tells you what people will do. It does not tell you what should be different when they are done. Without that difference being made explicit, the team can complete every task, celebrate the launch, and still have no reliable way to know whether the work was worth doing.

    We explain results before we understand them

    Human beings are very good defense attorneys for our own decisions.

    When a result is poor, circumstances were difficult. When a result is good, our strategy was smart. We might not say it that bluntly, but most of us can find a story that protects what we already believed about ourselves, our teams, and our choices.

    This is not because everyone is dishonest. It is because the story arrives almost immediately. We experience the result through our own expectations, identities, incentives, and incomplete view of what happened. By the time the meeting begins, each person might already have a different version of reality.

    The Plan-Brief-Execute-Debrief model Boucousis uses tries to slow that down with four questions:

    1. What was the objective?
    2. What result actually occurred?
    3. What caused the difference?
    4. What action will change before the next attempt?

    The sequence is important. Start with what you intended. Then describe what happened before explaining it. Otherwise, the explanation has a way of changing the standard by which the result is judged.

    You have probably seen this. A team misses its revenue goal, but the conversation quickly shifts to how much awareness the campaign created. A project is late, but everyone focuses on how much they learned while building it. A new system is barely used, but the implementation is called a success because it went live.

    Awareness, learning, and launching might all be valuable. They are not substitutes for the result you said you wanted before the work began.

    This is also why success needs to be debriefed. Success is much easier to accept and therefore easier to misunderstand. A good result can hide a bad process, an unrepeatable advantage, a near miss, or an assumption that happened not to hurt you this time. Research on after-event reviews found that people who examined successes and failures improved more than those who reviewed failures alone.

    If the campaign worked, you still need to know why. Otherwise, you may take the wrong lesson from it and confidently repeat the part that mattered least.

    This is not an excuse to avoid accountability

    Whenever a conversation turns toward systems, context, and root causes, someone worries that personal responsibility is about to disappear. If everything is a system problem, can anyone ever be held accountable?

    Of course they can.

    A person may have ignored evidence, violated a standard, failed to prepare, or simply not done the job. A leader may also have created incompatible incentives, withheld information, assigned responsibility without authority, or punished someone the last time they raised an uncomfortable issue. Both can be true.

    The point of a debrief is not to make responsibility disappear. It is to make responsibility more accurate.

    Most organizations use accountability too late. They invoke it after the result, when everyone is looking for the person who should own what went wrong. Real accountability starts before execution. It requires a clear objective, an owner, visible constraints, agreed evidence, and the ability to question the plan before everyone commits to it.

    It also requires people to be able to say what actually happened.

    If telling the truth about the work is dangerous, the debrief is theater.

    Boucousis describes debriefing as nameless and rankless. I like the intention, but rank does not disappear because the highest-ranking person says it has. Everyone watches what happens to the first person who contradicts the leader, admits an embarrassing mistake, or says that the favored strategy did not make sense.

    If that person is humiliated, interrupted, ignored, or quietly punished later, the organization still learns a lesson. It is just not the lesson the leader intended. People learn that protecting the story is safer than examining the result.

    Psychological safety sometimes gets described as making everyone comfortable. That is not how I think about it. It is what makes productive discomfort possible. Research on debriefing and psychological safety connects it with speaking up, sharing information, and learning from errors. Safety makes the evidence available. Accountability makes the evidence matter.

    Root cause can become too neat

    A few years ago, I wrote about how businesses tend to self-diagnose and then go straight to a specialist. Revenue is down, so the company decides it has a website problem and hires someone to build a new website. Six months later, the website is new, the underlying problem remains, and everyone is looking for the next solution.

    A debrief should help with this, but only if people resist the desire for an explanation that feels cleaner than reality.

    Organizations like a single root cause because a single cause creates a sense of control. The campaign failed because the offer was weak. The product was late because one team missed a handoff. The client left because the account lead did not communicate.

    Maybe. But complex outcomes usually emerge from several conditions interacting with one another. A weak offer might have worked with a different audience. A missed handoff might not have mattered if the project had enough slack. Poor communication might have been recoverable if months of inconsistent delivery had not already weakened the relationship.

    The goal is not to make the past look inevitable. The goal is to improve what the organization believes before it acts again.

    Sometimes that means ending the debrief with, “We do not know yet.” I would rather hear that than a confident story supported by little evidence. “We do not know whether the audience or the offer was the primary constraint, so our next test will separate them” is a useful conclusion. It turns uncertainty into a better next action.

    This is where red teaming can help. The red team is not there to complain or to make the meeting more dramatic. Its job is to ask what would make the preferred explanation false, which assumptions the next plan depends upon, and what an outsider might notice that the team has learned to ignore.

    AI will make the learning problem more obvious

    AI lowers the cost of doing things. We can produce analysis, copy, code, plans, reports, experiments, and variations much faster than before.

    That sounds like an unqualified advantage until you remember that organizations already have trouble learning from the amount of work they do now.

    When production becomes cheaper, the bottleneck shifts. Judgment, attention, and organizational memory become more valuable because the business now has more actions, results, and explanations to sort through.

    AI can help an organization remember. It can also help the organization repeat a bad explanation much faster.

    AI can compare a stated objective with the reported result. It can find contradictions across accounts, identify causes that recur across projects, retrieve a prior lesson during planning, and red-team a proposal before reality does it for you. Research systems such as Reflexion have even shown that an AI agent can use feedback, create a reflection, retain it as memory, and improve a later attempt.

    But storing a meeting transcript is not the same as creating organizational memory. A report is not intelligence simply because it contains more information. The lesson has to show up when someone is about to make a relevant decision, and it has to change what the person or system does.

    AI also cannot decide which purpose is worth pursuing, which tradeoff is acceptable, or whether a local success damaged the larger system. If the destination and values are vague, AI can help you move faster without helping you move in a better direction.

    What I would actually put in a debrief

    I do not think every project needs a long, ceremonial meeting. In many cases, 15 focused minutes would be an enormous improvement over the hour-long status meetings people are already attending.

    I would make the team answer these questions:

    1. What did we intend to change? Not what did we intend to do. What effect were we trying to create?
    2. What actually happened? Describe the result before anyone explains it.
    3. What evidence supports our explanation? Separate what we know from what we think.
    4. What else could explain the result? Give someone permission to challenge the explanation everyone prefers.
    5. What will we do differently next time? Name the action, the owner, and the condition that should trigger it.
    6. Where will this lesson live? Put it somewhere the next person will encounter it before repeating the decision.
    7. When will we find out if we learned the right lesson? The corrective action needs its own review.

    That final question matters. A corrective action is still a hypothesis. It might be reasonable and still fail. If no one checks, the organization can turn a mistaken lesson into a new standard operating procedure and call that learning.

    What changed because of the conversation?

    The advantage is not simply moving faster. It is learning faster.

    A business with a high learning rate does not need every decision to be right. It needs wrongness to become visible, discussable, memorable, and useful. It needs leaders who can tolerate evidence that complicates their own story. It needs success to be examined instead of merely celebrated.

    When the debrief is over, ask one last question: What changed because of this conversation?

    If the answer is nothing, you had a meeting. You did not debrief.

    This essay develops ideas from my conversation with Christian “Boo” Boucousis, CEO of Afterburner and a former Royal Australian Air Force fighter pilot. Watch or listen to “Why Fighter Pilots Debrief Everything” on The Unfolding Thought Podcast.

    Christian “Boo” Boucousis, former fighter pilot and CEO of Afterburner.
    Christian “Boo” Boucousis, CEO of Afterburner and former Royal Australian Air Force fighter pilot
  • When Success Makes a Soccer Club Weaker

    When Success Makes a Soccer Club Weaker

    What happens when a small youth soccer club develops a team that becomes better than the supposedly elite clubs around it?

    In an open system, the team would earn stronger competition and the club would gain status. In much of American youth soccer, there is no automatic path upward. The club’s best players are more likely to leave for organizations that already possess the right league badge. The original club can point to those departures as evidence that its coaches did good work, but the team that produced the evidence has been dismantled.

    Success can make the institution that created it weaker.

    That paradox reveals an information problem disguised as a competition problem. Parents are buying player development they cannot directly inspect, so they rely on league badges, wins, roster placement, facilities, and social proof. Then, when a smaller club produces convincing evidence of development, the system can move that evidence to organizations whose status has already been established.

    A system that cannot reliably elevate good development or expose poor development cannot tell parents which is which. Eventually, the appearance of development becomes easier to sell than development itself.

    This was the tension I kept hearing in my second interview with Rory O’Neill on The Unfolding Thought Podcast. Rory has spent years coaching and building youth soccer programs. Much of our conversation concerned closed leagues, pay-to-play, promotion and relegation, and the incentives around player movement. Underneath those issues is a harder question: how can a market reward good development if the people buying it cannot confidently recognize it?

    The person paying and the person developing are not the same

    In a simple purchase, one person may play every important role. I buy a sandwich, eat it, decide whether it was good, choose whether to return, and suffer the consequences if it makes me sick.

    Youth sports divide those roles among several people:

    • The parent pays.
    • The child receives the coaching.
    • The coach claims the expertise to evaluate development.
    • The parent usually decides whether the family stays or leaves.
    • The child bears most of the long-term consequences.

    Those forms of power can point in different directions. A child may need harder competition, less playing time, a different position, more unstructured practice, or an honest explanation that progress has stalled. None of those experiences is guaranteed to make the payer happy today.

    Calling the parent “the customer” is commercially accurate and developmentally incomplete. Calling the child “the customer” sounds principled but ignores who controls the revenue. The mistake is assuming that one word can describe a relationship in which payment, benefit, expertise, choice, and risk are divided.

    When satisfaction is visible and development is not, the system learns to sell satisfaction.

    Parents are buying something they cannot easily inspect

    Economists describe some expert services as credence services. Even after buying them, the customer may not be able to determine whether the right service was provided. Most patients cannot independently evaluate a medical diagnosis. Most car owners cannot inspect a transmission repair. Most parents cannot watch a practice and determine whether the coach made the right tradeoffs for a player’s development over several years.

    Youth development is especially difficult to judge because the result is partly counterfactual. The relevant question is not simply, “Is this player better than last year?” A growing child receiving almost any regular practice may improve. The harder question is, “How much better would this child have become in a different environment?” Families never get to observe both futures.

    When buyers cannot inspect the underlying quality, they look for signals. A winning record is a signal. A famous coach is a signal. Travel is a signal. Facilities are a signal. Being selected for an “elite” roster is a particularly powerful signal because it tells the parent that another authority has already evaluated the child.

    Signals are not useless. A strong league may provide better competition. A selective roster may contain better players. Winning may reflect good coaching. The danger comes when a signal becomes easier to produce and sell than the result it is supposed to represent.

    That distinction matters in a closed system. MLS NEXT membership is awarded through an application and evaluation process that considers philosophy, governance, coaching, player development history, affordability, geography, and other factors. Those are reasonable things to examine. But membership is still a selected certification, not a competitive rank that every club can continuously earn on the field. To a parent, the badge can therefore look like objective proof even though the route to obtaining it is more complicated.

    The short feedback loop usually wins

    A player’s development may take ten years. A club’s renewal decision arrives every season. A parent’s frustration can arrive after one lineup.

    The shorter loop produces louder information. The club sees a complaint, a departure, or an unpaid invoice immediately. It may not know for years whether a technically gifted 12-year-old learned to solve problems, whether a late-maturing player received enough patience, or whether a confident 15-year-old learned to accept responsibility.

    Organizations tend to manage what they can observe. Research on performance measurement and incentives has long warned that measurable proxies can distort behavior when they are mistaken for the harder-to-measure goal. In youth soccer, renewals, wins, league status, and roster size are easy to count. Development is not.

    This does not require bad people. A caring coach can know that a player needs a difficult truth. A well-intentioned director can know that a team should emphasize learning rather than a weekend result. But if telling the truth causes the family to leave, while reassurance protects the revenue that pays the coach, the business model has made honesty expensive.

    A coach can be serving the child and risking the customer at the same time.

    A more honest hierarchy would help, but it would not solve the whole problem

    Rory argues that promotion and relegation would make American soccer more meritocratic. A team that performs well could earn stronger competition. A club could not preserve status without repeatedly earning it through open competition simply because it already belonged to the right league.

    That would create useful information. Results would have consequences, and success could open a path that is currently controlled by membership decisions. In that sense, promotion and relegation is not just a sporting preference. It is a mechanism for testing claims.

    But a more objective team hierarchy is not the same as an objective development system. A club can win by recruiting the strongest current players rather than improving the players it has. It can favor early-maturing children, use conservative tactics, narrow positions, and reduce experimentation. A standings table can tell us which team won. It cannot tell us how much each child learned.

    The United States Olympic and Paralympic Committee’s American Development Model emphasizes age-appropriate development, access, quality coaching, fun, and attention to skill and effort. Those goals remind us that competitive results are one signal among several. Replacing a league badge with a winning record would improve one feedback loop while leaving the larger measurement problem intact.

    Success can make the club that produced it weaker

    Consider a small club that develops an unusually strong team. If that team cannot earn access to a higher level of competition, its best players may leave for clubs that already possess the desired badge. The original club can point to those departures as evidence that its coaches did good work, but the team that produced the evidence has been dismantled.

    Success has made the institution that created it weaker.

    That is more than an issue of fairness. It is a loss of information. Parents evaluating the original club no longer see the strong team. They see the remaining roster and the higher-status clubs that absorbed its players. The visible proof migrates away from the people who created it.

    There are attempts to address this. The MLS NEXT Development Grant Program can reward a qualifying elite academy when a player reaches specified professional milestones. That is meaningful progress, but eligibility is narrow and the grant goes to the immediately preceding qualifying academy. Many origin clubs and earlier coaches remain outside the mechanism.

    International soccer offers a broader idea. FIFA’s Clearing House distributes training compensation and solidarity contributions to clubs involved in a player’s development. The details are complex, and no payment system perfectly identifies causal contribution. The important principle is that development should leave some value behind for the institution that helped create it.

    If success causes your best evidence to leave, the system is not rewarding development. It is consuming it.

    Development needs a better scoreboard

    No single measure will solve this. That is the point. When the goal is complex, a responsible system uses several imperfect signals and makes their limitations visible.

    A club that claims to develop players should be able to show more than trophies and placements. It should be able to explain what it is trying to develop, how coaches assess progress, and what families should expect when the child’s needs conflict with the team’s short-term result.

    • Are individual goals documented and revisited over time?
    • Do assessments include technical, tactical, physical, and psychological development rather than one coach’s overall impression?
    • Can another qualified observer review a player’s progress?
    • Do players receive meaningful opportunities to apply what they are learning?
    • Does the club track voluntary retention, player enjoyment, affordability, injuries, and burnout alongside wins?
    • When a player advances elsewhere, does the originating club receive financial or reputational credit?
    • Are families told clearly that development does not guarantee a roster label, starting role, scholarship, or professional future?

    FIFA’s talent-identification guidance calls for a clear philosophy, defined player profiles, comprehensive scouting environments, player observation, data analysis, structured selection, and a process that can operate over the long term. A child cannot be reduced to one tryout, one coach, or one team result. Neither can the quality of the environment developing that child.

    Make the truth less expensive

    Transparency will not remove conflict. It can make conflict more honest.

    Before a season begins, clubs can separate the development promise from the status promise. They can explain how playing time is decided, what “elite” means, what evidence coaches use, and how a family can appeal or seek an independent view. They can make it easier for a child to move when another environment would be better. They can reward coaches for long-term progress and candor, not only wins and renewals.

    Parents also have a role. If we say we want development but punish every decision that makes our child uncomfortable, the club learns what we actually value. If we treat a badge as proof, we help turn the badge into the product. If we demand guarantees that no honest coach can make, reassurance will eventually displace judgment.

    The goal is not to make parents passive. It is to give them better information and clearer expectations so that dissatisfaction can reveal a real problem rather than merely the distance between a development process and an imagined outcome.

    This is not only a youth sports problem

    The same information problem appears in schools, healthcare, consulting, nonprofits, elder care, and business services. One person may pay, another may use the service, a third may evaluate quality, and the consequences may arrive after the contract ends.

    Customer satisfaction matters in all of those settings. It can tell us whether the payer received the experience they expected. It cannot, by itself, tell us whether the institution fulfilled its purpose. Leaders need to ask who pays, who benefits, who can judge quality, who decides whether the relationship continues, and who bears the cost if it fails. If the answers name different people, the most visible feedback is not necessarily the most important.

    American youth soccer does not lack people who care about children. It lacks a reliable way to keep care, truth, revenue, and development pointing in the same direction. Until development becomes more visible and more valuable to the institution producing it, status will remain easier to market than progress.

    The question is not whether American youth soccer has coaches capable of developing players. It is whether the system can recognize their work before success dismantles the proof.

    Listen and read further

    Rory O’Neill and Eric Pratum on The Unfolding Thought Podcast.
    Listen to Rory O’Neill on The Unfolding Thought Podcast.
  • The Cloud Has a Utility Bill. Who Pays When the Forecast Is Wrong?

    The Cloud Has a Utility Bill. Who Pays When the Forecast Is Wrong?

    What happens when a technology company explores building the same data center in several states, encourages each one to compete for the project, chooses the place that offers the best combination of price and speed, and walks away from the rest?

    Long before the decision is made, utilities and communities have already begun studying substations, transmission, generation, reserve capacity, land, incentives, and staffing. That planning costs real money. Some equipment and contracts may need to be reserved years before anyone knows whether the server racks will arrive.

    Who bears that cost when the company chooses somewhere else? Usually not the company that created the competition.

    The company keeps the option to change its mind. Utilities, communities, and ratepayers can be left with the cost of preparing for a future that never arrives.

    That is the less obvious infrastructure problem underneath the AI boom. It is not simply that data centers need enormous amounts of electricity. It is that technology companies can make fast, reversible bets while the systems competing to serve them must begin making slow, difficult-to-reverse commitments.

    That was the tension I kept hearing in my interview with Peter Kelly-Detwiler on The Unfolding Thought Podcast. Peter has spent decades in electricity markets. He describes data centers that place enormous interconnection requests because access to power may determine whether an AI investment succeeds. He also describes utilities trying to distinguish committed demand from speculative demand when the cost of guessing wrong can remain on customer bills for decades.

    AI moves in quarters. The grid moves in decades.

    The cloud has encouraged us to think of computing as nearly weightless. A company adds capacity, moves a workload, or releases a new model, and the change appears to happen everywhere at once. The physical system beneath that experience moves differently.

    Power plants, transmission corridors, and substations are not software deployments. They require land, equipment, permits, financing, engineering, and public consent. Once built, they are expected to serve customers long enough to recover their cost.

    Meanwhile, the demand forecast is changing at software speed. Lawrence Berkeley National Laboratory’s 2025 update estimated that U.S. data centers used about 4.7 percent of the nation’s electricity in 2024. Its reference case reaches 11.8 percent in 2030, while the modeled range runs from 9.5 to 15.3 percent. The authors caution that those bounds are not a confidence interval and that the most extreme assumptions are unlikely to occur together. The uncertainty is not a flaw in the research. It is evidence of the problem planners face.

    The North American Electric Reliability Corporation’s 2025 long-term assessment says data centers account for most of the projected increase in electricity demand over the next decade. It also describes projects that slow down, disappear, or consume far less than originally requested. In Texas, planners reduced projected demand from newer data centers after operating sites used, on average, about half of the capacity they had requested.

    The same forecast can therefore contain a real signal and a large amount of noise. The system may need much more power. It does not follow that every proposed megawatt will appear where and when an application says it will.

    A request for power is not the same as demand

    A developer benefits from preserving options. If it can apply for capacity in several regions, it can compare timelines, incentives, energy prices, regulatory conditions, and construction risks before choosing a site.

    That is rational behavior for the developer. It becomes a system problem when every option is treated as a commitment.

    If five utilities each prepare to serve the same project, society may reserve equipment, engineering time, and capital for four facilities that will never exist. If every utility heavily discounts every request, the one project that is real may arrive before the grid is ready.

    Traditional forecasting tries to estimate which future is most likely. That is necessary, but it cannot answer the more important governance question: who should carry the cost when the forecast is wrong?

    The problem is not uncertainty. The problem is allowing one party to create uncertainty while another party pays for it.

    A forecast should not be a blank check

    When a utility builds dedicated infrastructure for a large customer, the customer is receiving something more valuable than electricity. It is receiving an option on future capacity.

    An option has value even if it is never exercised. Equipment may be ordered. Other customers may be told that capacity is unavailable. A transmission plan may be changed. Capital that could have served another need may be committed.

    The contract should make that option visible. Deposits, milestone payments, minimum-demand charges, exit fees, and long-term commitments are not punishments for innovation. They are ways to keep a private option from becoming a public liability.

    This principle is now moving into regulation. The Federal Energy Regulatory Commission has called for cost-recovery agreements that protect other customers if infrastructure is built for a large load that does not arrive as planned. The point is straightforward: the party best positioned to judge whether a project is real should retain a meaningful share of the risk that it is not.

    A forecast should guide an investment. A contract should decide who carries the risk if the forecast is wrong.

    Speed to power has a price

    Peter uses the phrase “compute heat rate” for the electricity price at which the value of additional computation no longer justifies operating the data center. The concept matters because it reveals how differently an AI company and an ordinary customer may value the same megawatt-hour.

    For most businesses and households, electricity is a meaningful operating expense. For a company racing to train or deploy a valuable model, electricity can be the input that determines whether billions of dollars of chips produce revenue or sit idle.

    That can make time-to-power more important than the price of power. It explains the interest in co-located generation, private power plants, batteries, and temporary arrangements that let a project begin operating before the larger grid connection is ready.

    There is nothing inherently wrong with paying for speed. The problem comes when the price is hidden in another customer’s bill, in reduced reliability, or in infrastructure that remains after the business case disappears.

    The best new load may be a flexible one

    A data center is often described as a load, as though it were simply an enormous appliance. That leaves out an important possibility. Some computational work can move in time, move to another location, or briefly draw on onsite storage and generation.

    That flexibility can be valuable because the grid is built for its most difficult hours, not its average hour. A facility that avoids adding to the annual peak can consume more total electricity without requiring the same amount of new capacity.

    Electric Power Research Institute modeling found that shifting portions of data-center demand away from constrained hours reduced peak demand, the need for firm capacity, and the average price of electricity supplied to the facilities. EPRI is also testing operational approaches through its Data Center Flexible Load Initiative.

    Flexibility should not be treated as a hopeful description. It should be a measurable operating commitment. Different workloads have different latency, availability, and geographic constraints. A promise to reduce demand is useful only if the grid operator knows how much, how quickly, for how long, and with what consequences.

    The same is true of reliability. NERC’s work on emerging large loads warns that data centers can change consumption quickly enough to create risks the grid was not designed to manage. The facility cannot claim the reliability benefits of grid connection while treating its own electrical behavior as someone else’s problem.

    Build what remains useful when the forecast is wrong

    Uncertainty does not justify paralysis. It should change the order in which investments are made.

    Some improvements create value across many possible futures. Better sensors and dynamic line ratings can reveal when existing transmission lines safely carry more power. Advanced conductors can move more electricity through an existing right-of-way. Storage can reduce short peaks and improve power quality. More planners, engineers, and regulatory capacity can shorten decisions without pretending the decisions are simple.

    The Department of Energy describes these grid-enhancing technologies as faster ways to unlock capacity from infrastructure that already exists. They do not eliminate the need for new generation and transmission. They buy time, improve utilization, and remain useful if a particular data center never appears.

    Larger dedicated investments should follow stronger evidence of commitment. They can be staged against financing, land control, equipment orders, construction progress, and binding load agreements. The evidence required should rise with the irreversibility of the decision.

    • How much of the requested load is supported by committed capital and construction milestones?
    • Which investments benefit the wider system and which exist only for this customer?
    • What flexibility can the customer demonstrate and contractually guarantee?
    • Who pays if the project arrives late, operates below forecast, or disappears?
    • Which decisions can be staged, and which would be difficult to reverse?

    The grid does not need certainty about AI. It needs commitments proportional to the uncertainty AI creates.

    Planning is becoming a form of risk design

    The AI electricity debate is often framed as a choice between acceleration and restraint. Build everything quickly and risk higher costs, stranded assets, and reliability problems. Move slowly and risk losing investment, innovation, and economic opportunity.

    That framing is too narrow. The more useful question is how to make a sequence of decisions that remains sensible across several plausible futures.

    We do not need to know exactly how many proposed data centers will be operating in 2035. We need connection rules that distinguish curiosity from commitment. We need contracts that put project risk close to the party creating it. We need flexible operating agreements that make new demand useful to the grid when possible. We need public investments that retain value even when a private forecast fails.

    AI may transform the economy. It may also pass through investment cycles that make today’s confident forecasts look naive. The grid has to function in either case.

    That is why the deepest infrastructure skill is no longer prediction. It is designing commitments, options, and safeguards so that we can build before uncertainty disappears without pretending uncertainty has disappeared already.

    A household should not become an involuntary venture investor merely because someone else’s investment happens to plug into its grid.

    Listen and read further

    Peter Kelly-Detwiler and Eric Pratum on The Unfolding Thought Podcast.
    Listen to Peter Kelly-Detwiler on The Unfolding Thought Podcast.