Inbound & Agile

Category: Leadership

  • 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 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.”

  • 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.
  • When the Process Outlives the Problem It Was Built to Solve

    When the Process Outlives the Problem It Was Built to Solve

    An institution can be orderly, disciplined, and increasingly ineffective at the same time.

    That kind of failure is hard to see because it still looks like competence.

    The weekly report arrives on time. Every project clears the required stage gate. The dashboard is green. The audit finds no missing fields. Yet customers are leaving, quality is slipping, decisions take longer, and the people closest to the work have learned that raising an anomaly creates more trouble than ignoring it.

    Nothing is obviously broken because the organization has become excellent at demonstrating fidelity to its process. The difficulty is that fidelity to process and fidelity to purpose are not the same thing.

    That distinction is what I kept returning to after my conversation with Eliot Frick on The Unfolding Thought Podcast. Frick makes a much larger philosophical argument about whether modernity has entered the final stage of its life. His description of late-stage systems is immediately recognizable in organizations.

    A system begins with generative power. It solves problems that could not previously be solved. Its methods become credible because they work. Over time, however, the methods become inseparable from the system’s identity. When results deteriorate, the system rarely concludes that its operating assumptions may be exhausted. It concludes that people are no longer following them faithfully enough.

    So it adds oversight, tightens compliance, and treats deviation as the cause of decline. The process that once produced the outcome becomes the ritual through which the organization proves it still deserves to exist.

    A process is a memory of a problem

    Most recurring processes began for a reason. Someone shipped defective work, exposed the company to risk, made an expensive decision without enough evidence, or forced other people to reconstruct information that should have been recorded. A checklist, review, approval, or report was created to keep that failure from recurring.

    When the process works, the original problem becomes less visible. New employees encounter the solution without experiencing the conditions that made it necessary. They know the form must be completed but not what judgment the form was meant to improve. They know who must approve a decision but not what uncertainty that approval was supposed to reduce.

    The process is therefore a kind of organizational memory. It preserves an answer after the question has faded.

    That can be useful. We do not want every generation of employees to rediscover fire safety or financial controls from first principles. But a memory becomes dangerous when the organization cannot distinguish the enduring purpose from the historical method. Conditions change while the ritual remains. Eventually, people measure whether the process occurred because they have lost the ability to measure whether it still helped.

    Success teaches a system what to stop noticing

    Every successful institution develops an operating grammar. It creates categories, incentives, reporting systems, professional language, and approved ways of reasoning. This grammar lets large groups coordinate. It also makes the organization increasingly good at recognizing what it already knows how to see.

    James March described a related tension as the difference between exploiting old certainties and exploring new possibilities. Exploitation improves what the organization already knows how to do. Exploration searches for something better but produces uncertain returns. March’s warning was that adaptive systems often refine exploitation more quickly than exploration. That can make them effective in the short run and self-destructive over time.

    The problem is not that managers foolishly choose the old over the new. The old process comes with evidence, owners, budgets, benchmarks, and political support. The alternative begins as a question. One side can produce a forecast. The other can only promise learning.

    The imbalance compounds. The more an organization invests in a process, the more careers, systems, and explanations depend on that process remaining legitimate. Evidence that fits the current model is easy to absorb. Evidence that challenges the model arrives looking incomplete, undisciplined, or irrelevant.

    A failing institution often becomes more faithful to its process as it becomes less capable of producing its purpose.

    Threat makes the rulebook harder

    Declining results ought to create curiosity. In practice, they often create rigidity.

    Barry Staw, Lance Sandelands, and Jane Dutton’s work on threat rigidity describes two common responses to adversity: information processing narrows and control constricts. Organizations rely more heavily on familiar knowledge, reduce the number of voices involved, centralize authority, and formalize procedure.

    Those responses are understandable. A threat creates urgency, and coordination can matter more during an immediate crisis. The problem comes when a short-term crisis response becomes the operating model for a system whose assumptions are failing. The organization reduces variation at precisely the moment it most needs alternatives.

    This creates a cruel feedback loop. The system produces weaker results. Leadership tightens the system to protect performance. Tighter control suppresses dissent and experimentation. The organization receives less information about why the system is failing. Its leaders become even more convinced that inconsistent execution is the problem.

    People who question the process are then easy to cast as undisciplined. Yet they may be carrying the information the process was designed to exclude.

    Collapse stories can be another form of loyalty

    Frick makes a surprising argument about stories of collapse. We assume that someone predicting the end of a system has escaped its influence. Often the opposite is true.

    The defender says the institution must be saved. The critic says it must be destroyed. Both keep the institution at the center of the imagination. The defender uses its categories to explain what must continue. The critic uses the same categories to explain what must end. Neither has necessarily described what could make the old conflict less important.

    This pattern appears in organizations whenever two factions fight over control of a process whose usefulness neither side is examining. One department wants stricter enforcement. Another wants the process abolished. Both assume the available choices are compliance or rebellion.

    The opposite of defending a failing system is not attacking it. Both can keep the system at the center.

    A more useful question is what problem the process was built to solve and whether that problem still exists in the same form. If it does, perhaps the method needs repair. If it does not, the fight over the method may be consuming attention that belongs somewhere else.

    A new system may not win the old argument

    Thomas Kuhn’s account of scientific revolutions is helpful here. Transformative ideas do not always emerge by accumulating better answers inside the accepted model. Anomalies build until a different framework can organize them.

    A new framework may not defeat the old one by its own measures. It can change which observations matter, which problems deserve attention, and what counts as an explanation. Questions that once felt decisive can become smaller or disappear.

    Organizations routinely make this transition harder than it needs to be by requiring every experiment to justify itself with the current system’s metrics. But those metrics contain assumptions about value, time, quality, and risk. An idea that tests the assumptions cannot always prove itself using measures designed to preserve them.

    This does not mean experiments should escape accountability. It means their first obligation may be to produce information rather than scale, efficiency, or immediate financial return. Leaders need to know what the experiment is trying to learn and what evidence would cause it to stop. That is different from demanding that it look like a small version of the established business.

    Leadership is the preservation of options

    Frick uses the word “aperture” for a protected opening through which unfamiliar possibilities can develop. The metaphor matters because most new ideas do not begin with enough power to survive the full force of an established institution.

    A leader does not need to believe every unconventional proposal. Most will be incomplete, and many will fail. Leadership requires preventing the current operating system from eliminating all unfamiliar ideas before any can produce evidence.

    Amy Edmondson’s research on psychological safety helps explain one condition for such an opening. Teams learn when people believe they can take interpersonal risks. That does not mean conflict disappears. It means uncertainty, error, and disagreement can enter the conversation without immediately becoming evidence that someone does not belong.

    Authenticity matters too. Research by Charlan Nemeth, Keith Brown, and John Rogers found that a genuinely held minority position produced better quantity and quality of solutions than several forms of assigned devil’s advocacy. An organization does not receive the full benefit of dissent by appointing someone to perform disagreement inside a meeting whose real boundaries remain untouched.

    Leadership does not require knowing what comes next. It requires refusing to let the present eliminate every alternative.

    What protecting an aperture looks like

    Protected exploration does not have to mean an innovation lab separated from the real work. It can begin with a few operating choices:

    • Separate delivery work from exploratory work so the two are not judged by identical expectations.
    • Give small experiments explicit sponsors, modest budgets, and expiration dates.
    • Ask which measures reflect the purpose and which merely prove compliance.
    • Record anomalies before explaining them away.
    • Invite authentic dissent from people who actually hold a different view.
    • Require experiments to produce learning before requiring them to produce scale.
    • Periodically ask what problem each recurring process still solves.
    • Retire rituals whose connection to outcomes can no longer be demonstrated.

    These practices will not reveal the next paradigm on command. That is not the promise. Their value is that they preserve variation long enough for the organization to learn from it.

    Jim Dator’s four generic futures include continuation, collapse, discipline, and transformation. Frick’s argument rearranges that sequence, but the categories remain useful. Organizations are usually comfortable planning for continuation. They can imagine collapse because fear supplies the story. They understand discipline because tightening control feels actionable.

    Transformation is harder because it cannot be fully described in the language of the system it may replace.

    The practical question is therefore not whether modernity, an industry, or a company is truly at the end of its life. The better question is diagnostic: are our institutions still producing results, or are they spending more of their energy demonstrating loyalty to the way those results used to be produced?

    A process deserves protection when it remains connected to purpose. When that connection is gone, enforcing the process more intensely will not restore it. Leadership begins by noticing the difference and preserving enough room for another possibility to become visible.

    Listen and read further

    Eric Pratum and Eliot Frick on The Unfolding Thought Podcast.
    Listen to Eliot Frick on The Unfolding Thought Podcast.
  • Fake News Works Because Trust Usually Does

    Fake News Works Because Trust Usually Does

    Most of what holds modern life together is something we rarely notice: we trust what we have not verified.

    When we pay for a product, we trust that the thing inside the box is the thing described on the outside. We trust that it will not poison us, catch fire, or quietly fail in a way the seller already knew about. We also trust that if the product turns out to be bad, the seller or manufacturer will make it right.

    They may have warranties, contracts, reputations, and laws encouraging them to do so. But none of those lets us verify the product before every purchase or guarantees that someone will behave honorably afterward. The transaction happens first. Proof, if it comes at all, comes later. Every ordinary purchase therefore includes a small, unsecured loan of confidence.

    The same thing happens when we eat at a restaurant, turn on a faucet, board an airplane, fill a prescription, or cross a bridge. We do not run a chemical analysis on lunch. We do not inspect the water system, audit the maintenance log, or calculate the load-bearing capacity beneath our feet. We rely on long chains of people and institutions whose work we will never personally examine.

    Trust is not merely a soft virtue. It is infrastructure. It lets us act without requiring every person to become an investigator, engineer, chemist, lawyer, and auditor before breakfast.

    News used to sit inside that same background of ordinary trust. A reader could disagree with a newspaper or think a television network was biased while still assuming that reporters were trying to describe events that had actually happened and that editors would not knowingly publish an invention as fact. That trust was never perfect and never deserved to be absolute. But it was substantial enough that a headline could function as information rather than the beginning of a forensic investigation.

    Now the same screens, formats, and social signals carry reporting, opinion, advertising, satire, propaganda, and synthetic material produced at almost no cost. We still have to trust because nobody can verify everything. Yet we are less certain which signals deserve that trust. That is what makes fake news such a difficult problem. It exploits a habit we cannot simply abandon.

    That was the idea I kept returning to after my conversation with Ming Ming Chiu on The Unfolding Thought Podcast. Chiu studies how people communicate, create, mislead, disagree, and learn together. At first those subjects may look like separate research programs. I think they belong to one problem: how do groups remain open enough to think together without becoming easy to manipulate?

    The usual answer to misinformation is some version of “be more skeptical.” That is directionally right but structurally incomplete. A society in which nobody trusts anyone may circulate fewer lies. It will also struggle to circulate knowledge, organize work, or solve problems. We need a better process for deciding what deserves trust without destroying trust itself.

    Most of life runs on unverified trust

    Every claim carries more possible questions than we have time to ask. Who produced it? What did they observe? What did they omit? Which incentives shaped the presentation? Could an alternative explanation fit the same evidence?

    Trust compresses those questions. It substitutes a working judgment about a person, institution, or process for a complete investigation. That is not intellectual laziness. It is how finite people operate in a world with more information than any one mind can evaluate.

    This is why falsehood cannot be understood only as bad content. It is also an attack on the systems people use to economize attention. A fabricated story borrows the visual grammar of journalism, the intimacy of a friend’s recommendation, or the authority of a confident speaker. It tries to collect the benefit of trust without paying the costs that make trust warranted.

    A society that trusts nothing may be harder to deceive. It will also be unable to do much of anything.

    The dependence runs both ways. Lies need a background of truth to remain cheap. If every email were fraudulent, email fraud would stop working. If every headline were invented, headlines would lose their power. Deception is parasitic. It needs ordinary reliability to create the expectation it violates.

    A lie designed to move is different from a lie designed to hide

    Chiu and his colleagues make a useful distinction between personal lies and fake news. A personal lie often tries to become invisible. It may be designed to end an inquiry, avoid a consequence, or keep someone from acting. Fake news is usually built for the opposite purpose. It wants attention. It wants movement. It asks the reader to click, share, fear, donate, vote, buy, or join.

    That difference changes the writing. A message built to travel may address “you,” ask questions, activate emotion, or attach itself to an identity. It does not have to offer the most coherent account of reality. It has to become consequential before careful judgment catches up.

    We also make a mistake when we treat every share as evidence of belief. People pass information along for many reasons. They may find it funny, alarming, identity-affirming, or simply worth asking others about. Someone can share a claim while still wondering whether it is true. Yet distribution systems often collapse all of those motives into the same observable event: engagement.

    That ambiguity is useful to a manipulator. The platform sees circulation. Other people see apparent social proof. The original sharer’s doubt disappears from view.

    The attacker needs a conversion, not a consensus

    We tend to imagine propaganda as an attempt to make most people believe the same false thing. Sometimes it is. But mass agreement is not always necessary.

    A scammer does not need everyone to respond. A political operation may need only a small number of people to become more afraid, more cynical, less likely to vote, or more willing to act. A fabricated health claim may be commercially successful if it reaches one unusually vulnerable audience. The relevant question is not always, “How many people believed this?” It may be, “Did the right person act?”

    Misinformation does not need universal belief. It needs the right person to act.

    Artificial intelligence makes that problem more precise. A message no longer has to be merely plausible to a demographic category. It can be adjusted to an individual’s interests, language, anxieties, and prior responses. In a series of experiments involving 1,788 participants, researchers found that language-model-generated messages became more persuasive when they were personalized with information about the recipient.

    The old economics of persuasion required choosing a message that was acceptable to many people. The emerging economics can generate many messages and discover which one moves each person. This is not just a bigger megaphone. It is a feedback system.

    Distrust is a defense that destroys the asset

    If deception exploits trust, the obvious defense is to withdraw trust. But skepticism applied without discrimination can deepen the same problem it is meant to solve.

    People rarely stop trusting altogether. More often, they transfer trust. When institutions, journalists, scientists, and unfamiliar people are treated as presumptively corrupt, identity becomes the shortcut. The trustworthy source becomes “someone like us.” A community that rejects external authority may become more dependent on insiders, even when those insiders have weaker methods and stronger incentives to distort.

    That is one reason echo chambers are resilient. They do not eliminate trust. They concentrate it and make it conditional on belonging. Evidence from outside the group becomes suspicious because of where it came from. Evidence from inside acquires credibility because it confirms the relationship.

    “Trust nothing” therefore cannot be the foundation of media literacy. Nobody has enough time or expertise to verify everything from first principles. The more durable goal is calibrated trust: confidence that rises and falls with the quality of the process, not with the familiarity of the speaker.

    Politeness is part of the truth-seeking system

    This is where Chiu’s work on creativity and disagreement becomes unexpectedly relevant.

    It is tempting to treat politeness as a matter of tone, useful for comfort but secondary to whether an argument is correct. Chiu’s research suggests something more consequential. In a study of group problem solving, disagreement was associated with more small creative advances, while rude actions were associated with fewer. In a separate study of online political debates, polite disagreement was associated with receiving more audience votes and winning more often.

    The point is not that civility makes a claim true. It is that respectful communication helps keep potentially useful contributions in the system long enough to be examined. Rudeness changes what people are willing to say, how carefully others listen, and whether a partial insight survives contact with the group.

    Correctness is not self-executing. A correct person who cannot be heard, or who causes everyone else to retreat into defensive positions, may contribute less to a group’s eventual understanding than an imperfect person who makes continued inquiry possible.

    This distinction matters online, where we frequently confuse aggression with rigor. Humiliating someone for sharing a false claim may establish the responder’s intelligence to an audience. It may also make the original sharer less willing to reconsider, and it signals to everyone watching that uncertainty carries social risk.

    If our goal is truth rather than performance, the question is not merely whether a correction is accurate. It is whether the correction improves the group’s ability to think.

    Add friction to the claim, not the relationship

    One of Chiu’s own practices offers a practical starting point. When he disagrees with someone, he first asks, “What is this person saying that I think is right?”

    That question is not an obligation to manufacture agreement. It is a way to separate respect for the person from acceptance of the claim. It forces the listener to understand before attacking and gives the conversation a piece of shared ground. From there, the evidence, logic, and assumptions can be tested more directly.

    Respect people. Test claims.

    The same separation can shape platforms and institutions. Friction should be attached to the moment a claim moves, not to a person’s basic membership in the conversation. Ask whether someone has read the article before sharing it. Surface the source. Show the date. Make uncertainty visible. Add a pause before a highly emotional claim is amplified.

    Small prompts can matter. A meta-analysis of 20 experiments involving 26,863 participants found that brief reminders to think about accuracy reduced intentions to share false news by about 10 percent. That will not solve coordinated deception. It does show that some sharing happens because the environment makes reaction more immediate than reflection.

    A ten percent reduction may sound modest. At the scale of a social platform, modest changes in the probability of sharing can alter what millions of people encounter. More importantly, an accuracy prompt does not require treating every user as an enemy. It interrupts an action while preserving the relationship.

    Shared reality is maintained, not inherited

    Individuals cannot outwork industrial-scale deception. Media literacy matters, but no curriculum can make every person an expert in medicine, economics, climate science, warfare, law, and every other subject that may appear in a feed. Personal vigilance is one layer of defense, not an excuse for platforms and institutions to avoid responsibility.

    Healthy information systems need provenance, transparent moderation, meaningful appeal, and friction that reflects the possible harm of amplification. They need institutions willing to show their work and correct errors without pretending that error and fraud are the same thing. They need education that teaches people how knowledge is made, not only how to spot a suspicious headline.

    Most of all, they need norms that let scrutiny and cooperation coexist.

    Chiu’s research points toward a useful symmetry. Misinformation becomes powerful when it exploits our relationships while bypassing our judgment. Productive disagreement does the reverse: it protects the relationship so that judgment can become more demanding.

    That is disciplined trust. It does not ask us to believe people because they are familiar, polite, or on our side. It asks us to preserve enough mutual regard to investigate claims together, then place confidence in processes that expose themselves to evidence, correction, and challenge.

    The opposite of fake news is not a world in which nobody believes anything. It is a world in which trust is earned, claims encounter thoughtful friction, and disagreement helps us get closer to the truth instead of driving us farther apart.

    Listen and read further

    Eric Pratum and Ming Ming Chiu on The Unfolding Thought Podcast.
    Listen to Ming Ming Chiu on The Unfolding Thought Podcast.