Inbound & Agile

Category: Change

  • 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.
  • A Better Statistic Does Not Mean a More Stable Life

    A Better Statistic Does Not Mean a More Stable Life

    For decades, one of the most familiar explanations for persistent poverty in America began with the timing of childbirth.

    Have a child too young, the story went, and education becomes harder, work becomes less stable, marriage becomes less likely, and poverty becomes more difficult to escape. Delay parenthood, finish school, find work, and the odds should improve.

    Young women changed their behavior.

    The teen birth rate fell 78 percent between 1991 and 2021. In her episode of The Unfolding Thought Podcast, sociologist Kathryn Edin describes an even more striking generational shift inside the birth cohort she helps lead. About 40 percent of the mothers had given birth before age 22. Among their daughters, the figure was about 15 percent.

    Then Edin adds the sentence that should unsettle the entire causal story: the young adults are not doing well.

    The long-term verdict is not yet in. The Future of Families and Child Wellbeing Study is collecting its age-27 wave during 2026. But the evidence already presents a challenge. If people change the behavior that was supposed to explain the outcome, and the outcome does not improve proportionally, the explanation was incomplete.

    Teen parenthood can make an already difficult path harder. That is not in doubt. What deserves more scrutiny is the leap from “this event increases risk” to “this event explains the system.”

    A better statistic can be a genuine achievement while still concealing an unstable life.

    We changed the sequence, not the odds

    The appeal of a behavioral explanation is that it offers both a cause and a remedy.

    If early childbirth causes poverty, reducing early childbirth becomes an anti-poverty strategy. If dropping out causes poverty, increasing enrollment becomes an anti-poverty strategy. If unemployment causes poverty, moving people into jobs becomes an anti-poverty strategy.

    Each proposition contains truth. None is sufficient.

    A sequence of milestones can describe the lives of people who became economically secure without identifying what made those milestones valuable. Education pays when the credential is credible, the institution is affordable, and the labor market rewards it. Work pays when hours are dependable, wages exceed the cost of showing up, and one sick child does not end the job. Delayed parenthood creates options when the intervening years contain real opportunities to build a life.

    Otherwise, the sequence becomes a set of instructions for waiting.

    A policy can change the sequence of a life without changing the odds that govern it.

    Annual income erases time

    Edin’s most important distinction may not be between poverty and prosperity. It may be between low income and instability.

    Annual income compresses twelve months into one number. Two households can report the same income while living in different economic worlds. One receives a predictable paycheck every other Friday. The other moves through full weeks, shortened weeks, canceled shifts, emergency expenses, and months when earnings and bills arrive in the wrong order.

    The Federal Reserve’s 2024 household survey found that 29 percent of adults experienced at least occasional month-to-month income variation. Eleven percent said that variation made it difficult to pay bills. Among adults with family income below $25,000, 19 percent struggled with bills because their income varied.

    The damage is not limited to the missing dollars. Volatility creates secondary costs.

    • A late rent payment becomes a fee and then an eviction filing.
    • An unreliable car causes a missed shift, which reduces the next paycheck.
    • A temporary move changes a child’s school and a parent’s commute.
    • A short workweek makes a training payment unaffordable, turning an unfinished credential into debt.

    Averages treat these events as fluctuations around a mean. Families experience them as a sequence in which one disruption changes what becomes possible next.

    Poverty is not only a shortage of resources. It is a shortage of planning horizon.

    Welfare reform strengthened work and weakened the floor

    The honest account of welfare reform is neither triumph nor catastrophe.

    Edin credits the Earned Income Tax Credit with helping make low-wage work pay. Research by Bruce Meyer and Dan Rosenbaum found that EITC expansions accounted for a large share of the increase in employment among single mothers during the period they studied. More mothers entered formal employment, and many families benefited.

    At the same time, the replacement of Aid to Families with Dependent Children with Temporary Assistance for Needy Families removed the guarantee of cash assistance and gave states much wider discretion over the funds.

    A Congressional Research Service analysis estimated that 79 percent of people eligible for family cash assistance received it in 1994. By 2018, 26 percent did. A more recent CRS review notes that the number of families receiving assistance fell from 5.1 million in 1994 to about 1 million in 2024. It also explains that caseload reduction itself helped states meet federal work standards, whether or not the families outside the program were employed.

    That is a measurement problem disguised as an administrative success.

    A smaller caseload may mean fewer families need help. It may mean fewer eligible families receive it. It may mean the application process is harder, the benefit is less useful, or the state spent the money elsewhere. The number cannot tell us which story is true.

    A safety net has not succeeded because fewer people touch it. It has succeeded when fewer ordinary shocks become catastrophes.

    The success sequence assumes a success structure

    The young adults in Edin’s cohort have absorbed the message. They are delaying children, trying to finish school, and trying to establish themselves in the labor market.

    But the institutions supporting that sequence are uneven.

    College is not one experience. A student who attends full time with family support is not following the same path as a student moving in and out of courses around changing work schedules. Starting college is not finishing. Finishing is not necessarily obtaining a credential with labor-market value. Edin describes young adults trying to follow the script in fits and starts, including some who attended for-profit trade schools and were left with credentials that did little for them.

    The safety net introduces another paradox. Much of it is organized around dependent children. Several major benefits are available only, or far more substantially, to households raising children. Young adults who delay parenthood may therefore spend precisely the years when they are trying to build stability with less support than they would receive after having a child.

    This is not an argument for early parenthood. It is an argument against a system that tells people to postpone a milestone and then withholds much of its support during the postponement.

    The success sequence is usually presented as personal discipline. Its results depend on public and economic structure: stable work, useful education, affordable housing, transportation, health coverage, and enough cash continuity to survive a setback without abandoning the plan.

    The success sequence assumes a success structure.

    Meaning is not a luxury good

    Edin complicates the story further when she describes what motherhood meant to the women she interviewed.

    Many did not treat a child as a casual decision or marriage as unimportant. They often held marriage to an extraordinarily high standard and saw motherhood as one of the few available roles through which they could create meaning, identity, and a contribution that mattered.

    That finding is easy to mishandle. It does not make the economic demands of early parenthood disappear. It does reveal an assumption embedded in middle-class advice: people can defer meaning because institutions will provide other credible sources of progress while they wait.

    A good job offers more than wages. It offers mastery, relationships, identity, and a future a person can imagine inhabiting. Education can do the same when it provides belonging and visible progress rather than repeated administrative friction. Communities do it by giving people places to gather, contribute, and be known.

    When those institutions fail, postponement is not experienced as patient investment. It can feel like life has been placed on hold without a reliable date for its return.

    Social infrastructure belongs in the mobility equation

    Near the end of the episode, the conversation moves from income and family formation to bowling alleys, libraries, cafés, religious congregations, nonprofits, and the places where people repeatedly encounter one another.

    These can sound like amenities beside the seriousness of wages and housing. They are part of the economic system.

    A large Nature study of social capital and mobility found that economic connectedness, measured through friendships across class lines, was strongly associated with upward mobility. A companion study found that institutions matter not only because they expose people to one another, but because their structure affects whether exposure becomes friendship.

    A room containing different kinds of people is not yet a relationship. A program that makes people interact once is not yet social infrastructure. Trust is built through repeated contact, shared work, and enough time for people to become more than categories to one another.

    Those relationships can carry information, references, transportation, childcare, expectations, and the quiet confidence that someone will answer when a plan goes wrong. They extend the planning horizon.

    Measure whether a life can absorb a shock

    The decline in teen births is real progress. The mistake would be treating it as proof that the original theory of poverty was correct.

    A more serious evaluation would ask what happened after the statistic improved:

    • How often does a household fall below a minimum cash floor during the year?
    • How predictable are work hours thirty days in advance?
    • How long does recovery take after a car repair, illness, or missed paycheck?
    • Does starting education lead to completion, and does completion lead to earnings?
    • How many moves, school changes, and job losses follow one income interruption?
    • Does a young adult have dependable relationships beyond the household?
    • Can a person make a six-month commitment with reasonable confidence about next month?

    Those measures are less tidy than a birth rate, poverty line, enrollment count, or welfare caseload. They are closer to the thing policy is supposed to improve.

    A stable life is not one in which nothing goes wrong. It is one in which ordinary problems remain ordinary.

    Edin’s work asks us to look past the apparent solution and investigate the life that followed. Young women changed. The statistic changed. If the promised mobility does not follow, the next question is not what else is wrong with them.

    It is what we failed to change around them.

    Listen and read further

    Eric Pratum and Kathryn Edin on The Unfolding Thought Podcast.
    Listen to Kathryn Edin on The Unfolding Thought Podcast.
  • Blame Is What Organizations Use Instead of Learning

    Blame Is What Organizations Use Instead of Learning

    A project misses its deadline. A customer leaves. A machine fails. A campaign spends the budget and produces almost nothing.

    The review begins with a reasonable question: What happened?

    But the question rarely remains open for long. Someone was careless. Someone did not communicate. Someone lacked urgency. Someone should have known better.

    Once the organization has a name, it also has a remedy. Give feedback. Add training. Put a note in the performance review. Replace the person if the failure was serious enough. The meeting ends with the satisfying feeling that accountability has occurred.

    Then a different person encounters the same incentives, the same missing information, the same overloaded process, and the same failure happens again.

    The organization did not solve the problem. It purchased narrative closure at the cost of understanding.

    In her episode of The Unfolding Thought Podcast, Factor.AI co-founder Lindsay McGregor describes what she calls the blame bias: our tendency to explain poor performance through the character of the person closest to the outcome, even when the surrounding system did more to produce it.

    That insight is often softened into a pleasant leadership slogan: blame the system, not the person. But the implication is more demanding than the slogan. System thinking does not remove accountability. It expands accountability beyond the person with the least power to change the conditions.

    Blame is what organizations use when they want the appearance of accountability without the work of learning.

    Blame compresses the causal field

    Every meaningful organizational outcome has more causes than can fit comfortably into a meeting.

    A missed deadline may involve a poor individual decision. It may also involve an unrealistic estimate, a sales promise made without delivery input, shifting priorities, a dependency no one owned, incentives that rewarded saying yes, and a reporting process that made trouble visible only after recovery was impossible.

    Blame compresses that causal field into a person. It converts a difficult investigation into a familiar moral story: a responsible person would have produced a different result.

    Social psychologist Lee Ross gave the fundamental attribution error its name in 1977. We tend to overestimate stable traits when explaining another person’s behavior and underestimate the situation in which the behavior occurred. In ordinary language, we see what someone did and quickly decide what kind of person would do it.

    Distance makes the story easier. An executive sees a careless operator. A person standing beside the machine sees the awkward control, the rushed handoff, the warning light everyone has learned to ignore, and the production target that makes stopping the line feel dangerous.

    The executive may have more authority and more data. The operator often has more causal knowledge.

    Blame gives an organization a culprit. Accountability gives it a next action.

    The same people can produce a different organization

    McGregor illustrates the power of context through NUMMI, the automobile plant General Motors and Toyota operated together in California.

    The plant’s previous workforce had been described as unreliable, antagonistic, and nearly impossible to manage. Toyota reopened the facility with many of the same workers but a different operating system. Employees were trained to identify problems, stop production, suggest improvements, test ideas, and treat quality as something they helped create rather than something management inspected after the fact.

    The people did not receive new personalities. The work gave different behaviors a reason to emerge.

    A California Management Review study of NUMMI describes how broad job classifications, teamwork, job rotation, employee involvement, and continuous improvement replaced direct supervision as the primary mechanism for performance. The point was not simply to be nicer to workers. It was to make their judgment part of the production system.

    W. Edwards Deming estimated from his experience that 94 percent of troubles and possibilities for improvement belonged to the system and were therefore management’s responsibility. The number should not be treated as a universal law. Its distribution of responsibility is the more important point.

    The higher a person sits in the organization, the more power that person usually has to shape goals, incentives, workload, information flow, staffing, tools, decision rights, and consequences. Yet performance conversations often place the greatest explanatory burden on the people with the least authority over those conditions.

    A system explanation does not say nobody is responsible. It asks whether responsibility has been assigned in proportion to power.

    The system is not an alibi

    There is an obvious objection. Some people lie, neglect their obligations, mistreat colleagues, or knowingly violate an important rule. A leader who explains every action through context can become as unserious as one who explains everything through character.

    System thinking should not erase agency. It should improve diagnosis.

    Four questions belong in the same review:

    • What choice did the person make?
    • What conditions made that choice more likely, more rational, or harder to detect?
    • Who had the authority to change those conditions before the event?
    • What individual and system changes will make recurrence less likely?

    An employee can be responsible for deception while a manager is responsible for a target that rewarded it. A manager can be responsible for ignoring a warning while an executive team is responsible for a culture in which delivering bad news ends careers. These responsibilities do not cancel one another.

    A system explanation does not erase individual responsibility. It reveals who had the power to prevent recurrence.

    Designing for the exception changes everyone’s work

    Bad actors create another system problem: they are memorable.

    McGregor describes the remote employee discovered to be working two full-time jobs. The violation is real. The leader’s anger is understandable. The danger comes next, when the organization redesigns work as though every employee were waiting for a chance to cheat.

    More monitoring appears. Decision rights narrow. Work becomes visible through activity rather than results. Approval steps multiply. The system may catch another offender. It also teaches conscientious employees that their judgment is not trusted and that appearing busy is safer than experimenting with a better way to work.

    Control is not free. It consumes attention, delays decisions, encourages performance theater, and transfers discretion away from the people closest to the problem.

    Research by Richard Ryan and Edward Deci on self-determination theory identifies autonomy, competence, and relatedness as conditions that support intrinsic motivation and healthy self-regulation. A control added for one exceptional case can weaken those conditions for everyone else.

    The design question is not whether to trust people blindly. It is how to contain misconduct without making the misconduct the model of human behavior on which the entire organization is built.

    When leaders design for the worst employee they can imagine, they often create the worst workplace everyone else has experienced.

    Blame destroys evidence

    The deepest cost of blame is not hurt feelings. It is lost information.

    James Reason distinguished the person and system approaches to error in a foundational article on human error. The person approach treats errors as products of inattention, carelessness, poor motivation, or moral weakness. Its remedies include retraining, new procedures, discipline, and shame. The system approach begins with the fact that people are fallible and asks how working conditions can prevent an error or limit its consequences.

    A blame-oriented review teaches employees to manage exposure. They disclose less, describe uncertainty more carefully, and wait until a problem is undeniable before attaching their names to it. The organization receives cleaner reports and worse knowledge.

    Amy Edmondson’s study of psychological safety and learning behavior found that teams where people felt safe taking interpersonal risks engaged more in behaviors such as discussing errors, seeking feedback, and experimenting. Learning behavior, in turn, helped explain performance.

    This does not mean every mistake should feel comfortable. It means the organization must be able to hear an accurate account before it decides what the account requires.

    A good metric can look like bad performance

    Systems also determine what counts as evidence.

    In the Toyota system McGregor describes, pulling the andon cord made a problem visible and could stop production. A manager optimizing only for short-term output might see fewer cord pulls as improvement. A manager responsible for learning might worry that fewer pulls meant employees had stopped surfacing problems.

    The same number can therefore describe two different organizations. One has fewer problems. The other has fewer reported problems.

    Most performance systems are much better at counting completed work than adaptive work. They measure units, calls, revenue, utilization, deadlines, and hours. They rarely measure useful questions, early warnings, experiments, prevented errors, or ideas that improved the process.

    When the visible score rewards output and the invisible score contains learning, people rationally protect output. Then leadership interprets the resulting silence as evidence that the system works.

    Accountability should end with a changed system

    A learning review should not begin by assuming innocence. It should not begin by assuming guilt either. It should begin by preserving the size of the question.

    Useful reviews ask:

    • What did the person know at the time, and what could that person reasonably have known?
    • What made the action seem sensible, necessary, or safe in that moment?
    • Would a capable peer in the same conditions have been likely to act differently?
    • Which target, workload, handoff, tool, incentive, or norm increased the probability of failure?
    • Where could an earlier signal have changed the outcome?
    • What individual choice requires correction?
    • What system change will make the better choice easier, earlier, or more visible?
    • Who owns that change, and how will the organization know it worked?

    If the review ends only with feedback for the employee, the organization has probably stopped one level too soon. If it ends only with a vague promise to improve the culture, it has stopped several levels too early.

    Accountability becomes real when it creates an obligation to repair. The employee may need to change a behavior. The manager may need to change the work. The executive may need to change the incentive that made the behavior predictable. Each owner should leave with an action proportionate to the power that owner had over the conditions.

    Learning requires a longer explanation

    Blame survives because it is emotionally efficient. It turns ambiguity into certainty, causation into character, and management failure into an employee problem.

    Learning is slower. It asks leaders to understand work they may have only seen through dashboards. It makes them examine controls they approved, incentives they praised, and constraints they never personally experienced. It may reveal that the person who made the visible mistake was adapting to a less visible mistake in the design.

    That is not softness. It is a more exacting form of accountability because it refuses to confuse punishment with prevention.

    Blame asks who deserves the pain. Accountability asks who owns the repair.

    Listen and read further

    Episode artwork for Lindsay McGregor’s appearance on The Unfolding Thought Podcast.
    Listen to Lindsay McGregor on The Unfolding Thought Podcast.
  • A Strategy That Cannot Fail Is Not a Strategy

    A Strategy That Cannot Fail Is Not a Strategy

    A leadership team asks for a strategy. What it really wants is a guarantee.

    The executives want to know which campaign will produce the forecasted revenue, which technology will create the promised efficiency, which market will grow, and which reorganization will solve the problem without creating another one. The strategist can provide research, models, experience, and a recommendation. What the room wants is certainty.

    So everyone participates in a familiar performance. Assumptions become projections. Projections become targets. Targets become commitments. The presentation grows more precise as the underlying situation remains stubbornly unpredictable.

    If the plan works, its authors were prescient. If it fails, they can point to the data, the methodology, the consultant, or the industry standard. Every decision was defensible. No one quite decided.

    This is not strategy. It is responsibility laundering.

    In his episode of The Unfolding Thought Podcast, strategist Steve Kozel describes strategy as a series of nested choices. At any altitude, he argues, strategy requires multiple feasible options, an understanding of the situation, a desired effect, and a choice among the alternatives.

    That definition carries a consequence organizations often try to avoid: if the alternatives are genuinely feasible and the future is genuinely uncertain, the choice can be wrong.

    A strategy that cannot fail is not a strategy. It is either an operating procedure, a foregone conclusion, or a story told to make uncertainty feel controllable.

    Strategy begins where prediction ends

    There are decisions for which certainty is a reasonable expectation. Payroll should run. An invoice should calculate correctly. A proven manufacturing process should stay within tolerance. When cause and effect are stable and repeatable, leaders should demand reliability.

    Strategy is different because it concerns choices whose value depends on reactions that have not happened yet. Competitors respond. Customers reinterpret what they want. Employees change the plan while implementing it. Technology alters the economics. The choice itself changes the environment in which its success will be judged.

    Frank Knight drew a useful distinction in Risk, Uncertainty, and Profit. Risk describes situations in which probabilities can be estimated. Uncertainty describes situations in which the probabilities themselves are not reliably knowable.

    Most organizations are comfortable managing risk. They create ranges, reserves, scenarios, and controls. They struggle when uncertainty cannot be converted into a percentage. The absence of a reliable probability feels like the absence of management.

    But uncertainty is not a temporary defect in the strategy process. It is the condition that makes strategy necessary. If the correct action and its outcome were already known, the organization would not need a strategist. It would need an operator.

    A plan becomes strategy only when it contains a consequential choice and accepts the possibility of being wrong.

    Certainty has a job inside the organization

    The demand for certainty is not simply a reasoning error. It serves an organizational purpose.

    A confident forecast lets a project receive funding. A familiar methodology reassures procurement. A famous consultancy gives an executive cover. A dashboard suggests control. A best practice allows everyone to say the decision met the accepted standard.

    These things reduce personal exposure even when they do not reduce uncertainty. That difference matters.

    Kozel calls the deeper pattern “Fear OS,” an unspoken operating system in which people are expected to maintain control, avoid failure, produce positive metrics, and explain the world as though it were more rational than it is. Under those conditions, the safest decision is often the one whose failure will be easiest to defend.

    This helps explain why a company can be filled with intelligent people and still repeat choices no one strongly believes in. Each participant is responding rationally to a local incentive. The analyst avoids an unsupported recommendation. The manager protects the quarterly number. The executive selects the respectable vendor. The board receives a forecast with enough detail to feel governed.

    The organization has not eliminated uncertainty. It has distributed responsibility so thoroughly that the eventual result will seem to belong to no one.

    Best practice cannot create strategic advantage

    Best practices are valuable when the goal is dependable execution of a known activity. I want the people operating an airplane, administering medication, or securing financial data to use procedures that have survived serious scrutiny.

    The problem begins when leaders ask best practice to answer a strategic question.

    Michael Porter’s explanation of strategy centers on a unique position, trade-offs, and fit among activities. Strategy requires choosing what not to do. An organization that copies the accepted practices of its category may become more competent. It does not become meaningfully different.

    This is the contradiction Kozel surfaces in the episode. Organizations want a proven solution and an advantage. They want evidence without experiments. They want innovation without the waste, ambiguity, and failed attempts through which new knowledge is produced.

    If every competitor follows the same research, hires the same experts, adopts the same technology, and optimizes against the same benchmark, best practice becomes a convergence mechanism. It improves the category while compressing the differences within it.

    Best practice can improve execution. It cannot explain why the company should win.

    An experiment is not an excuse

    None of this means leaders should become casual about evidence or celebrate failure as a cultural virtue.

    “We are experimenting” can become its own form of responsibility laundering. Teams can use the language of learning to excuse poor preparation, unclear goals, oversized bets, and projects that continue long after the original hypothesis has collapsed.

    A real experiment is more demanding than a confident plan because it must say what is not known and how the organization intends to learn it.

    Amy Edmondson describes an intelligent failure as an undesired result in new territory. The attempt should pursue a meaningful goal, rest on an informed hypothesis, and be no larger than necessary to produce the needed knowledge.

    Those constraints turn experimentation from a slogan into a management discipline.

    • What specific assumption are we testing?
    • What evidence would increase or reduce our confidence?
    • What is the smallest credible action that can produce that evidence?
    • What downside are we exposing, and who has agreed to it?
    • When will we stop, expand, or change direction?
    • Where will the learning alter a decision, budget, process, or belief?

    The purpose of an experiment is not to make failure harmless. It is to buy information at a price the organization can afford.

    Learning must reach the assumptions

    Organizations often claim to learn while protecting the belief that produced the disappointing result.

    A campaign misses its target, so the team changes the creative. A product struggles, so marketing receives a larger lead goal. A transformation stalls, so managers schedule more training. The response corrects activity without reconsidering the governing assumption.

    Chris Argyris distinguished this kind of correction from deeper learning. Single-loop learning asks how to perform the existing approach more effectively. Double-loop learning asks whether the underlying goals, rules, and assumptions still deserve to govern the work.

    In his work on defensive reasoning, Argyris argued that capable professionals often respond to difficulty by avoiding examination of their own contribution to it. Expertise can make the defense more articulate without making it less defensive.

    This is why adding more data does not necessarily make an organization more empirical. Data can test a claim. It can also decorate a conclusion the organization is unwilling to reconsider.

    Double-loop learning creates a more uncomfortable review:

    • Did the execution fail, or was the strategic premise wrong?
    • Are we improving the offer, or only improving the way we promote it?
    • Does the metric represent value, or is it merely easy to make rise?
    • Are we asking a team to solve a problem whose cause sits above its authority?
    • What would we stop believing if we took this result seriously?

    If every review ends with a better way to execute the same idea, the organization is not learning strategically. It is becoming more efficient at defending the past.

    Exploitation wins the quarterly argument

    Even leaders who understand this problem face a structural disadvantage.

    James March’s classic paper on exploration and exploitation in organizational learning describes the tension between refining what an organization already knows and searching for new possibilities. Exploitation produces clearer, nearer, and more predictable returns. Exploration is uncertain, delayed, and frequently disappointing.

    That asymmetry gives the known approach an advantage in every budgeting cycle. The established product has revenue. The existing channel has benchmarks. The familiar process has owners. The new option has a hypothesis.

    March warned that adaptive processes can become effective in the short run and self-destructive in the long run because organizations refine exploitation faster than exploration. The company becomes increasingly competent at a world that is becoming less relevant.

    This is another reason certainty can be dangerous. It does not merely misdescribe the future. It systematically moves resources toward whatever already has a history.

    The most dangerous experiment is the one an organization is already running while pretending the outcome is guaranteed.

    What honest strategy sounds like

    An honest strategy does not replace confidence with vagueness. It makes confidence proportional to evidence.

    It names the choice. It explains why this option is preferable to other feasible options. It distinguishes what is known from what is assumed. It defines the risk the organization is accepting. It makes clear who owns the decision. It identifies the earliest evidence that would justify changing course.

    It also separates commitments from predictions.

    A leader can commit to allocating resources, protecting an experiment, reviewing evidence, and changing direction when the premise no longer holds. A leader cannot honestly commit that customers, competitors, employees, regulators, and technology will behave according to the forecast.

    This kind of strategy can sound less impressive in the room. It replaces false precision with boundaries, hypotheses, and decision rules. It admits that expertise improves a choice without making the future obedient.

    That is not weaker leadership. It is leadership willing to remain responsible after the illusion of control has been removed.

    The choice comes before the certainty

    Organizations do not need more comfort with failure in the abstract. They need a more precise relationship with uncertainty.

    Preventable mistakes should be prevented. Known processes should be reliable. Large irreversible choices deserve more scrutiny than small reversible ones. Evidence should shape judgment wherever evidence exists.

    But strategy cannot wait until the uncertainty has disappeared. By then, the choice may be obvious, the advantage may be gone, or the organization may have spent years perfecting the wrong thing.

    The discipline is to decide without pretending to know, act without becoming reckless, and learn without protecting the assumptions that made the action possible.

    Strategy is not a promise that the future will obey. It is a commitment to choose, learn, and remain accountable before certainty arrives.

    Listen and read further

    Episode artwork for Steve Kozel’s appearance on The Unfolding Thought Podcast.
    Listen to Steve Kozel on The Unfolding Thought Podcast.
  • Organizations Cannot Change Until They Grieve What Is Ending

    Organizations Cannot Change Until They Grieve What Is Ending

    A leadership team learns that the future it planned for is not going to arrive.

    The market moved. The merger did not create the expected value. A technology changed the economics of the business. Customers stopped behaving the way the strategy assumed they would. The product that once organized the company’s identity is becoming less important every year.

    So the leaders schedule a strategy session.

    They bring in new data, debate scenarios, revise the forecast, rename a few priorities, and leave with a transformation plan. Yet the plan still depends on the future they were supposed to have abandoned. The same products must remain central. The same people must retain authority. The same capabilities must be protected. The same story about why the organization matters must survive.

    The strategy is new. The future underneath it is not.

    In her episode of The Unfolding Thought Podcast, futurist and death doula Juli Rush brings an unusual idea into the work of foresight: sometimes a future has to be treated as something that is dying.

    That language may sound too intimate for a strategy meeting. I think that is precisely why it is useful.

    Organizations are usually comfortable discussing change as an analytical problem. They are much less comfortable discussing it as a loss. But a future can disappear before a company, community, or person has emotionally and structurally stopped living inside it. When that happens, more evidence does not necessarily create movement. The organization may understand what has changed and still be unable to act as though it understands.

    A plan is never just a plan

    We like to imagine that a strategic plan is a neutral description of choices, resources, and expected results. It is also a collection of promises.

    It promises a salesperson that a market will continue to value the relationships she spent twenty years building. It promises a product leader that the expertise which made him important will remain important. It promises an executive that the organization he knows how to lead will still exist. It promises employees that the sacrifices they made were part of a story that leads somewhere.

    Those promises are rarely written in the plan. They are still real.

    This is one reason organizations can acknowledge a change intellectually while rejecting its consequences operationally. They accept that the market is different, then preserve the budget built for the old market. They say the business must become more customer-centered, then leave decision rights with the functions furthest from the customer. They describe a legacy product as declining, then require every new investment to protect its revenue, status, and internal constituency.

    The future in a strategy is not just a forecast. It is a promise about what will matter, who will matter, and which sacrifices will have been worthwhile.

    Research on organizational identity helps explain why this is so difficult. In one study of employees going through a corporate takeover, people adjusted better when they could experience either continuity with a valued identity or the gain of a credible new one. When neither path was available, strong identification with the old organization could deepen the loss.

    The implication is not that leaders should preserve everything people value. That would make meaningful change impossible. It means that ending a plan can also disturb status, competence, community, memory, and identity. If leaders discuss only the business case, they leave the rest of the transition to happen underground.

    Facts cannot end a future people are still living inside

    When change stalls, leaders often assume the missing ingredient is information.

    They commission another market study. Add detail to the transformation roadmap. Ask finance for a more precise forecast. Build a larger dashboard. Bring in a consultant to repeat what people inside the organization have already been saying.

    Sometimes better evidence is exactly what is needed. Sometimes the evidence has already done its job.

    The problem is that evidence can tell us a choice is no longer working without dissolving our attachment to it. In Barry Staw’s classic research on escalation of commitment, people became more willing to invest in a failing course of action when they felt personally responsible for having chosen it. More information did not automatically produce better correction. Responsibility could become a reason to defend the past.

    Organizations make this effect collective. A strategy acquires sponsors, specialists, reporting lines, incentives, rituals, and language. Entire careers grow around making it appear sensible. By the time the evidence turns, the organization is not reconsidering an isolated decision. It is reconsidering part of itself.

    This is why a declining initiative can survive years of disappointing results. Each round of investment is described as the final adjustment needed to prove the original idea. Each delay protects the people involved from having to decide what the ending means.

    There is an important difference between uncertainty and ambiguity. Uncertainty means we do not yet know which outcome will occur. Ambiguity can mean we do not even agree on what is ending, what remains, or what the loss should be called.

    Psychologist Pauline Boss developed the idea of ambiguous loss to describe losses that resist clean resolution. The organizational version is obviously not equivalent to the disappearance or psychological absence of a loved one. The concept is still useful. A company can announce a transformation while leaving the former system half-alive. People lose familiar roles but continue to be measured against their old responsibilities. A strategy is declared over, but no one can say which commitments are actually released.

    Nothing has clearly died, so nothing can be fully grieved or replaced.

    Resistance is often grief with an operational vocabulary

    In business, grief rarely announces itself as grief.

    It sounds like a request for more proof. A concern about timing. A debate over scope. A warning that customers are not ready. An insistence that the old process needs one more quarter. A proposal to run the new model without taking resources from the old one.

    Any of those objections might be valid. Treating every disagreement as an emotional reaction is a convenient way for leaders to avoid criticism. But treating every objection as an analytical judgment is equally convenient. It allows the organization to negotiate indefinitely with a future that has already left.

    Resistance is often grief with an operational vocabulary.

    The popular habit of assigning organizational change to a neat sequence of grief stages is not especially helpful. Grief is not a predictable staircase, and employees are not patients to be managed through denial on the way to acceptance. People can support a strategic decision and still mourn what it removes. They can be excited by a new role and miss the competence they felt in the old one. They can disagree with the decision for sound reasons while also feeling threatened by it.

    The useful move is not diagnosing people. It is making the losses discussable.

    What will employees no longer be proud of? Which relationships will become less central? What expertise will carry less authority? Which customer, community, or tradition will feel abandoned? What story about the organization will no longer be true?

    If leaders cannot answer those questions, they probably do not yet understand the change they are asking other people to make.

    Hospice: stop treating the incurable as a turnaround

    Rush has since formalized her work as a framework of Hospice, Vigil, and Compost. Those three words describe work that conventional strategy often skips.

    Hospice begins with a difficult distinction: what needs more effort, and what needs an ending?

    Leaders are rewarded for fixing, growing, and sustaining things. Ending something can feel like admitting that the prior investment was mistaken. That makes it tempting to label every failing system a turnaround opportunity.

    But purpose and mechanism are not the same. A university can remain committed to learning while ending a program. A company can remain committed to customers while retiring the product through which it once served them. A nonprofit can remain committed to a need while admitting that its intervention no longer produces the intended result.

    Hospicing a future does not mean declaring the work worthless. It means stopping the attempt to make one mechanism immortal.

    Practically, I would ask leaders to make the ending specific:

    • Which assumption no longer deserves to organize the strategy?
    • Which product, process, role, or investment will stop?
    • What evidence made that decision necessary?
    • What would cause the organization to reconsider?
    • Who has the authority to prevent the ending, and for how long?

    Without that specificity, “letting go” becomes another slogan sitting on top of unchanged budgets and power.

    Vigil: stay with the loss long enough to learn

    Organizations are often impatient with the space between an ending and a replacement.

    A product is discontinued on Monday and the new growth narrative is presented on Tuesday. A reorganization is announced with a slide explaining why everyone should be excited. A founder leaves, and the company immediately declares that the culture will not change.

    That speed is meant to create confidence. It can also communicate that what people invested in the old future is inconvenient.

    A vigil does not require months of therapeutic meetings or ceremonial language that feels false to the organization. It requires enough attention to distinguish what should end from what should be honored.

    What did the old strategy make possible? Which capabilities did it create? What did people learn while making it work? Which relationships and values should survive its end? What harm did it cause that should not be romanticized?

    This matters because organizations frequently make one of two mistakes. They preserve the old system so completely that the new future cannot emerge, or they denounce the old system so completely that people who built it hear the change as an indictment of their contribution.

    Neither produces honest learning.

    The vigil is where a company can say, “This served us, and it cannot take us where we need to go.” Both halves of that sentence matter.

    Compost: carry forward what can still feed the system

    Compost is not preservation. The original form does not survive.

    What survives are elements that can become useful in a different system.

    A legacy product may contain customer knowledge the new team needs. A discontinued program may have built a community that should not disappear with its budget. A failed strategy may reveal a capability the organization undervalued because it was attached to the wrong outcome. A departing leader may carry relationships and tacit knowledge that cannot be transferred through a folder of documents.

    Composting asks a harder question than, “What lessons did we learn?” Organizations answer that question too easily. They produce a list, thank the team, and return to work.

    The better question is, “Where will the lesson live?”

    Will a decision rule change? Will a metric disappear? Will authority move? Will a capability receive funding? Will a hiring profile change? Will a customer promise be rewritten? If the old strategy taught something but nothing in the operating system changes, the organization did not compost the experience. It buried it.

    This cannot become change-management theater

    There is an obvious danger in bringing the language of grief into organizational life. Leaders can use it to make a decision feel humane without making the decision itself more honest.

    A ritual cannot compensate for a deceptive explanation. Listening sessions do not create dignity if the outcome was predetermined and leaders pretend otherwise. Honoring the past is not meaningful if employees absorb every cost while executives protect their own roles. Asking people to grieve can become insulting when the organization refuses to name who chose the change and who benefits from it.

    If nothing stops, loses funding, or gives up authority, the ending was symbolic.

    The work of ending a future does not replace strategy. It makes strategy testable.

    A responsible ending should show up in the allocation of money, attention, people, and decision rights. It should release someone from an obsolete expectation. It should create room for a new experiment that the old strategy would have crowded out. It should make clear which part of the organization’s identity is a purpose worth carrying and which part was only one temporary expression of that purpose.

    The most strategic question may be: what has ended?

    Most strategy processes begin with some version of, “What future do we want?”

    It is a necessary question. It may not be the first one.

    Before an organization can choose a future, it may need to identify the future it is still trying to recover. The market position that is not coming back. The growth curve that depended on temporary conditions. The career ladder built for work that no longer exists. The cultural story that stopped matching employees’ experience. The strategy that succeeded once and became an identity long after it stopped being an advantage.

    Rush’s insight is not that organizations should become preoccupied with endings. It is that beginnings become more credible when endings receive real attention.

    The future is not built on an empty site. It is built amid structures, promises, loyalties, and expectations that already occupy the ground. Some deserve to be restored. Some deserve to be repurposed. Some deserve a careful ending.

    The strategic discipline is knowing which is which, and having the courage to make the distinction operational.

    Sometimes the most responsible beginning looks a little like a funeral.

    Listen and read further

    Episode artwork for Juli Rush’s appearance on The Unfolding Thought Podcast.
    Listen to Juli Rush on The Unfolding Thought Podcast.
  • AI-Driven Task Displacement

    AI-Driven Task Displacement

    While many people say things like “AI will not replace people”, “it will create more abundance”, “it will change what we do but not the need for us”, etc, I’d like you to consider the history of the horse.

    In the 1800s, horses were used to move carriages, people, equipment, and more. In places like New York City, a horse taxi that used a single horse and could carry typically 1-2 people + a driver required the driver or business owner to have at least 3 horses. 1 for trips, another to rotate out with (because horses need rest), and a third in case you had a problem with 1 of the first 2. A horse-bus (it’s a thing) might have used up to 6 or 7 horses at a time to pull the horse-drawn version of those famous London, double-decker red buses. All of these horses cost money, required housing, and needed to be fed and otherwise cared for. They also defecated everywhere to the extent that there were predictions that the amount of horse feces in NYC streets would reach the third story windows of nearby buildings.

    Enter the automobile. Sure, they were unreliable and expensive at first, but as they improved, they replaced horses not just on city streets, but on farms and in other places as well. Soon enough, they were cheaper and easier to own than horses, and because horses are not super adaptive to work tasks beyond pulling, carrying, and hauling (all things the automobile quickly did net-net better or cheaper), there was nowhere for horses to go, but to be relegated to tasks that were economically relatively unimportant and for which there was low demand (racing, show training and jumping, and so on).

    Horses could not retool or retrain. Their territory, the tasks they specialized in, were encroached upon by technology, and in some sense, horses went into technology-driven unemployment. While he was not talking about technology at the time, John Stuart Mill famously said, “Demand for commodities is not demand for labor.” Just because people needed to get themselves and things from one place to another did not mean that they needed horses to do it.

    As AI continues to evolve, I think you should consider whether or not humans will reach the position of the horse in more and more areas that we previously thought we (and not technology) were ideally suited for. When humans are displaced by technology, people won’t stop wanting things, but they won’t need other people to produce those things for them.

    When ATMs were first developed, many people said they would kill the job of tellers. In reality though, demand for tellers went up. Why? Because while ATMs displaced tellers from their traditional roles, ATMs freed up tellers to do higher-touch tasks that ATMs could not. This is the type of story you will often hear from people that say that AI will not replace humans. It might displace us, but demand will remain for things that humans and not technology can do.

    Okay, maybe, but do you believe that humans are so unique that we will never get to a place that the horse encountered where there is no remaining task that is uniquely suited to humans that is also in high enough demand that most people can both be employed and make a reasonable wage?

    See-order kiosks stand inside a McDonald’s Corp. restaurant in Peru, Illinois, U.S., on Wednesday, March 27, 2019. McDonald’s Corp., in its largest acquisition in 20 years, is buying a decision-logic technology company to better personalize menus in its digital push. Photographer: Daniel Acker/Bloomberg

    Look at self-service kiosk-based ordering in places like McDonald’s or order-via-app at Starbucks. These businesses are able to employ the same number of front-line service workers while selling more product. And, sure maybe they have to employ more burger flippers or baristas, but we all know that many aspects of making food or drinks can be automated too. And, any price v. quality tradeoffs that currently weigh in favor of humans will only increasingly shift in the direction of technology such that more and more businesses will, in the words of JSM, be able to sell more of their commodities, but not need to employ nearly as much labor.

    Now, maybe you say that human beings value more than price. Sure. Many people do, but while many might like a handmade sofa or custom-tailored suit, more people choose IKEA and are able to access their goods at a much lower cost than those that choose to buy handmade. Demand for “humanmade” products in many areas is simply much less than it once was. Because while humans by default value others’ hard work, they value access and price more in most cases. Think of someone in the third world getting access to a pair of Nikes for the first time. They can buy the Nikes for $X or they can buy some handmade shoes of the same quality for $3X. Which do they choose?

    As technology increasingly displaces humans, we must shift to areas unique to us, but just because there is still the need for a manager at McDonald’s when the cashier position is replaced by technology does not mean that I might be qualified today, even able to be trained to do that job, or that that job is in an area I can or am willing to live in. Horses ran out of places they could be displaced from and to. The same will happen to humans.

    Think of the difficulty of finding employees in recent years. Everywhere I’ve been in the country, people have said that they can’t find enough employees, and yet, many people weren’t working. Why? One reason was that the match between skills needed and skills available did not exist. Technology-driven task displacement will only exacerbate this.

    A further complicating factor is minimum wages. Labor Department research long ago showed that increases in minimum wage often have a perverse impact on people at the lowest end of the skill spectrum. If I run a Subway shop and employ high school students for $Y/hour and the minimum wage goes up 25%, I might find that it’s not worth employing those high school students because the quality of their work simply is not up to the level I’m required to compensate them. As a result, I might not employ those high students and instead employ a recent graduate, which takes that graduate out of the mix for jobs they might actually be better suited for. The outcome is people at the lower end of the skill spectrum not being gainfully employed and employers at the higher end of the skill spectrum not being able to find people they need to.

    Or, let’s say I can’t find that recent graduate. I might not employ the high school students. I might simply employ no one and choose to do what I’ve seen from many service businesses in the US in recent years. I’ll just close my doors at certain times or on certain days because I’d rather sell a product that my customers are willing to pay for than sell one that is below the quality they’re paying for and have that result eventually in people not wanting to buy from me at all. The outcome is fundamentally the same. I need a certain quality of labor at a certain cost. I can’t find it, and yet, some segment of the market remains unemployed. In this case, I’m just waiting for the day that I can afford to replace my potential human labor with a cheaper alternative

    Add to this that–as technology-driven task displacement occurs and more people are left looking for new jobs, retraining, having to move to where work is available, and so on–there will be a greater supply of humans and, I believe, in many cases a static or even decreasing level of demand for those humans. When supply is high and demand is low, it creates less incentives for employers to either pay well or to provide a high-quality work experience. This will further drive people out of the labor market. It’s as if the horse is still needed, but the job sucks so much that they refuse to do it. They’d rather lay around.

    If I have $50k in college loan debt and have to work a crap job or do nothing at all, I might actually choose at some point to leave the labor market, file Chapter 7 or 13 bankruptcy, and then file an adversary proceeding to claim that repayment of my loans would cause undue hardship. If I have for example a BA in Business Administration, $50k of college debt, can only find a sandwich-making job paying $15/hour that might not be enough to even live on, and have the only other option being to move to an expensive city like San Francisco where a bartending gig might pay $60/hour or go back to school to get a new degree, a judge might reasonably say that working a low-paying job I’m ill-suited, moving to an expensive city far away to work a higher-paying job I’m ill-suited for, or taking on yet more debt is really no option and therefore void my loan obligations.

    Great for me. I’m still unemployed though, because like the horse, technology encroached on the tasks I could uniquely do until there were no more tasks left for me to be displaced to or the available options were so unattractive that I simply chose not to participate in the labor market anymore.