Bryan J. Kaus

To copy an example of success, without understanding it with the aid of theory, may lead to disaster.

- W. Edwards Deming

At the General Motors assembly plant in Fremont, California, cars used to come off the end of the line useless, unable to move. They had to be towed away to be fixed. That was considered normal.

By the early 1980s Fremont had the worst vehicle quality in General Motors, a history of wildcat strikes, absenteeism that made staffing a daily improvisation, and a relationship with the United Auto Workers that had stopped resembling a negotiation. GM closed it.

In 1984 the same building reopened as NUMMI, a joint venture with Toyota, with Toyota managing the operation. Roughly 85 percent of the start-up workforce were UAW members from the plant that had just failed. Another five to ten percent came from a shuttered Ford facility down the road in Milpitas. The people GM management had privately regarded as the problem, including the men with reputations as agitators, walked back through the same gate.

Inside of a year the plant went from the worst quality in General Motors to the best.

Same building. Same workers. Same union. The thing that changed was the production and management system.

The lesson everyone drew, and the one that mattered

The obvious conclusion was that GM should copy Toyota, and GM was in an unusually good position to do it. This was not industrial espionage or reverse engineering from the outside. GM owned half of NUMMI. It could send its own managers through the plant, and it did.

It still took the better part of two decades to move much of that capability into the rest of the company, and by most accounts the transfer was partial at best.

The usual explanation is that GM’s culture resisted it, which is true and also unhelpful, because culture is the name we give to a mechanism we have not bothered to better define and specify. The more useful account is that what traveled from Japan to Fremont was not a set of practices. It was a set of conditions that made those practices rational for the people actually executing them.

An Andon cord that lets a line worker stop production only functions if management has genuinely accepted the cost of a stopped line and will not punish the person who pulled it. Standardized work only improves anything if the people doing the work own the standard and can change it. Small-lot production without buffer inventory requires strong supplier relationships and equipment reliability that take years to build and that expose you badly if either fails. Problem-solving at the line requires that surfacing a defect be safe for the person who found it.

Toyota did not ship a toolkit to Fremont. It shipped the playbook and the conditions, and it spent enormous effort installing them, and the tools then worked.

What GM subsequently tried to move into its other plants was the visible layer. The cords, the boards, the terminology, the kaizen events. Those artifacts were photographable and could be installed quickly. The conditions underneath them could not, and without the conditions the artifacts were theatre, and the workforce identified them as theatre immediately, because workforces always do.

The precedent traveled once and failed to travel the second time, and the same company was on both ends of it.

Sixteen samples

I read across a lot of sectors, on the theory that the way things actually work tends to repeat from one to the next, and the clearest version of an idea often turns up a long way from where you need it. This one turned up in a medical journal.

Most of what the world knows about Ebola comes from two strains. There is a third, called Bundibugyo, different enough that the people studying it were candid that the existing knowledge might not carry over. And the entire evidence base for it, across every outbreak on record, is sixteen samples.

Sixteen. That is what a response team works from when an outbreak starts and the decisions cannot wait. The only real body of knowledge available is about two related but genuinely different viruses, and nobody gets to postpone the call until better evidence shows up.

That is not a failure of science. Rather, it is the ordinary condition of any consequential decision, and it is where an operator or an investor lives most days.

What this costs in business

The transfer error is the most common analytical mistake I encounter, and it almost never looks like a mistake while it is being made. It looks like pattern recognition, which is what we pay experienced people for.

Margins from one geography imported into another where the competitive structure and conditions are entirely different. Adoption curves observed among early users applied to a mass market that wants a different thing for different reasons. A management team that performed brilliantly at one scale, in one industry, in one capital environment, hired on the assumption that the performance was the person rather than the fit. Commodity behavior from one regime carried into another where the marginal supplier has changed or economics are different - think conventional oil and gas versus renewables; I’ve written on this exact point before. Returns generated across a decade of nearly free capital extrapolated into an environment where money costs something. Project economics from one power market moved into another with a different market design, a different queue, and a different regulator.

In each case the precedent is real. Somebody did achieve that margin, that adoption curve, that return. The reasoning failure is not fabrication, rather it is the silent assumption that the result was produced by the thing you can see rather than by the surrounding conditions you did not inventory.

The trap is that a precedent is not evidence in the way it feels like evidence. That the past resembles the future, or that this case resembles that one, is not something the numbers prove. It is something we assume, and then arrange the numbers on top of. That does not make experience useless. Experience is information. It makes it a tool with a stated tolerance, and using a tool past its tolerance is how people get hurt and results miss the mark.

So the question to ask of any analogy is not whether a precedent exists. Precedents always exist, and a diligent analyst can find one for nearly any proposition.

The question is which causal conditions produced the precedent, and which of those conditions are present here.

Then, immediately: which are absent, and do the absent ones strengthen the case, weaken it, or break it entirely.

Where this argument breaks

The failure mode of everything above is an organization that has learned to say “our situation is different” and has thereby immunized itself against all outside evidence and blind belief in its own exceptionalism.

That sentence is the single most common defense of a bad practice inside a struggling business. Every plant is different. Every market is different. Every customer relationship is unique. All of which is true and none of which is a reason to keep doing something that works nowhere. Demanding perfect transferability before acting is not rigor, but rather, it is polite paralysis, and the people who practice it usually get to be right about the risk and wrong about the outcome.

The right posture is the one those same doctors took. Faced with sixteen samples, they did not throw up their hands and they did not pretend the gap was not there. They used what they had, said plainly where they expected it to fail, and built a way to find out fast from the people closest to the outbreak.

That has three parts.

1. Use the imperfect precedent, because it is what you have.

2. Name precisely where you expect it to fail.

3. And build the mechanism that will tell you which, fast, from the people on the ground.

The last part is the one many companies skip. Headquarters tends to see the pattern across the portfolio and mistakes that for seeing the mechanism. The plant manager, the field engineer, the regional commercial lead, and the crew running the equipment know which conditions are actually present in a way no comparative analysis will surface. And I say this as someone who started their career in a plant. Excluding them is not merely a discourtesy. It removes the only instrument capable of measuring whether the analogy holds and the principles can be deployed effectively.

The Point Taken

Experience is worth a great deal, and it is worth almost nothing in the form most people carry it. What most of us store is outcomes. What transfers is mechanisms, and the two get filed in the same drawer.

Three questions before you rely on any precedent, whether it is a case study, a prior success of your own, or the thing that worked at the last company.

1. What were the conditions that produced that result, stated specifically enough that someone could check whether they exist here.

2. Which of those conditions are absent in this case, and is any single absence sufficient to break it.

3. And who on the ground would know first if the analogy is failing, and what would have to be true for them to tell you in time.

Subscribe now

© 2026 23.5 Strategies; The Point Taken™

By Bryan J. Kaus

There is nothing more difficult to take in hand, more perilous to conduct, or more uncertain in its success, than to take the lead in the introduction of a new order of things.

- Niccolò Machiavelli, The Prince, 1513

Tesla lost roughly fourteen percent of its value in a single day after its second-quarter report.

The reason was not hard to find. The company generated $4.7 billion of operating cash in the quarter and spent $5.8 billion building things. Free cash flow turned negative for the first time in two years. Capital spending more than doubled from a year earlier, and management guided to more than $25 billion for the full year, nearly triple what the company spent in 2025. To help fund it, Tesla arranged a credit facility of up to $30 billion.

That is enough to make any serious investor stop and look harder.

Capital has consequences. Money spent today has to become more money tomorrow, or it was never investment at all - it was speculation. Tesla still earns most of its revenue selling cars, and most of the programs now drawing its capital and its attention have yet to prove they can pay for themselves. The market was not wrong to want evidence.

But I think the reaction missed the more interesting thing.

Tesla is not spending more to build better cars. It is trying to become a different company.

That distinction is the whole essay.

Follow the capital

Companies describe themselves as transforming all the time. A new slogan. A reshuffled org chart. An innovation office. A few extra slides in the investor deck. The underlying business goes on doing exactly what it did before.

A real repositioning looks different. Capital moves. Facilities change. Talent gets redirected. Management accepts near-term pain because it believes the future company will need capabilities the present company does not yet have.

The budget is ALWAYS where you find the truth. A company will tell you many things about itself in its language. It tells you what it actually believes with its money, its people, its equipment, and its attention. On that measure, Tesla’s spending is not the behavior of a car company buying growth or innovating in its lane. It is the behavior of a company changing what it is.

The stated destinations for the capital are compute and data centers, expanded manufacturing and research capacity, company-operated fleets of AI-enabled assets, and the service and charging infrastructure to run all of it. This is a stunningly obvious observation once you pause on it for a moment. Operating expenses jumped forty-seven percent in the quarter, driven by AI, the Optimus humanoid robot, and the robotaxi program. Tesla has also begun building the unglamorous physical scaffolding that autonomy actually requires: cleaning, maintenance, charging, security, teleoperation, fleet management. None of that is a laboratory experiment. Those are actual assets, buildings, and payrolls.

Cars remain central to the story. But their role is quietly shifting. They are still products sold to customers. They are also becoming data-generating machines, distributed computing and power platforms, nodes in a network, and the first commercial expression of a broader ambition to connect software intelligence to the physical world.

None of this means Tesla will succeed. It means that judging the company only as a carmaker placing expensive side bets may be the wrong frame entirely.

The identity gap

There is an awkward stage in any real repositioning. The old business still explains the income statement. The new business increasingly explains the capital account. The company has started to move, and the financial evidence has not caught up.

I think of that as the identity gap.

Inside it, everyone is looking at a different clock. Management sees the system it intends to build. Investors see the cash leaving. Employees feel the shift in resources and status. Customers still judge the product in front of them. And the board has to decide how long to fund the difference between what the company earns today and what it is trying to become. That is the crux of it.

Markets tend to struggle in that space. Not because they are foolish, but because the evidence is genuinely incomplete. The old metrics no longer capture the strategy. The new metrics are too immature to price accurately.

That is uncomfortable. It is also where the most consequential corporate decisions get made.

An operator’s view of the same gap

I have watched a version of this from the inside.

When Phillips 66 was spun off in 2012, the market reached for the most legible frame it had and saw a refiner. That was fair on the numbers. It was also incomplete. I remember working through exactly this with the excellent Ed Hirs, one of the leading market voices on our presence at the time, whose words were misreading who we were. And in doing so I realized it was not that Ed was being unfair. It was that we had been unclear about what we were actually going to be when we grew up, even though we had already put far more in front of the market than the market had absorbed. Management was building something the reporting structure did not yet show: logistics, chemicals, storage, pipelines, export access, a connected physical network meant to earn more durably than refining margins alone ever could, funded by the cash the refineries threw off. For a while the two descriptions did not match. Over time, capital, disclosure, and results converged, and the market came to understand a company that was not the one it had started pricing years earlier. That was a remarkable thing to watch and to be part of.

I raise it only in passing, because it taught me a distinction I have never stopped using.

A strategic repositioning can be real before its economic success is proven. Those are two separate judgments, and confusing them is where most of the analytical errors live. Management can correctly identify what a company must become and still sequence the capital badly. It can hit every construction milestone and still earn an inadequate return, no matter how thorough the scenario planning. The market can misunderstand what a company is becoming and still be right to question how the transition is funded and how the balance sheet is positioned. Both can be true at once. Tesla deserves that same double-vision.

Others have crossed the gap

Some companies make it through.

For years, the market understood Microsoft through Windows, personal computers, and packaged software licenses, even as management was rebuilding the company around cloud infrastructure and recurring subscriptions - and, of course, the Steve Ballmer launch-day dance routines. The new identity did not become credible because leadership kept saying “cloud.” It became credible when commercial-cloud revenue climbed from roughly twenty billion dollars toward fifty billion within a few years, and Office customers moved to recurring subscriptions. The story did not carry the transition. The reported economics did.

Amazon is a slightly tighter version. For most of its life the market read it as a thin-margin online retailer that reinvested every dollar it could find. The infrastructure built to run that store became Amazon Web Services. By 2025, AWS generated about $128.7 billion in sales and $45.6 billion of operating income, the majority of Amazon’s profit. Only when revenue, operating profit, and customer usage made the shift measurable did the market re-rate the whole enterprise.

That is the tell. A repositioning that is just a proclamation is a slogan. Words. Anybody can do that. A repositioning that earns a new valuation frame is one that eventually produces its own irrefutable, tangible, touchable evidence.

Tesla has not yet crossed that line. Which is exactly why this period matters so much.

More than one man

Any honest analysis of Tesla crashes head-on into Elon Musk, and there is no point pretending otherwise.

I will say plainly that I have reservations about the way markets sometimes value anything with his name attached. Enthusiasm expands simply because he is involved, and reputation becomes a valuation input long before any of the real economics show up. So I try to look beneath it, and across it, and around it.

This quarter offered a clean example. Tesla put $2 billion into xAI in January. Weeks later, when SpaceX absorbed xAI, that stake converted into a sub-one-percent position in SpaceX. After SpaceX went public in June, Tesla booked roughly a billion-dollar gain on the holding. On the earnings call, management walked through the growing web of connections among Musk’s ventures: chip fabrication with SpaceX, Starlink hardware in the Cybercab, shared robotics work. Follow those arrows far enough and the org chart stops resembling a corporate structure and starts resembling a family tree.

Some of that may carry real strategic logic. But capital deployed to build compute, factories, autonomy, and robotics belongs squarely inside the repositioning thesis, while capital and paper gains that exist because the chief executive’s companies orbit one another are a different category. Proximity to Musk is not a sound strategy. It is a fact about the map.

And yet Tesla cannot be reduced to the man either. Whatever you make of his forecasts or his conduct, the institution has accumulated real capability: engineering, manufacturing, battery and power electronics, software, energy, supply chain. In the quarter, Tesla delivered a record 480,126 vehicles, its first year-over-year delivery growth in roughly two years, and deployed 13.5 gigawatt-hours of energy storage. Those are not decorative bullet points on a slide. That is real, touchable, tangible output.

Past success deserves consideration. It does not deserve exemption from scrutiny. Musk’s record genuinely changes the odds and the speed-to-market potential. But every celebrated builder eventually meets a problem that does not yield to the methods that produced the earlier victories. Confidence can curdle into overconfidence. Speed can become haste. Stars lose their shine eventually, sometimes because the environment changed, sometimes because the institution grew too dependent on the star to challenge the thesis at all.

The real question is narrower and harder. Do Tesla’s accumulated people, assets, data, and operating capability give it a legitimate right to win in physical AI, and is the organization disciplined enough to find out honestly when they do not?

The operator’s problem

Repositioning a company at this scale is not a communications exercise. It is an actual physical operating problem, and it looks much the same in a refinery, a software company, or a car plant. Management has to run two systems at once.

The first job is to protect the business that pays for the transition, and it is the one that most often gets missed. I saw this across a good part of the renewables and transition wave. Too many of those efforts set their economics, capital structures, and cash flows on the wrong footing from the start, which I believe explains much of the sector’s retrenchment after the boom years of 2018 to 2023. The ones still standing are, by and large, the ones that secured advantaged feedstocks and optimized their supply chains for genuinely low-cost inputs before the music stopped. If you had oil and products, the disciplined move was to use their cash-cow properties to build a proper beachhead for the transition initiative, so it had a fighting chance when the wind shifted and the favorable economics, especially the ones buoyed by incentive subsidies, eroded and the business got stress-tested. That is the world you have to plan to survive in. Prove survival through that stress test and you are viable. If not, it is a bad bet, because it is chance rather than calculated risk.

The same logic sits directly under Tesla. Automotive revenue was roughly $20.5 billion of the quarter’s $28.2 billion. The future may be broader than cars, but the present depends on them completely, and the core supplies far more than cash: manufacturing knowledge, supplier relationships, customer access, real-world data, and the plain institutional credibility that lets the company raise its bets at all. The most common way transformations fail is that leadership treats the core as yesterday’s problem before the new business can carry the weight. The old engine gets to become less important. It does not get to be neglected while it is still running the whole thing.

The second job is the opposite discipline: giving the new business enough separation to breathe. A new operating model rarely survives inside the metabolism of the old one. Force it through the habits of the core and the core rejects what does not fit. Cut it loose entirely and it loses the assets and data that were the whole reason to attempt it under one roof. There is no formula for that balance, at Tesla or anywhere else.

Then there is sequencing. A budget is really a decision about what does not get funded this year - anyone who has been through the process knows it. The core, the new capabilities, the balance sheet, and the shareholders all draw on the same pool of cash, and the order in which those claims get paid is not an accounting detail. It is the strategy. Tesla ended the quarter with about $43.5 billion in cash and says it can throttle spending if the ramp comes slower than planned. That flexibility is worth something only if management is willing to use it. Once a strategy becomes fused to the identity of a celebrated founder, slowing down starts to feel like weakness rather than discipline. Part of a board’s job is to protect management’s ability to change the sequence without treating every revision as a betrayal of the vision.

The deepest discipline applies to any transformation: define the evidence before enthusiasm becomes immune to it. Agree in advance on what would actually prove the repositioning is working, and make it operating truth rather than demonstration. For Tesla that means autonomous miles and safety performance, fleet utilization, cost per mile, the manufacturing cost of Optimus, and, above all, the rate at which the new businesses stop leaning on automotive cash. Without those gates, every delay gets recast as further investment and every overrun as proof the prize is even bigger. That is not strategic patience. It is narrative immunity, and it is how good companies talk themselves into bad outcomes.

Finally, preserve reversibility. The goal is not to eliminate risk, which would eliminate the possibility of transformation. The goal is to make sure a single wrong assumption cannot take the whole institution down with it.

The investor’s problem

Turn the same problem around to the outside, and it belongs to any company caught in the identity gap. An investor needs two theses, not one.

The first is about the company that already exists. What are the present businesses actually worth on current revenue, margins, competition, and capital intensity? For Tesla, that is automotive, energy, and services, valued honestly rather than assumed away.

The second is about the company being built. Those options should not be marked at zero because the outcome is uncertain, and they should not be capitalized as though the uncertainty were already resolved. Each bet comes with a gate you can watch: what has to work technically, what regulators have to permit, what the economics look like at scale, how much more capital the road demands. And the question that quietly undoes more investors than any of the others, in any transformation story: how much of the happy ending is already sitting in the price.

The discipline is old and unglamorous, whether the company makes cars, software, or steel. Value the business you can see. Identify the embedded options. Assign probabilities you can defend. Update as the evidence changes. Possibility is not probability, and uncertainty is not zero.

Who carries the transition

Shareholders are never the only people asked to fund a transformation.

Employees in the core will watch status, resources, and promotions drift toward the new thing. The veteran who knows why the body line jams on a humid morning, and how to keep it moving anyway, notices when the interesting money and the interesting problems have quietly relocated to another building. A company can lose its best operators from the core long before the new organization is ready to catch them, and those are the people it misses first.

Customers are entitled to ask whether service and quality still get the attention they deserve. A car company’s reputation is won and lost in the service bay and at the charging stall on a cold night, not on the earnings call. No company gets to claim it is building the future while degrading the experience of the people currently paying for it.

Suppliers, regulators, and communities each carry their own piece: tooling decisions made on assumptions about future volume, and new factories, data centers, and fleets that each demand their own permission to operate. Technical capability does not automatically grant that permission. Legitimacy has to be built alongside the assets.

The board’s task is to know which of these stakeholders is absorbing the cost of the transition, and whether management has been honest with them about the trade being asked.

What would change the thesis

A thesis worth holding, about any company, names the conditions under which you would give it up. Without them, it is not a thesis. It is a hope with a spreadsheet attached.

For Tesla, strengthening evidence would be autonomy expanding safely at scale, improving robotaxi economics, Optimus doing economically useful work, and the new businesses generating their own cash. Weakening evidence would be repeated delays without improved economic visibility, escalating capital requirements, deterioration in the automotive core, and, most telling of all, management that refuses to revise its assumptions when the evidence moves against it.

The honest test is the same for any enterprise that sets out to become something else. Over time, the uncertainty should narrow. If each passing year requires a larger narrative to explain why the economics remain somewhere just beyond the horizon, the story is not progressing. It is receding.

The Point Taken

Tesla has entered the largest investment period in its history while the businesses that investment is meant to build remain unproven. That is a gamble, and the market is right to price it as one. But it is not a random pile of speculative projects. There is a coherent logic connecting vehicles, batteries, storage, manufacturing, compute, fleets, and robotics. That logic may prove right. It may also badly overestimate how easily one set of capabilities transfers into another. The verdict is genuinely open, and anyone who tells you otherwise is selling something.

None of this is really about one company. Every enterprise that sets out to become something new lives in the gap for a while: the old identity explains the earnings, the new one explains the capital, and everyone attached to it is asked to fund the distance between the two. The discipline is the same each time. Protect the engine that pays the bills. Give the new thing room without cutting its supply lines. Decide in advance what would prove you right, and what would prove you wrong. Keep enough in reserve that one bad assumption cannot end the whole story.

The present income statement tells us what Tesla has been. The capital program tells us what management believes it must become. The operating evidence, and only the operating evidence, will eventually tell us whether the transition was real. The market is right to question the cost.

It may still be underestimating what is being gambled for.

© 2026 23.5 Strategies; The Point Taken™

Bryan J. Kaus

Men, it has been well said, think in herds; it will be seen that they go mad in herds, while they only recover their senses slowly, and one by one.

- Charles Mackay

Watch enough financial news and you stop hearing the content. You start hearing the appetite underneath it. The same story, told with the same conviction, by the same people who were selling a different story with the same conviction a quarter ago. Nobody sounds unsure. Everybody sounds early. And the room leans in, not because the argument is sound, but because it is exciting, and because everyone else is already leaning in.

Last week I wrote about the ways we misread the past. Survivor bias, confirmation bias, the quiet editing that turns a lucky run into a law. If it survived, we tell ourselves, it must be so. That sentence is comfortable. It is also the machinery that has emptied fortunes for four hundred years. This week I want to follow it one layer down, to the thing that makes the machinery run. Not the error itself, but the hunger for it.

Charles Mackay catalogued the wreckage in 1841. Holland lost its head over tulip bulbs, a few of them briefly priced above a working man’s house. France handed its economy to John Law and his Mississippi paper, and the crowd pressed so hard into the Rue de Quincampoix that a hunchbacked man made a living renting out his back as a writing desk. England answered with the South Sea Bubble. Mackay’s subject was never really the flowers or the shares. It was the crowd, and the fact that the same fever kept finding new hosts. The costumes change. The disease does not.

Error, the oldest acquaintance

The chapter of Mackay that stays with me is not the bubble arithmetic. It is a quieter one he called the love of the marvelous and the disbelief of the true. He put the mechanism plainly. Of all the offspring of Time, error is the most ancient, so old and familiar an acquaintance that Truth, when she finally arrives, is met like an intruder.

Read that twice. It is not that people are stupid. It is that a thrilling falsehood and a dull fact never compete on level ground. The falsehood is already seated at the table, comfortable, welcome, wearing the face of a friend. The fact knocks at the door and is asked to explain itself. We do not weigh the two. We carry a standing preference for the one that flatters us, moves fast, and promises more than any ledger can support.

That preference is not a relic of a credulous age. It is human wiring, and it sits in the analyst and the allocator as surely as it sat in the tulip trader. We like to think we retired it with data. We mostly just gave it faster tools.

The warrant does the work

You do not have to reach back to Holland for the pattern. Look at the last decade.

Theranos told the world that a single drop of blood could run hundreds of tests, and the world believed it, not because the machines worked, but because the story arrived with a warrant. The board was a wall of statesmen and generals: two former secretaries of state, two former secretaries of defense, a former senator. At its peak the company was valued near nine billion dollars. What it lacked was a device that did what it claimed, and the people saying so, a young whistleblower and a reporter, were disbelieved and pressured long before they were believed. The marvelous claim carried a warrant. The plain truth did not. Holmes is now serving more than eleven years.

FTX ran the same play in a different market. A rumpled young founder who had supposedly cracked crypto, wrapped in the language of doing the most good, on the magazine covers and the conference stages, courting regulators with a reformer’s smile. The warrant was the halo. The dull truth was that customer money was not where he said it was. The crowd disbelieved that until the withdrawals could not be met and the whole thing came apart in a week. He is serving twenty-five years.

WeWork is the quieter case, and maybe the more instructive, because no one went to prison. It leased office space and lost money doing it. That is a dull, true sentence. The marvelous version, that it was a technology company elevating the world’s consciousness, carried it to a forty-seven billion dollar valuation, right up until the prospectus forced the plain numbers into daylight and the offering collapsed in about six weeks.

Notice what these share. None of them was unknowable. In every case someone was saying the true thing out loud, and sophisticated boards and famous investors disbelieved it, because it lacked a warrant and because it was not the exciting story. That is not a failure of information. It is the love of the marvelous, wearing this decade’s suit.

The efficient sameness

Individual frauds are the loud version, and they are almost comforting, because they come with villains and verdicts. The version I actually watch for is quieter, and it has no perpetrator at all. It looks like efficiency.

The mania of this moment is not one wrong idea held by everyone. It is the decision, made by nearly all of us at once, to outsource judgment to the same source and call the result thinking. The tools are genuinely capable. They can rationalize a position faster than most of us can frame the question. That speed is real, and I will use it. But speed is not sense, and a confident answer is not a considered one.

When everyone reaches for the same easy button, the outputs converge. Every memo reads well. Every deck lands. None of it is anyone’s own. Follow that out a few orders and you arrive at the part that should worry anyone who cares about growth rather than the next quarter. Innovation is a function of divergence. It comes from the outliers who trust their own ground truth over the confident consensus, the ones willing to be wrong in a direction no one else is looking. Flatten that variance and you do not get a boom. You get a very efficient sameness.

The image is the monoculture. A single crop, bred for yield, planted fence to fence, posts the best numbers per acre anyone has seen, right until one pathogen finds the weakness they all share. The field traded adaptive variety for short-run efficiency, and the trade looked brilliant until it wasn’t. Homogenized judgment is the same bargain. High yield now, thin resilience later, and a slower arrival of whatever was supposed to come next.

There is an optimistic case, and I want to be fair to it. Perhaps a few crack the code, build enormous winners, and the losers fall away. Even then I am not comforted. If novelty concentrates into a single winning source, you have built something efficient and fragile at the same moment. Growth over decades is a recombination problem. It needs many independent experiments to draw from. Narrow the pool to one and you have not secured the future. You have mortgaged it.

The worship of the thief

Mackay has a chapter that reads strangely until you sit with it. He called it the popular admiration for great thieves. His subject was the highwayman and the robber, the bandit the public did not condemn but celebrated, cheered toward the gallows and sung about in ballads for a generation. The crowd did not merely tolerate the man who took what was not his. It admired him for the taking.

We have not outgrown this. We have only refined it. Before the verdicts came in, we put these same founders on the covers and the stages and asked them how they did it. We reserve a particular reverence for the one who seems to have cracked the code, who games a structure everyone else agreed to play straight and narrates the extraction as vision. A dull edge does not draw a crowd. A daring theft does.

The reason this matters, beyond the moral of it, is that the admired thief is usually the same figure as the single winning source from a moment ago. When a crowd decides in advance to worship whoever cracks the code, it has already handed concentration its warrant. The applause is not incidental to the flattening. It is the mechanism that licenses it.

One by one

There is a trap in writing any of this, and it is worth naming before it swallows the argument.

Mackay is the herd’s favorite book. Everyone who has ever quoted the line about men thinking in herds pictures himself as the lone sane figure watching the stampede thunder past. That is the comfortable read, and it is the wrong one. The delusion is never the other crowd. The discipline Mackay is actually asking for is harder and lonelier: not diagnosing someone else’s tribe, but dissenting from your own.

Look again at his line on recovery. Crowds go mad together, all at once, for free. They come to their senses slowly, and one by one. That asymmetry is the whole argument. Madness is collective and costs nothing. Sense is individual and costs something every time. It means cultivating your own knowledge past the base you were handed, doing the work when the easy button is right there, and holding a boring true thing while the room celebrates an exciting false one. Nobody hands you that. You build it alone, or you do not have it.

That is the part I will not surrender. The tools are marvelous and I will use them. But this is still our world to reason about, and reasoning is not something you can outsource without eventually forgetting how it was done.

The Point Taken

Mackay borrowed a small story from Lord Bacon that says more than any bubble ledger.

An old woman refuses to believe her sailor son could have seen a flying fish. Her Bible makes no mention of such a creature, so it cannot be. In the same breath she gladly believes he hauled a golden, jewel-encrusted wheel up out of the Red Sea, because her Bible tells her Pharaoh drowned there, and a sensible captain confirmed it. The marvelous falsehood arrived with a warrant, so she welcomed it. The plain truth arrived without one, so she turned it away.

That old woman is every board that waved through the statesmen and disbelieved the whistleblower. We accept the marvelous when it carries a stamp of authority and reject the true when it shows up unaccredited and inconvenient. It is the same appetite that priced a tulip above a house, and it has not aged a day.

The crowd will always love the marvelous. It costs nothing to join and it feels like insight. The only defense anyone has ever found is unfashionable, unscalable, and entirely your own.

Recover your senses one by one, and do not wait for the herd to go first.

Subscribe now

© 2026 23.5 Strategies; The Point Taken™

Bryan J. Kaus

Sure thing. Save this card and watch it closely. Samuel Benner, 1875

There is a chart that keeps coming back.

Sepia paper, hand lettering, sawtooth peaks marching from 1924 out to 2059. Three rows, labeled panic, good times, hard times. It resurfaces every few years with a line attached, and the line is always some version of the same claim: an Ohio farmer in 1875 predicted 2008.

The dates land close enough to matter. 1927, near the crash of 1929. 1999, just ahead of the technology collapse. 2007, just ahead of the mortgage crisis. Off by a year or two, and the concession does real work, because a near miss reads as humility and humility reads as credibility.

It is a compelling object. It is also the most expensive idea in finance, wearing a costume.

The chart itself is harmless. Almost nobody is allocating against it. What is not harmless is the appetite that keeps pulling it back to the surface, because that appetite does not stay in the antique aisle. It shows up in trading accounts. It shows up in board packets and price decks. It showed up in the risk models of the largest financial institutions in the world, and it cost roughly a decade of growth. And it is now being wired, quietly and at scale, into the systems we are asking to think on our behalf.

Nobody sets out to destroy capital. That is the thing worth sitting with. Capital gets destroyed sideways, by people who were being careful, using good tools, right up until the moment the tool stopped describing the world and started replacing the judgment that was supposed to check it.

The chart is just the cleanest specimen of the impulse. So start there, then follow it into places where the money is real.

Start with the artifact, because the artifact is already lying

Before we evaluate the prophecy, look at the paper.

The card circulating as Benner’s 1875 forecast is not straightforwardly Benner’s. The “Periods When to Make Money” broadside is a business card of the George Tritch Hardware Company of Denver, compiled in 1872 and carrying copyright dates from the 1880s and 1890s. Benner published Benner’s Prophecies of Future Ups and Downs in Prices in 1875, a study of hog, corn and pig iron prices, and his original forecast ran out somewhere around 1891. The version now in circulation, the one that reaches 2059, is a later extension. Attribution between the two men is genuinely contested, and the chart most people are passing around is not the chart most people think they are passing around.

This is not a footnote. It is the first data point.

A complicated nineteenth century argument about agricultural and industrial commodity prices, of uncertain authorship, extended by a hardware merchant, has been compressed into seven words: a farmer predicted 2008.

The compression is the product. Every step that made the story shareable also made it less true. If the artifact cannot survive a provenance check, it is unlikely to survive a capital allocation decision.

Prediction by proximity

Grant the chart its best case anyway. Assume Benner drew every line himself.

The apparent accuracy is manufactured by proximity and by elastic categories.

“Panic” can absorb a recession, a correction, an inflation, a geopolitical rupture, or an event in an adjacent year. “Good times” can mean growth, high prices, or merely a moment that later looks like a decent exit. “Hard times” can mean a bear market, weak sentiment, or any period in which something eventually turned out to be cheap. Define the target broadly enough and widen the window enough, and history will always hand you a match.

But close is not a neutral word when you are moving money.

An investor who sold in 1927 did not simply dodge October 1929. He also handed back two years of one of the great bull markets in history, and he had to survive professionally while doing it. Someone who braced for the 2019 panic year and positioned defensively gave up a strong year in equities, and then the event that arrived had nothing to do with a nineteenth century commodity cycle. It was a virus.

Being early is indistinguishable from being wrong for anyone who has to report to a board, an investment committee, or a margin clerk.

And notice what happens to the misses. They vanish. The hits get screenshotted. This is survivorship bias applied not to funds but to ideas: a century and a half of charts, waves, cycles and warnings have been produced, nearly all of them forgotten, and the handful that can be retrofitted to later events keep getting rediscovered. The survival of a forecast is then mistaken for evidence of its accuracy.

Cycles are real. Clocks are not.

None of this requires denying that cycles exist. They plainly do, and anyone who has run a physical business has lived inside one.

Credit loosens. Leverage builds. Capacity gets added because returns look attractive and everyone is looking at the same returns. Supply overshoots. Prices break. Weak balance sheets fail, assets change hands cheaply, and the cycle reloads. Anybody who has watched a refining margin cycle, or a shale capex cycle, or a tanker rate cycle, knows the shape by feel.

Recurrence is real. Periodicity is fiction.

A capital cycle is not a planetary orbit. It runs on human decisions, and humans learn, institutions change, technologies emerge, central banks intervene, and, decisively, the act of anticipating the pattern changes the pattern. Markets are reflexive. They respond not to events but to beliefs about events, then to the strategies built on those beliefs, then to the consequences of thousands of participants adopting the same strategy at the same time.

October 1987 is the cleanest illustration. Portfolio insurance was designed to protect institutions by selling into declines. It was sound enough for one investor. Deployed simultaneously across the market, the selling triggered declines, which triggered more selling. The model did not merely observe the risk. At scale, it manufactured it.

Hold that thought. We are going to need it again shortly.

How capital actually dies

Long Term Capital Management is the case everyone reaches for, and usually for the wrong reason.

LTCM was not following anything as crude as a sepia chart. It was arguably the finest concentration of financial intellect ever assembled in one firm, including two Nobel laureates, trading small and genuinely observed pricing relationships among similar securities. It returned roughly twenty percent, then forty three, then forty one, then seventeen.

Here is the detail that matters, and it is an operator’s detail, not a mathematician’s.

At the end of 1997, LTCM returned capital to its investors without reducing the size of its book. The positions stayed. The equity underneath them shrank. By year end the fund was carrying something on the order of thirty dollars of debt for every dollar of capital.

Nobody voted to increase risk. Risk increased anyway, as a mechanical consequence of a decision that looked like discipline. That is how capital dies. Not in a dramatic wrong call, but in a structural change that no one re-underwrote because the model still said the trades were sound.

When Russia defaulted in August 1998, the spreads that were supposed to converge diverged, in nearly every case at once. Liquidity vanished. Positions that appeared diversified turned out to be one position: a leveraged bet that normal relationships and normal liquidity would return on a schedule the fund’s balance sheet could survive. It could not. On the twenty third of September, fourteen firms put roughly three and a half billion dollars into the fund in exchange for ninety percent of it, in a recapitalization the Federal Reserve Bank of New York brokered without lending a public dollar.

The lesson is not that the models failed. Every model fails to represent reality in full. That is what a model is for.

The lesson is that model confidence, leverage, concentration and illiquidity were stacked on top of one another until a temporary analytical error became a terminal one.

Ten years later the same epistemic mistake showed up wearing better clothes. Mortgage securities were rated using models built on historical default behavior from a period in which national home prices had not fallen, with assumptions about correlation across regional housing markets that turned out to be badly wrong. When prices fell everywhere at once, instruments assumed to be diversified failed together.

The Financial Crisis Inquiry Commission put it more plainly than any of us could. Its conclusion was that financial institutions and the rating agencies had come to treat mathematical models as reliable predictors of risk, and that in doing so they let the models stand in for judgment.

They let the models stand in for judgment. That is the whole essay in a line.

The regulators have not forgotten. In April of this year the Federal Reserve, the OCC and the FDIC rescinded the model risk framework that had governed banks since 2011 and replaced it with revised interagency guidance, on the view that models are simplifications whose usefulness and whose limitations arrive together, and that material model risk survives even rigorous validation.

That is not an argument against models. It is an argument against worshipping them.

The same error, in a nicer suit

Everything above happens in boardrooms too, and it is harder to see there because it is denominated in slides rather than basis points.

A single price deck carries a ten billion dollar project. Utilization is assumed to climb in a smooth line because it climbed in a smooth line before. Synergies are modeled as though identifying them in a spreadsheet makes them operationally inevitable. Demand is extrapolated from the most recent three years because that is the dataset most readily at hand. The base case hardens into the plan, the plan hardens into the budget, and by the time anyone asks what happens if the deck is wrong, the steel is on order.

The precision of the presentation conceals the fragility of the assumption. A strategy is not robust because the model balances.

And the failure is almost never the forecast. It is that no one wrote down which two or three variables the entire conclusion was resting on, and no one went back to look at them when the world moved. The forecast was wrong in the ordinary way all forecasts are wrong. The capital died because nobody had built anything that could survive being wrong.

The new pattern machine

Which brings us to the part of this I find genuinely worth worrying about.

The tempting move is to ask whether AI is the new railroad, the new dot com, the new tulip. It is the wrong question, and it is wrong in an instructive way, because it is a question about where we sit on a cycle. It assumes the schedule.

The better question is what happens when the tool we increasingly use to reason about the future is, by construction, a pattern extractor trained on the past.

A large model does not know that 2026 is not 2007. It knows what text about 2007 looked like. It is exceptionally good at producing the most probable continuation of a pattern, and it is exceptionally fluent while doing it, which means it delivers regression toward the historical consensus in the register of confident analysis. It is, in a precise sense, a machine for manufacturing the feeling the Benner chart manufactures. The pattern is found. The pattern is stated cleanly. The uncertainty is smoothed off, because uncertainty is not what fluent text sounds like.

Then recall 1987. The danger of portfolio insurance was not the logic. It was the correlation. Thousands of institutions running the same rule turned a hedge into an accelerant.

We are now installing broadly similar models, trained on broadly overlapping data, into the analytical layer of the same industries, at the same time, reaching similar conclusions from similar priors, and telling everyone the same thing at once. That is not a bubble question. That is a correlation question, and it is the one worth asking early.

The agencies are already asking it. Alongside the revised model risk guidance, the OCC, Federal Reserve and FDIC have said they intend to issue a request for information on model risk management that specifically addresses banks’ use of AI, including generative and agentic systems. That is the right question arriving at the right time, which is unusual enough to notice.

The correct posture is not to refuse the tools. The tools are extraordinary and I use them daily. The correct posture is to remember what they are: instruments that compress the past into a confident sentence. Confidence is the output. It is not the evidence.

The frictionless era

Meanwhile every remaining obstacle to acting on a hunch is being removed.

Prediction markets have industrialized. Total volume across CFTC registered prediction markets ran past twenty five billion dollars in 2025, and the number of listed event contracts went from roughly sixteen hundred a day in April 2025 to about a hundred and sixty two thousand a day a year later. That is a hundredfold increase in the supply of things to have an opinion about, and the CFTC is now building a framework to govern it.

Prediction markets are useful. They force beliefs to be quantified, they aggregate dispersed information, and they update. But a contract at seventy cents does not establish that an event is seventy percent likely. It establishes that capital is currently clearing at seventy cents, given the participants, liquidity, incentives and contract language in that market at that hour. That is information. It is not permission to stop thinking.

At the same time the friction on the retail side is gone. FINRA has eliminated the pattern day trader designation and the twenty five thousand dollar minimum equity requirement that stood since 2001, replacing them with real time intraday margin standards. The SEC approved it in April. It took effect the fourth of June, with firms phasing in through October of 2027, so your broker’s treatment may still vary.

Lower the barrier and you have not created an edge. You have only removed the thing that was slowing down the mistake.

The evidence on that is not close. Barber and Odean studied sixty six thousand four hundred sixty five households at a discount broker from 1991 to 1996. The most active traders earned 11.4 percent annually against a market returning 17.9. The average household turned over three quarters of its portfolio a year to underperform. Their explanation was overconfidence, and their title has aged better than anything else written on the subject: trading is hazardous to your wealth.

When every belief can instantly become a position, uncertainty starts to feel like a problem demanding action. A headline demands a trade. A chart demands an entry. A model output demands a decision. But activity is not analysis, and the ability to transact is not the presence of an opportunity.

The question is never whether the thing can be traded. It is whether you know why the person on the other side is willing to trade it with you.

What discipline actually looks like

The alternative to false certainty is not paralysis, and it is not the fashionable pose that nothing can be known. Businesses can be valued. Capital cycles can be read. Some outcomes are plainly more likely than others. The discipline is simply to keep three things separate that weak analysis always collapses into one.

A thesis explains why something creates value, and it names the causal mechanism. A forecast estimates how that mechanism might unfold. A decision determines how much capital, time and reputation to commit given that the forecast may be wrong. Strong analysis holds all three apart. Weak analysis fuses them into a single confident story and calls the story conviction.

In practice that means a small number of questions asked before the money moves, and asked again after it has.

Which is observation and which is interpretation. Revenue grew is an observation. Revenue will keep growing because the company has reached escape velocity is an interpretation wearing the clothes of an observation.

What is the base rate. Before asking whether this acquisition will work, ask how acquisitions of this type have generally worked. The specific case has to earn its departure from the record.

Which two or three assumptions are carrying the entire conclusion. Almost every model is driven by a handful of variables, and finding them is worth more than another decimal place on the output.

What would prove me wrong, and what would I do about it. If every decline is a buying opportunity and every delay is temporary and every contradictory fact is the market failing to understand, the thesis has stopped being a thesis and become an identity.

What happens if I am directionally right and temporally wrong. This is the one that kills people. LTCM’s trades eventually converged. The fund was not there to see it. Liquidity and timing are not secondary details. They determine whether you are still in the room when you turn out to have been correct.

Good leaders are not the ones with the best forecasts. They are the ones who know which assumptions matter, who size their bets accordingly, who protect the downside, who stage commitments where they can, who preserve the option to be wrong, and who update faster than their competitors.

Their advantage is not clairvoyance. It is preparedness.

The Point Taken

The Benner chart is not worthless. Its value is simply not the value assigned to it.

It is evidence that the hunger for a schedule is very old. It shows that investors have always converted recurring human behavior into claims of fixed numerical law, and that the conversion has always felt like insight rather than like appetite. That it still circulates, and still persuades serious people who allocate real money, tells us how little the hunger has changed in a hundred and fifty years.

We still want the card. We still want someone to tell us when to buy, when to sell, and when the panic arrives. We still prefer a clean line to an untidy distribution. And we will take that line from a nineteenth century hardware catalogue, a prediction market price, an analyst target, or a language model, because what we are actually purchasing is not information. It is relief.

The relief is the product being sold. The capital is what pays for it.

Use patterns as questions. Use probabilities as estimates. Use models as tools, and know precisely where each one stops describing the world.

Then build something that does not require prophecy to survive.

The future does not belong to those who predict it most confidently. It belongs to those who allocate capital, attention and effort most intelligently while knowing they may still be wrong.

Subscribe now

© 2026 23.5 Strategies; The Point Taken™

By Bryan J. Kaus

The first principle is that you must not fool yourself, and you are the easiest person to fool. - Richard Feynman, Caltech commencement, 1974

The captain had forty years at sea. He had taken ships through this channel in fog and in blackout, read currents that would have put a lesser sailor on the rocks, and in four decades he had never once left a hull where it did not belong. On the night the ship went aground, none of that saved him. The tide sat differently. The load rode differently. One variable he had watched a thousand times lined up in a way he had never quite seen. His experience told him where the deep water was. The water had moved.

We have a phrase for this. We say his experience worked against him. It is a strange thing to say, because experience is supposed to be the asset, the thing we pay for in gray hair and close calls. But the phrase is honest about something most of us would rather not examine. What the captain trusted on that last night was not the channel. It was his belief about the channel. And belief, it turns out, is built out of repetition, not out of the thing itself.

The mind counts the sides

David Hume mapped this machinery three centuries ago. Even where there is no such thing as chance in the world, he argued, our ignorance of the real cause of an event produces belief all the same. Watch a die in your hand before a throw. The side supported by more faces turns up more often as the mind runs the possibilities, and that frequency of rehearsal is what we come to feel as likelihood. Belief, in Hume’s telling, is nothing grander than a conception held more vividly than imagination alone can manage. The concurrence of many views prints the idea harder. It gains force. That force is the whole of what we call conviction.

Hume was careful to separate two things we tend to blur. Some causes are uniform and have never once failed. Fire has always burned. Water has drowned every person held under it long enough. Gravity has not taken a day off. Those we can lean on. But most of what we forecast in a market, a business, or a life is not that. It is the die, not the flame. And with the die, the mind still manufactures the same steady sensation of knowing, on evidence that does not warrant it. The feeling of certainty and the actual odds are two different quantities. We are wired to confuse them.

When everyone rehearses the same view

Turn that private machinery loose in a crowd and you get the oldest story in finance. Charles Mackay catalogued the pattern more than a century ago. Dutch households mortgaged their homes for tulip bulbs. All of London chased the South Sea Company. In France, a Scottish gambler named John Law sold shares in a company that held the monopoly on Louisiana, promised the territory was thick with gold, printed the paper money people used to buy the shares, and watched the whole thing climb nearly twentyfold before it collapsed in 1720 and erased a generation of savings. Not one buyer had ever laid eyes on Louisiana. It was mostly mosquitoes. In each case the crowd was not so much lied to as reinforced. Every neighbor buying was another view concurring, another face added to the die, until the imprint was total and the belief felt exactly like fact, right up to the morning it did not.

The same reflex wears a suit. Michael Porter, in his work on competitive strategy, described companies falling into herd behavior, each imitating its rivals on the assumption that the others must know something it does not. Everyone benchmarks against the same best practices, everyone drifts toward the same position, and the industry converges until the firms are hard to tell apart. Porter’s warning was blunt. Operational effectiveness is not strategy. Doing the same thing faster is not an edge, it is a race everyone is already running. The essence of strategy, he wrote, is choosing what not to do. That is a discipline of trade-offs, and trade-offs are uncomfortable, which is precisely why the crowd prefers the warmth of the consensus view.

“We have always done it this way” is the boardroom translation of Hume’s die. It is a belief assembled from repetition and mistaken for a law of nature. It is where money gets left on the table, where the position gets missed, where a good company runs aground in a channel it has navigated for years.

The job is not the crystal ball

Markets punish this error on a schedule. The models that ran Long-Term Capital Management were built by Nobel laureates on a recent, calm stretch of history, and a Russian default that sat outside that history took the fund apart in a matter of weeks. The mortgage machine of 2008 rested on a quiet assumption that home prices would not fall across the whole country at once, because in the data on hand they never had. The one scenario that mattered was the only one the model could not see. In the spring of 2020, crude oil printed a negative price. Sellers were paying buyers to haul the barrels away, a thing that could not happen, because the paper market had never counted the storage that was already full and the oil that had nowhere physical to go. In every case the certainty was real. The odds were never what the certainty claimed.

This is why I distrust anyone selling a single confident number about the future. The honest posture is not prediction. It is preparation. When I put a $157 mark on crude, I was not forecasting that the tape would print it. I was naming a boundary, a level of pressure at which the physical system would be forced to reconfigure, and asking the only questions that matter at a boundary. What breaks first. What moves next. Which levers are in my hand if it comes. I have spent enough time in the rooms where a number like that gets argued to know that the people worth listening to are never the most certain ones. They are the ones holding the most branches at once. A scenario is not a guess in better clothes. It is a rehearsal that leaves you standing when the die lands somewhere you did not expect. You do not need to see the future. You need to have already walked the branches.

A note on the machines that agree with us

There is a modern wrinkle worth flagging, and it is only a note, because the failure underneath it is human and old. We now carry tools that are very good at handing our own views back to us. A recent review by psychiatric researchers, surfaced in the Wall Street Journal, named three traits that let a chatbot warm its way into a person’s thinking. It flatters, agreeing rather than challenging. It mirrors your vocabulary and cadence until it sounds like you. And it tailors itself to everything it has learned about you across past conversations. Any one of these is mild. Together, the researchers called it an amplification spiral, a loop in which the machine keeps affirming a view until the view drifts loose from reality.

Read that as a warning about far more than software. Matching another person’s syntax and rhythm is a known way to build rapport. It is taught in negotiation and on the public stage precisely because it lowers resistance. A hyper-detailed profile of who you are is what lets a message feel as though it was written from inside your own head, whether the sender is a chatbot, an advertiser buying your attention, or a vendor seeking your business. The defense is the same one Hume’s die demands. Trust, then verify. I use these tools every day, and I build friction in on purpose, because a voice that only ever agrees with me is not a counselor. It is a mirror, and a mirror has never once caught my error. That is also why I read outside my field, and why I go looking for the argument that contradicts something I already believe, to see whether it holds water. Experience is not a feeling of certainty. It is a larger pile of data points to draw from, and some of the most valuable points in the pile are the ones that prove you wrong.

The assumption the physical world does not honor

Nations make this mistake at scale, and here the industrial and energy story I spend most of my time inside becomes the case study. For a generation the consensus held that manufacturing was a relic, that a mature economy naturally graduates from making things to servicing them, that globalization would lift every boat. It was a confident belief, rehearsed across boardrooms and editorial pages until it carried the weight of law. Vaclav Smil, in Made in the USA, took that belief apart. No advanced economy prospers for long without a strong and innovative manufacturing base, he argued, and the retreat of American industry was neither the tidy evolutionary step the consensus described nor something that had to happen.

The part that should hold your attention is the mechanism. Cheap energy built American industrial dominance, and rising energy costs in the 1970s helped unwind it. That is not sentiment. That is a physical constraint overriding a paper assumption, the same pattern that puts a negative print on a crude screen. For anyone weighing the industrial future now, as capital moves back toward the grid, toward domestic production, toward the molecules and electrons that sit underneath everything else, the lesson is to hold the consensus loosely. The story everyone is repeating is the die, not the flame. The opportunity tends to sit exactly where the crowd’s certainty is thinnest.

The Point Taken

Strategy works best when agility and humility are built into it. That is not complacency, and it is not permission to go slack. It is the opposite. It is composure. When a decision carries weight, gather your thoughts before you move, and take the emotion out of your thinking, because emotion is how you lose composure, and lost composure is how you underperform. Demand clarity as the price of continuing. If the picture is not clear, that is information, not a reason to press ahead. Stay fixed on your purpose while everything around it moves. Control the situation by restating your position plainly, to yourself and to the room, so that the crowd’s noise never quietly becomes your belief.

This is negotiation training, but it does not stay at the table. It is how you read a market, run a business, and think through any decision where the sides are uncertain and the mind is trying to hand you a certainty it has not earned. Weigh the possibilities. Name your levers. Then act, and keep watching, because the honest operator carries the conviction and the doubt in the same hand.

The die does not care how sure you are. Prepare as though it already knows.

© 2026 23.5 Strategies; The Point Taken™

by Bryan J. Kaus

When we try to pick out anything by itself, we find it hitched to everything else in the universe. John Muir, My First Summer in the Sierra

The price of urea fell by more than half this spring, and almost no one outside agriculture noticed. In late February, a ton barged up from New Orleans ran between $455 and $470. During the worst of the fighting around the Strait of Hormuz it touched $782. By late June it had slid to roughly $340, the lowest level of the year. Markets read that decline as relief. The strait was reopening. The shooting was winding down. The cargoes would flow.

The physical system tells a slower story.

Most people read Hormuz as an oil story. It is. But it is also a fertilizer artery, and fertilizer is the quiet hinge between energy and food. A falling urea price says that traders expect supply to return. It does not say that the supply has returned. Those are different facts, and the distance between them is where this piece lives.

Fertilizer is where energy becomes calories.

Fertilizer is energy in agricultural form

Start with nitrogen, because nitrogen is the nutrient that carries most of modern yield. The feedstock is natural gas. EIA describes the chemistry plainly: ammonia is made by the Haber-Bosch process, which pulls hydrogen from natural gas through steam methane reforming and combines it with nitrogen taken from the air. Ammonia is then the base for the nitrogen fertilizers a farmer actually applies, urea and UAN and anhydrous ammonia among them. In the United States, ammonia is used mainly as fertilizer or as feedstock for fertilizer. The molecule is, in effect, natural gas wearing an agricultural uniform.

Phosphate runs on a second, less obvious input: sulfur. Sulfur becomes sulfuric acid. Sulfuric acid, mixed with mined phosphate rock, makes phosphoric acid. USGS describes the rest of the chain directly: treating phosphate rock with sulfuric acid produces phosphoric acid, the basic material for most phosphate fertilizers, and reacting that phosphoric acid with ammonia yields DAP and MAP. So the two workhorse nutrient families both trace back to the energy complex. One starts at a gas field. The other starts at a sulfur cargo coming off a refinery.

A bag of fertilizer, read closely, is a balance sheet of energy inputs. That is not a metaphor. It is the supply chain.

Hormuz is not only an oil chokepoint

The Strait of Hormuz closed on February 28, after the operation known as Epic Fury and the Iranian response that followed. The world watched crude and LNG, and for good reason. But the Persian Gulf is also one of the most concentrated fertilizer corridors on earth. By Argus Media’s estimate, the region normally supplies roughly a third of the world’s traded urea, and most assessments put the Gulf at close to half of seaborne sulfur. When the strait shut, those volumes did not reroute. They sat behind the chokepoint, physically trapped, because Gulf fertilizer cannot easily find another door.

A two-month agreement reopened commercial traffic in mid-June, without tolls. That is genuine progress. It is also where the paper story and the physical story split. Vessels move through the strait in a priority order, and fertilizer is near the back of it. Passenger ships first, then oil and gas, then fertilizer and sulfur. Bloomberg Intelligence counted roughly 40 loaded fertilizer vessels, mostly urea, still waiting behind the strait in the days after the agreement. India alone reported sixteen stranded ships bound for its ports, including eight carrying about 330,000 tons of urea and four carrying about 257,000 tons of DAP, with ammonia and sulfur behind them. Restarting an idled urea plant is not a light switch. There is lag built into the system before that material reaches a dealer, let alone a field.

This is the part worth sitting with. The urea price fell on the expectation of reopening. The cargoes that would justify the price are still in the queue. Marine insurance on some routes has run 300 to 600 percent higher, adding roughly $40 a ton to delivered cost, and total landed cost on affected lanes is up somewhere between 12 and 25 percent. The screen says relief. The water off Musandam says wait. Markets reprice hope on a headline calendar. Physical systems normalize on their own.

The United States is more insulated than most, which is worth saying so the picture stays honest. Domestic plants supply the overwhelming majority of American ammonia, so the nitrogen buffer at home is real. But even here, a meaningful share of urea and phosphate consumption rides on Gulf exports that have to clear Hormuz. Insulation is not immunity, and the countries with neither are the ones to watch.

The farmer sees it as working capital

For the operator on the ground, a fertilizer shock arrives first as a cash-flow problem, not a price headline. Inputs get bought before the crop generates a dollar. USDA’s Economic Research Service has it as a major line: from 2010 through 2019, fertilizer ran 33 to 44 percent of corn operating costs, and the relationship has held since. Most US corn goes in the ground in April and May, with inputs often purchased months ahead. So the farmer is financing chemistry on credit, in advance, against a harvest that does not yet exist.

When that input gets more expensive or less certain, the farmer rarely stops cold. More often the response is to optimize. Prepay earlier to lock a price. Delay and gamble on a cheaper window. Substitute one nutrient blend for another. Apply somewhat less than the agronomic ideal and accept the yield risk. Each of those choices protects the balance sheet. Each one also moves a small amount of risk downstream, from this season’s cash position into next season’s output. The decision looks financial. The consequence is biological.

The consumer sees it months later

None of this reaches a grocery shelf quickly, and anyone who promises that it will is selling drama. Fertilizer disruptions run on agricultural calendars. A crude shock can show up at the pump in days. A fertilizer shock shows up through tender prices, dealer inventories, prepay behavior, planting choices, application rates, yield outcomes, and processor margins, over many months, and only if several other things break the same way.

That conditionality is the whole point. Weather can absorb a lighter application rate, or it can punish it. A strong currency can swallow a higher import bill, or a weak one can magnify it. Credit availability, carryover stocks, and local subsidies all sit in the path and can blunt or amplify whatever started upstream. This is a risk-transmission argument, not a prediction of scarcity. The chain is real, and it is also contingent at every link.

Where it does travel, it travels quietly. Corn and soybeans are not only food. They are feed, which means fertilizer cost can surface later in beef, pork, poultry, dairy, and eggs. Corn is also ethanol feedstock, so the loop can even run back toward fuel, energy into fertilizer into corn and partway back into energy. By the time any of it reaches a household budget, the original cause is several steps removed and effectively invisible. Consumers see bread, rice, and meat. They do not see ammonia, sulfur, or a ship riding at anchor off Oman.

Where the pressure lands first

The cleanest place to watch the chain complete itself is an import-dependent economy with thin foreign-exchange reserves. Bangladesh is the current example. On June 26 the World Bank approved $1.1 billion in emergency support, explicitly tied to fertilizer, fuel, and food pressure from the Middle East conflict. Of that, $300 million is earmarked to import 600,000 metric tons of fertilizer, half of it urea, enough to cover roughly 1.4 million hectares of rice. The country imports more than 85 percent of its fertilizer, and the two seasons this financing protects account for about 90 percent of national rice production. Half the population works in agriculture. Earlier in the disruption, four of the country’s five domestic fertilizer plants had gone idle.

That is the asymmetry, stated in numbers. A well-capitalized farmer absorbs a higher input bill on credit. A poorer one applies less and hopes the weather cooperates. A wealthy government subsidizes imports. A constrained one rations scarce dollars across fuel, food, fertilizer, and debt service, and has to choose. The same shipping disruption produces a manageable margin squeeze in one place and a sovereign financing event in another. Energy logistics, followed far enough, becomes social stress.

The Point Taken

Energy security and food security are usually discussed as separate files, handled by separate people, debated in separate rooms. They are not separate systems. They are one system, joined by chemistry, by logistics, by financing, and above all by timing. Natural gas and sulfur enter at one end. Calories come out the other. A chokepoint in the middle does not have to stop a single food shipment to raise the price of food. It only has to disrupt the inputs that make food possible, and then wait.

What I would watch from here is not the urea screen, which has already priced the hope. I would watch the physical tells: how fast the stranded vessels actually clear the strait once energy cargoes take their priority slots, how quickly idled Gulf urea capacity restarts, what India and Brazil pay in their next tenders, what dealer inventories and farmer prepay behavior look like into the application window, and which import-dependent governments follow Bangladesh to the financing window. Those are the readings that tell you whether the paper recovery and the physical recovery are converging or still drifting apart.

There are two clocks here, and the falling price invites you to read the wrong one. The backlog clears in months. The shock clears in seasons. The price has already passed through this year’s planting math, and a field that got less nitrogen this spring will not report back until the fall harvest, and that grain will not move into feed, then meat and dairy, until the year after. The institutions are already pricing the longer one. The World Bank wrote Bangladesh’s fertilizer support to cover seasons that run into 2027. That is not a relief check. It is the system conceding that this is a multi-season problem.

The strait will reopen on a political timeline. The fertilizer will move on a logistical one. The crop will respond on a biological one. And the grocery bill, if it moves at all, will move last and most quietly of all, long after the headline that started it has scrolled away.

The molecule moves first. The grocery bill moves last.

Subscribe now

© 2026 23.5 Strategies; The Point Taken™

By Bryan J. Kaus

For a successful technology, reality must take precedence over public relations, for nature cannot be fooled. - Richard Feynman

On Thursday, June 18, the Federal Energy Regulatory Commission ordered the six grid operators under its jurisdiction to defend or rewrite the rules that govern how data centers and other large customers connect to the power system. The same day, in Austin, the Public Utility Commission of Texas approved a new process for its own grid to study large loads as a group rather than one at a time.

Texas runs its grid the way it does in order to stay outside federal jurisdiction. That is the entire point of ERCOT. So when Washington and Austin reach for the same lever on the same day, with no coordination and no common boss, it tells you something worth hearing. The thing they are both responding to is not a question of who holds authority. It is a question of physics, and of arithmetic.

The arithmetic is the part worth sitting with.

What FERC actually did

The Commission’s tool was a show-cause order under Section 206 of the Federal Power Act, issued to PJM, MISO, the Southwest Power Pool, the California ISO, ISO New England, and the New York ISO. Each operator has sixty days to justify why its current tariffs remain just and reasonable, or to propose changes. Each has thirty days to file a separate report explaining how it will keep enough generation on the system to serve the load it is being asked to connect. Together these regions serve roughly two hundred million people.

Strip away the procedural language and the order is about one thing. Cost. The Commission flagged several areas for reform, and the load-bearing one is the prevention of cost-shifting. It directed each region to show how it keeps the expense of connecting a hyperscale data center on the party that requested it, rather than on the household three counties over. It pointed toward arrangements that would have developers post money up front, so that a project which never gets built does not leave its bill behind for everyone else to pay.

This is the right place to slow down. The fight over speed-to-power is usually framed as an engineering problem. How fast can we energize. The more honest framing is an allocation problem. The cost of underbuilding is paid by the company that waited. The cost of overbuilding is paid by everyone else. A developer who reserves five hundred megawatts and builds fifty has not made a private mistake. He has consumed studies, planning, and upgrade commitments that the system priced in and that someone now has to absorb. Speed-to-power is not free. The question is never only how fast. It is how fast, for whom, and on whose bill.

The Texas number

Texas gives you that arithmetic in its rawest form. ERCOT defines a large load as a single site that draws more than seventy-five megawatts, enough to power something like nineteen thousand homes on a peak afternoon. It is currently tracking more than 438 gigawatts of such requests. Nearly nine in ten of those gigawatts are data centers.

Hold that figure against the grid it wants to join. ERCOT’s all-time peak demand, the most electricity Texas has ever pulled at once on the hottest August evening in its history, is about 85.5 gigawatts. The request queue is five times the largest load the system has ever served.

It is not real. Not all of it. A request to join a queue is close to free, and a free option gets exercised early and often. The same project gets registered in three places. A site is reserved against a financing round that has not closed. When Enverus screened the Texas queue project by project, it estimated that when this first round settles, something on the order of twenty-two gigawatts will actually qualify as ready. Against 438 requested. The rest is intention, not load.

That gap is what the new process exists to find. Rather than study each request in the order it arrived, ERCOT will study them together and sort them into three buckets: projects mature enough to count, projects that still have to prove themselves, and projects that do not make the cut. The old rule was first come, first served. The new rule is closer to first prove you are real. Reserving capacity will require site control, financial security, and a credible commitment to energize, not a place in line and a hope.

A free queue measures appetite. Batch Zero measures what is real.

The same frontier, approached from two sides

Look closely and the federal order and the Texas process are wrestling with the identical move. A large load that cannot get through the queue fast enough has an obvious workaround. Bring your own generation and sit next to it, drawing less from the grid or nothing at all. Co-location, in the language of the filings.

This is the part the old tariffs never contemplated. A data center parked beside its own gas plant or behind its own meter is neither a normal load nor a normal generator, and the rules were written for one or the other. FERC’s order spends much of its length on exactly this, directing operators to write clear terms for co-located generation and behind-the-meter arrangements. In Texas, Enverus counts roughly a dozen gigawatts of projects that have already skipped the queue this way. Both grids arrived at the same frontier from opposite directions. The load is trying to route around the constraint, and the regulator is trying to write the rule before the routing becomes the norm.

Not a step behind

It is easy to read all of this as regulators caught flat-footed, and easier still to make it a morality tale about government always lagging the market. That reading is lazy. The interconnection rules now being rewritten were sound for the world they were built for, a world where load grew a percent or two a year and the hard problem was connecting generators, not customers. The data center did not break the rules. It changed the magnitude of the thing the rules were measuring, and it did so faster than any planning cycle is designed to absorb.

What you are watching is a system retrofitting itself in public. FERC opened its first show-cause on co-located load in early 2025, ordered PJM to fix its tariff at the end of that year, and widened the order to all six operators six months later. ERCOT spent the same period discovering that a process built to study requests one at a time could not survive a single year in which it received more large-load requests than in the three years before it combined. Neither moved early. Both moved. Adapting to a change of this size is not an indictment of the institution. It is the work.

The Point Taken

The takeaway is simpler than the headlines. A request to connect is not a reservation. The grid spent years treating the two as the same and quietly putting the cost of the gap on everyone’s bill. On June 18, FERC and Texas began, separately and on the same day, to move that cost back onto the party making the request. What follows is a fight about cost allocation, not technology, and it gets decided in the fine print.

Underneath it sits one question. When a new kind of customer arrives wanting power at a scale and speed the system was never built to deliver, who carries the risk that the customer is wrong about itself?

The old answer, by default and without anyone choosing it, was the ratepayer. The connection got built, the load came in light or never came at all, and the cost stayed on the system. What FERC and Texas did on the same Thursday was begin, clumsily and incompletely, to move that risk back toward the party making the claim. Prove the load is real. Post the security. Carry the cost of your own optimism.

That is not hostility to growth. It is the precondition for it. A grid that cannot tell a real megawatt from a hopeful one will either choke on caution or drown in cost, and neither one builds anything. The decisive work of the next two years lives in the details, the penalty rates and the security postings and the cost-recovery terms that the financial engineers are modeling right now. That is where it gets settled whether speed-to-power is a privilege the system extends to those who can prove they will use it, or a subsidy it quietly hands to those who cannot.

The queue is full of real intention, much of it serious. But you do not build steel and copper against a hope, and a grid that tries will always overshoot or fall short. Make the promise pay to prove itself, and you build enough for the demand that is real.

Subscribe now

© 2026 23.5 Strategies and The Point Taken™

The fuel aviation bet its future on could cover seven and a half days of a single oil shock. The shortfall was never in the refinery capability. It was in the physics of feedstocks.

By Bryan J. Kaus

“You only find out who is swimming naked when the tide goes out.” - Warren Buffett

The Strait of Hormuz went quiet in the last days of February. The first two months of the year had been ordinary in fuel markets, prices sitting in the low-to-mid two-dollar-a-gallon range for weeks. Then the war started, the tankers stopped, and within weeks jet fuel in the United States ran from roughly two and a half dollars a gallon to nearly five. Carriers cut marginal flying, redeployed aircraft to higher-yield routes, and paid whatever the next cargo cost. They also reached, by reflex, for the fuel they had spent a decade calling the future of flying.

It was not there.

Add up all the sustainable aviation fuel the world will make this year and you have enough to replace about seven and a half days of what the Strait took off the market. Not seven and a half days of global jet demand. Seven and a half days of the disruption alone. The hedge that was supposed to matter most in a supply shock turned out to be a rounding error in the one that came.

The gap the crunch exposed

The Wall Street Journal said it plainly in early June. Airlines had called sustainable fuel the future, and when the crisis arrived there was almost none of it to reach for. The arithmetic is not subtle. SAF made up about six-tenths of one percent of jet fuel last year. Total jet consumption runs near three hundred million tonnes a year. SAF output sits around two million tonnes. Four years ago the international aviation body expected this year’s volumes near five million tonnes. We are at less than half of that, and growth is slowing, not accelerating.

The head of the airline trade group called the progress disappointing and pointed at fuel producers and at policy. Both deserve a share of the blame. Neither is the constraint that actually binds. A decade of mandates in Europe and the UK, billions in premiums paid by airlines for the little SAF that exists, and the product still has not reached commercial scale anywhere on earth. When effort that large produces a result that small, the problem is not effort or ambition. It is physics and geography.

It was never the refinery

Here is what the run of disappointing numbers tends to obscure. The technology works.

The dominant production route, hydroprocessed esters and fatty acids, is mature and commercial. It accounts for roughly nine in ten gallons of SAF made today. The fuel is a true drop-in. It burns like Jet A, moves through the same pipelines, fuels the same engines, needs no new aircraft and no new airport plumbing. The plants exist. Many are converted renewable diesel units that already know how to run the process. If the molecule works and the units run, the obvious question is why there is so little of it.

Because the process eats lipids. Used cooking oil, tallow, animal fats, vegetable oils. And there is not enough of that to matter at the scale aviation burns fuel. The refinery was never the bottleneck. The grease was.

The tell was the fraud

When a market starts counterfeiting its own raw material, that tells you exactly where the scarcity sits.

Europe now imports something like four-fifths of the “used” cooking oil it consumes. China supplies close to forty percent of that. Malaysia exports roughly three times more used cooking oil than the country could plausibly collect, which is not a logistics miracle. It is virgin palm oil wearing a waste label to capture the green premium. The pattern got serious enough that a bipartisan group of farm-state senators wrote to the EPA, Customs, and the trade representative asking for enforcement. The United States, a country of restaurants and fryers, now imports a waste commodity it produces in abundance, because demand for the molecule has outrun the molecule.

Run the volumes and the ceiling is obvious. Ryanair alone would need every drop of used cooking oil collected in Europe to fly an eighth of its schedule. Global targets for 2030 would require at least twice the used cooking oil that the United States, Europe, and China can collect combined. You can legislate a blending mandate. You cannot legislate grease into existence.

Which means it is agriculture and logistics

Reframe the whole problem and it stops looking like a fuel question.

SAF at scale is a collection problem. The feedstock molecules are real and they exist, but they sit dispersed across millions of fields, fryers, and rendering lines, and somebody has to aggregate them, verify them, and haul them to a converter. That is supply chain work. It is trucks, rail, storage, chain-of-custody documents, and the unglamorous discipline of knowing that the thing in the tank is the thing on the certificate. The pathways that scale are the ones that plug into a collection and logistics network that already exists, not the ones that require building that network from nothing.

Some of the answer is agronomic. Cover crops like winter camelina and carinata can be layered into existing rotations and fallow ground, adding oilseed feedstock and farmer revenue without displacing food acres. That is promising and slow. The machine that already exists, fully built and fully paid for, is corn.

The clearest route to scale

The pathway with the cleanest line to volume is alcohol-to-jet, and in the United States that means ethanol.

The country already makes on the order of sixteen billion gallons of ethanol a year, almost all of it from corn, sitting on top of an agricultural and rail logistics system that took a century to build and is already amortized. The feedstock aggregation problem that strangles the grease pathways is, for corn, already solved. That capacity could in principle yield roughly ten billion gallons of renewable jet, close to forty percent of the twenty-five billion the country burns in a year. The plants are starting to come. Gevo’s Net-Zero One in Lake Preston, LanzaJet’s Freedom Pines in Georgia, Summit’s next-generation projects. Industry’s own assessment is that ethanol-to-jet is one of the few routes that can plausibly reach real commercial scale. The capital-heavy routes, Fischer-Tropsch gasification and power-to-liquid e-fuels, are real and probably necessary by mid-century, but they are next-decade stories that require building both the conversion plants and the feedstock systems at once. The grease route hits its ceiling around 2030 no matter how many units get built.

And every pathway here still assumes the fuel has to pour into the engines already flying. Change the engine and you change the question. Hydrogen, batteries on the short hops, airframes built to burn less, those are real and worth chasing. But they replace the fleet rather than fuel it, which makes them a mid-century project, not a this-decade one. For the aircraft already in the sky, the choice is drop-in liquid or nothing, and drop-in liquid is bounded by what the land can grow and the waste stream can yield.

Now the catch, and this is where the original framing needs sharpening. The constraint on the most scalable pathway is, at this moment, policy. Sustainable fuel runs about twice conventional jet at the market and four to five times where mandates force it, so the economics never close on their own, and a production credit was meant to bridge the difference. Instead it pays more to put a gallon of ethanol into a gas tank than into a wing, and it takes about one and six-tenths gallons of ethanol to make a gallon of jet. The tax bill passed last July cut the SAF credit from a dollar seventy-five to a dollar even while preserving the road-fuel value. So the single pathway with built-in logistics scale is the one current incentives quietly underpay.

That is the honest answer to whether this was a policy problem or a feedstock problem. It was both, in a specific order. Policy decides which feedstock gets chased. Feedstock decides whether the chase ever reaches scale. The mandates summoned a decade of demand for waste oils. They could not summon the oils. The binding constraint, the one that does not bend to a signature, is the physical, agricultural, logistical one underneath.

The diesel echo

There is a second-order loop worth holding in view. The same disruption that broke the jet market is hitting diesel, because refiners prioritize diesel over jet when they allocate constrained barrels. American farmers are absorbing the worst diesel shock since 2022 right now. And the most scalable SAF feedstock is grown, harvested, and hauled with that diesel. The resilient fuel of the future runs, today, through an agricultural system that is exposed to the very shock it is meant to hedge. Resilience that runs through the field runs through the diesel pump too. Real damage from a supply shock tends to arrive on a lag, downstream, after the headline price has already moved on.

The Point Taken

For a decade the industry treated sustainable aviation fuel as a problem of chemistry and subsidy. The crunch after Hormuz revealed it as a problem of collection and logistics wearing a chemistry costume. The pathway with the clearest route to commercial scale is not the most elegant molecule or the cleverest process. It is the one already sitting on an agricultural supply chain that exists, paid for, and running.

That pathway can make SAF a viable product. Where the feedstock economics already work, low-carbon ethanol in the corn belt, waste oil with provenance you can actually verify, SAF earns a durable place in the blend and stands on commercial logic rather than mandate. Everywhere it leans on imported grease or fuel that still has to be invented, it stays boutique and subsidy-fed. The work was never inventing the fuel. The work is assembling the feedstock honestly, at volume, and pointing the incentives so the gallon flows to the aircraft instead of the automobile.

None of that makes the fuel a failure. It makes it a hedge, which is a more honest thing to be than a replacement. The blend that exists today is too small to save an airline in the week a strait closes, and no credit conjures more of it overnight. But a barrel that draws on more than one source is a sturdier barrel over the years it takes to build that source, and a hedge like that is worth paying for on its own terms. The case for sustainable fuel was always a case for optionality. It was never a case for substitution.

The future of flight was never going to be refined into being. It was going to be grown, collected, and hauled. We just kept studying the wrong end of the supply chain.

By Bryan J. Kaus

“Lose money for the firm and I will be understanding. Lose a shred of reputation for the firm and I will be ruthless.” — Warren Buffett, addressing Salomon Brothers employees as interim chairman, 1991

I recently reread Alice Schroeder’s The Snowball and found myself going down a rabbit hole on John Meriwether.

Part of it was the book. Schroeder’s account of Warren Buffett’s interim chairmanship at Salomon Brothers during the 1991 Treasury auction scandal is one of the more revealing leadership case studies in modern finance, and Meriwether sits at the center of it. Part of it was research I have been doing on the side for a book of my own, where the patterns of competitive intensity, technical mastery, and the cultural gravity of high-performing organizations keep showing up across industries.

For those who don’t know the name, John Meriwether was vice chairman of Salomon Brothers and head of its fixed-income trading operation in the 1980s and early 1990s. Salomon was, at the time, the dominant bond house on Wall Street, and Meriwether’s group was the most profitable corner of it. He left Salomon in 1991 after the Treasury auction scandal that nearly destroyed the firm’s independence. He then founded Long-Term Capital Management in 1994, a hedge fund built around mathematical sophistication and elite personnel including two Nobel laureates, which became famous for both its early returns and its collapse in 1998, a collapse so consequential that the Federal Reserve facilitated a private rescue to prevent broader market damage.

John Meriwether is not a simple villain.

That is what makes the story useful.

It would be easy to write him off as another Wall Street figure undone by ego, leverage, and cleverness. But that would miss the more interesting lesson. Meriwether was not obviously incompetent. Quite the opposite. He was brilliant, intensely competitive, respected by sophisticated investors, and surrounded by some of the most talented people in modern finance.

That is the point.

The danger was not stupidity.

The danger was intelligence without enough humility.

This is the paradox of the specialist. Mastery of the mechanics, paired with insulation from the system the mechanics operate inside.

Meriwether’s career sits at the intersection of two of the most revealing financial stories of the late twentieth century: the Salomon Brothers Treasury auction scandal and the collapse of Long-Term Capital Management. In one, a dominant bond-trading culture pushed past legal and ethical limits in the world’s most important government securities market. In the other, a hedge fund filled with elite traders and Nobel-level intellectual firepower built a strategy so sophisticated, so leveraged, and so widely entangled that its failure threatened broader market stability.

The common thread is not that any one person caused every failure around him. That would be too easy and probably unfair.

The better question is more uncomfortable.

What happens when technical excellence becomes so profitable that people stop challenging the culture around it?

That question matters far beyond Wall Street.

It matters in investing. It matters in leadership. It matters in energy, technology, private equity, banking, trading, corporate strategy, and any business where specialists begin to believe that mastery of the game exempts them from the discipline of the system.

The Issue

There is a recurring pattern in business failures.

The first stage is competence.

A person or team finds an edge. They understand a market better than others. They see relationships others miss. They move faster. They know the mechanics. They are rewarded for insight, execution, and results.

The second stage is status.

The team becomes special. They are the profit engine. They are the group everyone needs but few fully understand. Their results create deference. Their language becomes more technical. Their judgment is questioned less. Their internal culture tightens.

The third stage is exemption.

Rules begin to feel like friction. Risk controls feel like bureaucracy. Compliance feels like something for people who do not understand the trade. Outsiders are tolerated but not respected. The line between superior insight and special permission begins to blur.

The fourth stage is fragility.

By the time the failure becomes visible, the issue is no longer just one bad trade, one bad decision, or one bad actor. It is a system that allowed competence to become insulated from accountability.

That is why Meriwether’s story is worth revisiting.

Not to relitigate every fact.

Not to prosecute a man decades later.

But to examine the risk of elite performance cultures that lose the ability to self-correct.

The Salomon Lesson

At Salomon Brothers, the issue was not simply one trader’s misconduct.

Paul Mozer, the head of Salomon’s Government Trading Desk, submitted false bids in U.S. Treasury auctions. The purpose was to acquire more securities than Treasury rules permitted, including bids submitted in customer names without authorization. In the February 21, 1991 five-year note auction, Salomon submitted a $3.15 billion bid in its own name and additional unauthorized $3.15 billion bids in the names of customers Mercury Asset Management and Quantum Fund. In the May 22, 1991 note auction, the firm controlled more than 90 percent of the issue, far exceeding the 35 percent limit set by the Treasury.

That is not a paperwork problem.

That is a direct challenge to market integrity.

The Treasury market is not a casino side room. It is the financing mechanism of the United States government and a reference point for the global financial system. If the Treasury auction process is compromised, the issue is bigger than one firm’s profit and loss statement.

There is a deeper risk here worth naming. A high-performing trading culture can become so focused on the mechanics of the game that it loses sight of the rules of the stadium. The trade is the game. The market is the stadium. The financial system is the city. The economy is the country. Each layer outward has its own rules, its own legitimate expectations, and its own consequences for being wrong. A team that masters one layer can forget the layers above it exist. When that happens, what looks like a technical question inside the trade is often a structural question about the system.

The SEC proceeding is also important because of what it says about senior leadership. John Meriwether, vice chairman of Salomon and head of fixed-income trading, was Mozer’s direct business line supervisor and was first alerted to the problem on April 24, 1991. He took the matter to Salomon president Thomas Strauss and chairman John Gutfreund, and the issue moved into senior management discussions.

The SEC was careful. The respondents were not charged with participating in the underlying violations. The Commission did find that supervision was deficient and that the matter was not promptly reported to the government. After the initial disclosure to management, additional unauthorized bids occurred. The matter was not publicly disclosed until August. Meriwether resigned from Salomon on August 18, 1991, the same day Gutfreund and Strauss resigned.

That distinction is the lesson.

The failure was not merely “someone did something wrong.”

The failure was that a high-performing organization did not respond with enough urgency when it learned that something was wrong.

That is often how institutional risk works.

The first violation may be misconduct.

The second failure is tolerance.

This is why Warren Buffett’s reputation line has endured. Buffett, whose Berkshire Hathaway was Salomon’s largest shareholder, stepped in as interim chairman during the cleanup. His warning to Salomon employees was direct: lose money and there may be understanding; lose reputation and the response becomes ruthless. He understood that certain losses are financial, while others are existential. A firm can survive a bad quarter. It may survive a bad trade. It may survive a cycle. But once it loses trust with regulators, counterparties, customers, and employees, the damage becomes harder to measure.

That is not corporate poetry.

That is risk management.

The Ethicality of Proximity

The Salomon scandal raises a leadership issue that is more subtle than “do not break the law.”

Of course, do not break the law.

But the harder lesson is about proximity.

What do leaders tolerate from the people closest to them?

Most institutions do not fail because every person is corrupt. They fail because the people who know better convince themselves that a problem is isolated, manageable, technical, or not yet urgent enough to force a confrontation.

That is the ethicality of proximity.

It is easy to be principled about bad behavior in the abstract. It is harder when the person involved is a star performer, a trusted lieutenant, a rainmaker, a technical genius, or a member of the inner circle.

The closer the person is to the profit engine, the more expensive integrity feels.

That is exactly why integrity matters.

If the standard changes when the offender is valuable, then the standard was never the standard. It was branding.

This is a core leadership lesson. High-performing toxic talent is a tail risk. Not because talent is bad, but because organizations often become too dependent on it. When one desk, one trader, one executive, one engineer, one dealmaker, or one strategist produces extraordinary results, the organization may unconsciously reprice the rules around that person.

At first, the tolerance looks practical.

Then it becomes cultural.

Then it becomes dangerous.

The LTCM Lesson

After Salomon, Meriwether built Long-Term Capital Management.

In some ways, LTCM looked like a cleaner version of the Salomon bond arbitrage culture: elite talent, sophisticated models, enormous credibility, and a strategy built around exploiting small pricing differences across markets. Founded by Meriwether in February 1994, LTCM became famous for its mathematical sophistication, elite personnel including two Nobel laureates, and early returns of 20 percent in 1994, 43 percent in 1995, 41 percent in 1996, and 17 percent in 1997.

Those numbers create belief.

Belief attracts capital.

Capital attracts more opportunity.

Opportunity justifies more complexity.

Complexity requires more confidence.

Confidence invites more leverage.

By the end of 1997, LTCM was holding about $30 in debt for every $1 of capital. The fund had also returned capital to its investors without reducing the scale of its investments, which further increased its already high leverage.

That is the part of the story that matters for investors.

LTCM was not simply making reckless directional bets in the common sense. The premise was more refined. It looked for spreads that should converge over time. It sought to profit from relative mispricings. It believed diversification across markets and sophisticated modeling reduced the risk.

That is what made the failure so dangerous.

The strategy was not obviously foolish.

It was logically elegant.

But logic can become a trap when the real world stops behaving like the model.

In 1998, the Asian financial crisis deepened, and Russia suddenly devalued its currency and stopped payments on its debt in August. Investors rushed toward safer and more liquid investments. LTCM’s convergence trades diverged rather than converged. In a September 2 letter to investors, Meriwether disclosed that the fund was down 44 percent for the month of August and 52 percent year-to-date.

The issue was not that the mathematics had no value.

The issue was that the mathematics could not fully capture panic, liquidity, crowded positioning, counterparty behavior, forced selling, and the reflexive nature of markets under stress.

That is the difference between the map and the territory.

A model can describe relationships.

It cannot guarantee the world will honor them on schedule.

The Hubris of Precision

There is a special kind of danger in strategies that are mostly right.

A foolish strategy may fail quickly. A brilliant strategy may work long enough to become overtrusted.

That is what makes precision seductive.

When the model works, it feels like understanding. When the returns compound, it feels like proof. When smart people agree, it feels like validation. When counterparties extend credit, it feels like market confirmation.

But the market can validate a strategy for years before revealing the flaw.

The flaw may not be in the central case. The central case may be excellent. The flaw may be in the assumption that the central case can be levered aggressively because the probability of deviation appears low.

Leverage is a multiplier of ego.

When confidence is high and the model is working, leverage feels like a tool. When the world stops behaving, leverage becomes the mechanism through which being right becomes irrelevant.

That is the high cost of being right.

If the trade is right but the timing is wrong, leverage can make being right irrelevant.

If the relationship converges eventually but financing disappears first, the model can be right and the portfolio can still fail.

If the asset is mispriced but liquidity vanishes, the investor can be correct and insolvent.

If the probability distribution excludes panic, the tail can own the firm.

That is why humility is not a personality preference in investing.

Humility is a risk-management tool.

Too Interconnected to Fail

LTCM’s failure became a systemic concern because of how deeply connected it was to the financial system.

In September 1998, fourteen banks and brokerage firms invested $3.6 billion to prevent LTCM’s imminent collapse. The Federal Reserve Bank of New York facilitated the arrangement but did not put public funds at risk. The purpose was to avoid a disorderly fire sale of LTCM’s positions into already turbulent markets.

That detail matters.

The rescue was not a bailout in the simple taxpayer sense. But it was a warning about interconnection. LTCM was large, leveraged, and intertwined enough with major counterparties that its failure could have transmitted stress through the system.

This is one of the most important lessons for modern markets.

Risk is not only what is on your balance sheet.

Risk is also where you sit in the network.

A trade can be rational in isolation and dangerous in aggregation. A fund can be hedged on paper and exposed in liquidity. A balance sheet can look sophisticated and still be vulnerable to everyone needing to exit at once.

Crowding is not always visible.

Correlations are not stable in crisis.

Liquidity is not a constant.

Counterparties are not passive.

Markets are social systems wearing mathematical clothing.

That does not mean models are useless. It means models need supervision by people who understand their limits.

The Redemption Trap

There is also a human dimension to the story.

After Salomon, LTCM can be read in part as a redemption project. Meriwether had been forced out of one of the most powerful firms on Wall Street after a scandal that damaged reputations and nearly destroyed the institution. LTCM offered a way to rebuild around the thing he knew best: bond arbitrage, elite talent, and market structure.

There is nothing inherently wrong with redemption.

People should be allowed to learn, rebuild, and contribute after failure.

The danger is when redemption becomes proof-seeking rather than learning.

If the deeper lesson of Salomon was about culture, supervision, ethics, and accountability, then the question becomes whether LTCM truly absorbed that lesson or simply rebuilt the same elite-specialist model outside the corporate shell.

That is not a personal accusation.

It is an institutional question.

After LTCM, Meriwether launched JWM Partners in 1999, using a similar relative-value bond arbitrage strategy with more conservative leverage of roughly 15 to 1 compared to LTCM’s earlier 30 to 1. The flagship Relative Value Opportunity II fund was closed in July 2009 after losing 44 percent between September 2007 and February 2009 during the global financial crisis.

There is a pattern worth considering.

Not because people should never return to what they know.

But because repeated reliance on the same core method after repeated stress events raises the question of whether the lesson was fully learned.

A master craftsman can rebuild after a bridge collapses.

But at some point, he has to ask whether the problem was the weather or the blueprint.

The Modern Parallel

This is where the story becomes relevant again.

Markets today are filled with Meriwether-like temptations.

Not necessarily Meriwether-like people. That would be too easy. The issue is not personality replication. It is structural similarity.

We see elite teams with complex strategies.

We see crowded trades dressed as independent insight.

We see private credit structures with limited transparency.

We see AI capital spending justified by strategic necessity.

We see high valuations supported by models that require generous assumptions.

We see hedge funds, venture funds, private equity sponsors, technology platforms, energy developers, and infrastructure investors all making claims about durable advantage.

Some of those claims will be right.

Some will be expensive.

The lesson of Meriwether is not “avoid sophistication.”

Sophistication is necessary. The modern economy is too complex for simplistic thinking. Energy markets, capital markets, AI infrastructure, derivatives, logistics, power systems, and global supply chains all require specialists.

The lesson is that specialization must remain accountable to judgment.

A model is not a conscience.

A return stream is not a culture.

A high Sharpe ratio is not a strategy if it depends on liquidity that disappears when needed most.

A brilliant trader is not a reason to suspend supervision.

A profitable desk is not a waiver from ethics.

A winning streak is not a substitute for humility.

The Leadership Lesson

For leaders, the lesson is not merely “hire ethical people.”

That is necessary, but insufficient.

The real task is building systems where ethics, risk, and accountability remain stronger than status.

That means leaders must ask uncomfortable questions before the crisis:

Who is allowed to say no?

Who understands the trade besides the trader?

Who has authority to stop the machine?

Who benefits from not seeing the problem?

Who is too valuable to challenge?

What are we calling a technical issue that is actually an ethical issue?

What assumptions would have to fail for this strategy to become dangerous?

What happens if liquidity disappears?

What happens if everyone else is in the same trade?

What happens if our best person is wrong?

Those questions are not signs of distrust.

They are signs of seriousness.

A strong culture does not assume bad intent. It assumes human vulnerability. People rationalize. People compete. People protect status. People confuse loyalty with silence. People convince themselves the problem is manageable until it is not.

This is why ethics and risk management belong together.

Ethics without risk discipline can become slogans.

Risk discipline without ethics can become cleverness.

Both are needed.

The Investor Lesson

For investors, Meriwether’s story is a reminder to look past pedigree.

Pedigree matters, but it is not enough.

A team can be brilliant and still fragile.

A strategy can be sophisticated and still overlevered.

A model can be statistically impressive and still incomplete.

A manager can have a record of success and still carry unexamined patterns.

When evaluating a company, fund, or management team, the question is not merely whether they are smart.

The harder questions are different.

How do they behave when they are wrong?

Do they disclose bad news quickly?

Do they respect constraints?

Do they understand liquidity?

Are incentives aligned?

Is leverage visible?

Is risk distributed or concentrated?

Is the culture open to challenge?

Does management learn from failure, or explain it away?

That last question may be the most important.

Everyone has failures.

Not everyone metabolizes them.

The real test of leadership is not whether someone has ever been wrong. It is whether being wrong changes how they lead.

The Point Taken

John Meriwether’s career is not a morality play about one man’s flaws.

It is a case study in the risk of elite competence without enough humility.

At Salomon, the lesson was ethical and supervisory. A firm’s most profitable culture can become its greatest vulnerability if performance earns insulation from accountability.

At LTCM, the lesson was mathematical and systemic. A strategy can be elegant, diversified, and historically supported, yet still fail if leverage, liquidity, crowding, and panic are underestimated.

In both cases, the warning is the same.

Being smart is not enough.

Being right is not enough.

Winning is not enough.

The highest-risk people and organizations are not always the obviously reckless ones. Sometimes they are the most capable, the most credentialed, the most confident, and the most admired. They know the game so well that they begin to forget the game exists inside a larger system.

That is the paradox of the specialist.

That is the danger.

Technical discipline without ethical discipline is incomplete.

Mathematical precision without systemic humility is fragile.

Competitive intensity without moral boundaries becomes tail risk.

The market does not only punish ignorance.

Sometimes it punishes brilliance that forgets to doubt itself.

That is the real lesson.

The high cost of being right is paid when intelligence becomes conviction, conviction becomes leverage, and leverage meets a world that refuses to behave.

Subscribe now

Share

By Bryan J. Kaus

“The first principle is that you must not fool yourself, and you are the easiest person to fool.” — Richard P. Feynman

Part 1 made the case that AI demand is showing up in the physical infrastructure layer: utility capital plans, midstream backlogs, financing packages, construction labor, and the storage and pipeline footprint behind it. The demand is real. The buildout is real. The system is being asked to deliver more than it has in decades.

This is the part where discipline matters.

The Problem

The market tends to compress complexity into a label.

AI infrastructure.

Power demand.

Data centers.

Natural gas.

Grid modernization.

Transmission.

Nuclear.

Cooling.

Transformers.

Each label contains real opportunities. Each also contains ways to lose money.

That is where discipline matters.

The market can be directionally right about a theme and still overpay for the wrong assets inside it. Railroads. Telecom fiber. Shale. Renewables. The first wave of LNG. E-commerce logistics. Cloud. Every major buildout attracts capital. Every major buildout creates winners. Every major buildout also creates overcapacity, failed projects, and narrative-driven investments that do not earn their cost of capital.

The operators know this.

When asked whether Kinder Morgan would consider another pipeline expansion into the Northeast given how tight gas supply into New England has become, CEO Kim Dang did not soften the answer. The need was clearly there, she said. But the company would have to have certainty on state permits and the commercial support to underwrite the project. “We’ve gone down that road once. We wrote off a fair amount of capital and I think that’s not something that we are interested in doing again.”

That is the operator’s lesson made explicit.

A project can have real demand sitting in front of it and still destroy capital if the permitting environment, the contract structure, or the commercial backing is not there. Discipline is not abstract. It is what an operator who has actually written off capital sounds like when asked to do it again. And this happens all the time.

AI infrastructure will not be exempt from this pattern.

The buildout is real.

But the buildout will not be evenly distributed.

Some projects will be backed by investment-grade customers, long-term contracts, strong interconnection positions, credible power supply, reasonable leverage, and assets with value beyond one customer or one use case. Others will depend on speculative load, weak tenant credit, unrealistic timelines, high leverage, uncertain interconnection, rising construction costs, and a perfect capital market.

Those are very different investments.

This is why the Richard Kinder frame is so useful.

He is not telling the market to buy the slogan.

He is pointing to the constraint.

And in infrastructure, constraint is often where value is created.

Constraint as Opportunity

A constraint is not merely a problem.

A constraint can be an investable bottleneck.

If power is scarce, the assets that can deliver power become more valuable.

If gas deliverability is scarce, pipelines with the right location and capacity become more valuable.

If transmission is scarce, utilities and developers with viable projects, regulatory support, and cost recovery mechanisms become more important.

If transformer supply is scarce, procurement discipline and equipment access matter.

If interconnection is scarce, projects already positioned in the queue have an advantage.

If credible load is scarce, counterparties and contracts become decisive.

If capital is scarce, balance sheets matter.

This is where investors need to separate exposure from advantage.

Exposure is easy.

Advantage is harder.

A company can mention data centers on an earnings call and have no durable economic edge. Another company may never use flashy AI language but own the corridor, pipe, substation, generation site, water rights, or contracted position that actually matters.

That is the difference between thematic investing and underwriting.

Smart capital does not chase “AI infrastructure” in the abstract. It asks who the customer is, whether the load is contracted, whether the counterparty is creditworthy, who pays if the project is delayed, who absorbs cost overruns, whether generation and transmission and gas deliverability are actually available, whether the interconnection is real, whether the permitting path is credible, whether the equipment is available, whether the return is regulated or contracted or merchant or speculative, and whether the asset still has value if the original demand case is reduced.

That last question may be the most important one.

The best infrastructure investments usually have multiple ways to win. They serve durable demand. They sit in constrained corridors. They have credible customers. They benefit from real physical scarcity. They generate cash flow even if the most aggressive version of the theme does not materialize.

The weaker investments depend on the perfect version of the story. Perfect load growth. Perfect financing. Perfect timing. Perfect technology adoption. Perfect permitting. Perfect utilization. Perfect customer economics.

Perfect stories are expensive.

And fragile.

The Financing Layer

The capital requirement is also where this cycle connects to the credit markets directly.

If the world needs trillions of dollars of AI-related data center and hardware investment, and hyperscalers fund only part of that from internal cash flow, the rest has to come from somewhere. Credit markets, asset-backed finance, partnerships, project finance, leases, private capital, and structured vehicles all become part of the AI capital stack.

That changes the risk profile.

A model may be software. The financing is not.

Debt has a maturity.

Projects have budgets.

Equipment has lead times.

Power has availability constraints.

Counterparties have credit quality.

Capital has a cost.

There is also a structural timing mismatch that is forcing the system to adapt. Rabobank framed it clearly this week: grid interconnection timelines of 36 to 84 months are structurally incompatible with data center build cycles of 12 to 24 months. With grid connections taking up to seven years, hyperscalers are increasingly moving behind the meter, with more than 130 GW of capacity now proposed across the United States. Gas accounts for more than 80% of that announced behind-the-meter total.

That is the tangible version of “AI has a pipeline problem.”

The model may scale quickly.

The grid does not.

When the AI story is told only through the lens of productivity, software adoption, and model capability, it can feel abstract. When it is told through the lens of power, pipelines, utilities, and project finance, it becomes more concrete.

That is healthier.

The more capital-intensive the cycle becomes, the more important it is to ask whether the cash flows justify the investment.

There is also a tenant-credit issue. Not all AI demand is created equal. A lease or project backed by a major hyperscaler with a fortress balance sheet is different from one backed by a newer AI cloud provider, a speculative tenant, or a customer whose economics depend on future capital markets remaining wide open.

The market will increasingly need to distinguish between real contracted demand and aspirational demand, between investment-grade counterparties and weaker tenants, between regulated cost recovery and merchant exposure, between essential infrastructure and theme-adjacent assets, between durable bottlenecks and temporary shortages.

That is where winners and losers will separate.

The Jobs Story

There is another dimension that deserves attention.

This buildout is not just financial. It is industrial.

If even a meaningful portion of the planned AI and power infrastructure buildout occurs, it will create work across the physical economy: pipeline construction, electrical work, civil engineering, data center construction, gas generation, transmission and distribution, substations, control systems, cooling, maintenance, field services, project management, welding, heavy equipment, environmental review, land work, and operations.

Meta’s El Paso project is one example. Meta has said the facility is expected to support over 4,000 construction workers at peak and more than 300 permanent jobs once completed.

That does not make every project good.

It does not eliminate environmental concerns, community concerns, affordability concerns, water concerns, reliability concerns, or capital discipline concerns.

But it does mean the AI story is not confined to Silicon Valley, Seattle, Austin, or Wall Street.

The digital economy is creating demand for people who build things.

That matters.

One of the recurring mistakes in modern markets is treating “technology” as if it floats above the real economy. It does not. Technology eventually lands somewhere. It lands in land use, energy use, water use, labor markets, municipal planning, utility bills, capital budgets, and local politics.

AI is no different.

The model may be virtual.

The buildout is not.

The Energy Stack

This is also an energy-stack story.

The market likes to talk about energy transition as if sources simply substitute for one another. In practice, systems stack. Demand grows. Reliability matters. Intermittency matters. Dispatchability matters. Location matters. Existing infrastructure matters. Time matters.

Data centers need reliable power.

That does not mean only natural gas will win. It does not mean renewables do not matter. It does not mean nuclear will not be part of the mix. It does not mean storage, demand response, efficiency, or grid optimization are irrelevant.

It means the system has to work.

Hyperscalers may want clean power. Utilities may need dispatchable generation. Grid operators may need reliability. Customers may demand affordability. Regulators may demand prudence. Communities may demand water and land-use protections. Investors may demand returns.

All of those pressures will meet in the same place.

The physical system.

Allianz described AI as likely imposing the largest sustained demand shock on U.S. electricity infrastructure in decades, with data-center power consumption expected to nearly double by 2030, lifting the sector’s share of total U.S. electricity demand from roughly 5% to around 9%. Although planned generation additions look sufficient on paper, data centers may absorb nearly half of projected new capacity, leaving thin margins if electric-vehicle adoption or industrial electrification accelerate faster than expected.

That is the energy-stack problem.

AI is not the only demand source.

Manufacturing, electrification, cooling load, transportation, LNG, industrial growth, and normal economic activity are all competing for the same system.

Natural gas is positioned to matter because it can provide dispatchable power at scale, and because the pipeline and generation ecosystems already exist in large parts of the country. But that does not make every gas project attractive. The winners will likely be the projects tied to credible load, strategic locations, and disciplined contracts.

The same applies to transmission. Transmission is essential, but transmission is difficult. It faces permitting, siting, cost allocation, landowner concerns, and regulatory complexity. Everyone wants more deliverable power. Fewer people want the line near them or the cost on their bill.

That is the real constraint.

Not desire.

Execution.

The Local Constraint

There is also a local politics and community acceptance layer.

The proposed Stratos data center in Utah is a useful illustration. The project, developed by Kevin O’Leary’s (aka “Mr. Wonderful”) O’Leary Digital, would span 40,000 acres of private land plus 1,200 acres of military and state-owned property in Box Elder County. At full buildout, the campus would reach 9 gigawatts, all produced on-site through a connection to the Ruby Pipeline, more than double Utah’s current statewide electricity use of roughly 4 gigawatts. The site lies on the northern shore of the Great Salt Lake, and protests have raised concerns over Utah’s water shortages and the struggling Great Salt Lake, which supports millions of migrating birds and is drying into a serious dust problem impacting public health.

Whether that specific project ultimately proves viable is not the point.

The point is that physical infrastructure has a constituency.

It has neighbors.

It has environmental constraints.

It has water constraints.

It has ratepayer implications.

It has local political risk.

That is why the phrase “AI infrastructure” is too clean. The reality is messier. Every serious project has to move through a world of people, permits, power, water, land, cost recovery, and execution.

The market can model the load.

Operators still have to build the system.

Winners and Losers

There will be winners and losers.

Some utilities will grow rate base intelligently and protect affordability. Others may face pushback if customers believe they are subsidizing speculative load growth.

Some midstream companies will convert tight capacity and customer demand into attractive contracted projects. Others will chase marginal expansions that depend on optimistic forecasts.

Some data center developers will secure power early, build in the right locations, and match capacity to real demand. Others will discover that land without power is not a project.

Some hyperscalers will earn attractive returns because AI becomes embedded in high-value workflows, enterprise software, advertising, cloud services, cybersecurity, industrial systems, and consumer products. Others may spend heavily to maintain competitive position without earning proportional incremental returns.

Some investors will own the bottlenecks.

Others will own the narrative.

That is the difference.

The winners will be the ones closest to indispensable infrastructure, credible demand, and disciplined execution. The losers will be the ones that confuse thematic exposure with economic moat.

The Point Taken

Richard Kinder’s comments are useful because they bring the AI conversation back to earth. He is a sage voice - and I was stunned when he appeared on Bloomberg TV and the hosts said they had to look him up - the man is a legend.

The question is not whether AI is real.

It is.

The question is whether the physical system can scale fast enough, cheaply enough, and reliably enough to support the demand being assumed, and which projects, companies, and operators will capture durable value in the process.

That is where the constraint becomes both a risk and an opportunity.

The risk is that markets overbuild the wrong assets, finance weak projects, assume every announced data center becomes real load, and price infrastructure as if execution were automatic.

The opportunity is that credible demand is colliding with constrained systems: gas pipelines, power generation, transmission, substations, equipment, cooling, storage, construction labor, and capital.

Some constraints will become investment opportunities.

Some constraints will become growth bottlenecks.

Some projects will create durable value.

Some will become stranded monuments to a cycle that moved faster on paper than in the field.

That is why this is not a call to chase everything attached to AI.

It is a call to underwrite the physical chain with discipline.

Contracted demand matters.

Counterparty quality matters.

Power availability matters.

Gas deliverability matters.

Permitting matters.

Capital structure matters.

Execution matters.

Time matters.

The AI economy may be built in code, but it will be constrained by concrete, copper, steel, gas, water, wires, workers, and time.

That constraint is not just the risk.

Properly underwritten, it may also be the opportunity.

Subscribe now

By Bryan J. Kaus

“We shape our buildings; thereafter they shape us.” — Winston Churchill

AI feels weightless from the user interface.

A prompt goes in. An answer comes out. A model writes, codes, summarizes, searches, analyzes, drafts, edits, translates, and reasons in seconds. The experience is digital, frictionless, and almost abstract.

The infrastructure behind it is anything but.

Behind the intelligence is land. Power. Gas. Wires. Water. Steel. Cooling. Transformers. Substations. Turbines. Pipelines. Permits. Construction crews. Financing markets. Interconnection queues. Skilled labor. Regulatory approval. Community acceptance. And time.

That is the part of the AI story the market is still working through (and expectations will temper to reality).

The last piece I wrote on AI focused on capital governance: the risk that when everything feels possible, companies begin treating possibility as priority. AI has become large enough that it is no longer just a technology story. It is a capital-intensity cycle. A balance-sheet story. A credit story. A management discipline story.

This is the next layer.

AI may be built in code, but it will scale through infrastructure.

And infrastructure has physics.

The Issue

Richard Kinder has been one of the clearer voices on this point.

That matters because Kinder is not selling an AI model. He is not underwriting a software multiple. Kinder Morgan is a physical infrastructure company. It moves energy. It sees demand through contracts, utilization, bottlenecks, expansion projects, creditworthy customers, and whether someone is willing to commit capital for deliverable capacity.

That vantage point is useful.

On Kinder Morgan’s first-quarter 2026 earnings call, Kinder said he had looked back at his own prior comments about natural gas demand and found that, “in almost every case,” the projections he made turned out to be understated. His explanation was straightforward: demand for natural gas, driven primarily by LNG feedgas and increased utilization of natural gas for electric generation, has grown faster than expected. He then added that recent events had made the outlook even more positive.

The data point that jumps out is electric generation.

Kinder cited S&P Global Market Intelligence reporting that utilities plan to add 153 gigawatts of gas-fired generation capacity over the next several years, primarily to serve data centers, with the bulk of that coming online by 2030. He noted this was twice the estimate from the same group a year earlier and reflected plans to build about 210 additional natural-gas-fired facilities. Kinder Morgan’s own forecast now sees total U.S. gas demand reaching approximately 150 Bcf per day in 2031, growth of about 27% from this year. His summary was simple: “the natural gas story has legs.”

What sits behind that headline is more interesting.

On the same call, Kinder Morgan’s president Dax Sanders told analysts that the company is in various stages of development on projects serving more than 10 Bcf per day of natural gas demand in the power generation sector and over 3 Bcf per day in the LNG sector. These are not booked projects in the $10.1 billion backlog. They are what is moving through the operator’s commercial pipeline behind the headline number.

That is the deeper read.

Not a slogan.

Not a pitch deck.

A demand forecast tied to generation, data centers, LNG, and pipeline capacity, with another layer of identified opportunity sitting beyond what has already been formally committed.

The important nuance is that this is not a claim that every announced data center will be built. That would be the wrong conclusion. Some projects will not get built. Some will be delayed. Some will be repriced. Some will fail on interconnection. Some will fail on permitting. Some will fail because financing conditions change. Some will fail because the customer economics of AI take longer to prove out than the capital spending assumed.

But not every project has to be built for the infrastructure requirement to be enormous.

That is the point.

A discounted version of the AI and data center buildout can still create a massive need for power generation, gas deliverability, pipeline capacity, transmission, substations, cooling systems, construction labor, and capital.

The opportunity is real.

So is the constraint.

The Physical Chain

The AI economy is creating a surge in expected power demand. That part is no longer theoretical.

American Electric Power recently raised its five-year capital investment plan to $78 billion, up from $72 billion, citing demand growth in key states including Indiana, Ohio, Oklahoma, and Texas. AEP signed 7 gigawatts of new large-load agreements during the first quarter, primarily in Ohio and Texas, bringing total expected incremental load to 63 gigawatts by 2030, with AEP Texas accounting for 41 gigawatts of those commitments.

That distinction matters.

Demand is one thing.

Deliverable power is another.

A data center does not run on market narrative. It runs on electrons. Those electrons have to be generated, transmitted, delivered, stabilized, cooled, backed up, and paid for.

That is where the infrastructure chain matters.

Data centers require power.

Power generation often requires gas.

Gas requires pipelines.

Pipelines require permitting, rights-of-way, compression, steel, construction, regulatory approval, and customers willing to sign contracts.

Transmission requires lines, towers, substations, transformers, land, interconnection, permitting, and cost recovery.

Cooling requires water, equipment, planning, and local acceptance.

Financing requires confidence that the demand, counterparty, contract structure, and return profile are real.

Every link matters.

Kinder’s Warning, and Kinder’s Opportunity

Richard Kinder’s argument is grounded but generally bullish.

That is what makes it useful.

He is not telling the market to chase every AI-adjacent story. He is saying the underlying demand has grown faster than expected, and the midstream sector, underpinned by long-term throughput agreements with investment-grade customers, offers a comparatively low-risk way to participate in that growth.

Kinder’s framing on the call was direct on positioning: he described Kinder Morgan’s pipeline network as “enormously advantaged by the sheer size and location” of its assets, located in the areas where gas demand is growing dramatically. The strategy he described is to expand and extend existing assets in an aggressive but disciplined manner, complete projects on time and on budget, and finance growth primarily with internally generated cash flow while maintaining a strong balance sheet.

That is an operator’s answer.

It is also an investor’s answer.

Own the scarce asset.

Contract with creditworthy customers.

Avoid speculative overreach.

Fund growth without breaking the balance sheet.

Execute.

That is very different from chasing the broad label of “AI infrastructure.” And there are so many out there simply chasing…

Kinder Morgan’s hard numbers support the case. In Q1 2026, the company reported utilization on its five largest gas pipelines exceeded 90%, up from 74% in 2016. Its project backlog reached $10.1 billion, with approximately 92% tied to natural gas projects and nearly 60% supporting power generation and local distribution company demand. New projects added in the quarter alone totaled $375 million, including three deals tied directly to data center load. The footprint behind those numbers is 78,000 miles of pipeline and 136 terminals, much of it sitting in the corridors where demand is moving fastest.

There is also a less-discussed competitive layer.

Natural gas storage as a structural differentiator. The company holds more than 700 Bcf of storage capacity, with additional expansions under evaluation. As data center load, LNG cycling, and weather-driven demand grow, the ability to inject and withdraw gas quickly stops being just inventory and starts being part of the system’s reliability infrastructure. Storage becomes a scarce resource, not a commodity service. That is the kind of asset detail most market commentary misses.

Again, that is not a promotional AI number.

It is an infrastructure signal.

A utilization problem.

A backlog.

A development pipeline beyond the backlog.

A storage moat.

A tightening system.

A customer base asking for deliverable energy.

Kinder Morgan also highlighted specific projects tied to this demand. Its Creekside Lateral project is designed to serve growing power generation, industrial, and data center demand in Central Texas and is supported by binding long-term contracts. The company is also advancing larger projects such as the Trident Intrastate Pipeline, intended to move significant volumes from Katy, Texas toward the industrial corridor near Port Arthur.

That is the real-world version of the AI story.

Pipe in the ground.

Contracts.

Customers.

Storage.

Steel.

Time.

The Buildout Is Real

The broader market evidence points in the same direction.

S&P Global’s Regulatory Research Associates now forecasts approximately $1.3 trillion of aggregate capital expenditures for U.S. energy utilities between 2026 and 2030, as companies seek to modernize infrastructure and add generation capacity in response to heightened energy demand from large-load customers, particularly data centers that support AI, digital services, and cloud infrastructure. Data centers and other large industrial loads are expected to add 374 TWh of energy demand and over 45 GW of peak load through 2035, according to analysts at S&P Global Energy CERA.

That turns AI into a utility-capex story.

It is not simply about model performance.

It is about financing the physical layer of the digital economy.

Morgan Stanley has estimated that global data center capacity could increase by a factor of six over the next five years, with spending on data centers and hardware alone reaching roughly $3 trillion by the end of 2028. Roughly half of that funding is expected to come from hyperscaler cash flows, with the rest financed through outside channels of the credit markets.

Meta’s planned El Paso data center illustrates the scale. Meta is working with Morgan Stanley and JPMorgan Chase on a roughly $13 billion financing package for the project. Meta had previously increased its investment in the planned El Paso AI data center by more than sixfold to $10 billion, aiming to reach 1 gigawatt of capacity ahead of the facility’s projected 2028 opening.

One gigawatt is not a rounding error.

It is a power-plant-scale requirement attached to a digital business model.

That is the shift.

AI is pulling the physical economy forward: utilities, pipelines, gas generation, substations, transmission lines, construction, turbines, engineering firms, project finance, land development, equipment suppliers, cooling systems, water infrastructure, and skilled trades.

Williams is seeing the same kind of demand from the pipeline side. In Q1 2026, Williams upsized its Transco Power Express project, increasing capacity to 750 million cubic feet per day, aimed at serving rising energy demand from AI-driven data centers in Virginia. The company is on track to deliver 2026 adjusted core earnings at the higher end of its forecast range, supported by data center and LNG export demand.

Exelon is another current example. The company raised its four-year capital expenditure plan to $41.7 billion, projecting 7.9% rate base growth. Executives said the data center pipeline is increasingly backed by FERC-approved Transmission Security Agreements, which have now secured approximately $1 billion of collateral. Transmission rate base is now projected to grow at 16% through 2029.

The pattern is becoming visible.

Utilities are increasing capital plans.

Midstream companies are adding pipeline capacity.

Hyperscalers are arranging large project financings.

Developers are signing long-term leases.

Construction and equipment ecosystems are being pulled into the cycle.

This is not merely a technology trade.

It is an industrial buildout.

The Point Taken

AI may feel weightless at the user interface. The system that powers it does not.

Utility capital plans are rising. Midstream backlogs are expanding, and operator commercial pipelines sit deeper still. Financing packages are being assembled at scales the industry has not seen in a generation. Construction labor, equipment, transmission, gas deliverability, and storage are all being pulled into the same cycle.

The demand is real.

The physical chain is real.

The system is being asked to deliver more than it has in decades.

That is the part of the story the market is still working through.

The harder questions come next. How the buildout will actually be funded. Where the local constraints will bind. Which operators capture durable value, and which projects end up as stranded monuments to a cycle that moved faster on paper than in the field.

Stay tuned for part two next week…

Subscribe now

By Bryan J. Kaus

“The art of economics consists in looking not merely at the immediate but at the longer effects of any act or policy; it consists in tracing the consequences of that policy not merely for one group but for all groups.” — Henry Hazlitt, Economics in One Lesson

For weeks I have written about the gap between what the screen sees and what the system already knows. The screen trades headlines, sentiment, Fed expectations, and the hope that geopolitical risk resolves quickly. The system carries molecules, vessels, contracts, inventories, and the operating decisions companies have already made.

That gap is where the mispricing lives.

Last week’s April Producer Price Index report is the system speaking.

The Bureau of Labor Statistics reported that final demand PPI rose 1.4 percent in April, the largest monthly increase since March 2022. Year over year, producer prices rose 6.0 percent, the largest twelve-month increase since December 2022. Nearly 60 percent of the monthly increase came from final demand services, and goods prices moved sharply higher. Gasoline jumped 15.6 percent. Jet fuel and diesel followed. Industrial chemicals and transportation-related categories all contributed.

The market was surprised.

It should not have been.

The Print

A 1.4 percent monthly print is not a small number. It is not the largest in history, and it is not by itself proof of a regime change. But it is the kind of number that does not arrive in isolation, and it is the kind of number that confirms what the physical economy has been signaling for two months.

For anyone outside the economic weeds, PPI is short for Producer Price Index. It measures the prices producers receive at the factory gate, the wholesaler’s loading dock, and the input stage of the value chain, before those costs flow to the consumer. CPI, the Consumer Price Index, is what households feel at checkout. PPI is what is already moving through the pipes before they feel it.

That is the entire point of watching PPI.

Inflation rarely begins at the grocery shelf, the restaurant menu, the airline ticket counter, or the home improvement aisle. It begins in crude oil, diesel, jet fuel, natural gas, fertilizer, chemicals, packaging, freight, warehousing, labor, insurance, financing costs, and the countless hidden inputs that sit inside the real economy long before a consumer sees a final price.

The market often misses this part.

A stock price can reprice in seconds. A vessel takes weeks to arrive. A refinery cannot instantly reverse years of underinvestment. A farmer cannot ignore fertilizer costs. A manufacturer cannot pretend freight, diesel, chemicals, labor, and working capital do not matter. A retailer cannot absorb every upstream increase forever without damaging margins, reducing investment, cutting labor, or raising prices.

Eventually, physical constraints become financial consequences.

That is what April PPI is beginning to show.

The Q1 Read

One reason this print felt jarring is that Q1 earnings commentary, in many cases, has remained relatively calm.

There is logic to that.

Q1 was not a clean read of the current environment. Much of the quarter operated under more normal conditions. The first strikes on Iran hit on February 28. For roughly two-thirds of the quarter, the system was functioning as expected. Even where volatility began appearing in March, the full operating impact would not necessarily show up immediately in reported earnings.

Financial statements look backward.

Supply chains operate in motion.

Markets try to trade forward.

That timing mismatch creates room for complacency. If two-thirds of a quarter operated before the full shock was visible, then Q1 earnings can appear solid while the next layer of cost pressure is already moving through the system. That does not make the commentary dishonest. It makes it incomplete.

April PPI matters because it is one of the first broad statistical signals that the physical pressure is entering the reported data.

Bigger Than Crude

The easy version of the story is that crude rose, therefore producer prices rose.

That is true. It is also incomplete.

Crude moved from roughly $60 to $100 since February. That alone is enough to ripple through dozens of downstream categories. But the disruption around the Strait of Hormuz is not only an oil story. It is an energy story, a fertilizer story, a logistics story, an agriculture story, and ultimately a consumer story.

Hormuz matters for crude oil and LNG. It also matters for urea, ammonia, sulfur, methanol, aluminum, helium, and a long list of commodities tied to industrial and agricultural supply chains. Roughly one-third of global seaborne fertilizer trade typically moves through the Strait. The Gulf region accounts for roughly 49 percent of globally traded urea exports and 30 percent of ammonia. U.S. urea at the New Orleans port is up more than 25 percent since late February.

That kind of disruption does not stop at the fuel pump. It moves into farm economics, crop production costs, and eventually food prices.

A farmer facing higher fertilizer costs is not dealing with an abstract geopolitical risk premium. He is dealing with cash costs, planting economics, credit requirements, and margin risk. If those costs rise and stay elevated, they show up somewhere.

In lower margins.

In higher food prices.

In reduced acreage or changed crop choices.

In greater financial stress across the agricultural supply chain.

They do not disappear.

The Pass-Through

This is where public commentary often becomes too simplistic.

When companies raise prices, the immediate accusation is greed. Sometimes that is fair. Companies with strong pricing power can protect or expand margins when the market gives them room.

But many cost increases are not that simple.

A business facing higher input costs has a limited set of choices. It can absorb the pressure through lower margins. It can offset through productivity. It can reduce spending. It can cut labor. It can delay capital investment. It can raise prices. It can do some combination of all of the above.

For a time, companies absorb the pain. Many do. But there is a limit. If input costs rise materially and persistently, absorbing all of it becomes a threat to the operating plan. That means less cash for maintenance, hiring, growth, debt service, inventory, technology, and reinvestment.

So when producer costs rise, the pressure eventually moves downstream.

That is not a moral statement. It is operating math.

The Consumer Math

This is the part of the chain where the situation becomes more delicate.

Headline consumer spending can look resilient even when households are under real pressure. Consumers keep spending by using credit, drawing down savings, delaying large purchases, trading down, or shifting from discretionary to essential categories.

That is not the same as strength.

The numbers are starting to tell that story. U.S. credit card debt recently topped $1.33 trillion, a new all-time high. The personal savings rate dropped to 4.0 percent in Q1, down from 6.2 percent in early 2024. Average credit card APRs sit above 21 percent. Spending continues to move, but increasingly it moves on credit rather than on income.

That is a fragile foundation if upstream costs keep working their way down the chain.

If producer inflation feeds into consumer prices at the same time households are leaning more heavily on credit, the consequences run beyond inflation alone. Slower growth. Margin compression. Tighter financial conditions. A consumer who keeps spending until the math no longer works.

This is not a catastrophe forecast. It is a warning against complacency.

The Tanker

Even if the geopolitical situation improved tomorrow, the cost shock would not vanish.

That is another point markets tend to underappreciate.

Supply chains have inertia. Ships are already in motion. Inventories are already positioned. Refinery runs, procurement decisions, hedges, freight contracts, purchase orders, crop plans, and pricing actions are already underway.

The economy turns more like a tanker than a speedboat.

Once a disruption enters the system, it takes time to work through. Even a resolution today would leave months of adjustment behind it. Inventories have to be rebuilt. Supply routes have to normalize. Risk premiums have to reset. Buyers have to regain confidence. Producers have to decide whether lower costs are durable enough to change pricing again.

Saudi Aramco has gone on record that full normalization could push into 2027 if the Strait of Hormuz is not sorted by mid-June. That is not coming from a pessimist. It is coming from the operator sitting at the center of global crude flows.

The question is not, “Will this resolve?”

The better question is, “How much of the cost shock has already entered the system, and where will it surface next?”

April PPI gives part of that answer.

For Operators and Allocators

The practical takeaway is not panic. Panic is rarely useful.

The better posture is disciplined realism.

For investors, the test is pricing power. Some companies can pass through costs without major volume loss. Others cannot. Some businesses benefit from energy tightness. Others are exposed to it. Some industrial companies have supply-chain flexibility. Others are operating with very little room for error.

For management teams, the questions have changed.

Where are we exposed to energy, freight, chemicals, fertilizer, packaging, or working capital pressure?

Where are we assuming cost normalization that may not arrive quickly?

Where do we have pricing power, and where are we simply hoping customers will absorb increases?

Where are inventory decisions protecting us, and where are they tying up cash?

Where are we mistaking Q1 resilience for full-year durability?

For policymakers, the lesson is structural. Energy security, logistics, agriculture, refining capacity, LNG, fertilizer, and industrial resilience are not separate policy categories. They are part of the same operating system.

When that system tightens, consumers eventually feel it.

The Point Taken:

The April PPI report is not proof of disaster.

It does not mean recession is inevitable. It does not mean every company will miss earnings. It does not mean the consumer collapses tomorrow. It does mean upstream inflation pressure is real, broad enough to matter, and now visible in the data.

That should not be a surprise.

The market can trade optimism, AI enthusiasm, Fed hopes, and the assumption that geopolitical risk will fade. Some of those narratives may prove partially right.

But narratives do not move barrels, ships, fertilizer, diesel, or freight.

The physical economy eventually imposes its discipline.

PPI is the smoke under the door. The fire alarm comes later.

The screen will trade what it wants. The system will eventually impose what is real. The job of an operator, an investor, or a policymaker is to read both, and to know which one moves the world.

Share

Subscribe now

By Bryan J. Kaus

“Publicity is justly commended as a remedy for social and industrial diseases. Sunlight is said to be the best of disinfectants; electric light the most efficient policeman.” — Louis D. Brandeis, Other People’s Money

The SEC’s proposed rulemaking around Form 10-S looks, at first glance, like a technical filing change.

It is not.

Disclosure cadence is not just paperwork. It is part of the plumbing of public markets. It shapes how information moves, who receives it, how quickly expectations adjust, and how much trust investors place in the market itself.

Transparency has a cost.

Opacity has one too.

The Issue

There is a legitimate argument for reducing the reporting burden on public companies.

Anyone who has been close to the earnings process knows the quarterly cycle is not just a press release, a filing, and a call. It is a governance machine.

Business units close the books. Finance reconciles the numbers. Legal reviews the disclosure. Investor relations calibrates the message. Management prepares for the call. Boards engage with performance, risk, capital allocation, and narrative. Control processes are tested. Variances are explained. Assumptions are challenged.

The quarter becomes a recurring checkpoint.

That checkpoint consumes time, money, management attention, and organizational energy. For smaller issuers, the burden can be especially heavy.

That is not an unserious point.

Quarterly reporting can reinforce short-term behavior. Management teams can become overly focused on the quarter, the guide, the whisper number, and the market’s reaction to whether earnings came in a few cents ahead or behind expectations. A company can be doing the right long-term things and still be punished for timing issues, temporary working capital movements, margin compression, maintenance, project ramp-up costs, or investments whose returns will not appear inside a ninety-day window.

Strategy is not built in ninety-day increments.

Competitive advantage does not mature on a reporting calendar.

Plants, pipelines, logistics systems, refineries, brands, customer relationships, data centers, trading organizations, management systems, and cultures do not compound because a filing deadline arrives.

That is the strongest case for reform.

But it is not the whole case.

The Problem

The problem with quarterly reporting is not that investors receive too much information.

The problem is that too much of the market has learned to overreact to the wrong information.

That is different.

A quarterly filing can become theater. So can an earnings call. So can guidance. So can the cottage industry of expectation resets, sell-side models, management commentary, and post-earnings scorekeeping.

But less frequent reporting does not automatically make management teams more strategic.

A good company can think long term while reporting quarterly.

A weak company can think short term while reporting twice a year.

The filing schedule is not the strategy.

The deeper risk is that reducing mandatory disclosure does not eliminate the market’s demand for information. It simply changes where that information goes.

Markets do not stop needing current data because companies provide less of it. The information demand migrates.

Large institutional investors will still build models. Credit analysts will still perform channel checks. Banks and rating agencies will still seek insight. Private data vendors will still sell signals. Alternative data providers will still scrape, track, image, monitor, and infer. Investors with the resources to buy better information will continue to do so.

Satellite imagery, credit card data, supply-chain tracking, web traffic, shipping data, commodity flows, app downloads, hiring trends, customer checks, and industry surveys become more valuable when public reporting becomes less frequent.

In other words, the clock does not disappear.

It becomes private.

That is the risk: information stratification.

The ordinary public investor, the one public markets are supposed to include, may be pushed further from the center of the information flow. The largest and best-resourced market participants will still find ways to estimate what is happening between formal disclosures. Smaller investors may simply be asked to wait.

That matters.

Public markets depend on a shared baseline of timely, comparable, reliable information. The baseline does not have to be perfect. It never is. But if the baseline becomes too thin, confidence erodes.

And once confidence erodes, the cost of capital usually follows.

The Current Context

This debate is also arriving at a strange moment for markets.

We are living in an environment of algorithmic trading, alternative data, private credit growth, prediction markets, geopolitical volatility, and instantaneous reaction to headlines. Information now moves through more channels, at higher speed, with more participants trying to monetize timing advantages.

That does not mean every market move is manipulation.

It does mean disclosure gaps matter.

Recent concerns around unusual crude oil short activity and prediction-market behavior are not proof of anything about this SEC proposal. They should not be treated that way. But they are a reminder of a broader principle: when information moves markets, the timing of information becomes valuable.

The wider the gap between public disclosures, the more valuable that gap becomes.

In a slower market, six months may sound reasonable.

In today’s market, six months can be an eternity.

Supply chains move faster. Working capital decisions move faster. Commodity markets move faster. Capital markets move faster. Customer behavior moves faster. Investors react faster. Technology has made closing processes more efficient, but it has also made the market less patient.

So the question is not simply whether quarterly reporting is burdensome.

It is whether semiannual reporting fits the velocity of the modern market.

The Governance Machine

There is another point that should not be lost.

Quarterly reporting is not only an external disclosure process. It is an internal operating discipline.

The quarter forces management to reconcile the story with the numbers.

It forces business leaders to explain what changed.

It forces finance teams to close the books.

It forces legal and disclosure teams to review risk.

It forces boards to stay current.

It forces executives to look at performance before small misses become larger issues.

That rhythm can be exhausting. It can also be useful.

In many companies, the issue is not that management has too many reporting obligations. It is that management does not always have enough timely command of the business. A recurring disclosure cycle can serve as a stress test. It can reveal whether the company actually understands its own drivers: margin, volume, price, working capital, capital spending, customer demand, operating reliability, and cash conversion.

Removing the checkpoint does not fix a poor strategy.

It may only delay recognition.

That is why the better reform may not be less frequent disclosure. It may be better disclosure.

Streamline the 10-Q. Reduce duplication. Eliminate boilerplate. Make MD&A more useful. Focus on what actually changed. Explain business drivers with more clarity. Stop rewarding narrative gymnastics around immaterial quarterly variance. Push management teams to discuss capital allocation, cash generation, risks, and long-term value creation with more substance.

There is a difference between reducing burden and dimming the lights.

The first is good governance.

The second is dangerous.

The Market Signal

There is also a practical investor-relations question.

If the rule is adopted and remains voluntary, what will the market infer when a company elects semiannual reporting?

That answer will vary by company.

A stable, mature, highly transparent issuer with conservative disclosure practices may be viewed differently from a promotional company in a volatile sector. A utility or midstream company may be assessed differently from a speculative technology company, a levered roll-up, or a business dependent on aggressive non-GAAP adjustments.

But the signaling risk is real.

High-quality companies may continue reporting quarterly because transparency itself is a signal of confidence. Companies with significant institutional ownership may find that investors expect the current cadence to continue. A move to semiannual reporting could save cost, but it may also invite questions.

Why less visibility?

Why now?

What should investors assume during the gap?

Does management want to reduce noise? Or reduce scrutiny?

That may not be fair in every case. But markets are not obligated to be generous. If investors believe a company is reducing visibility, they may demand a higher return for holding the stock.

The result could be ironic.

A rule designed to reduce burden and improve public-market attractiveness could, for some companies, increase the cost of capital.

The Broader Point

The decline in public listings and the attractiveness of private markets are real issues. But quarterly reporting is only one part of that system.

Private capital has grown. Private credit has grown. Venture capital has grown. Secondary markets have expanded. Companies can stay private longer. Founders can preserve control. Large incumbents can acquire promising businesses before they mature into public companies. Litigation risk, regulatory complexity, compensation disclosure, governance expectations, and activist pressure all shape the decision to go public.

Reducing the reporting cadence may help at the margin.

It will not, by itself, restore the public-company model.

That requires a broader conversation about why companies go public, why they stay private, what public investors need, and how to preserve the public market as a place where ordinary investors can participate in economic growth.

The question should not be framed as quarterly reporting versus long-term thinking.

That is too neat.

The better question is this: how do we reduce unnecessary burden without weakening the information foundation that makes public markets work?

The Point Taken:

Quarterly reporting is imperfect.

But imperfection is not the same as failure.

The quarterly system can encourage short-termism, narrative management, and excessive focus on near-term expectations. That should be acknowledged. But the answer to quarterly capitalism is not necessarily less disclosure. It is better disclosure.

Public companies should not be managed to satisfy a quarterly narrative.

But public investors still deserve timely information.

Those ideas can coexist.

The market still needs a clock, not because every business truth reveals itself every ninety days, but because public capital depends on a shared understanding of time, performance, and accountability.

Once information becomes stratified, the weighing machine becomes a house of mirrors.

The right goal is not disclosure for disclosure’s sake.

It is disclosure good enough, timely enough, and credible enough that public markets remain public in substance.

Not merely in name.

By Bryan J. Kaus

"Show me the incentive and I will show you the outcome." — Charlie Munger

Earnings season has offered a lot of insight across the spectrum. I was looking through the outcomes last week and found a few things that made me want to look a little deeper.

For instance, Amazon’s Q1 2026 8-K disclosed something worth pausing on.

The company reported $16.8 billion in pre-tax gains in non-operating income from its investments in Anthropic. That is a paper markup, reflecting Anthropic’s most recent funding round at a $380 billion valuation. In the same window, Amazon agreed to invest up to $25 billion more in Anthropic, on top of $8 billion already committed, while Anthropic committed to spend more than $100 billion on Amazon Web Services (AWS)‘s technologies over the next 10 years, including up to five gigawatts of capacity on Amazon’s custom Trainium chips.

None of that is improper. Mark-to-market accounting on equity investments is standard. The strategic logic may be sound. Anthropic is a real company with real revenue.

But sit with the architecture for a moment. A strategic investor takes a larger stake in a customer. The customer commits to enormous future spend back to the strategic investor. The customer’s valuation rises. The strategic investor reports paper gains on its existing stake. Those gains contribute to near-term reported income.

That is the AI capital cycle in miniature. It is real. It is strategic. It is possibly excellent. And it is also a closed loop that is harder to evaluate cleanly than current valuations suggest.

The lesson here is not really about Amazon, or Anthropic, or AI specifically. It is about capital governance. How organizations behave when the upside feels unlimited and the urgency to act is intense. How investors price overlapping claims on the same future profit pool. How boards and management teams allocate capital when every initiative carries the rhetorical weight of inevitability.

AI is the current case study. Capital discipline is the lesson.

What the Earnings Cycle Actually Showed

The numbers from the past two weeks were not subtle.

Alphabet Inc. reported Q1 2026 revenue of $109.9 billion, with Google Cloud growing 63% to $20 billion and backlog nearly doubling sequentially to over $460 billion. Capex in the single quarter was $35.7 billion. Management raised full-year 2026 capex guidance to $180 billion to $190 billion and signaled that 2027 capex will “significantly increase” compared to 2026.

Amazon reported AWS growth of 28% and confirmed approximately $200 billion in capex for 2026.

Microsoft posted fiscal Q3 capex of $30.88 billion, up 84% year over year, with AI revenue surpassing a $37 billion annual run rate.

Meta raised its 2026 capex range to $125 billion to $145 billion, up $10 billion at both ends, and tapped the bond market for up to $25 billion in long-dated investment-grade debt to help fund the spend.

Aggregated, the four major U.S. hyperscalers are now expected to deploy close to $725 billion in capital expenditure in 2026, roughly double the prior year’s already-record level. Goldman Sachs noted that hyperscaler capex would need to reach $700 billion in 2026 to be in line with the peak of spending during the late 1990s telecom investment cycle.

AI has moved beyond product narrative. It is now a capital-intensity cycle, and once a technology becomes a capital-intensity cycle, the questions change. Not whether the technology is real, but who governs the spend. Who earns the return. Who owns the bottleneck. Who owns the customer. Who is converting capability into durable advantage. Who is simply spending to stay in the story.

When Everything Is Possible

The deeper lesson is not limited to AI. It applies any time the upside appears large enough that organizations begin treating possibility as priority. That happens more often than people admit.

Every operator has seen some version of it. A digital transformation that swallows the IT roadmap. A new commercial platform that becomes a five-year program nobody can shut down. A refinery optimization project that grows into an enterprise initiative. An ERP implementation that turns into a generational capital outlay. A pricing system rebuild. An acquisition integration that loses its scope. A supply-chain redesign that keeps adding workstreams.

The pattern is almost always the same. The strategic logic is sound. The opportunity is real. The business case is compelling. The urgency is high. The organization mobilizes.

Then the scope expands.

More stakeholders want in. More use cases get added. More vendors appear. More pilots launch. More capital is requested. More dependencies emerge. The project becomes too broad to manage cleanly and too important to challenge directly.

At that point, the risk is no longer the idea. The risk is governance. Capital projects fail less often because the original ambition was irrational. They fail more often because the organization loses control of scope, sequencing, accountability, and return discipline.

The question is not whether AI deserves capital. It does. The question is whether companies are governing AI capital like real capital, because that is what it is now.

Data centers, chips, power, cooling, cloud capacity, software architecture, model development, enterprise workflow redesign, training, cybersecurity, and change management are not innovation expenses dressed up as capex. They are real capital commitments with real opportunity cost. Every dollar allocated to AI is a dollar not allocated to safety, maintenance, customer service, R&D, balance-sheet flexibility, leadership development, or core execution.

When everything is possible, governance becomes strategy. Without it, possibility becomes sprawl, and sprawl is where capital goes to disappear.

Booking the Future

The architecture in that opening disclosure is not new in pattern.

Enron’s distortion was not optimism. It was the conversion of expected future contract economics into reported present value before the cash had unfolded. Future profit was pulled forward.

The current AI cycle is not doing that in the same accounting sense. The hyperscalers are real companies with real cash flow, real assets, and real customers. This is not Enron in any literal sense.

But valuation can produce a similar psychological effect. When markets reward companies today for future AI optionality, they are discounting a future that has not yet been competitively, operationally, or financially settled. That is normal in equity markets. The risk comes when that future gets counted too confidently, too broadly, or multiple times across overlapping participants.

Optionality is not cash flow. Partnerships are not profits. Capex is not return. Exposure is not advantage.

The investor’s job is to ask the basic governance questions without flinching. Is the demand organic? Is the customer economically independent? Would the customer buy the same capacity absent the investment? Is the contract profitable after the capex required to serve it? How concentrated is the exposure? What happens if funding markets tighten?

These are quality-of-earnings questions. They are also quality-of-governance questions.

The risk is not that any single arrangement is wrong. The risk is that markets begin counting the same future value multiple times, in multiple companies, on the assumption that the future has already arrived.

Capex Is the Test

The spend is now too large to treat casually. When a single hyperscaler raises 2026 capex guidance to $190 billion, the analysis changes. When the four largest hyperscalers approach $725 billion in aggregate, it changes again.

Pivotal Research projects Alphabet’s free cash flow to plummet almost 90% this year to $8.2 billion from $73.3 billion in 2025. Morgan Stanley analysts project Amazon free cash flow turning negative by almost $17 billion in 2026, while Bank of America analysts see a deficit of $28 billion. Barclays estimates Microsoft free cash flow will slide by 28% this year before popping back up in 2027. Meta, which raised the upper end of its capex range by $10 billion, just placed up to $25 billion in 40-year debt to help fund the spend. S&P assigned the new debt investment-grade and maintained its stable outlook, while noting that Meta’s massive investment in AI was “starting to affect credit metrics.”

That last phrase is worth re-reading.

This is no longer purely an equity-growth story. It is a balance-sheet story. A credit story. An infrastructure story. A capital-allocation story. That changes what the analysis has to do.

Is capex growing faster than incremental gross profit? Is AI revenue growing faster than depreciation? Are gross margins expanding or compressing? Is free cash flow improving or weakening? Are useful-life assumptions realistic? Are companies funding AI from operating cash flow or from debt?

These are not anti-innovation questions. They are capital-discipline questions. They are the questions any responsible operator asks before committing capital that cannot easily be redeployed if the original thesis is wrong.

GPUs Are Not Dams

Not all capex is created equal.

A refinery is a 40-year asset. A hydroelectric dam can run for a century. A bridge built well today is still moving traffic when our grandchildren are working. The depreciation curve on long-lived industrial capital is generous because the asset earns over decades.

AI capex does not work that way.

Two thirds of Microsoft’s capex this quarter went to short-lived assets, primarily GPUs and CPUs. These are not 20-year assets on a steady depreciation curve. They are short-lived hardware on a generation cycle, where today’s leading-edge chip becomes tomorrow’s stranded inventory faster than most balance sheets are designed to absorb.

That changes the math.

When you fund a $190 billion capex budget partly through long-dated debt, you are not just betting on the technology. You are betting that the return will materialize faster than the asset becomes obsolete. You are betting that compute pricing holds while supply expands. You are betting that customer demand keeps pace with your depreciation schedule.

The hyperscalers may well win that bet. Their cash flow, customer base, and pricing power give them real ability to absorb shorter useful lives. But the bet exists, it is large, and it deserves to be named.

Capital that depreciates fast must earn fast.

That is not a critique of AI. It is an accounting observation. And it is one of the cleanest tests of whether the spend is being governed like real capital.

The Operator’s Concern

There is also a management failure mode that maps cleanly onto this cycle.

If management teams come to believe the highest-return incremental dollar is always an AI dollar, capital and attention flow toward AI by default. In some places that is right. In many it is not. Without discipline, AI becomes the answer before the question is clear, and exposure becomes a substitute for strategy.

That is the governance version of the productivity-and-headcount mistake I have written about before in The AI Dividend. Reducing labor cost is not the same as expanding capability. Activity is not the same as advantage. The market often cannot tell the difference in the first quarter. It tells the difference eventually.

The Same Test, at Smaller Scale

The hyperscalers are the visible case because the numbers are public, the capex is enormous, and the earnings cycle forces transparency. But the more dangerous version of the same test is happening one ring out.

Every company in every sector is now running some version of this. The bank with an enterprise AI strategy. The manufacturer integrating AI into maintenance and quality. The retailer rebuilding its pricing engine. The insurer overhauling underwriting. The healthcare system deploying clinical decision support. The professional services firm trying to figure out what AI does to its hourly model.

These are real initiatives, and many will create real value. But they face the same governance risk as the hyperscaler buildout, with one important difference. The hyperscalers can absorb the spend. They have the balance sheets, the customer base, the cash flow, and the optionality to fund a multi-year capex cycle and survive substantial write-downs.

Most companies cannot.

For companies in non-tech sectors, AI investment is real capital allocation against a tighter budget, with less margin for error, in environments where the use cases are less mature, the vendors less vetted, and the internal capability to evaluate the spend is still being built.

The savings story is usually the entry point. AI promises to reduce headcount, accelerate cycles, lower error rates, and improve margins. Some of that is real. But the savings in the back half of the implementation often come with frontloaded costs that do not get fully accounted for. Consulting fees. Integration work. Software licenses. Change management. Training. Severance. Productivity loss during transition. And the lost institutional capability that walked out the door.

The mistake is treating cost shift as value creation. Cutting headcount to pay for software licenses is not a productivity gain. It is a rearrangement of the P&L. Reducing labor cost is not the same as growing the business. Replacing one expense line with another is not capital efficiency. It is rearrangement dressed up as transformation.

The discipline is to insist on the harder version of the question. What measurable improvement did we earn? Did cycle time actually fall? Did customer experience actually improve? Did margin expansion compound? Did the freed capacity get redeployed to growth, or did it simply leave? Are we delivering on the promise, or only promising it?

That is what manifesting the promise means. Real returns. Realized. Measured. Defended. Not announced. Not modeled. Not assumed.

Every company is now running its own AI cycle. The ones that come out of it stronger will be those that govern the spend like real capital, prove the returns, and resist the temptation to confuse activity with progress.

That discipline is not unique to AI. It never was. It is the discipline of effective management. Capital allocation. Resource stewardship. The honesty to do hard accounting on your own initiatives, and the willingness to admit when an experiment did not earn its keep.

AI is the test of the moment. The discipline is the test of the operator.

What Good Capital Governance Actually Does

The mature phase of this cycle will require more than excitement. It will require governance. Not bureaucracy. Not slow committees. Not paralysis dressed up as prudence. Speed is not the enemy of discipline. Lack of structure is.

Good capital governance does five things.

It frames investment as a portfolio. AI initiatives are not isolated experiments. They are capital projects that compete with one another and with everything else the company could fund. Treating them as a portfolio forces honest tradeoffs.

It uses real stage gates. Not bureaucratic checkpoints. Real ones. Define the problem. Prove the use case. Scale the deployment. Measure the return. Decide what comes next. Each gate is a chance to confirm or kill.

It commits to incremental gains. Systemic transformation usually fails. Compounded incremental improvement usually succeeds. The discipline of asking what the next measurable gain looks like protects against the sprawl of trying to AI-enable everything at once.

It assigns operational ownership. Every funded initiative needs a person whose career is tied to delivering the return. Without ownership, every project is everyone’s, which is to say no one’s.

It includes the courage to stop. The hardest discipline in any capital cycle is killing a project that has support, momentum, and political weight. Companies that cannot stop weak initiatives cannot allocate capital well, regardless of how many they start.

The questions a good governance process answers are practical. What problem are we solving? What is the expected return? What is the cost of being wrong? What has to be true for this to work? What are we not funding because we are funding this? When do we stop?

That last question is the one most organizations skip. In a hype cycle, companies are rewarded for starting things. The best operators are also willing to stop them. That is capital discipline.

Where the Discipline Lives for Investors

The investor’s posture follows from the same logic. The answer is not to avoid AI. That would be as undisciplined as buying everything with AI exposure. The better posture is selectivity, anchored to capital governance rather than narrative.

There is opportunity in bottleneck assets. Power, grid equipment, transformers, switchgear, cooling, specialized real estate, transmission, water. These are the constraints behind the spend, and the constraints often get priced last.

There is opportunity in disciplined hyperscalers that can show real return on capital, customer demand, utilization visibility, and the ability to slow or redirect spend if the cycle turns.

There is opportunity in workflow owners that already control the enterprise system of record and can embed AI into how work actually gets done.

There is opportunity in proprietary data owners. Models can commoditize. Unique, permissioned, high-quality data is harder to replicate.

And there is real opportunity in real-economy adopters that use AI to improve an already strong business. Energy. Industrials. Logistics. Manufacturing. Insurance. Healthcare. Distribution. The best AI investment may not be an AI company at all. It may be an industrial operator using AI to improve asset utilization, maintenance, forecasting, pricing, safety, or working capital.

That is where AI becomes operating leverage. And operating leverage paired with discipline is how durable value gets built.

A Responsible Capitalist View

Capitalism works when capital moves toward productive opportunity, when innovation is rewarded, when risk-taking is encouraged, and when weak projects are allowed to fail.

But capitalism also requires discipline. It requires distinguishing value creation from valuation expansion. It requires accepting that real technology can produce poor investment returns in parts of the first wave. It requires the humility to recognize that being right about a future does not guarantee being paid for it.

The responsible capitalist welcomes AI. The responsible capitalist also demands evidence.

Real returns. Real productivity. Real customer value. Real cash conversion. Real resilience. Real strategic clarity.

Not exposure. Not announcement. Not optionality. Not narrative.

There will always be money in the high-risk phase. Some operators are very good at trading volatility, ambiguity, and momentum. Some will do well in this cycle. But the broader system needs more than that. It needs durable companies, productive investment, resilient infrastructure, human capability, and disciplined capital allocation. It needs innovation without hollowing out the enterprise.

That is not anti-growth; it is grounded capitalism.

The Point Taken:

The lesson from this earnings cycle is not that AI should be dismissed. It is that AI has moved from a technology narrative into a capital-allocation cycle. That changes the standard of proof.

Markets no longer need only to know who is exposed to AI. They need to know who can govern the investment. Who can prioritize. Who can sequence. Who can measure return. Who can stop weak projects. Who can prevent sprawl. Who can turn capability into operating leverage. Who can avoid mistaking activity for strategy.

That discipline is not new, and it is not unique to AI. Every capital cycle in history has rewarded the operators who could distinguish exposure from return, activity from advantage, and announcing the future from earning it. AI is the current test of that distinction. It will not be the last.

The early winners in any cycle are often those with exposure.

The durable winners are those with discipline.

The right posture is not rejection. It is disciplined participation.

Fund the future.

Govern it like capital.

Manifest the return.

Do not book it before it is earned.

By Bryan J. Kaus

“Energy is the only truly universal currency, and nothing, from galactic rotations to ephemeral insect lives, can take place without its transformations.” — Vaclav Smil

For months I have written about the gap between paper barrels and physical ones. The screen moves on headlines. The system moves on molecules. That gap is where most of the mispricing in this market lives, and where the real risk and the real opportunity tend to compound.

Last week, the Federal Reserve Bank of Dallas issued a rare interim update to its Energy Survey. In central banking, “interim” is a polite way of saying volatility has outpaced the calendar. It was not a procedural pulse check. It was an audit of a system under strain.

The data inside is a sobering reality check for anyone expecting a clean snap back to normal.

What the Operators Are Actually Doing

The most telling number in the report is not about prices. It is about discipline.

Nearly 70 percent of large E&P firms reported no change to their 2026 drilling plans, despite a sustained run of WTI well above $75 and stretches above $100. The operators with the largest production base in the country are choosing to wait. They have lived through enough cycles to know what the screen does not yet price in.

The headline can resolve. The system does not snap back when it does.

Tanker flows do not reset overnight. They are rerouted, adding thousands of miles and weeks of duration to global supply chains. Insurance and logistics costs stay sticky long after the chokepoint reopens. Thirty-six percent of executives now expect shipping costs from the Persian Gulf to remain structurally higher by $2 to $4 per barrel even after the conflict ends. That is a permanent friction tax on the global cost of movement.

There is also the matter of new layers of bureaucratic heat. Some reports out of Tehran indicate a push for legislation that would charge ships transiting the Strait of Hormuz in local rials. Whether it becomes a formal toll or simply a sanction-bypassing maneuver, the intent is unmistakable. The cost of moving oil through the world’s most critical chokepoint is being structurally repriced.

The Q1 Mirage

Before going further, it is worth being honest about what the Q1 earnings prints actually show.

January and February were largely normal. The first U.S. and Israeli strikes hit Iran on February 28. For roughly two-thirds of the quarter, the system was operating as expected. The real volatility started in March, and even in March, the early action was an emotional trade. War talk pushed prices up. Ceasefire rumors and peace-talk headlines pulled them back. The market spent weeks oscillating around a question it could not answer: how much of this disruption is going to manifest physically?

That answer is now arriving.

While the screen was trading sentiment, the physical system was tightening. Tanker rerouting. Refinery slate constraints. Storage filling in the Gulf with nowhere to ship to. By early April, jet fuel had moved from $2.39 a gallon on February 27 to a peak of $4.78 on April 2, before settling near $3.51 by mid-month. The benchmark European jet fuel price hit a record $1,800 per ton on March 18.

Q1 results are a rearview-mirror look at a partially stable system. The real weight will be measured in Q2 and Q3, and we are starting to see what that looks like.

Larger Than 1973

It is worth pausing on the scale of what we are inside of.

The 1973 oil embargo removed roughly 4.5 million barrels per day from global supply, about 7 percent of the market at the time. The current closure of the Strait of Hormuz disrupts up to 20 million barrels per day, roughly one-fifth of global petroleum consumption. The International Energy Agency (IEA) has called it the largest oil disruption on record, and even after accounting for pipeline workarounds through Saudi Arabia and the UAE, the real bottleneck still sits closer to 9 million barrels per day. That is more than twice the 1973 number.

There is a tendency to assume U.S. domestic production insulates us from global tightness. It does not.

The market for oil is global and interconnected. Record U.S. exports mean that when a refiner in Asia or Europe goes short, the market draws supply out of U.S. terminals to fill the void, and domestic prices rise to clear. Production at home does not mean prices at home are protected. Prices are set at the margin, and the marginal buyer is now competing globally for every barrel.

The U.S. refining complex itself is not a closed loop either. American refineries are configured for specific crude slates, and a meaningful share of those slates depends on Canadian heavy crude moving south through pipelines. Domestic light crude alone cannot run our refineries the way they were built. In a tight market, the Lifeline from Alberta is as load-bearing to U.S. fuel supply as anything we drill in the Permian.

Geography and physics do not care about borders or economic nationalism.

The Input Stack

To navigate this market, you have to stop treating energy as a sector and start treating it as the foundation of a stack.

Energy sits at the top of nearly every input chain in the modern economy. It is the prerequisite for extraction, manufacturing, logistics, refining, and power generation. Whether you are building an AI data center or growing wheat, you are buying a molecule and converting it into a margin.

When you add heat to the top of that stack, it does not stay there. It ripples outward through every node that depends on it, and the further down the chain you go, the more compounded those costs become by the time they reach the consumer.

Leonard Read illustrated this in his 1958 essay, I, Pencil, later popularized by Milton Friedman. Read observed that not a single person on earth knows how to make a simple number two pencil from scratch. It requires cedar from Oregon, graphite from Sri Lanka, rubber from Malaysia, and the coordinated effort of millions of people who will never meet, all moved and refined and assembled by what Friedman called the magic of the price system. Every one of those nodes is a conversion of Smil’s universal currency.

When the cost of that currency moves by a fraction of a penny at the transport stage, it compounds. By the time the graphite is refined and the pencil reaches a shelf in Ohio, the incremental energy costs have been baked into replacement value. This is why inflation is never one number. It is a thousand small adjustments working their way through a chain, and it stays sticky because every entity along the way is trying to recoup the margin they lost when their inputs spiked.

The fertilizer chain shows the same physics. Up to 30 percent of global fertilizer trade moves through the Strait of Hormuz. When the cost of moving natural-gas-derived fertilizer rises, the impact is not contained at the pump. It shows up on the dinner plate. The price of corn and wheat is as much a function of that strait’s plumbing as it is of the weather.

The Coping Canary

If you want to see this transmission in real time, look at the airlines.

They are the canary in the cockpit because fuel is their largest, most immediate, and most non-negotiable cost. They do not have the luxury of waiting two quarters for the plumbing to adjust. They feel the heat the moment the tanker is delayed.

The earnings revisions of the last few days illustrate the pivot from a Year of Growth to a Year of Coping.

United Airlines cut its 2026 EPS guidance from a range of $12 to $14 down to $7 to $11, citing a $340 million increase in fuel expense in Q1 alone and announcing a 5-point capacity reduction for the rest of the year. United expects to recover only 40 to 50 percent of the second-quarter fuel hit through revenue, climbing to as much as 80 percent in the third quarter and approaching full coverage by year end.

Alaska Airlines withdrew its full-year guidance entirely, citing a $600 million Q2 fuel hit and a $3.60 per share earnings headwind. The company expects to pay $4.75 a gallon in April and $4.50 across the quarter, nearly double the early-year price. Alaska began tankering fuel from Singapore to Seattle in March because West Coast refinery margins had pushed jet fuel another 20 cents per gallon higher.

Europe is in worse shape. Roughly 75 percent of European jet fuel imports come from the Middle East. The IEA’s director told reporters in mid-April that Europe had “maybe six weeks” of jet fuel left at the prevailing pace. Lufthansa Group announced it would cancel 20,000 short-haul flights through October. Slovenia introduced fuel rationing in late March, the first EU country to do so since the start of the crisis.

The response across the industry is a predictable three-step dance. Squeeze margins. Cut capacity. Pass the cost to the consumer. The fare increases of 15 to 20 percent and the bag fee hikes are not greed. They are the physical manifestation of an energy shock moving through a stack. The airlines have already pulled the easy levers. What remains is structural, and structural cuts to growth are what coping looks like.

This is just one example.

Disciplined Execution

I do not believe in the theatrical labels of bulls and bears. I believe in disciplined execution and the fundamentals of a clear market read. Discipline does not mean avoiding every bump in the road. It means controlling the controllable so you can navigate cycles when they turn, and they always turn.

The market is currently celebrating headline resolution (or lack thereof - it can, as we’ve seen change by the day, the hour and even the minute). The physical system is still executing a painful adjustment. The 70 percent of operators sitting on their drilling plans are not being timid. They are choosing the weight of the system over the noise of the screen.

For the management teams and investors I work with, the framework is straightforward.

Audit the slates, not the sentiment. Value a company on its exposure to the input stack and its ability to maintain pricing power as replacement costs rise. Look past the magic words and the latest pivot. (I wrote about that valuation discipline more directly in The Multiple Is in the Message)

Capital discipline is the north star. High prices are not a signal to chase. They are a signal to protect the balance sheet and wait for physical certainty. The 70 percent of E&P operators who are effectively holding their drilling plans understand this. They are choosing the weighing machine over the voting machine.

Watch the canary. The fuel shortages already emerging in Europe are an early warning of what happens when the cost of movement becomes a physical constraint, not just a line item. Whatever is happening in aviation now will work its way into freight, into industrials, into anything that runs on a margin and depends on a barrel.

The Point Taken

The market is currently celebrating headline resolution while the system finishes a physical adjustment that has barely begun to manifest. Q2 and Q3 are where the real weight lands. Inflation is not one number. It is a thousand small adjustments working their way toward the consumer, and the airlines are showing you in real time how that math eventually arrives.

Markets move faster than systems. Systems are what move the world.

Subscribe now

By Bryan J. Kaus

“In the short run, the market is a voting machine but in the long run, it is a weighing machine.” — Benjamin Graham

This past week, a company declared something. Not a dividend or a debt offering, but a change of identity. That company was Allbirds and it did not merely announce a new product line. It changed its entire category.

The company once synonymous with wool sneakers and sustainability recently announced its intention to become NewBird AI, a business focused on GPU-as-a-Service and AI infrastructure. The market did what markets often do when a familiar brand borrows the vocabulary of the future: it reacted first and asked questions later.

In a single trading session, the company’s valuation surged by over $100 million.

This is not a new story. We have been here before: the “Tronics” craze of the 1960s, the “.com” boom of the 1990s, the Blockchain rush of 2017. Today, the magic word is AI.

But the useful lesson is not simply that markets are irrational. The real lesson for executives and investors is more nuanced: markets must classify a company before they can value it. The language a management team uses to describe itself materially influences the peer set, the risk discount, and the earnings multiple applied by analysts. This makes corporate storytelling powerful. It also makes it dangerous.

Three Ways to Change Your Story

Not all repositioning is the same. Companies usually fall into one of three categories when they change how they describe themselves, and the difference between them is enormous.

The first is cosmetic repositioning. This is the “magic word” strategy: tronics, dot-com, blockchain, AI. The underlying business has not materially changed, but the label has. It is a costume change.

The second is a real strategic pivot. This is when the business model actually changes: new assets, new talent, new customers, new capital allocation, and a genuinely different risk profile. These moves are rare and difficult. They do happen. Companies evolve dramatically. Nintendo began with playing cards. Toyota’s roots trace through textile machinery. Samsung began as a trading company. Nokia started with paper. Companies can become something fundamentally different from what they were. But that process leaves operational fingerprints, and it takes time.

The third is valuation clarification. This is when a company is being misread by the market and needs to articulate its actual business mix more clearly. This is not hype. It is capital markets hygiene. And it is the most underappreciated of the three.

Before you can clarify value, you have to understand where you actually stand. A great story about a weak business buys time, but it does not build durable value. A mediocre business with a great story may travel further than it deserves. A strong business with a great story is the optimal zone. But a strong business with no story, or the wrong one, leaves real value on the table. That last case is the one that gets underestimated. The problem is not always hype. Sometimes it is silence, or imprecision, or a lazy default to the most obvious label.

The P66 Case Study

I first saw the power of the classification gap during the Phillips 66 spin from ConocoPhillips.

I was one of many informed observers in those early days, not the architect but close enough to the decisions to carry the lessons forward. The market was prepared to tag the new entity as “just a refiner.” In 2012, refining was viewed as cyclical, capital-intensive, and unglamorous compared to the explosive growth of the shale boom. Upstream was where the excitement was. Refining felt like the old world.

But the operational reality was different. Phillips 66 had Midstream assets, a world-class Chemicals joint venture, and a Marketing business with its own earnings profile. Those businesses had different valuation implications. Refining was a relatively low multiple business. Midstream was higher. Chemicals was higher. Marketing was higher. If the market anchored the entire company to refining, the other segments essentially disappeared from the valuation.

The job was not to invent a story. It was to make sure the market did not underwrite the company as something less than it already was - and to evolve that narrative as the company evolved. That is what has always drawn me to the investor relations discipline. It is not storytelling alone. It is storytelling meets finance, and the two have to be in the room together.

The Trap of Ambiguity

This is where most management teams make a different kind of mistake.

Fearing they might miss a specific valuation category, they cram their earnings scripts and investor materials with every label that might attract attention. They want to be AI-enabled, SaaS-adjacent, platform-native, and sustainability-focused all in the same breath. They treat their mission statement like a list of hashtags, hoping the algorithm of the market will pick them up.

It rarely works the way they intend.

Complexity creates a discount. When management cannot clearly explain what the company is, and more importantly what it is not, investors get nervous. If an analyst cannot draw a clean box around the business model, they cannot model future cash flows with confidence. Instead of capturing multiple valuation premiums, the company ends up with a sprawl discount.

If you try to be everything, you risk being misread as nothing.

I recently advised a PE-backed startup caught in exactly this fog. They were selling themselves as a horizontal, do-it-all data tool. By defining a clear wedge, identifying the one specific industrial problem they solved better than anyone else, we moved the conversation from “software cost” to “asset optimization.” Being willing to say no to the sprawl widened the valuation. The discipline to exclude was the unlock.

This is a lesson I have had to apply to my own narrative as well. When your experience spans operator, investor, and advisor, people do not always know how to classify you. The cost is real. Unreadable is undervalued, and the burden of clarity is always yours to carry.

The AI Accountability Gap

For the Allbirds-to-NewBird pivot to hold its value, it must move past the press release.

AI is real. It will change business. It will produce genuine winners. Companies that are not traditionally thought of as technology businesses will use AI to transform supply chains, pricing, maintenance, procurement, and capital allocation. Some of them will become materially better businesses as a result.

But “using AI” is not the same as “being an AI company.” A retailer using AI to optimize inventory may be a better retailer. An energy company using AI for scheduling or commercial optimization may be a better energy company. Neither automatically deserves a software multiple unless its economics begin to resemble software economics.

For Allbirds, the questions are straightforward. Where is the GPU infrastructure? Who are the technical operators? What does the capital allocation between the old and new business actually look like? What are the power and utilization assumptions? A pivot that does not change the financial statements within four to eight quarters is not a pivot. It is a press release.

The market will eventually ask for the evidence. It always does.

The Point Taken

The market does not only value what a company owns. It values what it understands.

Clarity is a valuation tool. When used correctly, to reveal the real structure of a complex portfolio or the genuine wedge of a startup, it is one of the most powerful levers available to leadership. When used incorrectly, to chase a fashionable category or paper over strategic ambiguity, it creates a gap between the story and the business that execution will eventually expose.

Say what you are. Say what you are becoming. Make the aspiration clear. But make sure the operating reality can carry the weight of the message.

Because a name can move a stock. Only a business can hold the value.

Subscribe now

“Financial capital moves at the speed of light, but physical capital moves at the speed of a loaded tanker. In the world of molecules, there are no shortcuts.” — Vaclav Smil

By Bryan Kaus

Markets are currently trading on hope.

Over the last 24 hours, global equities have pushed higher and crude has softened on whispers of renewed diplomacy. To the casual observer watching the ticker, it looks as though the risk premium is being “priced out” of the system. But as we’ve explored before, there is a fundamental disconnect between the “paper” market’s reaction to a headline and the physical world’s ability to heal.

Regardless of the political optimism in Washington, we cannot ignore the plumbing. The market is pricing de-escalation faster than the physical world can reflect it.

The “Void” in the Shipping Lanes

Yesterday, Heather Conley, a Nonresident Senior Fellow at the American Enterprise Institute (AEI), provided the necessary corrective on Bloomberg News. She noted that the tankers that cleared the Strait of Hormuz just prior to the commencement of hostilities on February 28 are only just now reaching their destination ports.

Because tankers move at a sluggish 10 to 15 knots, the physical reality of the last six weeks is only beginning to manifest at the dock. Even if the current “double blockade”—where Iran demands tolls and the U.S. Navy blockades those who pay—ended tomorrow morning, there is a “void” in the supply chain. Ships that would have set sail in March simply aren’t there. That gap is a physical certainty; de-bottlenecking a chokepoint doesn’t instantly manifest fuel in Tokyo or Rotterdam—it simply restarts a 45-day clock.

The Resonance Effect: A Market Risk Mosaic

While the media fixates on the price of a barrel, the “operator” watches the knock-on effects that define the real economy’s margin structure.

• Aviation: Jet fuel prices have more than doubled since the conflict erupted, far outpacing the rise in crude. Delta Airlines recently forecast a $2 billion hit to fuel costs, forcing airlines to cut capacity and add refueling stops. The IATA has warned that even if the Strait reopens, it will take months for refined product supplies to recover.

• Chemicals & Plastics: Ethylene prices—the building blocks of everything from packaging to medical supplies—have hit all-time highs. While U.S. producers using ethane (natural gas) have a temporary feedstock advantage over international competitors using naphtha (oil), the sheer uncertainty has caused a wave of stalled projects and construction delays.

• Logistics & Freight: Diesel prices are up roughly 50% since the war began. This isn’t just a line item; it is an existential threat. Nearly 18% of small U.S. truckers have already halted or scaled back operations. In the competitive trucking industry, fuel surcharges are passed through to shippers within a week, eventually hitting the price of every consumer good on the shelf.

Infrastructure as a Symptom

If you want to see how this physical strain is manifesting domestically, look at the plumbing. Oneok (OKE) recently moved to reverse a segment of the Magellan pipeline to push product toward the Gulf Coast. Why? Because our refining complex is being drained by an unprecedented global pull.

In March, U.S. clean petroleum product exports hit a record 3.11 million barrels per day, surging to replace disrupted Middle East supplies for Europe, Asia, and Africa. We are not an island; we are the world’s balancing refiner. When the global system is short, our domestic molecules move to the highest bidder.

The Leadership Lesson: Trimming the Sails

I once worked with a Global SVP who was in a state of near-paralysis over a sudden shift in political and regulatory conditions. She was looking for a way to reverse the tide. I told her: “You can’t change the weather, but you can trim the sails.”

Leadership isn’t about wishing for a better hand; it’s about the Trader’s Mindset. It is about taking the terms and conditions you’ve been dealt and finding the opportunity within those constraints. A true operator removes emotion from the equation, digests the data midday on silent, and monitors the waves before they hit the shore. Optimism is useful for culture, but it is not a risk-management framework.

Why Q1 is a False Signal

As we enter the Q1 earnings season, expect a “mixed bag.” But be careful how you read it. The conflict began at the end of February, meaning the first quarter only had one month of direct exposure—much of which was cushioned by pre-war cargoes and inventory levels. If Q1 results look buoyant, it isn’t proof of immunity; it is proof of the lag. In the physical world, the bill for March and April won’t fully come due until Q2 and Q3.

Conclusion: The Mirror and the Telescope

The mistake is assuming that a recovery in market sentiment equals a recovery in the physical world. As the economist Charles Gave famously noted:

“The financial markets are a telescope into the future, but the supply chain is a mirror of the past. When the mirror breaks, the telescope is useless.”

Right now, the mirror is broken. We are looking at a future through the telescope that the physical world simply cannot deliver yet. The market can price hope in an afternoon, but the real economy takes a quarter to tell the truth.

Stay rational, watch the input costs, and remember: molecules move at their own pace, regardless of what the headlines say.

The Point Taken:

The real economic impact of the Iranian conflict is only now beginning to clear the ports. Do not mistake a relief rally in the paper market for a resolution in the physical one. Watch the vibrations — the things like Oneok flow reversals, trucking exit rates, and jet fuel crack spreads and so much more — those are the real indicators of the strain yet to come.

Subscribe now

By Bryan Kaus

“As a trader you often walk on the blade. Be careful and don’t step off.” — Marc Rich, as quoted in The King of Oil by Daniel Ammann

There are moments when markets stop trading narrative and start pricing constraint.

This is one of them.

A great many people looked at the volatility in crude across March and saw what they usually see: a geopolitical spike, a temporary dislocation, a price move that would mean-revert once the situation stabilized. The futures curve reflected that assumption. Prompt prices surged, but the back of the curve stayed lower, an implicit bet that this, too, would pass.

That bet may be wrong.

The issue is not that the market failed to notice physical tightness. It noticed. What it underestimated was duration. And in a market defined by physical constraint, duration is everything.

Because the longer the disruption persists, the tighter the system becomes. Inventories draw. Replacement economics harden and get sticky. Procurement behavior shifts from opportunistic to defensive. A few tankers slipping through under friendly flags do not offset global thirst.

The system is still short. And everything downstream from crude, freight, products, refining runs, petrochemical feedstocks, logistics capacity, and industrial planning, tightens with it.

That is what many participants, and certainly many commentators, have not fully internalized.

So rather than being a fear trade waiting to normalize, this is a physical constraint that compounds.

The Curve Was Telling the Truth

Backwardation is one of those terms that is easy to define and often poorly understood.

In plain English, it means the market is paying up for immediacy. The prompt barrel is worth more than the future promise. Time is no longer a source of comfort. Rather, it is part of the problem.

That is exactly what you would expect in a physically constrained market. As of this week, Brent futures were trading around $110, up more than 60% since the war began. But that number understates the stress. Physical spot Brent, actual cargoes for near-term delivery, recently traded above $140, a premium of more than $30 over paper. The front-month WTI spread over the second month hit $16.70 per barrel, the widest ever recorded.

This is the market’s way of saying: I need the barrel now.

A market in contango can tolerate delay. Storage has value. Patience has value. A market in backwardation is telling you something different. It is telling you that prompt supply has become scarce enough that the near-term barrel carries a premium simply because it solves an immediate problem in the physical world of demand.

And the back of the curve, still priced for normalization, may be telling you more about hope than reality.

Paper Logic in a Physical Market

The volatility across March reflected a market trying to price something it does not handle well: a disruption with no clear end date.

Financial markets are built to reprice stories. They do it instantly, often violently. But the assumptions embedded in that repricing tend to anchor to historical patterns. The pattern says: supply shocks are temporary, arbitrage restores order, logistics adjust, and prices normalize. Usually, that pattern holds under normal disruptions.

This time, it may not. This is not normal.

Because physical systems cannot reconfigure themselves on command. Ships still have to sail. Cargoes still have to be scheduled. Crude grades still have to fit refinery slates. Storage still has limits. Logistics still have sequence. And when a chokepoint controlling roughly 20 percent of global seaborne oil goes effectively offline, there is no quick workaround. There is only reallocation of remaining supply, competition, and strain.

That is why geographic distance does not insulate anyone. An economy may sit far from the Strait geographically and still remain tightly tied to the pricing system, the freight system, the trade system, and the inflation system that follow from it. We do not live in isolated commodity ponds. We live in one global pool of risk transmission.

And the longer the constraint persists, the more that risk compounds.

Duration Changes Everything

It is important to say clearly what we do not yet know.

We do not know the full extent of the damage to infrastructure across the region. We do not know how quickly assets, routes, and normal shipping patterns recover. We do not know which secondary disruptions prove transitory and which begin to harden into longer-duration planning assumptions.

But what we do know is that the structure of the disruption matters more than the initial price spike.

A short disruption is painful but manageable. Inventories absorb the shock. Procurement adjusts at the margin. Prices spike and then retrace. That is the pattern the back of the curve is betting on.

A prolonged disruption is different. It changes working-capital needs. It changes procurement behavior from just-in-time to just-in-case. It changes inventory strategy. It changes the way refiners think about slate flexibility and the way traders think about freight exposure and hedging. It changes inflation expectations. And eventually, it changes policy.

The IMF has already signaled what is coming. Even a rapid end to hostilities, IMF Managing Director Kristalina Georgieva said this week, would still mean lower growth and higher inflation. “All roads now lead to higher prices and slower growth.” The impact, she noted, ripples through oil and gas shipments and into related supply chains: fertilizer, food, and the most vulnerable importing economies.

And if the war is not rapid? If the constraint persists into a second month, a third?

Then the back of the curve is not a forecast.

It is a hope.

The Shock Does Not Stay in One Lane

This is one of the most underappreciated features of energy shocks.

They do not arrive everywhere at once.

They propagate.

First through crude. Then through freight. Then through diesel and jet. Then through transport, industrial inputs, chemicals, margins, and eventually broader prices and expectations.

The point is not only that costs rise.

It is that rhythm is disturbed.

Modern economies need rhythm more than drama. They need enough stability to plan, finance, hire, ship, hedge, and build. They do not need violent oscillation in basic inputs. Energy shocks rarely remain confined to energy. They move outward, often in waves, and their effects tend to linger longer than the initial headlines do.

We are already seeing that transmission in operational terms. Vietnam Airlines has cut domestic flights. AirAsia X has tankered fuel before flying into tighter markets. Air India has added refueling stops. That is exactly how these shocks move: from screens, to schedules, to costs, to broader economic consequence. And all of those roads ultimately lead to consumers’ wallets.

That is why the inflation question matters. Not because every spike instantly becomes a permanent inflation regime, and even now some downward revisions to growth remain relatively tempered. But because once higher energy costs begin working their way through freight, products, and supply chains, the echo can persist even after the first shock has faded from the front page.

Policymakers are already acknowledging as much. New York Fed President John Williams noted this week that higher energy prices affect both inflation and disposable income simultaneously, creating a dual pressure on economic demand. The pass-through, he said, will take months to work through airfares, goods, and services.

This is the boring nature of monetary policy, and it is boring for a reason. Stable systems build better than chaotic ones.

A plant can tolerate some rain and some sun.

It does not grow well in a hurricane.

The Human Reality Still Matters

It is worth stating something that should not need saying but often gets lost once market commentary takes over.

Behind the curves, the freight rates, the margins, and the policy debates sits the older and harder reality of war itself. Human beings and civilian systems bear the cost first and longest.

That does not make the market consequences less important. It is part of why they matter. When physical infrastructure is threatened or damaged, the spreadsheet is not the first casualty. The human world is. Any serious business-minded analysis should be able to acknowledge that without turning itself into a slogan.

How a Derivatives Strategy Can Still Go Wrong

This is where the market-structure lesson becomes more than academic.

A sophisticated company can still lose a great deal of money in a market like this. Not necessarily because it failed to understand oil. But because it structured its risk around a more normal kind of dislocation, and a more normal duration.

That distinction matters.

Phillips 66’s recent 8-K filing is a useful example precisely because it does not read like a speculative confession. The company routinely carries net short derivative positions in crude, refined products, NGLs, and renewables feedstocks as economic hedges for certain physical positions tied to its assets. It also disclosed that sharp price moves created approximately $900 million of preliminary pre-tax mark-to-market losses, while the associated increase in current market value of the underlying physical inventory was not reflected in book value. As of March 31, the company’s net short crude-and-products derivatives position was about 50 million barrels, and the quarter saw roughly $3 billion of cash collateral outflow on derivative positions.

That is the more serious point. In a physically constrained market, a hedge does not have to be reckless to become painful. It only has to be mismatched in timing, basis, or liquidity.

A firm may hedge benchmark price exposure but remain vulnerable to basis blowouts between the benchmark and the actual delivered grades it needs. It may hedge the flat price and underestimate what happens when prompt spreads explode. It may protect one layer of economic exposure while leaving itself open to freight, quality, timing, or replacement-cost dislocation. It may be directionally reasonable and still get crushed by collateral calls or the speed of mark-to-market losses before the commercial system has had time to reprice.

That is the trap. The paper loss can arrive immediately. The physical offset may be economically real, but slower, partial, or invisible in accounting at the moment the collateral is due. That is how a strategy intended to dampen volatility can become a source of earnings damage and cash strain.

Experience can help in normal markets. It can also hurt in abnormal ones. Why? Because experience often teaches you how markets usually dislocate. Usually, supply finds a path. Usually, arbitrage restores order. Usually, benchmarks and physical reality do not drift too far apart for too long. Usually, a position that looked prudent in a calmer regime does not become a near-term earnings and funding problem.

But when the disruption is both geopolitical and physical, and when it persists longer than the models assumed, intuition can become a liability. That is when prompt spreads gap wider than expected. That is when basis detaches. That is when replacement economics overwhelm the elegance of the paper hedge. And that is how a strategy designed to reduce risk can, in the wrong regime, become a source of it.

How To Navigate It Better

The lesson here is not that firms should never hedge. It is that they should hedge with firm grounding in physical constraint and more respect for duration risk.

In a market like this, the right posture is not heroism or speculation. It is layered discipline.

That means respecting the difference between benchmark price risk and delivered-system risk. It means keeping tenors aligned to actual commercial exposure. It means thinking through prompt-spread risk, basis risk, quality differentials, freight, and liquidity, not just the headline direction of crude. It means stress-testing not only whether the position works if the market moves against you, but whether the company can withstand the collateral, accounting, and earnings consequences if the move is fast, disorderly, and physically grounded.

It also means leaving room for reality. If the physical market is screaming scarcity, the prudent response is not to lean too hard into the comforting assumption that price will normalize on your schedule. It is to recognize that the benchmark may move sharply, the physical barrel may move more sharply still, and the bridge between the two can become expensive very quickly.

In practical terms: less directional bravado, more basis awareness, tighter mapping of paper to physical, stronger liquidity planning, and more humility about how long a market can remain dislocated when actual molecules are trapped.

What This Means for Strategy

All of this leads back to a broader business point.

Physical markets still discipline the actual trade.

In periods of stability, it is easy to believe everything can be abstracted. It is easy to mistake clean models for durable understanding. It is easy to think that with enough financial sophistication, most risks can be transformed into something manageable.

Then the system tightens.

And the old truths reassert themselves.

Balance-sheet strength matters. Working-capital flexibility matters. Storage matters. Supply diversity matters. Operational optionality matters. Management teams that understand not just price, but operational realities, logistics, timing, quality, and physical substitution, become critical in times like these.

In calmer periods, all of that can look boring.

In stressed periods, it looks like competence.

This is why the conversation should not end with whether the market overreacted or whether the price spike was “just fear.” The better question is what the move revealed and what it continues to reveal the longer the constraint persists.

To me, it revealed once again that when the physical world gets tight, the penalties for confusing financial elegance with operational resilience can be severe.

The practical lesson is not that every shock can be predicted. It is that serious operators should build and manage as though physical constraint is always possible, even in periods when markets look calm and frictionless.

That requires a different mindset. It means treating logistics, working capital, storage, procurement flexibility, and balance-sheet strength not as dead weight, but as strategic assets. It means understanding that benchmark prices are useful, but they are not the whole system. In stressed markets, delivered reality can separate violently from the paper hedge. It means resisting the temptation to sound clever when the better posture is disciplined. When the physical market is tightening, the goal is not to be heroic. It is to remain solvent, flexible, and positioned to act while weaker hands are forced into reaction.

Most of all, it means remembering that the first move is rarely the last move. Energy shocks propagate. They move through freight, products, input costs, margins, and expectations. The firms that navigate them best are usually not the ones with the flashiest market view. They are the ones with enough liquidity, optionality, and operational understanding to absorb the first-order shock without becoming captive to the second- and third-order effects.

That is the real lesson.

Not that volatility exists.

But that physical systems punish overconfidence, and they punish it more severely the longer the constraint compounds.

The Point Taken:

Many people saw the March volatility and priced in a reversion.

That may have been the wrong bet.

Because this is not a fear trade waiting to normalize. It is a physical market repricing scarcity in real time, and the longer the disruption persists, the tighter everything downstream becomes.

That is what backwardation was saying. That is what spot premiums were saying. That is what the $30-plus differential between paper and physical was saying.

And that is why the shock will not stay confined to commodity screens. It will move outward through freight, products, margins, inflation, and eventually policy.

The lesson is not merely that energy still matters way more than it sometimes gets credit for. It is that physical constraint still matters, and duration determines whether it fades gently or compounds dramatically and painfully.

Paper can move instantly. Molecules cannot.

So the right way to think going forward is not to chase perfect predictions. It is to build resilience before the next disruption arrives: stronger balance sheets, better liquidity discipline, tighter mapping between paper risk and physical exposure, and more respect for how timing, basis, and replacement cost behave when the system is under strain.

That is what serious risk management looks like.

That is what serious strategy looks like.

The market can argue about the story.

The curve usually tells you when the physical world has started voting.

Subscribe now

“A productivity gift reveals management. It does not replace it.”

By Bryan J. Kaus

Much of the discussion around AI still focuses on adoption.

Who is using it.
Who is behind.
Who is automating faster.
Who is reducing cost first.

Those questions matter.

But they are not the most important ones.

The more serious question is what management does with the productivity dividend once it arrives.

Because when a company becomes materially more productive, there are two ways to respond.

One is to cut headcount, trim cost, optimize the existing box, report improved margins, and congratulate yourself on efficiency.

The other is to ask a harder question:

What can we now build, enter, improve, or scale that we could not pursue before?

That is the real divide.

And in the AI era, I suspect it will become one of the clearest tests of management quality.

The Easy Move

There is nothing inherently irrational about using productivity gains to reduce cost.

Sometimes that is exactly the right move. Some organizations are bloated. Some workflows are wasteful. Some tasks should be automated. Some overhead deserves to disappear.

Any serious operator should be willing to say so plainly.

But too many management teams stop there.

They treat a productivity gain as though its highest use were simply labor subtraction. If the company can produce the same output with fewer people, the logic seems obvious: remove the people, keep the output, expand the margin.

From a narrow financial perspective, that can look prudent.

From a broader strategic perspective, it can be remarkably unimaginative.

Because productivity is not only an opportunity to shrink the cost base. It is an opportunity to widen the possibility set.

And that is where the real distinction begins.

A Productivity Dividend Is a Management Test

This is why the AI conversation is so often framed too narrowly.

AI is not merely a technology story.

It is a management story.
A capital allocation story.
An operating model story.
An imagination story.

When a company receives a productivity windfall, it has effectively been handed a strategic dividend. Time is freed up. Labor intensity falls. Analysis becomes faster and cheaper. Throughput can rise. Friction declines. Coordination improves.

That newly available capacity can be harvested, or it can be redeployed.

That choice is not technological.

It is managerial.

And the answer reveals whether leadership sees the enterprise as something to be optimized within an inherited box, or as a platform from which to build something larger.

Builders and Financial Engineers

This is where the distinction becomes useful.

Financial engineers look at productivity gains and ask how quickly they can turn them into lower expense.

Builders look at productivity gains and ask what new value can now be created.

Financial engineers focus on extraction.

Builders focus on redeployment.

Financial engineers optimize around the edges of the current model.

Builders use the gain to widen the model itself.

The financial engineer’s approach is familiar:

We can automate portions of finance, legal, procurement, customer service, or middle management.
We can flatten the structure.
We can reduce headcount.
We can improve margins.
We can satisfy the market for a few quarters.

Again, none of that is automatically wrong.

But it is incomplete.

The builder’s approach sounds different:

We can deepen customer coverage.
We can accelerate product development.
We can improve forecasting, maintenance, logistics, and commercial coordination.
We can strengthen the data spine of the business.
We can pursue an adjacent segment that was previously too labor-intensive.
We can redeploy people into judgment-heavy work that compounds advantage over time.

That is what actual operators do.

They do not only ask what can be taken out.

They ask what can now be put in.

The Difference Between Preservation and Creation

Stewardship matters. Discipline matters. Cost control matters. A serious enterprise cannot be run on romanticism alone.

But there is a critical difference between preserving value and creating it.

A company can become more efficient without becoming more formidable.

It can become leaner without becoming more intelligent.

It can improve quarterly optics without improving its long-term position.

That is why productivity gains are so revealing. They expose whether management has a theory of expansion or merely a habit of optimization.

Leaders who only know how to take cost out tend to treat every gain the same way. They reduce expense, protect the near term, and call it discipline.

Sometimes that is discipline.

Sometimes it is simply a failure of imagination dressed up in the language of prudence.

Real builders think differently.

They understand that if the system has become more productive, the question is not only what can be removed.

It is what can now be attempted.

Edge Optimization Is Not a Growth Philosophy

This matters because many companies have spent years optimizing around the edges.

Flattening here. Outsourcing there. Eliminating a layer. Tightening a workflow. Rationalizing a function. Pushing another initiative through the machinery of efficiency.

Some of that was necessary.

But edge optimization is not the same thing as building.

In fact, many organizations have become so conditioned to think in terms of cost takeout that they no longer recognize a productivity gain as an offensive asset. They see it as a chance to refine the current machine rather than a chance to expand its reach.

That is a problem.

Because the firms that compound over time are rarely the ones that only get tighter. They are the ones that learn how to convert discipline into new capability.

They use stronger economics to fund stronger positions.

They use better throughput to deepen the moat.

They use freed capacity not just to defend margins, but to build the next leg of the business.

That is a very different mentality.

Why Markets Often Reward the Smaller Move

One reason this distinction gets obscured is that markets often reward the smaller move faster than the bigger one.

Cost cuts are legible.

Headcount reductions are legible.

Near-term margin gains are legible.

Analysts can model them quickly. Management can explain them cleanly. The optics are immediate.

Redeployment is harder.

New initiatives carry uncertainty. Adjacent expansion takes time. Capability-building is messier than cost reduction. The numbers are less immediate and less tidy.

Which means many management teams will be tempted to choose the version of AI that flatters them fastest.

That temptation should not be confused with strategy.

And it should not be confused with leadership.

Real leadership is not merely producing a cleaner quarter.

It is converting a productivity gain into a stronger enterprise.

What Builders Actually Build

So what does builder behavior look like in practice?

It means using productivity gains to go on offense.

That may mean building better internal tools and data infrastructure. It may mean stronger commercial intelligence. It may mean better maintenance planning, faster customer response, improved asset utilization, or more disciplined capital allocation. It may mean accelerating innovation. It may mean entering an adjacent market, launching a new service line, or deepening coverage in places the business previously could not reach economically.

Most of all, it means seeing efficiency not as the destination, but as fuel.

Fuel for new output.
Fuel for better strategy.
Fuel for stronger positioning.
Fuel for compounding capability.

That is the distinction.

Productivity is a gift.

Imagination determines whether it becomes a dividend or a dead end.

The Next Sorting Mechanism

Over the next several years, I suspect AI will function as a revealing force.

It will reveal which management teams actually know how to build.

Not just how to trim.
Not just how to narrate.
Not just how to produce better optics.
But how to expand the frontier of the business when newly available capacity shows up.

That is a much rarer skill.

And it is the one that should matter most.

Because firms that merely optimize around the edges may improve short-term economics while quietly hollowing out imagination, competitiveness, and strategic depth.

The firms that truly win are more likely to be those that treat productivity gains as a platform for expansion rather than contraction.

They will understand that the real question in the AI era is not:

How many people can we eliminate?

It is:

What can we now build that we could not build before?

The Point Taken:

AI is not just a technology test.

It is a management test.

More specifically, it is a test of whether leaders think like builders or financial engineers.

Financial engineers receive a productivity gift and use it to shrink the organization more neatly.

Builders receive a productivity gift and use it to expand the organization’s capacity to create value.

That is the real divide.

The next winners will not simply be the companies that adopt AI.

They will be the companies led by management teams with enough imagination and courage to redeploy the AI dividend into growth, resilience, capability, and new forms of value creation.

Productivity without imagination is just better shrinkage.

And better shrinkage, by itself, is not a strategy.

Subscribe now

Share

“Give a man a dole … and you save his body and destroy his spirit. Give him a job and you save both body and spirit.” — Harry Hopkins

By Bryan J. Kaus

A great deal of the discussion around artificial intelligence, universal basic income, and the future of work is too shallow.

Not because the disruption is imaginary.

It isn’t.

And not because the financial risks are trivial.

They aren’t.

But because too much of the debate begins with productivity and ends with income replacement, as though the central question is simply how many roles can be compressed, how many salaries can be removed, and how large a transfer may be needed to keep the system calm.

That is not the only question.

And in many ways, it is not even the right one.

The more serious question is this:

What happens when a society begins solving for efficiency while quietly stripping away purpose?

That, to me, is where the real issue begins.

The Simplification at the Center of the Debate

There is a growing temptation among policymakers, technologists, and corporate leaders to treat the next phase of disruption as though it were mainly a distribution problem.

If automation displaces enough workers, then the answer, we are told, is straightforward: provide some form of basic income, stabilize demand, reduce unrest, and move on.

Neat. Elegant. Technocratic.

And far too thin.

Because income is not the only thing work provides.

Work is not merely a mechanism for distributing cash. It is one of the principal ways people build competence, organize time, earn respect, develop identity, and remain tied to a broader civic and economic order. Brookings makes the point plainly: work is not just a source of income, but also of identity and self-esteem, and the factors most associated with meaningful work — autonomy, relatedness, and competence — matter far more than compensation and benefits alone.

That is why the thinner versions of the UBI debate feel incomplete to me.

Not because material support is unimportant. It is. Food matters. Shelter matters. Stability matters. Any serious person should be able to admit that social fracture becomes more likely when large numbers of people lose economic footing at once.

But too much of the conversation assumes that the primary human loss from disruption is wages.

In many cases, the deeper loss is role.

Why This Debate Is No Longer Theoretical

This is not purely academic anymore.

Reuters has reported that 2026 has already brought another wave of major layoffs tied to efficiency pushes and rising adoption of artificial intelligence tools. Amazon confirmed it would cut roughly 16,000 corporate roles in January. Block cut nearly half its staff, with CEO Jack Dorsey pointing explicitly to AI tools and smaller teams. Meta is reportedly planning layoffs that could affect 20% or more of its workforce as it offsets massive AI infrastructure spending. Atlassian cut roughly 10% of its workforce as it pivots toward AI and enterprise sales.

That does not prove AI is a fraud.

It is not.

Nor does it prove labor markets will never adjust.

Historically, they often do.

But it does tell us something important: we are no longer talking about a hypothetical future in which intelligent systems might begin to alter the labor equation. The adjustment is already underway in boardrooms, budget reviews, and operating models.

And once that is true, the question becomes larger than whether some kind of support will be needed.

The question becomes whether a society can remain healthy when a growing share of its people are treated as economically redundant.

The Human Question Beneath the Economic One

This is where the discussion becomes less fashionable and more serious.

For all the utopian talk that technology will free humanity for leisure, creativity, and self-actualization, there is a harder reality that tends to get ignored: many people do not flourish in the absence of structure.

They drift.

They detach.

They lose confidence.

They lose standards.

They lose the reinforcing experience of solving problems, being counted on, and seeing evidence that they matter to the functioning of the world around them.

That is not a moral judgment.

It is a systems observation.

A healthy society does not run on purchasing power alone. It also runs on responsibility, discipline, aspiration, routine, and the quiet dignity that comes from being useful.

The same society that celebrates freedom often underestimates how much of social order depends on shared routines of contribution. Work may be imperfect. Many jobs are tedious. Some deserve to be automated. Some organizational structures absolutely need to be redesigned.

But a society that removes work without replacing purpose is not becoming more advanced.

It is becoming more fragile.

What the Cash Research Actually Suggests

Even some of the research around unconditional cash points in this direction.

OpenResearch found that recipients of unconditional cash transfers were slightly less likely to be employed and worked an average of 1.3 fewer hours per week than control participants. At the same time, recipients were more likely to be actively searching for a job, and among job seekers were more likely to say that interesting or meaningful work was an essential condition of any job they would accept.

That is an important nuance.

The point is not that direct support has no place.

The point is that giving people cash does not erase the human desire for useful, meaningful contribution.

The body may be sustained.

The deeper need to matter remains.

We Have Seen Versions of This Before

History does not repeat exactly.

But it does rhyme, often uncomfortably.

When communities lose their productive base, the damage is rarely confined to wages. What tends to disappear along with the jobs is pride, continuity, confidence, institutional memory, and the local sense that effort leads somewhere.

That is one reason this debate cannot be reduced to whether a monthly payment is enough to keep households solvent.

Solvency matters.

It is not the whole story.

During periods of deep economic rupture, the challenge was never only how to preserve bodies. It was also how to preserve spirit. That was the deeper insight behind work-relief logic during the Depression. As Harry Hopkins put it, direct relief might save the body, but a job could save both body and spirit.

One need not romanticize every New Deal program to understand the force of that point.

The issue was never just sustenance.

It was purpose.

Why This Is Not Really About AI Alone

This is also why I do not think this should be treated as an AI story alone.

AI is simply the newest and most visible catalyst.

The broader issue is how modern institutions think.

For years, leaders have been trained to see labor primarily as cost, process primarily as throughput, and technology primarily as a tool for compression. So when a new capability appears that can draft, summarize, compare, classify, synthesize, code, and automate, the first instinct is often not institutional design.

It is labor subtraction.

How many fewer people?

How much faster?

How much cheaper?

How soon?

Those are not illegitimate questions.

But they are incomplete ones.

Because an institution can become leaner while also becoming hollower. A company can improve short-term efficiency while weakening long-term capability. A society can become more productive on paper while becoming less stable in practice.

That is the part many of the loudest voices still underestimate.

A Safety Net Is Not a Civilization

To be clear, this is not an argument against support.

Transitions can be brutal. Markets do not allocate pain evenly. Families cannot eat theory. There may well be periods where direct assistance is necessary, justified, and unavoidable.

Fine.

But we should be careful not to confuse emergency cushioning with a durable social philosophy.

A safety net is a support structure.

It is not a vision of human flourishing.

And when universal basic income is presented as though it were a sufficient answer to widespread displacement, it often reveals an oddly diminished view of the person. It assumes that if the check clears, the deeper problem is solved.

I do not think that is true.

Because people do not live by consumption alone.

They need structure.

They need competence.

They need responsibility.

They need to matter.

What Serious Leaders Should Actually Be Building

This is where the leadership question comes in.

The obligation of leadership is not merely to manage displacement after the fact. It is to design systems that preserve dignity through contribution.

That means building organizations and economies in which technology increases capability without quietly erasing the human role altogether.

Some jobs should disappear.

Some tasks should be automated.

Some workflows should absolutely be redesigned.

But serious leaders should not be asking only how many roles can be removed. They should also be asking what new systems of value can be built in which people remain useful, accountable, and capable.

That is a very different posture.

It points toward augmentation rather than blind subtraction.

It points toward hybrid operating models in which machines increase leverage but people remain close to judgment, accountability, exception handling, relationship management, and the messy reality of execution.

It points toward educational models built around competence rather than generic credential accumulation.

It points toward skilled trades, infrastructure, local enterprise formation, industrial capability, care work, technical oversight, and other forms of contribution that keep people attached to real responsibility rather than passive dependence.

In other words, the real design problem is not how to preserve every legacy role.

It is how to modernize without hollowing ourselves out.

The Deeper Leadership Failure

For years, executives have said that people are their greatest asset.

Yet when pressure rises, the language often shifts quickly: rationalization, flattening, optimization, redeployment, efficiency.

Some of that language is unavoidable. Leaders do have to allocate capital responsibly. Not every role should be preserved. Not every structure deserves to survive.

But if AI becomes a moral cover for headcount reduction without a serious reckoning with the broader social implications, then we should be honest about that.

Efficiency is not the same thing as stewardship.

A company may be entirely rational in deciding it needs fewer people.

But if enough institutions make that same decision at once, the result stops being a firm-level productivity story.

It becomes a societal one.

And societies cannot be managed indefinitely as though they were spreadsheets.

The Point Taken:

The real risk in an AI-shaped economy is not simply that machines may do more work.

It is that leaders may begin treating human beings as though income were the only thing work was ever for.

That is wrong economically, wrong socially, and wrong at the level of basic human nature.

Yes, societies may need stronger safety nets during periods of disruption.

But a safety net is not a substitute for purpose, and a transfer is not a substitute for dignity.

The harder challenge — and the more important one — is building institutions, industries, and systems of value in which technology increases productivity while human beings still develop mastery, still carry responsibility, and still believe they have a meaningful stake in the future.

The leaders who matter most in the next era will not be the ones who replace people fastest.

They will be the ones who build systems in which more capable people, supported by better tools, create more value without losing the human substance that made the system worth building in the first place.

Because the moment a society starts solving only for efficiency, it risks discovering too late that what it optimized away was not merely labor.

It was meaning.

Subscribe now