Kth Connection™ Podcast

Kth Connection

Kth Connection studies the overlooked patterns behind business, behavior, risk, technology, communication, and reinvention — turning data into insight and insight into impact. kthconnection.substack.com

  1. Aug 2

    What Is Left When the Tool Is Gone

    The martial arts have been a strange thing from the beginning. Look at how most men move through the world. They carry a quiet assumption that size settles the question, that the bigger and heavier man wins the real fight, and that training is ornament on top of what nature already decided. Across most of God’s creatures the assumption holds. Watch a few hours of David Attenborough and you will see the rule enforced in every habitat on earth. The larger bull holds the harem. The heavier male keeps the ground he is standing on. Mass and reach decide, and everything else in the animal arranges itself around that verdict. So most men pursue little formal training, and often no casual training either. Their bodies have already answered the question for them, or so they believe. Which is why the man who takes it up anyway is doing something peculiar. At its most ordinary the decision buys him a bit of lively exercise. At its highest it becomes a monument that a man builds to a realization about himself, which is that his distinction among God’s creatures is his capacity to go past what he was issued at birth. He can improve. He can decide to apply himself and become something his frame never promised. Very few animals get that option. The man who trains has noticed that he does. The bees There is a hard limit on how fast a bee can change. The Asian giant hornet raids honeybee colonies for a living. One hornet can kill dozens of bees a minute, biting the heads off workers and carrying the larvae home to feed its own young. A few dozen hornets working together can empty a hive of thirty thousand inside a few hours. The hornet’s armor is thick enough that a bee’s sting cannot reach anything that matters. On equipment alone the matchup is settled before it starts. The European honeybee, brought to Japan in the Meiji era for commercial beekeeping, meets that hornet and does the only thing it knows how to do. It stings. The stings accomplish nothing. The colony gets wiped out. The Japanese honeybee, native to the hornet’s own country, answers differently. When a scouting hornet arrives at the entrance, five hundred or more workers pile onto it at once and pack themselves into a tight ball with the hornet sealed inside. Then they vibrate their flight muscles, the same muscles they use to fly and to warm the hive through winter, and the temperature inside that ball climbs to roughly 46 degrees Celsius within five minutes. Their own carbon dioxide and humidity accumulate in the sealed space and drag the hornet’s tolerance down further. The hornet dies somewhere around 44 to 46 degrees. The bees can hold out to 48 or 50. The entire margin of victory is a couple of degrees, and the hornet is finished inside ten or twenty minutes. Same body. Same sting, never used. What changed was the answer. I should be honest that this is not a recent invention. Researchers began publishing on it in the 1980s, and the behavior is co-evolved, and no clever hive improvised it on the day a camera crew showed up. The lesson survives that correction, and I think it sharpens it. Here are two populations of honeybee facing an identical enemy with identical hardware. One of them has the technique and one of them does not, and that alone decides which hives are still standing in October. The technique is the whole margin. Killer bees make a smaller version of the same point. Africanized bees do carry a real genetic difference, descended from African stock that escaped a breeding program in Brazil in 1957 and crossed with local European lines. But their venom is chemically the same as any other honeybee’s. What makes them dangerous is the response pattern. They trip at a lower threshold and commit far more of the colony to the fight. They will pursue an intruder for a quarter mile where a European colony would have quit at the fence line. Same weapon, held by a different attitude. Stubbornness carries in the animal kingdom more often than the size rule would predict. The honey badger has loose, thick skin that lets it twist around inside the grip of something much larger and get its teeth into whatever is holding it, along with a tolerance for venom that would kill most animals its size. It has been filmed refusing to leave a carcass with lions walking in. A wolverine at forty pounds will run bears off a kill. What those animals are running is an attitude, and the attitude is worth more to them than the twenty pounds they are giving away. None of them chose it. The badger was born into its skin and the bee inherited its answer over a span of time none of us can wait out. A man gets to make the decision himself, and he gets to make it inside one lifetime, which is the part I keep coming back to. The tool in the hand The history I was taught, and repeated for years without checking, begins with civil disobedience. The story goes like this. In the Ryukyu Kingdom, in what we now call Okinawa, weapons bans came down from above. One is usually attributed to King Sho Shin in the early 1500s. Another followed the Satsuma invasion of 1609, when the Japanese took the islands and moved to keep arms out of local hands. Only the elite were permitted the official weapons of the age. So the people barred from carrying steel picked up what they were still allowed to own. The rice flail became the nunchaku. The handle of a grain mill became the tonfa. The sickle stayed a sickle and became the kama. The carrying pole across a man’s shoulders became the bo. It makes a good story. Poor farmers training in secret, turning the instruments of their labor into instruments of resistance. I have since learned that historians are not comfortable with it. The men whose forms actually survive were largely pechin, the lower and middle nobility of the kingdom, along with merchants and government officials. They served as police and bodyguards. Many of the weapons have Chinese antecedents that predate the bans entirely, arriving through the trade routes that made Ryukyu wealthy in the first place. Several scholars now argue the farming-tool account was assembled after the fact, to give weapons of uncertain parentage a lineage worth claiming. Something survives the correction, though, and I think it is the part worth keeping. The transfers of weapons did happen in some form. And when the kingdom collapsed and the pechin lost their positions, those men went looking for work. The traffic ran the other direction from the story I was told. Trained warriors became laborers, and they carried what they knew into the new work, and they learned to fight with whatever their new lives put in their hands. The direction reverses. The condition does not. A man with training and without permission will find the weapon inside the tool. If you want the version with a paper trail, look at Brazil. Capoeira developed among enslaved Africans and their descendants, and it was criminalized outright in the penal code of the new republic in 1890, two years after abolition. The eighteenth chapter of that code dealt with vagrants and capoeiras together. Practicing in the street carried a jail sentence, and foreigners caught doing it could be deported after serving the term. Men were arrested in numbers and sent to penal colonies. The art went underground and stayed there for four decades, kept alive in the margins by people the state had already decided were criminals. It came back in the 1930s, when Mestre Bimba performed for the governor of Bahia and convinced the authorities that the thing they had outlawed had cultural value. He opened the first academy in Salvador in 1932. By 1940 the new penal code did not mention capoeira at all. That is a fighting art surviving as an act of disobedience, documented, with dates and decree numbers. The Okinawan version may be half legend. The impulse the legend describes is real enough that another country wrote it into criminal law. How it spreads Having mastery of any particular implement in this way leaves a man holding a responsibility. Sometimes that responsibility runs no further than a father to his daughter or his son. Sometimes a friend recognizes the skill, and has the need, and asks. What passes between them is rarely the implement itself. What passes is the knowledge of how an ordinary object in the hand, shaped by the discipline of practice, stops being ordinary. The extension of that happens without anybody planning it. The son or the friend ends up serving the regime anyway, by conscription or by choice, and gets trained by formal warriors in formal weapons under formal instruction. He does not arrive empty. He brings the thing his father showed him in the yard. What comes back out of that man is a mix, and the mix can exceed both what he walked in with and what the army handed him. Then he goes home and shows somebody. So the spread of training begins peculiarly and inefficiently, moving between friend and adversary, generation past generation, in a direction no institution chose and no authority could easily interrupt. Everyone is eventually without This has always been a principle the poor had to employ, and the rich need it just as badly, because tools go missing. We are separated from them by surprise, or by the plain fact of standing somewhere we did not plan to be standing. What is left is the problem of mustering the little we have and using it to inflict enough harm in defense to stop what is coming at us, whether that is an animal or a man. Worse than the missing tool is the empty ground. No rock. No stick within reach. Nothing to pick up, and across from you someone stronger, sometimes mounted and armed, sometimes with three friends behind him. The situation screams the need and offers no answer. Right and wrong do not control that moment. Training and the will of the man control it. I am right I once heard a soldier on Jocko Willink’s podcast think back on an incident during wartime. He was a young man from the United Sta

    What Is Left When the Tool Is Gone
  2. Aug 1

    Nobody Gets Fired

    Most arguments about AI and jobs start with a dramatic premise. Mass layoffs. A displacement event. Some visible rupture you could point to on a chart and date precisely. I want to try a quieter version, because I think the quiet version is the one that is actually happening. Assume nothing dramatic. No layoffs beyond the normal churn. No wage cuts. No collapse in demand. The economy grows. Companies stay profitable. The people who have jobs keep them, and many of them get raises, because AI makes them more productive and their employers want to keep them. Change one variable. When someone leaves, the seat stays empty. Not every seat. Just a meaningful share of them. The role gets absorbed by the team, or by a system, or by some combination that nobody documents formally because documenting it would require a decision and the whole point is that no decision gets made. A req sits unposted. A backfill gets deferred to next quarter, then the quarter after that. Eventually the org chart is redrawn and the box is gone. This is not a hypothetical. It is the stated corporate posture. A survey of more than 350 public-company CEOs and investors, reported by Fortune in March, found roughly two-thirds planning to freeze or cut hiring through the rest of 2026. The Yale School of Management convened a CEO gathering late last year where about a third expected to hire and the rest expected to hold flat or shrink. Klarna ran the pure version of this experiment: a hiring freeze held for over a year, headcount down 22%, most of it through attrition. Meta announced in April that it would not fill roughly 6,000 open positions. The Bureau of Labor Statistics gives you the churn baseline. Somewhere around 5.1 to 5.2 million separations happen every month. That is roughly 61 million exits a year against a nonfarm base of about 160 million. The entire question is what fraction of those exits get replaced, and by whom. So: no crisis, no headlines, no severance packages. Just a slow reduction in the number of people whose earnings are subject to a specific federal tax. I want to work through what that does, because I think the answer is more specific and more uncomfortable than the general anxiety about AI and employment. It lands on one system, on a schedule we can already see, and it puts the federal government in a position it has not had to reckon with yet. What the payroll tax can and cannot reach Social Security is funded by a tax on wages. Employers and employees each pay 6.2% of covered earnings, 12.4% combined, up to a cap that sits at $184,500 in 2026. The self-employed pay the full 12.4%. In 2025, about 185 million people had earnings covered by the program. That produced $1.32 trillion in net payroll tax contributions. Add $58 billion from income taxation of benefits and $69 billion in interest on trust fund holdings, and total program income for 2025 came to $1.449 trillion. Payroll taxes are more than nine-tenths of it. Now consider what that tax does not reach. It does not reach corporate profits. It does not reach capital gains. It does not reach depreciation schedules on a GPU cluster. It does not reach the return on a data center. If a company replaces forty analysts with a software system and books the savings as margin, the payroll tax collects nothing from that margin. If the same company distributes the gain to shareholders, the payroll tax collects nothing from the distribution. The payroll tax is a levy on the act of employing a human being. That was a reasonable design in 1935 and a reasonable design in 1983, when the last major overhaul happened, because the overwhelming majority of national income flowed through wages, and there was no serious prospect of producing economic output without a proportionate number of people on payroll. That assumption is the thing under stress. And you do not have to take my word for it, because the actuaries have already started writing it down. The Trustees track something called the labor share of output: total labor compensation as a fraction of GDP. In the 2025 report they lowered the long-run assumption to roughly 61.2%, down from about 62.8% the prior year. Separately, the share of total labor compensation that falls below the wage cap has slid from 89.6% in 1983 to an average around 82.5% over the last decade. Earnings above the cap grew faster than earnings below it, so a rising share of compensation now sits outside the taxable base entirely. Both of those trends predate ChatGPT by decades. The payroll tax base has been eroding relative to the economy for forty years for reasons that have nothing to do with AI. What AI does is take an existing slope and steepen it. Where the program actually stands The 2026 Trustees Report came out on June 9. The numbers are worth having in front of you. The retirement fund, OASI, is projected to deplete its reserves in the fourth quarter of 2032. One quarter earlier than last year’s projection. At that point, incoming revenue covers 78% of scheduled benefits, which means an automatic 22% cut absent legislation. That is six years from now. If Congress merges OASI with the disability fund, which would require a change in law, the combined program runs to 2034 and pays 83% at depletion. The disability fund itself is in good shape. DI is projected to pay full scheduled benefits through at least 2100. Applications have been low for years and the disabled-worker rolls have been shrinking since 2014. Hold that thought, because it matters later. The 75-year actuarial deficit jumped from 3.82% of taxable payroll to 4.42%, a 16% increase in a single year. The Center for Retirement Research at Boston College framed the deficit growth as the real story in the report, well ahead of the depletion dates that got the headlines. Reserves fell from $2.721 trillion at the start of 2025 to $2.561 trillion at the end of it. Program cost of $1.609 trillion against income of $1.449 trillion, a $160 billion gap covered by drawing down the fund. The program has been running cash negative since 2021 and negative on non-interest income since 2010. None of this projection assumes AI-driven employment decline. The Trustees looked at the question and concluded, per the Center for Retirement Research’s summary of the economic assumptions memo, that it was too early to know how AI would affect productivity. That is an honest position for an actuary. It also means the official baseline treats a major potential shock to the wage base as a blank. The arithmetic of a slow leak Let me make the attrition scenario concrete enough to argue with. Sixty-one million separations a year. Suppose backfill rates drop by six percentage points and stay there. That is roughly 3.7 million positions a year that would have been refilled and are not. Sustained for five years, you get somewhere in the range of 18 million fewer covered workers, which is about 10% of the covered workforce. Six percentage points. Not a collapse. A shift in default behavior that no single company would need to announce. If covered payroll fell 10%, payroll tax revenue drops by roughly $132 billion a year at current levels. Stack that on the existing $160 billion annual gap and you are running close to a $292 billion shortfall before you account for the gap widening on its own, which it does. Rough arithmetic, and I want to flag it as rough rather than dress it up as modeling: reserves of $2.561 trillion under current projections run about eight years, implying average drawdown near $320 billion annually. Add the attrition leak and you are drawing closer to $450 billion. That pulls combined depletion from 2034 toward 2031, and OASI alone from late 2032 toward 2030. Two honest complications. First, composition. The roles going unfilled are disproportionately entry-level. Stanford’s Digital Economy Lab, working with ADP payroll records covering millions of workers, found employment for people aged 22 to 25 in the most AI-exposed occupations down roughly 13% relative to comparison groups since late 2022, while employment for workers over 30 in those same occupations grew somewhere between 6% and 12%. Their updated Canaries Dashboard puts the current early-career decline in exposed occupations at about 3.8% per year, sharpening after the first year. Entry-level workers earn less, so losing them costs less revenue per head than my arithmetic implies. The near-term hit is milder than 10%. That is the good news and it is temporary. Those are the people who would have been in peak earning years in the 2040s and 2050s, paying the maximum into the system while a very large beneficiary population draws from it. A missing 24-year-old is a small revenue loss today and a large one for thirty years starting around 2045. The program’s problem is structurally long-dated, and this particular leak is aimed exactly at the long end. Second complication, and this is the one I keep circling back to. The wage argument, and why the cap defeats it The obvious rebuttal to everything above: if AI makes remaining workers more productive, their wages rise, and payroll tax revenue holds up even with fewer people paying it. Fewer workers, higher average covered wage, same total. The mechanism is real. Brynjolfsson and his coauthors found that adjustment in AI-exposed occupations has come through headcount rather than compensation. Salaries in those fields have not meaningfully fallen. The Trustees themselves raised their near-term productivity and average earnings assumptions in the 2026 report, and that assumption change is part of what kept the combined depletion date from slipping to 2033. So why do I still think the revenue math breaks? Because of where the wage gains land relative to a cap that sits at $184,500. Only about 6% of workers earn above the cap. But the productivity gains from AI are not distributed evenly across the wage ladder. They accrue to the people whose judgment now multiplies across more output: senior en

    Nobody Gets Fired
  3. Jul 31

    The Shape Comes Before the Size

    The story goes that Vilfredo Pareto was walking through his garden one afternoon in the 1890s, noticed that a small handful of pea pods were producing most of the peas, and reasoned backward from there to a law of economics. It is a good story. I have heard it told at conferences by people who were very sure of it. I have never been able to find it in anything Pareto actually wrote. What he did do was less charming and more useful. He went through Italian property records and found that a small share of the population held most of the land. Then he checked Britain, and Prussia after that. The exact proportions moved around. The shape did not. He published it in Cours d’économie politique in 1896. Forty-odd years later Joseph Juran, working on quality control at Western Electric, saw the same shape in factory defects. A few causes were producing most of the failures. He needed a name and borrowed Pareto’s. Juran later admitted he had overreached, that Pareto had documented something narrower than what Juran was claiming under his name, and that others had described unequal distributions first. He also came to regret his phrase “the trivial many.” Late in life he preferred “the useful many.” I bring up the mythology because it has made the principle worse. Eighty-twenty got flattened into a time-management slogan, the kind of thing printed on a conference tote bag. That is a shame, because what it actually is happens to be rare and valuable. It is a forecasting instrument. It works on data you do not have yet. The claim For a large class of business quantities, we know the shape of the distribution long before we know its size. That sentence is the whole essay. We usually cannot say how big a number will get. We can very often say how it will be spread across the population producing it. And the spread turns out to answer most of the questions people actually need answered. Here is the question that prompted me to write this down. I have now had some version of it from four different early-stage operators in the past year: how many tokens are my engineers going to use? The CFO needs a budget line. The CEO needs to know whether this becomes a rounding error or a top-three expense. Nobody has more than a few months of history. The tool changes every eight weeks. Every top-down number they can find is a vendor’s total addressable market slide, which is worth roughly what it cost them. The good news is that they already have the answer sitting in their billing dashboard. They just have not looked at its shape. Two numbers you already have Here is the objection, and it is the right one to have. Mean and median are usually close together. For most things you measure, the ratio between them sits near 1. So how could I possibly be telling you to look for a four-to-one gap? Because both are true, and which one you get depends entirely on what kind of quantity you are measuring. Look at American households. The Federal Reserve measures the same people twice, once on income and once on accumulated wealth. Median Mean Mean ÷ Median Household income about $80,000 about $114,000 1.4 Household net worth $192,900 $1,063,700 5.5 Same country. Same households. The income numbers behave the way you expect, sitting close together, roughly bell-shaped, ratio near 1. The wealth numbers are a different animal. The average American household is a millionaire. The typical American household has less than two hundred thousand dollars. Both sentences are true and they describe the same population. Why the two behave differently The difference is not about money. It is about how the quantity accumulates. Income is mostly additive. You earn some each year. A good year adds to the pile. A great year adds more. The years do not multiply each other, and there is a practical ceiling on hours you can work. Additive processes make bell curves, and bell curves put the mean and the median in nearly the same place. Wealth compounds. Money you already have earns returns that become money you have, which earns more. Being ahead makes you more likely to get further ahead. That is a multiplicative process, and multiplicative processes make the long right tail that drags the mean away from the median. So the question to ask about any quantity you are forecasting is simple. Does having more of it make you likely to get more of it? Height does not work that way. Test scores do not. Neither does the time it takes to complete a routine task. Token consumption absolutely does. An engineer who gets good at running agents uses that skill on bigger problems, which teaches them to run longer jobs, which makes them better still. The eighth engineer is not eight times more diligent than the median one. She is on a compounding curve that the median engineer has not stepped onto yet. That is why token spend looks like wealth rather than income, and why the mean-and-standard-deviation instincts most of us carry will fail on it. The lookup table Divide the mean by the median. That single number tells you which world you are in. Mean ÷ Median What you have Top 20% holds about 1.0 a bell curve, none of this applies ~25% about 1.75 a 70/30 system 70% about 4.0 an 80/20 system 80% above 5 heavier than 80/20, treat with care 85%+ Two numbers, one division. Every billing dashboard can give you both, and it takes longer to open the export than to do the arithmetic. For the curious, the reason the table works: consumption data of this kind is usually close to log-normal, meaning the raw numbers are wildly skewed while their logarithms are roughly bell-shaped. For a log-normal, the mean divided by the median equals e^(σ²/2), where σ is the standard deviation of those logarithms. The σ that produces exactly 80/20 is 1.68, and e^(1.68²/2) works out to 4.1. So your z-score instincts were never wrong. They were being applied to the wrong scale. What the bell curve cannot do One more reason to take the ratio seriously, for anyone still inclined to treat 80/20 as a strong version of something familiar. In any normal distribution, the top 20% sits about 1.4 standard deviations above the mean. That is fixed geometry, and it is the whole reason bell curves are so well behaved. Now ask what share of the total that top 20% controls. With a standard deviation a quarter the size of the mean, which is a reasonably wide bell, the answer is about 27%. Twenty percent of the people holding twenty-seven percent of the stuff. Barely worth a meeting. To force a real 80/20 out of a normal distribution, you would need a standard deviation more than twice the mean. Run that through and about a third of your population lands below zero. Negative token consumption. Engineers who produce tokens on net. So this is not a matter of degree. A bell curve cannot generate 80/20 for anything with a floor at zero. Anyone budgeting a heavy-tailed quantity with bell-curve instincts will be wrong in a predictable direction, underestimating the top, and surprised by the same invoice every quarter. The thirty-second confirmation The ratio can lie to you when your population is small. One monster user swings a mean calculated over forty people. So there is a second check worth doing before you build anything on it. In a clean power law the shape repeats inside itself. If the top 20% holds 80%, then the top 20% of that group should hold 80% of the 80%. Top 4% holds 64%. One more turn and the top 0.8% holds 51%. Pull your actual top-4% share. If it lands near the square of your top-20% share, the structure is real and you can lean on it. If it comes in well under, you have a softer distribution with a fatter middle, and your forecasts should be gentler at the high end. If it comes in well over, go check whether one row in that export is a build server. Five minutes of actual work Before the worked example, the data-pull, because I have watched this stall at exactly this step. Export one month of consumption per user. One row per person, not rolled up by team. Most billing systems will give you this, and the ones that will not are usually hiding it in an admin export nobody has opened. Strip out service accounts and anything else automated first, because one misclassified build server will masquerade as your heaviest engineer. Sort descending. Compute the mean. Find the middle row for the median. That is your ratio. Then sum the top twenty percent of rows and divide by the total, and do the same for the top four percent. Three numbers, and between them they describe the entire structure of your consumption. It takes longer to schedule the meeting about it than to do it. Forty engineers Let me put numbers on it. These are illustrative, chosen for clean arithmetic rather than pulled from a specific company, but the structure is what I keep seeing. Forty engineers. Last month the org consumed 1.2 billion tokens. Mean consumption: 30 million per engineer. Median: about 7 million. Mean over median is four. So this is an 80/20 system, and I have learned everything I need to know about its shape from a division I could do in my head. That single ratio has already caught the most common budgeting error I see. If the CFO builds the plan from the engineer she talked to, she is probably talking to a median engineer, because most engineers are median engineers. Forty times seven million gives 280 million. The actual number is 1.2 billion. She is off by a factor of four before she has made a single assumption about growth. Meanwhile the eight heaviest users are burning roughly 120 million each and one of them is probably north of 250 million. That person is not abusing anything. That person has figured out how to keep an agent running while they do something else, which is exactly what everybody wants and what the whole purchase was for. Now, forecasting. The naive move is to fit a curve to three months of history and extend it. Do not do that yet. First find the ceiling, because a growth curve without a ceil

    The Shape Comes Before the Size
  4. Jul 31

    The Bar Was Already on the Floor

    I want to put a prediction on the record, and I want to be honest that it is the kind of prediction that could make me look either prescient or naive depending on which part of it you weigh. Here it is. Within ten years, the large data center will be understood as one of the better industrial neighbors a county can have, measured per dollar of output, per acre of land, and per resident living within a mile of the fence line. Not because the industry earned that reputation through good behavior. Some of the early behavior has been indefensible. It will happen because the comparison set is so degraded that clearing it takes very little. The second half of the prediction matters more than the first. When data centers start looking clean by comparison, the useful question will not be what the industry did right. It will be what we spent a century failing to ask of everybody else. The standard for acceptable harm in American industrial siting was set long before the first hyperscale campus broke ground, and it was set low on purpose. Data centers did not invent the terms. They inherited them, took advantage of them the way any capital allocator would, and are now getting the public scrutiny that the industries next door avoided for forty years. That gap between scrutiny and harm is the whole story. It is worth understanding carefully, because it determines what states should be doing right now, and because it explains why the fight over data centers is going to produce better environmental policy than the fight over the refineries ever did. What actually happened in Memphis I want to start with Memphis, because the case is being used by both sides to prove opposite things, and because getting it right is a precondition for making any larger argument. The short version, taken from the Southern Environmental Law Center’s own timeline of the dispute, is not flattering to xAI. Colossus 1 began operating in southwest Memphis in June 2024. To power it, the company ran as many as 35 gas turbines without air permits. Community groups documented them. The Shelby County Health Department held a public comment meeting on the company’s permit application, and the commenters who showed up were unanimously opposed. SELC, representing the NAACP, sent a sixty day notice of intent to sue under the Clean Air Act. Only after that notice did xAI remove the unpermitted units. It then received a permit for the fifteen that remained. Then the company did it again. For Colossus 2, xAI installed turbines at a site in Southaven, Mississippi, a few miles south across the state line. Plaintiffs’ filings cite 35 confirmed units as of late March 2026, with thermal drone imagery suggesting the number may have reached the mid forties. The Mississippi Department of Environmental Quality approved a permit for 41 turbines about three weeks after a contentious public hearing, which is a fast turnaround for a stationary source of that size. SELC filed an appeal arguing the permit rested on data that understated cumulative pollution from the site and its neighbors. On April 14, 2026, the NAACP and its Mississippi State Conference, represented by SELC and Earthjustice, sued xAI and its subsidiary MZX Tech under the Clean Air Act over 27 turbines operating without a permit. The plaintiffs later moved for a preliminary injunction. One detail in that record deserves more attention than it has gotten. SELC’s argument is that the Southaven parcel was chosen in part because Mississippi’s registration process for portable equipment moves faster than Tennessee’s federally delegated Title V program. If that characterization holds up, the siting decision was partly a permitting arbitrage. That is a familiar move in heavy industry. It is not a new sin invented by the AI business. The water side follows the same shape. Protect Our Aquifer reported that xAI drew more than 25 million gallons from Memphis Light, Gas and Water in a single month in early 2026, working out to roughly 812,000 gallons a day pulled from the Memphis Sand Aquifer, with summer demand expected to run higher. The group also found the company paying about $0.19 per hundred gallons against the $0.32 a residential customer pays. The promised fix, an $80 million greywater recycling plant designed to treat up to 13 million gallons a day of municipal wastewater for industrial cooling, broke ground in October 2025 and then stalled while the company moved resources to Colossus 2. In April 2026 the mayor and the utility publicly pressed for it to restart. By June the company had committed to resuming construction no later than the first quarter of 2027, though MLGW’s chief executive noted the original engineering team was gone and a new one had to be assembled. So no, xAI has not been a model citizen. I said earlier that the industry would look good by comparison, and I want to be precise about what that comparison actually is, because the honest version of it is stranger and more damning than the flattering version. The corridor was already a sacrifice zone Southwest Memphis did not become an environmental justice case in 2024. It has been one for half a century. Residents there live alongside a Valero oil refinery, a Nucor steel operation, a coal tar distillation plant, and something on the order of twenty other permitted industrial facilities handling asphalt, fertilizer, solvents, plastics, and coatings. Both Shelby County, Tennessee and DeSoto County, Mississippi have received failing grades for ozone from the American Lung Association. The Memphis area has been named an asthma capital. It fails national standards for smog. The clearest example is the one that took the longest to surface. Sterilization Services of Tennessee operated a medical device sterilization plant on Florida Street from the 1970s until 2024, releasing ethylene oxide into a residential neighborhood twenty four hours a day. Ethylene oxide is linked to leukemia, lymphoma, breast cancer, and stomach cancer. EPA concluded in 2016 that the gas was roughly thirty times more toxic to adults, and sixty times more toxic to children, than the agency had previously estimated. Analysis cited by the Union of Concerned Scientists found that ethylene oxide accounted for nearly 82 percent of the estimated cancer risk from toxic air pollutants in the census tract containing that facility. Residents did not learn any of this until an EPA public forum in October 2022, held at a church in the neighborhood. The plant had been operating for more than four decades by then. It closed in 2024, after sustained community organizing, having outlasted two generations of the people who lived downwind. Set the two stories side by side. A company runs unpermitted turbines for months and faces a federal lawsuit, drone surveillance, national press coverage, a governor’s-race-level political fight, and a preliminary injunction motion, all within eighteen months. Another company releases a potent carcinogen into the same city for forty years, and the neighbors find out about it at a church meeting because a federal agency finally decided to tell them. That asymmetry is the thing worth writing about. It is not evidence that xAI behaved well. It is evidence that the enforcement apparatus, the press attention, and the public appetite for a fight all scale with the visibility of the defendant rather than the magnitude of the harm. Elon Musk’s name on a facility generates more scrutiny in a year than ethylene oxide generated in forty. If you are a community organizer in South Memphis, this is infuriating in both directions. The new arrival gets fought hard, and the old damage was never fought at all. Where the low bar came from The extractive pattern here is old enough that treating it as a story about AI misses the point. The template goes like this. Outside capital identifies a place where land is cheap, labor is available, political resistance is thin, and some local resource can be drawn down without paying its replacement cost. The capital builds. Value is produced and shipped elsewhere. Wages stay for a while. The burden stays permanently. When the resource is exhausted or the economics shift, the capital leaves and the cleanup cost lands on whoever could not leave. Coal did this. Timber did this. Refining did this. Textiles did this and then did it again somewhere cheaper. The company town was the fully realized version. Every one of those industries arrived with a jobs promise, and most of the promises were kept for a while, which is exactly what made the arrangement durable. American environmental law then formalized the pattern in a way that is rarely discussed outside of regulatory practice. The Clean Air Act imposed strict requirements on new sources and much looser requirements on existing ones, on the reasonable-sounding assumption that the old plants would retire on a normal schedule and be replaced by cleaner ones. They did not retire. The regulatory asymmetry gave owners a direct financial incentive to keep aging equipment running rather than trigger New Source Review by modernizing it. The result was a permitting regime that systematically protected incumbents and put its full weight on newcomers. Layer a second structural problem on top. Permits are issued facility by facility and pollutant by pollutant. In most jurisdictions there is no test that asks what the cumulative burden already is on the people who live there. Each individual permit in South Memphis could be lawful while the aggregate air quality earned an F. That is not a loophole. That is the design. So when someone says the bar for data centers should be higher than the bar we set for existing industry, they are correct, and they are also describing an admission. If a 300 megawatt computing campus can be required to run closed-loop cooling, purchase emissions offsets, fund local training, and accept curtailment obligations, then the refinery down the road could have

    The Bar Was Already on the Floor
  5. Jul 31

    The Chess Game That Proved the Computer Was Broken

    In February 1967, Star Trek aired an episode called “Court Martial.” Kirk is on trial. The ship’s computer has produced a visual record showing that he ejected a pod during a yellow alert and killed a crewman. The record is clear. Kirk’s own memory contradicts it. The court believes the machine. While the trial proceeds on the starbase below, Spock sits in a briefing room playing three dimensional chess against the ship’s computer. McCoy walks in and calls him cold blooded for playing games while the captain’s career burns down. Spock replies that he has just won four games in a row. McCoy stops cold. That is impossible. Spock programmed the chess computer himself, using his own knowledge of the game. The most he should ever be able to achieve against it is a draw. In the episode Spock puts it plainly: the computer cannot make an error, and assuming he does not either, the outcome should be stalemate after stalemate. That is the clue that breaks the case. Someone tampered with the computer. The evidence against Kirk was fabricated. It is a well built scene. It also rests on a premise the writers considered too obvious to defend. In the 23rd century, aboard a starship crossing light years, a person beating a machine at chess is proof that the machine has been sabotaged. Thirty years later, on May 11, 1997, Garry Kasparov resigned after nineteen moves in the sixth game of his rematch against IBM’s Deep Blue. The match went to the machine, 3.5 to 2.5. It was the first time a computer had taken a full match from a reigning world champion under tournament conditions. Kasparov said afterward that he had lost his fighting spirit. The Enterprise’s chess computer was beaten by a person. IBM’s chess computer beat the reigning world champion. Star Trek’s 23rd century was overtaken in 1997, and the show never saw it coming. The shape of the error I wrote a while back about science fiction as a forecasting instrument, and about how often it lands specific inventions decades ahead of the engineering. Star Trek is the standard example, and it deserves the reputation. The show has a real hit rate on hardware. What interests me now is the shape of its misses. Forecasting errors are usually treated as noise. Somebody guessed, somebody was wrong, move on. But Star Trek’s errors are not scattered randomly. They point in one direction, consistently, across three decades and four series. The show was generous with physics. It handed out faster than light travel, matter transport, artificial gravity, and energy shields without much hesitation. It was stingy with computation. Every time the plot required a judgment call, a hunch, a moment of insight, or a game that mattered, the machine came up short and a person filled the gap. Sixty years later, we have none of the physics and considerably more than the computation. We cannot transport a paperclip across a room. We can hand a stranger a device that translates their speech into forty languages, writes a legal brief, and identifies a skin lesion. That inversion is the useful part. If you want to get better at reading the future, the interesting question is not which predictions were wrong. It is why the wrong ones cluster where they do. The hits, briefly Give the show its due first, because the record is genuinely good. The communicator. Kirk flips open a palm sized device and talks to a ship in orbit. The Motorola StarTAC, the first mass market flip phone, arrived in 1996 with the same gesture built into it. Martin Cooper, who made the first handheld mobile call in 1973, spent decades being credited with taking the idea from Star Trek, and for years he accepted the credit. He later corrected the record. The real influence was Dick Tracy’s wrist radio, which he read as a boy, and Motorola had been working on handheld cellular since the late 1950s. He has called the Star Trek attribution one of his own mistakes. The myth is now better known than the correction, which is its own lesson about how influence stories get written. The PADD. Officers walk around carrying flat glass slabs they read reports on. That one landed almost exactly, roughly two decades early. The talking computer. “Computer, locate Commander Riker.” Jeff Bezos has said the long term vision for Alexa was to become the Star Trek computer, something you could ask anything and converse with naturally. He grew up playing Star Trek in Houston, where one kid always had to play the computer. Amazon later added “Computer” as a selectable wake word on Echo devices. The reference is not subtle. The universal translator. Live speech to speech translation now runs on a phone. This one arrived early and quietly, and almost nobody treats it as science fiction anymore. The medical tricorder. In 2012 the Qualcomm Foundation and XPRIZE launched a competition named after the device, asking teams to build a portable unit that could diagnose thirteen conditions and monitor five vital signs without a clinician. Three hundred teams entered. In April 2017 the top prize went to Final Frontier Medical Devices, a family led team out of Pennsylvania headed by an emergency physician and his brother, for a system called DxtER. The prize existed because the fictional device was specific enough to be an engineering target. The replicator. The partial credit case. Star Trek imagined a wall unit that assembles physical objects from stored patterns, including hot food and Earl Grey tea. We have additive manufacturing that prints titanium brackets, dental aligners, houses, and, in laboratory settings, food. The direction is right and the gap is enormous. The show also treated replicator technology as a diplomatic instrument, handed to civilizations that needed it, which is a reasonable read on how manufacturing capability actually moves between economies. The rest. Video calls to Starfleet Command. Wall sized displays. The earpiece. Biobeds showing vitals continuously above a patient. Doors that open when you approach them, which the show pulled off in 1966 with a stagehand yanking a rope offscreen. That is a strong record for a series with a rope operated door. Where it went the other way Now the misses. They are more interesting. Chess, and every game after it Start where we started. “Court Martial” treats machine chess supremacy as impossible, and that impossibility is load bearing for the plot. The underlying model is that the computer is a recording of Spock. It holds his knowledge of the game, played back without error, and it cannot exceed him because there is nothing in it that did not come from him. A filing cabinet with a voice. Reasonable for 1967. Every decade since has dismantled it. Strategema Twenty two years later, in July 1989, The Next Generation aired “Peak Performance.” A Zakdorn master strategist named Kolrami comes aboard. Riker challenges him at Strategema and loses in about twenty three moves. Then Data plays him. Data is an android with a positronic brain, the only one of his kind in Starfleet, and Kolrami takes him apart in a matter of seconds. Data has something close to a breakdown over it and questions whether he can be trusted at his post. Eventually he plays Kolrami again with a different premise. He plays to prevent a conclusion, declining every opening and simply blocking. The game runs past thirty five thousand moves each. Kolrami throws down his controls and storms out. Data allows that he did not technically win, though he did, in his phrase, bust him up. Read that back with 2026 eyes. An artificial mind cannot beat an organic strategist at a board game, and its workaround is to be more patient. In 1989 that was a satisfying resolution about the limits of computation. In 2016 AlphaGo beat Lee Sedol at Go, a game whose branching factor was supposed to make it the wall that brute force could not climb. The following generation of the system learned it from scratch, with no human games as input, and surpassed every prior version. The premise of “Peak Performance” did not merely age badly. It reversed. Velocity In May 1998, the Voyager finale “Hope and Fear” opens with Janeway and Seven of Nine playing Velocity on the holodeck. Janeway wins six rounds out of ten. Seven is frustrated. She has Borg enhanced visual acuity and stamina and should, by her own accounting, win every round. Janeway tells her there is more to the game than stamina, and names intuition. Seven calls intuition a human fallacy. That scene states the show’s whole position on machine intelligence in about ninety seconds. The captain has a capability the enhanced being does not. It is unnamed, unquantified, and located precisely where measurement fails. That is the writing’s requirement rather than an accident of it. Whatever the machine cannot yet do gets promoted to the definition of the human. Then the machine does it, and the definition moves. Since 1967 that boundary has moved in one direction. Chess. Checkers, solved outright. Go. Heads up poker, including the bluffing, which was supposed to be the human preserve inside the human preserve. Protein folding. Language. Images. Code. Competition mathematics. Each time, the thing that fell was described beforehand as the thing that required judgment, feel, or intuition. Each time, after it fell, the description was quietly reassigned to whatever was still standing. I am not claiming the boundary vanishes. I am claiming that anyone whose position depends on where the boundary sits today is renting that position on a short lease. One android in a galaxy of ships Here is the structural miss underneath all of the others. The Federation spans thousands of worlds. It has replicators, warp drive, and a fleet. It has one Data. Machine intelligence in Star Trek is artisanal. It is a singular achievement by a lone genius, Doctor Noonien Soong, who dies without leaving reproducible instructions. Data is unique, precious, legally ambiguous, and irreplaceable.

    The Chess Game That Proved the Computer Was Broken

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Kth Connection studies the overlooked patterns behind business, behavior, risk, technology, communication, and reinvention — turning data into insight and insight into impact. kthconnection.substack.com