More from oftwominds-Charles Hugh Smith
The only "reform" that changes our lives in a fundamentally positive way is radical decentralization via distributing centralized power. Presidents like to deal with the CEOs of corporate monopolies for self-evident reasons: Rather than engage in the tedious, contentious herding-of-cats in nimble, dynamic, competitive sectors, the Prez makes a deal with the monopoly CEO and the deal is imposed on everyone down the political, corporate, workplace hierarchy. Centralized power makes a coup--a forced swap of leadership--meet the new boss, same as the old boss--easy. Financial coups are easier, too, with one central bank and one cartel of "too big to fail, too big to care" banks. Centralized power offers many other Irresistible Temptations. Reformers love centralized power because if they can grab control of it, they can force-feed their glorious reforms (or profit-maximizing schemes) down everyone's throats whether they agree or not: it is against the law to complain about corporate/state monopolies controlling our lives, everyone must install a Flock camera in their bedroom, no one can criticize the Supreme Leader in private, everyone must wear approved Silly Hats in public, etc. Oops, those reforms sound like an authoritarian, totalitarian state gone mad. Yes, precisely. All centralized power arrangements end up manifesting authoritarian, totalitarian extremes of madness, because that's the only possible outcome of centralizing power: petty dictators are soon running the asylum, and loving every minute of it. The patients, not so much. We see this everywhere now, as monopolies are manifestations of centralized power. This is why I call the status quo Privatized Totalitarianism as privately owned and operated monopolies / cartels have the same headlock on us as state monopolies, and the two work together, as this serves the interests of both: you make the Silly Hats, and we mandate their use, and penalize anyone attempting to modify your software, app, device or Silly Hat to evade your monopoly chokehold. We both get rich exploiting the powerless peasantry, so what's not to like? Politics now boils down to a Silly Hats slugfest over who gets control of the Privatized Totalitarianism casino. The only meaningful reform is to decentralize power by demolishing every monopoly and cartel and banning the aggregation of power. But what about "efficiency"? Yes, Privatized Totalitarianism is very "efficient"-- efficient at extraction, exploitation, surveillance, repression, propaganda, PR and social control mechanisms. If the public can "vote with their feet" by moving to a different physical location but they're still living in the same cartel-monopoly economy wherever they move, their "liberty" is illusory. It's like changing cabins in the gulag: maybe this hut has fewer leaks and fewer fleas, but it's still in the gulag. Just as what we're losing by using AI is invisible because we've lost the capacity to even see what's been lost, we've lost the capacity to see the systemic decay of the quality of our lives in the invisible gulag of Privatized Totalitarianism. So even as we thrill to some new novelty or tiny discount, we've lost the capacity to see what's been lost in the slow destruction of decentralized, competitive dynamism in favor of the profit-maximizing, sclerotic gulag we're all trapped in without even being aware that we're trapped, for the key to maintaining the kingdom is to foster the illusions of choice, liberty and competition while distracting us with ceaseless hype about new technologies, novelties and meaningless discounts as "competition" and "choice." It's like looking at a row of different brand products and then reading the fine print to discover that they're all owned by the same corporation. That's Privatized Totalitarianism, well cloaked behind carefully maintained illusions of choice, liberty and competition. And if you protest, it might get worse: "I am altering the deal, pray I don't alter it any further." The only "reform" that changes our lives in a fundamentally positive way is radical decentralization via distributing centralized power. Everything else is just changing huts in the gulag and being delighted with the steady stream of absurd parodies of novelty: "New gruel, new taste, now with micro-plastics!" New podcast: Charles Hugh Smith on the End Game of Repressed Interest Rates: Stagflationary Inflation followed by "Cold Turkey" (29:25 min) New collection of five intriguing stories: Jumble Bin Stories (Kindle $6, print $12) read samples for free (PDF) My book Investing In Revolution is available ($18 for the paperback, $24 for the hardcover and $8.95 for the ebook edition). Introduction (free) Subscribe to my Substack for free NOTE: Contributions/subscriptions are acknowledged in the order received. Your name and email remain confidential and will not be given to any other individual, company or agency. Thank you, Frank M. 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Our naivete is being exploited so ruthlessly and with such abandon that the golden age of the Big Con is consuming itself. Naivete is interesting because it's so easily confused with confidence, native optimism, youthful enthusiasm, and a host of delusions, including mistaking idealized fantasies and fairy tales as templates for the real world. In a culture that prides itself on not being a chump, to be called naive is an accusation: don't be naive means don't be an easily conned chump. In contrast, being confident, optimistic and filled with youthful enthusiasm are praised as the core of American Practicality, Idealism and Vigor, i.e. the can-do spirit. That naivete lends itself to youthful enthusiasm, sunny optimism and can-do confidence is rarely remarked upon. So what is naivete other than credulity? It's a willingness to trust leaders, institutions and mythologies without running them through common-sensically skeptical filters, and a belief that everything will work out just fine regardless of what happens if we just keep working hard and working smart. The difference between a willingness to trust leaders, institutions and mythologies, sunny optimism and youthful enthusiasm and naively mistaking self-serving fantasies and fairy tales for the real world is, well, there isn't any. Believing that an unending stream of patently transparent self-serving fairy tales accurately reflect "the real world" is the pinnacle of naivete, and that describes the entire American society, culture and economy: this is the golden age of the Big Con. Which brings us to Herman Melville's under-appreciated classic, The Confidence-Man. Why read a book from 1857 which flopped so badly as commercial literature that Melville stopped writing and ended his career as a customs official? Because this book masterfully explores the entire nature of trust, confidence and cons. Though the setting is a riverboat on the Mississippi River just before the U.S. exploded into Civil War, its insights cross cultural boundaries. This is not an easy book to read for several reasons. First, it is undoubtedly one of the first "post-modern" novels which breaks from traditional narrative storytelling. ( Another example: Dostoevsky's Notes From the Underground.) The Confidence-Man is a collection of 45 conversations between various people on the riverboat--beggars, absurdly dressed frontiersmen, sickly misers, shysters, patent medicine hucksters, veterans (of the Mexican-American War) and the "hero" in the latter part of the book, the Cosmopolitan. In typical Melville fashion, you also get asides--directly to the reader, in several cases, as if Melville felt the need to address issues of fiction outside the actual form of his novel. The lack of structure, action and conclusion make this a post-modern type book, but if you read each conversation as a separate story, then it starts to make more sense. For what ties the book together is not a story but a theme: the nature of trust and confidence. In a very sly way, Melville shows how a variety of cons are worked, as the absolutely distrustful are slowly but surely convinced to do exactly what they vowed not to do: buy the "herbal" patent medicine, buy shares in a bogus stock venture, or donate cash to a suspect "charity." In other chapters, it seems like the con artist is either stopped in his tracks or is conned himself. Since the book is mostly conversations, we are left to our own conclusions; there is no authorial voice wrapping up each chapter with a neatly stated ending. This elliptical structure conveys the ambiguous nature of trust; we don't want to be taken, but confidence is also necessary for any business to be transacted. To trust no one is to be entirely isolated. Melville also raises the question: is it always a bad thing to be conned? The sickly man seems to be improved by his purchase of the worthless herbal remedy, and the donor conned out of his cash for the bogus charity also seems to feel better about himself and life. The ornery frontiersman who's been conned by lazy helpers softens up enough to trust the smooth-talking employment agency owner. Is that a terrible thing, to trust despite a history of being burned? The ambiguous nature of the bonds of trust is also explored. We think the Cosmopolitan is a con-man, but when he convinces a fellow passenger to part with a heavy sum, he returns it, just to prove a point. Is that a continuance of the con, or is he actually trustworthy? The book is also an exploration of a peculiarly American task: sorting out who to trust in a multicultural non-traditional society of highly diverse and highly mobile citizens. In a traditional society, things operate in rote ways; young people follow in their parents' traditional roles, money is made and lent according to unchanging standards, and faith/tradition guides transactions such as marriage and business along well-worn pathways. But in America, none of this structure is available. Even in Melville's day, America was a polyglot culture on the move; you had to decide who to trust based on their dress, manner and speech/pitch. The con, of course, works on precisely this necessity to rely on one's senses and rationality rather than a traditional network of trusted people and methods. So the con man dresses well and has a good story, and an answer for every doubt. Our naivete is being exploited so ruthlessly and with such abandon that the golden age of the Big Con is consuming itself. Delusions are now the norm, as if our unhinged optimism that everything will turn out just fine as long as we believe in Technology and Finance, for that will be enough to stem the tsunami of consequences building up beneath the surface euphoria of stocks and AI making us all wealthy beyond measure. These are the joys of naivete. The tragedies are still over the horizon, a gathering storm we are unprepared for. NEW PODCAST: We Don't Have Capitalism Anymore--We Have Privatized Totalitarianism (60 minutes)(host Daniel H.) My book Investing In Revolution is available at a 10% discount ($18 for the paperback, $24 for the hardcover and $8.95 for the ebook edition). Introduction (free) Become a $3/month patron of my work via patreon.com Subscribe to my Substack for free NOTE: Contributions/subscriptions are acknowledged in the order received. Your name and email remain confidential and will not be given to any other individual, company or agency. Thank you, Joe S. 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The possibility that AI will end up unleashing waves of 'Anti-Progress'--malicious uses, untrustworthy output and uncontrollable floods of slop--also doesn't occur to those confined in the current belief construct. A funny thing happens on the way to understanding risk: we discover it's tricky. We think we see all the risks, and think we can mitigate or hedge those risks, but by its very nature, risk evades such simplistic filters and metrics. Risk remains hidden, offscreen, invisible, building up out of sight, awaiting a catalyst that's equally undetectable until it manifests, and after the fact, we look back and ask, why didn't we see that coming? Risk is tricky like that. It can lay dormant for decades and then erupt with little warning. Risk is tricky in other ways. In our hubris, we see the power and might of our technologies, systems and foresight, and reckon these are so robust they will easily survive any tectonic shift, as we've planned for emergencies. But our faith in the might of our civilization is itself a source of risk because the risk of Model Collapse--the breakdown not of a supply chain or technology but of our entire conceptual construct of how the world works--goes unrecognized because our confidence that our model maps the real world is so high that we are incapable of recognizing its drift into hallucination and civilizational psychosis. In other words, our confidence that our conceptual mythologies are accurately mapping the real world is itself a source of civilizational risk because this confidence makes it inevitable that we do more of what's failing, as the alternative--recognizing our conceptual models and mythologies are self-serving rationalizations that substitute artifice for realistic appraisals--is conceptually and emotionally impossible. Put another way: Emperor Norton's delusions of power and grandeur were harmless as long as he was recognized as delusional. But should Emperor Norton actually be given the power he believed was his to wield, then risk rises accordingly. Consider the bet being made globally that the current iteration of AI will be 1) immensely profitable (the most important thing in the Universe) and 2) immensely productive (secondary to immensely profitable but necessary as a motivation for everyone to throw trillions of dollars at purveyors of AI). The risk that this bet--and the assumptions that make it not only rational but pressing--is the equivalent of handing Emperor Norton the keys to the kingdom with little evidence he will be a wise leader, is unimaginable in the current model / mythology, and so therefore it doesn't exist. The worst that could possibly happen in the current model / mythology is a brief spot of bother in the stock market as euphoric overvaluations come down to Earth, and then the immense profits start flowing and markets rocket higher in a multi-decade Bull Market of AI Productivity. The possibility that the current iteration of AI is innately incapable of metaphorically boiling away the seas is not on the screen, any more than a stock market crash or social upheaval is on the screen. Yet if the fantasy of vast, unstoppable floods of profits driven by vast increases in productivity fail to materialize on a very short timeline, then both a stock market crash and social upheaval move from "impossible" straight through "unlikely" to "happening now," leaving everyone who thought they understood risk and were properly hedged against unwelcome change in a state of disbelief and wonderment. Risk is tricky that way. What's "impossible" in our current belief construct--a construct we mistakenly believe maps the real world perfectly--is a source of system-breaking risk that is invisible within the confines of this self-congratulatory belief construct. The possibility that AI will end up unleashing waves of Anti-Progress--malicious uses, untrustworthy output and uncontrollable floods of slop--also doesn't occur to those confined in the current belief construct. The risk may be of a magnitude and scale that switching AI vendors or platforms and approving policy tweaks won't fix the problem. My book Investing In Revolution is available at a 10% discount ($18 for the paperback, $24 for the hardcover and $8.95 for the ebook edition). Introduction (free) Become a $3/month patron of my work via patreon.com Subscribe to my Substack for free NOTE: Contributions/subscriptions are acknowledged in the order received. Your name and email remain confidential and will not be given to any other individual, company or agency. Thank you, Peter ($70), for your superb generous subscription to this site -- I am greatly honored by your support and readership. Thank you, Mark ($7/month), for your marvelously generous subscription to this site -- I am greatly honored by your support and readership. Thank you, Douglas H. ($70), for your massively generous subscription to this site -- I am greatly honored by your support and readership. Thank you, Vermont R.P. ($70), for your splendidly generous subscription to this site -- I am greatly honored by your support and readership. Go to my main site at www.oftwominds.com/blog.html for the full posts and archives.
The AI boom shares all the risk profiles of previous speculative manias but lacks society-wide benefits while generating fast-metastasizing negative consequences and costs. The idea that the current bubble in AI data centers is an echo of the railroad-construction bubble of the 1870s is appealing--but only half-right. The completion of the first transcontinental railroad in late 1869 sparked a speculative mania of raising capital to build railroads, which were seen as "can't lose" investments in a technology that lowered transport costs from $1 to ten cents. But not all routes had the potential to become profitable, and the resulting collapse of the railroad bubble devastated the developed-world economies, triggering a deep economic downturn from 1873 to 1879 that was called "The Great Depression" at the time (or "The Long Depression"). The term for speculative frenzies channeling vast sums into investments with difficult-to-assess risk profiles is mal-investment, and mal-investment on a large scale triggers financial panics and economic depressions in a well-understood feedback loop. Money invested in digging a mine that doesn't yield any gold can't be recovered. That capital is gone. There is an opportunity cost to every investment: that capital could have been invested in something else that was more productive than the speculative bet on something with unclear risks and payback. As the scale of losses become apparent, credit tightens and the pool of capital available shrinks. Short-term loans that can't be rolled over into longer duration loans trigger bankruptcies which quickly lead to bank runs (financial panics) and layoffs as businesses close. This decline in wages, revenues and the velocity of money is self-reinforcing, and the recovery process--being both financial and psychological--takes years. The parallels with the AI speculative investment mania are obvious. Just as any railroad was viewed as guaranteed to be immensely profitable because railroads generated enormous efficiencies that reduced costs, all AI is guaranteed to be immensely profitable because AI generates enormous efficiencies that reduced costs. But in the real world, use cases for specific railroads and AI applications are stretched along a spectrum which isn't visible in the early stages of a speculative boom. Individual use cases don't automatically guarantee an entire class of use cases will be successful. That one railroad--or application of AI--profitably reduced costs does not necessarily extend to all railroads or AI applications. Nobody wants to wait around for the long process of sorting which use cases are actually beneficial and which are mal-investments, as the big money is made by making big bets in the early days. Human greed is a remarkable force, especially when combined with self-serving hype and the euphoria of the herd running. In the current confluence of greed, hype and euphoria, the possibility that the inevitable aftermath of vast mal-investment is a Great Depression doesn't exactly resonate. AI isn't a railroad, it's the most amazing force in the Universe, etc. This is Wetware 1.0 in action: the psychology of speculative frenzies doesn't change, and so here we are--again. Those are the parallels of the railroad mania of the 1870s and the current AI mania. But that's only half the story. Railroads did dramatically lower costs, turning unprofitable ventures into profitable ventures not by reducing production costs but by reducing transport costs, which prior to railroads might equal production costs. The differences between railroads and LLM / generative AI are significant. While many railroads went bankrupt when the bubble burst, those that actually served expanding markets were eventually put to use as the tracks were still useful many years after being laid. A new locomotive type might enter service decades later, but the tracks remained useful and valuable for decades--with proper maintenance. The rails were not obsoleted every few years, nor did the the entire rail lines have to be replaced every few years. AI is not permanent. It is constantly being obsoleted. A new class of lower-power consumption chips could obsolete the current class of AI chips, requiring a mass replacement of the entire processing foundation of AI. Innovations in software could reduce the processing demands, turning existing data centers into expenses rather than profit generators. AI software that users download onto their own computers negates the need for "renting" data centers (i.e. buying processing power with tokens) by generating models from the user's own data. These are just a few potential forces undermining the utility, lifespan and profitability of the current build-out of data centers. While the cost structure of railroads were relatively straightforward, the costs of AI are complex and difficult to assess as initial costs are not total ownership costs, as maintenance expenses are still unfolding and future costs of resources and energy are trending higher. While the cost reduction and efficiency benefits of depending on AI are as yet unclear, the costs of sorting "good AI" from "bad AI" are already mounting as real-world expenses. The market continues to underestimate the AI slop problem and what it means for enterprise adoption and spending. Create enough hallucinated legal arguments, flawed engineering calculations and backdoor-ridden code, and the slop vats fill faster than our capacity to tell good work from bad, writes Tim Harford. How can we tell good AI from bad? (Financial Times) Cedar Owl recently published a comprehensive overview of the Total Costs of Ownership of AI / Robotics and concluded they may exceed the costs of human employees. Will the cost of an AI Robot be higher than the salary of a Human Employee? AI Robot vs. Human Worker Total Cost of Ownership (cedarowl.substack.com) "AI didn't remove cost--it changed where the cost lives." As for profitable use cases, it's too soon to tell. Individual cases don't necessarily scale to the entire sector or economy. The hype is AI is scalable and applicable everywhere, but this isn't what real-world experience is finding. Unlike railroads, whose cost-reduction benefits were immediate and measurable, the sum total of AI benefits is not just unclear but potentially negative. The negative effects of AI slop and malicious applications are already visible but the full consequences of their expansion cannot yet be determined. Recent polls reveal a profound skepticism in the younger generations whose lives will be most impacted by AI. Gen Z Is Using A.I., but Doesn't Feel Great About It. Only 15 percent said they saw A.I. as a net benefit. The structural limits of AI are equally visible but the full consequences of these multi-factor limitations cannot yet be determined. A recent article in Scientific American summarized one key limitation: the illusion that AI is "thinking," "understanding" and "reasoning": AI and human intelligence are drastically different--here's how: "They are extraordinarily powerful tools when used as what they are: engines of linguistic automation, not engines of understanding. They excel at drafting, summarizing, recombining and exploring ideas. But when we ask them to judge, we unintentionally redefine judgment--shifting it from a relation between a mind and the world to one between a prompt and a probability distribution." There are many other structural limitations whose nature limits "quick fixes." "To grow skills, people need to go through hardship. They need to develop the muscle to think through problems," he said. "How would someone question if AI is accurate if they don't have critical thinking?" "This is the contradiction that has many AI boosters talking out of both sides of their mouths: The use of coding agents is actively diminishing the very skills needed to effectively manage the coding agents." (via Manoj S.) CEOs are quietly realizing the AI replacement plan has a problem. Two problems, actually. "One: the token costs for running AI agents are now exceeding what they were paying the employees they fired. Two: when the tokens run out, the AI stops. Just stops. No continuity. No workaround. Just a spinning wheel where your workforce used to be." AI coding frontloads one form of productivity by backloading the entire system with higher maintenance costs down the line. These costs are not visible in the initial phase, and by the time they're piling up, it's too late to reverse these structural costs. The sums invested in AI data centers--and committed to planned data centers--are on a large enough scale that even the most robust economy is vulnerable to disruption when the revenues needed to justify these extraordinary sums fail to materialize and the total operational costs and costs of ownership become measurable. Matt Stoller offered an apt analogy of AI data center capital investments: But in a sense, the entire AI narrative is a bit like selling huge amounts of picks and shovels as everyone rushes to the mines, and then betting there will be gold when they all start digging. Much of the stock market is made up of investor speculation that pick and shovel companies are about to hit the motherlode. But we don't actually know how much gold there is, or even if there is any gold at all. So far, every powerful and rich person has insisted that there's so much gold we can't imagine it all, and anyone who thinks otherwise is a Luddite Marxist loser." Perhaps most importantly, once we subtract the hype, there is no evidence-based answer to the question: will our society / the public benefit from AI? Or are all the proposed benefits of reducing costs and generating innovations concentrated in the hands of AI's owners and corporate users? Cui bono--to whose benefit? What's being touted as beneficial to all--equivalent to railroads--is at this point only beneficial to owners and monopolistic-cartel corporations, the very asymmetry that is fast undermining the foundations of our social and economic systems. Put another way: is AI actually solving the core problems undermining our society and economy--systemic asymmetries of costs, wealth, power, agency and opportunity--or is AI adding new problems--brain rot, dependence on black box systems owned by a handful of tech corporations, AI slop, deepfakes, and a tsunami of malicious AI? For all these structural reasons, AI data centers are not the railroads of today. The AI boom shares all the risk profiles of previous speculative manias but lacks society-wide benefits while generating fast-metastasizing negative consequences and costs. My book Investing In Revolution is available at a 10% discount ($18 for the paperback, $24 for the hardcover and $8.95 for the ebook edition). Introduction (free) Become a $3/month patron of my work via patreon.com Subscribe to my Substack for free NOTE: Contributions/subscriptions are acknowledged in the order received. Your name and email remain confidential and will not be given to any other individual, company or agency. Thank you, Wade P. ($70), for your marvelously generous subscription to this site -- I am greatly honored by your support and readership. Thank you, Richard C. ($7/month), for your wondrously generous subscription to this site -- I am greatly honored by your support and readership. Go to my main site at www.oftwominds.com/blog.html for the full posts and archives.
Only then do we realize that by optimizing profit and efficiency, we've also optimized systemic failure. In my essay AI, Money, Human Nature and the Problem with Problems, I refer to boundary conditions but didn't offer a thorough explanation of the role this concept plays in understanding not just how the world works but more importantly, how things break down. Boundary conditions define what the system needs to function. The more complex the machine / system, the greater the number of conditions. For example, a car needs a source of power, fuel, tires, control mechanisms, seats, and so on--hundreds of components are required for the car to function optimally. Some boundary conditions are narrow--there's little or no wiggle-room in what the system needs to function. Everything has to function perfectly or the system breaks down. We can call these tight systems as there's very little leeway in what they need to function. In contrast, loose systems have boundary conditions with leeway: some components can fail or function poorly and the system will degrade--i.e. not operate optimally--but it will still function. Consider a tire. A tire is a fairly loose system. If the optimal tire pressure is 32 pounds, the tire will still function if pressure falls to 28 or is overinflated to 34 pounds. Now imagine a tire that fails if pressure exceeds 32.5 pounds or falls below 31.5 pounds. Those are unforgiving, tight boundary conditions with very little wiggle room. If tire pressure declines even slightly, it fails. Which tire do you want--the one optimized for price/efficiency or the one with looser boundary conditions? Our entire way of life is dominated by systems optimized for price/efficiency, not survivability when the system veers outside its boundary conditions. If a critical semiconductor chip fails in a modern vehicle, the vehicle breaks down and ceases to function. The chip controlled an essential subsystem, and once the chip failed, the subsystem failed, and the vehicle rolls to a stop: complete breakdown. Certain characteristics of systems create tight boundary conditions that we don't see until they break down. During the pandemic in the early 2020s, the supply chain of some semiconductors broke down, and as a result the production of cars and trucks that needed those chips broke down. Supply chains with single-source suppliers within long dependency chains (this part needs this part which needs this part) have exacting boundary conditions: since the supply chain depends on a single source for a critical part, if that supplier is disrupted, the entire chain breaks down. Since the economy is optimized to maximize profit, it's maximized for efficiencies which demand tight boundary conditions and lengthy dependency chains: the system only works if every component works perfectly and every condition is met. Centralization generates tight boundary conditions. Consider a mega-farm growing a single crop--a mono-crop that the region depends on. This centralized mega-system is optimized to maximize yield of a single crop via optimized subsystems: specific seeds, fertilizers, mechanized equipment, soil sensors, irrigation, harvesting and transport, and at the end, a market price for the crop that covers all the costs and yields a profit. Financially, this is an optimized system. In the real world, it is a system prone to failure due to its tight boundary conditions. A pest or plague that evades the genetically modified seeds' defenses can wipe out the crop, a sudden bout of extreme weather at the wrong time can wipe out the harvest, and a drop in the market value of the crop can make it unprofitable to even harvest, so it's left to rot or plowed under. Contrast this with a system of 100 independent, decentralized farms. Financially, this system is inefficient and not optimized to maximize profit, so it's anathema in a financial system that demands optimizing everything to optimize profits. Some of the farms will grow crops with low profit margins or non-optimal yields, and some will be inefficient due to raising a variety of crops instead of one financially optimized crop. When the pest, plague or price collapse wipes out the mega-farm, the system of 100 farms growing a variety of crops continues to function, albeit at a reduced yield as some farms will suffer lower yields and incomes while many will be unaffected. When a centralized system / mono-crop fails, everyone depending on that system / mono-crop starves. Once the system veered outside the boundary conditions, it broke down. Here's a graphic illustrating tight and loose boundary conditions: Analog - physical systems tend to be more forgiving than digital-dependent systems. When a bracket on a home appliance breaks, it's typically possible to substitute a non-optimized part to fix it. In other words, the manufacturer's bracket is nice to have but not essential, as some other piece of metal can be worked to serve the same function. When the digital motherboard on the modern appliance fails, there is no replacement except that exact board. Some other mix of semiconductors and circuitry can't be substituted. The appliance--or vehicle, digital device, etc.--is now a brick. And if that one component is no longer available, the appliance is unrepairable. In an old analog auto engine, if one of the four cylinders was no longer functioning optimally--the gasket was leaking, valves clogged, etc.--the engine would still function, albeit generating lower horsepower and dirtier exhaust. The majority of systems we rely on for life's essentials--water, power, food, transport, banking, healthcare, etc.--are now digitally dependent systems with tight boundary conditions. They work perfectly until some critical component in a dependency chain fails, and then the entire system fails. There are no replacements or substitutes for what failed, and so the entire system ceases to function. All the features of systems that optimize efficiency and profits tighten boundary conditions. Everything that widens boundary conditions--i.e. everything that increases survivability and flexibility--increases costs and reduces profits and optimization of efficiency: redundancy, warehousing of spare parts, constant training of personnel to deal with unlikely emergencies, etc. The vulnerabilities of our optimized way of life are hidden until systems veer outside their boundary conditions and break down. We've witnessed many such breakdowns as every system is optimized for efficiency and profit by stripping out redundancies, second suppliers, spare parts, analog backups in favor of digital efficiencies, etc. This is why we're surprised--and helpless--when they break down. We think they're robust because they work so well within their boundary conditions, but the narrowness of their boundary conditions makes them extremely sensitive to failures in critical components. This fragility is invisible until the system breaks down. Only then do we realize that by optimizing profit and efficiency, we've also optimized systemic failure. Go ahead and hold control-alt-delete, but the system won't reboot or repair itself, for it's been optimized to break down. My book Investing In Revolution is available at a 10% discount ($18 for the paperback, $24 for the hardcover and $8.95 for the ebook edition). Introduction (free) Check out my updated Books and Films. Become a $3/month patron of my work via patreon.com Subscribe to my Substack for free NOTE: Contributions/subscriptions are acknowledged in the order received. Your name and email remain confidential and will not be given to any other individual, company or agency. Thank you, Simons C. ($32.40), for your wondrously generous subscription to this site -- I am greatly honored by your support and readership. Thank you, Ferrema S. ($7/month), for your marvelously generous subscription to this site -- I am greatly honored by your support and readership. Thank you, Scott T. ($300) for your beyond-outrageously generous subscription to this site -- I am greatly honored by your support and readership. Thank you, Don A. ($70) for your enormously generous subscription to this site -- I am greatly honored by your support and readership. Go to my main site at www.oftwominds.com/blog.html for the full posts and archives.
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Shopify founder and CEO Tobi Lütke joins Shane Parrish to discuss AI agents, better decision-making, and the future of work. He explains how he uses an AI council to examine his hardest decisions and why taste, judgment, and responsibility become more valuable as AI becomes more capable. They go inside Shopify’s work with River, an … The post Tobi Lütke: AI Agents, Better Decisions, and the Future of Work appeared first on Farnam Street.
It is undeniable technology companies have found their most hospitable setting in the United States and while there are many reasons for the US dominance of technology, easier access to capital for young businesses has been a key ingredient. Venture capital in the US, in its institutional and organized form, can trace its roots back to the 1950s, and over the last few decades, it has generated its share of legendary investors. Vinod Khosla is one of those legends, and it is for that reason that I was surprised to see him tweet the following: I understand that utterances on social media, often in response to comments by others or made in anger, are often quickly regretted, and I believe (though I am not certain) that Mr. Khosla did not quite mean what he said here, confusing profitability with cash flows, and arguing that every business should put scaling ahead of profitability. That said, his view that scaling should be given priority over profitability is more the norm, than the exception, among many venture capitalists, and while it probably always has been the case, I believe the tilt towards scaling has become pronounced in the last two decades. In this post, I want to zero in on the scaling and profitability trade off, how the emphasis on the former over the latter plays out at start-ups and very young companies, and why we live with the consequences, whether they want to or not. Scaling versus Business Building To put the choices you will face on scaling up versus business building into perspective, let's assume that you are a founder, and that your start-up has a tested product and that you believe there is a market for that product. You can stay with what you have built and build a business to take advantage of the immediate market, focusing on financial health and profitability. The fact that you will stay small, and perhaps unrecognized in markets other than your own, is a minus, but there are pluses. You will have little need for external capital, and you will own much or all of the business, facing little pressure from outside to change the way you do things. Alternatively, you can take a more ambitious route, where you seek out a bigger market, augmenting existing or adding new products, and while that path will deliver larger revenues, you may have to work harder to get it to deliver profits and cash flows, and perhaps have to give up more of your ownership and control of that business. The Scaling Choice Before starting on the determinants of scaling, it make ssense to begin with the metric being scaled. For most businesses, it is revenues that is the chosen metric, with scale capturing how big revenues can become over time. With some earlier-stage businesses, many of which are pre-revenue, the metric can become a variable that these businesses hope to convert to revenues; with tech intermediaries and social media companies, it can be users or subscribers. Focusing on scale, though, there are factors that come into play that allow scaling to have a higher likelihood of success in some businesses than others: Market size: It is easier to scale up a company, if it is small player in a big market, than if if the market is small, and scaling up will quickly give you a dominant market share. That said, the way you describe your business, and then run it, can play a role in how big a market you will have for your products. In my posts on valuing Uber, for instance, I noted that describing it as a logistics company (car service, moving, delivery) rather than just a car service company could triple its potential market. Market growth: It is also easier to scale up a company if the overall market that it is targeting is also growing, since growth does not require going after competitors' customers. A smartphone company (Apple or Samsung, for instance) in 2010 had a growing market to work with, as customers switched from flip phones and smartphones made inroads into large emerging markets. In 2026, that advantage had largely dissipated, as the smartphone market has matured. Industry Structure: There is a natural structure to industries, driven by economics and business type, with some industries splintered across many players, and some concentrated in a few big players or even in a winner-take-all. You can scale up more in the latter, but you will have to confront the odds favoring you being one of the winners in the industry. Capital intensity: It is easier to scale up a business that does not require large capital investments to be able to generate more in revenues. Using Uber as an example again, scaling up was made easier in the early years, since it did not own the cars or hire the drivers that comprised its car service, and growth came quickly and with little added investment. Customer inertia: Businesses can grow faster and get bigger if there is less inertia among customers and more willingness to try out new products or services. At the risk of generalizing, this may explain why scaling up can happen more quickly in younger industries (like technology) than in older ones (health care, education). Key person(s): There are some businesses that are built around the specific skill sets of a person (usually a founder or business owner) and these skill sets are not easily transferred or taught to others. A master craftsperson, say a furniture-maker, will have a more difficult time scaling up that business, because without being able to pass his skills on to his or her apprentices (which can take time and require intense oversight), he or she is constrained in how much new business he can take on. If that craftsperson has a recognizable name, it is possible that you could build a scalable franchise model, as has been tried by some master chefs (Wolfgang Puck, Gordon Ramsey etc.) The graph below captures the scaling choices that companies make as a function of these factors: As you can see, some businesses can scale up quickly, some take more time to scale up and some never scale up, and the businesses that scale up quickly often scale down just as fast. Thus, the decision of whether to scale and how quickly to do so is as much driven by the nature of the business (capital intensity, industry structure, competition) and the characteristics of the market that it is targeting (size and growth, customer inertia). Business Building While having access to a big, growing market can allow you to scale up more quickly, your capacity to generate profits and build a business will ultimately come from other forces: Unit economics: Unit economics measures the profitability of the marginal unit sold by a business, and is thus determined by the price charged for that unit and what it costs the business to produce that unit. Businesses like software, where the marginal unit costs very little to produce and can still be priced highly, have superior unit economics and will find it easier to convert growing revenues into profits, since much of the increase in revenue will flow into profits. Conversely, businesses like electric cars, where each additional car sold costs money to make, will struggle to convert scaled up revenues to profits. Economies of scale: Businesses with large fixed costs, whether they be associated with maintaining platforms and infrastructure, or sales and marketing, face obstacles to profitability. While growing can provide scaling benefits, that works only if the fixed costs don't grow with revenues and if they are not so onerous, that you still have losses after scaling up. Competition: & Competitive Edges (moats): Large and growing markets provide businesses with opportunities to grow, but for that growth to translate into sustainable profits, these businesses will need pricing power and that power comes from barriers to entry that keeps new entrants out and gives existing players advantages. It is true that the operating choices that businesses make play out on both the scaling and profit dimensions, sometimes pitting them against each other. A decision to lower product prices may increase revenues at the expense of unit economic profits, and a decision to spend more on advertising and promotion may expand markets, but the higher marketing costs will impose a drag on profitability. One way to illustrate the combination of forces that go into business building is to to go back to basics, and to look at what lies under each one: As you can see, scaling up is not a mantra that automatically translates in profitability, and the pathway to profits will be determined by variables that are often out of the control of a business. Scale & Profitability Mixes With the multitude of factors determining both scaling potential and business model viability, it should come as no surprise that the outcomes that we observe can range the spectrum, starting with extraordinary companies that scale up quickly, while delivering huge profits, to companies that never scale up, either by choice or because they could not, and some of which never make money. Lightning in a Bottle: Are scaling and profitability mutually exclusive? Put differently, can a company scale up, while delivering profits and perhaps positive cash flows as it grows? The answer is yes, but it does require a fairly unusual combination of circumstances - a big and growing market, being an early entrant into the market with few competitors, low capital intensity and excellent unit economics. There are a few companies that meet these conditions, and we will call them "Lightning in a Bottle" firms, partly because they are rare, and partly because success can come from being at the right place at the right time. Google and Facebook, in their early years, were good examples, with revenues growing exponentially and profitability in place. Field of Dreams (Shoeless Joe Jackson version): As a baseball fan, I have always had a soft spot for the movie, Field of Dreams, where a farmer (Kevin Costner) builds a baseball field in the cornfields, and when asked why, responds with "if you build it, they will come". There are companies that seem to be built around this motto, where scaling up comes first, often accompanied by large losses, but with the promise that "if they build (revenues), they (the profits) will come. During Amazon's first decade and a half of existence, I described their business model as a Field of Dreams model, and gave credit for Jeff Bezos for being steadfast in not only telling this story, but also acting consistently with it, and carrying investors along. (If you are wondering what Shoeless Joe is doing in this story, I am afraid you have to watch the movie all the way to the end.) Field of Nightmares: Amazon was not the first successful Field of Dreams company, but as one of its highest profile winners, it gave rise to a legion of young companies, all labeling themselves the "next Amazon". Needless to say, Amazon's success came from being a disruptor of a huge business (retail), which had atrophied and weakened over time, and many of the Amazon wannabes that tried to imitate it managed to do so on the growth dimension, with immense amounts of capital invested in scaling up, but never turned the corner on profitability, partly because they had neither the unit economics nor the economies of scale to pull it off. Niche Star: Scaling is not always the optimal choice, and there are some companies that recognize this reality early, choosing to stay small and focusing on a portion of the market where they have decided advantages. To that extent that they can convert those advantages into premium pricing and niche market dominance, they can have values that are disproportionately large relative to their operating metrics, i.e., trade at high multiples of revenues and earnings. Ferrari, for instance, sells only a few thousand cars every year, but with an operating profit margin in excess of 20%, it trades at a market capitalization comparable to that of auto companies that sell hundreds of thousands of cars each year. Big and Broken: It is no secret that there are some businesses that start with business models with a fatal flaw, i.e,, a broken business model, and rather than being shut down, they are fed increasing amounts of capital and allowed to scale up. A real-estate based business that leases properties long term, and then sub-leases them short term, has a duration mismatch born in hell, and expanding it geographically and allowing it to lease hundreds of properties, as WeWork did, just makes it a really big, bad business. If you are puzzled as to why investors would supply capital to these businesses, you may want to read on. Small winner & Small losers: If you look at all businesses, private and public, most remain small, some due to business and industry structure and some because of owner constraints on capital and control. These small businesses, though, over time, bifurcate into good small businesses, earning more than their cost of capital and delivering value, and bad ones, earning less than the cost of capital, but still worth more as going concerns, than liquidated. Cut your losses: Finally, there are businesses that start up with dreams aplenty, and over time discover that they can neither scale up, nor make money. In the absence of capital infusions, these businesses fail early, but if capital providers keep funneling resources into these companies, they still fail, but do so later and with a much higher price tag. In the matrix below, with scaling on one axis and profitability on the other, I plot all eight of my scale/profit combinations: Any investor or founder who blindly follows the pathway of scaling first and profiting later for every business is using a cookbook approach to business building, and runs the risk of making small failures into big ones. The Tradeoff between Scaling and Profitability: Determinants As you review the factors that govern the trade off between scaling and profitability, it is clear that the right choice (on how much to scale) will depend on the firm, and that not every small firm is destined to become or be more valuable as a larger firm, and that not all large firms have the same profitability characteristics, once scaled up. That said, is it possible for firms to adopt scaling pathways that look, at least from a business standpoint, to be suboptimal? Of course! There are small firms that have viable pathways to scaling up that choose to stay small, and at the same time, there are small firms that are designed to be small, niche businesses embark on scaling that is value destructive, and the reasons are a mix of human frailties on the part of founders, system constraints (from governments and regulators), access to capital (too little or too much) and exit options (sell, liquidate or go public). 1. Founder Characteristics The founder or founders of a business not only play a key role in guiding the business through its early days, when most start-ups fail, but they also make key choices that can determine in its end game. In making these choices, they may be guided by the fundamentals we outlined in the last section, that affect scalability, but they are also a function of their personal make-up, on at least a couple of dimensions: Control versus Ambition: There is a natural tension between wanting to control the levers of decision-making in a business and scaling that business, since the latter almost always requires raising capital from providers who will either constrain your choices (if borrowed money is used) or demand a share of ownership rights (if equity). With the latter, founders will find their control diluted over time, and with enough scaling up, it is possible that founders end up with less than controlling stakes. For some founders, that fear of dilution and losing power over their business creations runs deep enough to stop them from embarking on growth plans, even though these plans make economic and financial sense.The flip side of control is ambition, and for some founders, the desire to build big businesses that are not restricted geographically or in product offerings can drive the decision to scale up, even though the fundamentals may not support that expansion. This works only if they can convince investors that their ambitions In fact, this tension between a founder’s need to be in control and that same founder’s desire to build big plays out in what Noam Wasserman called the Founder’s Dilemma, where to make a business bigger, its founder has to step down or at least compromise on control. Longevity versus Scale: There is an argument to be made that if your intent as a founder is to build a business that is long-lived, your odds of success improve if you keep your business smaller and more focused on what it does well. While there are many exceptions to this generalized rule, it is worth noting that some of the longest lived firms in the world are family owned small businesses, that serve a niche market, and are passed down generation to generation in the same family. It is also true that firms that see a sudden surge in revenues, usually as the result of an external factors or happenstance, often live to regret their good fortune, as they scale up overnight. In the aftermath of the Covid shutdown, for instance, firms like Moderna and Peloton boomed, but they also overreached, and did long-term damage to their business models. In summary, the choice between scaling and profitability will play out differently across businesses, depending upon what founders value most, thought it is healthy for an economy to a have a mix of founders, since it creates a mix of businesses. II. Access to capital It is true that businesses need access to capital, to varying degrees, to scale up, and the easier it is to raise that capital, the easier it is to make a business bigger. Capital can come from different sources, ranging from family wealth to venture capital to public equity, with each one carrying its pluses and minuses. Family (or friend) wealth: Every business, through human history, having lived through its early days (when failure risk is high and its products and services are still untested) has faced a choice of whether to stay small, serving a market that it knows and understands, or whether to get bigger, going after a bigger market. For much of that history, though, with businesses funded with family funds and access to capital was limited, most businesses chose the first path and remained small businesses, focusing on building business models that delivered profits, with wide differences in success rates. For a few, owned by wealthier families, access to a much larger pool of capital (from family savings and bankers willing to lend to these families) created family groups that dominated economies, and continue to do so in some parts of the world. Venture capital: The growth of public equity markets in the late 1800s and much of the last century did little to change the family control dynamic, since investors in those markets were primarily interested in funding larger companies with established business models. Recognizing this gap between capital need and capital access at younger businesses, and the opportunities that the gap presented, allowed for the rise of venture capital in the 1950s, primarily in the United States. These venture capitalists provided seed capital for start-ups, using winners to cover their failures, and got the bulk of their winnings when they exited these investments, either by going public or selling to another entity. Over the last few decades, venture capital has grown, and in the last 12 years, that growth has not let up: Source: NCVA 2026 Yearbook In this century, venture capital has also become more global, growing in Asia and Europe, but it is still true that it is easier for a small business to raise capital to scale up in the United States than it is in much of the rest of the world. Public equity: There are some growth businesses that bypass venture capital and go after public equity, a much bigger pool of capital and one that may give founders better terms. In some cases, this access to capital might be enabled by going public, even with unformed business models and little to show in terms of existing operations (revenues or earnings), but in most others, it takes the form of capital invested by larger, more mature public companies in return for a share of ownership. These investments may be labeled as strategic, but the motives for making these investments vary across companies. Some invest to get access to a promising technology or product. some to pre-empt competitors and some for the same reason that venture capitalists do. The bottom line is that businesses that seek out capital, whether from family, venture capital or public equity, have to accept that the capital providers will demand and usually get a say in business decisions, and the more capital you seek, the more sway they will have. III. Investor Preferences Businesses get their cues on whether to scale up or build business models from the investors who fund them, and much as founders want to map their own path, investor preferences matter, as do their end games. Put simply, a family that invests in a business with no plans for exit will choose a very different path for that business than a VC that invests in the same business with the intent of exiting that investment by selling it to another investor or company, or taking it public. Venture capitalists are often viewed as the sherpas who guided young businesses to success, both operationally and in markets, the mythology about venture capitalists and what they do has also built up. Since that mythology extends to almost every aspect of venture capitalist activity, perhaps the best way to dispel myths and bring in reality checks is to look at what venture capitalists are "assumed" to do in each phase, and contrast it with what they actually do: If you are reading this as a critique of venture capitalists, you are misreading it. My intent is not to paint a picture of venture capitalists as lazy and greedy, but to bring home the reality that given how venture capitalists invest, act and are judged, it is unrealistic to expect them to do the heavy lifting of building businesses for the long term and to even make business sense, when they talk about companies. There are two parts of the venture capital rulebook that you should focus on, to understand why many VCs prioritize scale over profitability. The first is that they price companies, rather than value them, and in a post from a few years ago, I made the argument in more depth. VC pricing based on what other venture capitalists are paying for similar businesses, often scaled to simplistic metrics, users and subscribers for pre-revenue companies and forward revenues or earnings in what passes for VC valuation: The second is that VC success is measured based on price at entry and price at exit on an investment, rather than the quality of the business built, and using that metric, the median venture capitalist has not been much better at harvesting alpha than the median mutual fund manager or PE investor: Cambridge Associates There are, of course, standouts in each of these categories, fund managers who have delivered well above the market, but in mutual funds and to an increasing extent, hedge funds, that success is fleeting. There are two aspects on delivering returns where venture capital stands out, relative to other active investing classes. The first is that failure, always a concern in investing, is much more a part and parcel of investing in venture capital than in other investing grouping. Put simply, not only are there more VC funds that go out of existence every year, but even the most successful VC funds lose on many or even most of the investments that they make, especially in angel financing deals. The second is that venture capital investing, when it works, can generate outsized returns on winners that (hopefully) cover the cost of failures. You can see both of these at play in the graph below, which looks at returns that VCs book when they exit investments: CF Private Equity, from Pitchbook data As you can see, across all the time periods, it is the top 10% of VC investments that deliver the bulk of returns to VC investors, and over time, that concentration has increased: in the 2023-2026 period, 80% of all returns to VC investors came from their top 1% of investments. The combination of these two forces (losses on most investments and outsized winners), i.e., the power law in venture capital, has two consequences. The first is that only about a quarter of venture capitalists in each year deliver above-average returns, making the average VC returns in the table above more palatable. The second is that success in venture capital, unlike in other areas of active investing (including mutual funds, hedge funds and even private equity), has been more enduring. The power law characteristic also feeds into VC incentives, leading venture capitalists to direct their capital more into chasing the biggest winners than in building businesses. In fact, the more top-heavy VC returns become, i.e., dependent on big payoffs, the more pressure venture capitalists feel (and pass on to their portfolio companies) to find the next big winner, pushing the ecosystem dangerously close to gambling. A Changing Game With the discussion of the scale versus profitability at the business level leading in, and the assessment of the incentives of capital providers following, I think that we are well positioned to examine how changes in public and private markets have increased business incentives to scale, as opposed to building business models. There are two developments, in particular, that have taken the tilt towards scaling in venture capital and made it even more pronounced - the entry of public equity into the funding of private businesses and the fading of reversal, as an antidote to momentum, in public markets. The Gray Market Effect For much of the last half of the last century, after venture capital established a presence in the United States, it remained the only or primary source of capital for young firms. That has changed especially int the last decade, as public equity investors have increased their investments in young, private businesses, supplementing venture capital in some and even displacing it in others. An early measure of this trend is captured in the charts below: Kwon, Lowry and Yiming (2020) While this graph looks at only the number of mutual funds investing in private businesses, and stops in 2016, there was a corresponding surge in capital invested by mutual funds in young, growth companies, with T.Rowe Price and Fidelity investing billions in high profile tech companies like Uber. They were joined by sovereign funds, who invested heavily in these companies either directly or indirectly, through stakes in entities like Softbank's Vision fund. We can debate the reasons for why we saw this surge, with fear over missing out (FOMO) and wanting to partake in tech playing roles, but whatever the reasons, capital access surged for young companies, especially in tech, during the period. In effect, rather than two mostly separated markets - one for young, smaller, private business dominated by VCS and one for larger companies more advanced in the life cycle, where public equity suppled the funds, a gray market was created where VC and public equity fund access allowed private businesses to stay private for longer. Public Markets: Momentum, Fundamentals and Reversals Public equity markets have always been momentum-driven, allowing traders who ride that momentum to prosperity, before bringing them down when the momentum shifts. At the same time, fundamentals act as an anchor, operating as a counter to momentum, leading to reversals and allowing investors to hold their own over time. While the congruence is not always perfect, scaling feeds into momentum and profitability is the most critical fundamental, and in markets with balance, when one gets out of sync, the other restores harmony. Over the history of stock markets, value investors have often claimed dominance, and pointed to the returns you could have earned by buying companies that look cheap on a value basis (low price earnings or low price to book) and waiting for price reversals. Traders push back by noting that over the same history, momentum has had a decisive effect on returns, especially over shorter time intervals. While the momentum effect shows up across the decades, there is evidence that the reversal effect has weakened over time, leaving investors who bet on mean reversion and a return to fundamentals in the lurch: The reasons given for this shift vary, and are often reflective of the biases of the investors giving the reasons. The Fed did it: For those who view central banks as all-powerful, and believe that the low interest rates of the last decade were their doing, those low rates have also become the proximate reason for market pricing behavior and reckless risk taking. Their argument is that interest rates that are close to zero induce investors to shift from bonds to stocks, and within stocks, to move from low growth, high earnings stocks to high-growth companies with little or negative earnings. The rise of passive investing: In the battle between active investing and passive investing, with ETFs supplementing index funds, the latter has had a decisive edge in terms of returns over the last two decades, and its share of the market now stands are well above 50%. There are some who argue that the flow of funds to passive investing vehicles has contributed to the increased power of momentum, since more new funds flow to the largest market cap companies than to the smaller ones. In addition, it is argued as the number of active investing declines, there are fewer investors looking at business models and profitability, reducing the pull of fundamentals on price. Public market composition: It is noteworthy that the reversal effect started weakening in the 1990s, a decade when young dot.com companies with unformed business models flooded the market, bypassing the more traditional route of using venture capital to grow. With these companies, where value is almost entirely driven by potential and not by operating metrics today, the catalysts needed for reversal may take longer to manifest. Information sources and access: It is undeniable that investors and traders get information from a wider ranges of sources now than two or three decades ago, with social media and online sources supplying information that used to come from newspapers and financial news channels. In additional to being less curated and controlled, that information is also instantaneously accessible to the public, and price reactions tend to follow. While I take issue with parts of each of these arguments, there is some truth to all of them, and they have contributed to making pushing back against momentum a more hazardous exercise for investors. The Consequences With larger amounts of capital being deployed by VCs at young, growth companies, substantial capital infusions from public equity funds into private capital markets, and public equity markets that are more used to and receptive to young company listings, it should not be surprising that it is changing how private companies behave. In the graph below, I look at the characteristics of companies going public in the United States, using the data that is generously made available by Jay Ritter; There are three clear changes over time that are visible in this graph: 1. Private businesses are waiting longer before going public: As you can see, the average age of a company going public has risen over time, with the median age rising about 11 years in the last 15 years. 2. Private businesses are scaling up (revenues) more, while waiting: While private businesses wait longer to go public, they are spending that time scaling up more than they used to. The inflation-adjusted revenues at the median IPO have tripled or even quadrupled, relative to IPOs in the 1980s. 3. Private businesses are deferring building business models & profitability: The most striking feature of the data, to me, is that while private businesses are waiting longer and scaling up more before going public, they also seem to be deferring business building for much longer as well. While it was routine for companies going public in the 1980s to be profitable (>80% were), less that a quarter of the companies that have gone public in the last decade have been profitable. While companies that are going public are bigger (in revenue terms) and less likely to be profitable, markets are attaching large market capitalizations to these newly minted companies, as you can see in this graph which zeros in on tech IPOs: You will also notice that companies going public are issuing smaller portions of their shares to the public, at least in the initial offering, suggesting that the need for capital that drove companies to go public has become less pressing over time, perhaps because of more capital access as private businesses. While the median market cap of a company going public in the last six years has exceeded a billion, the largest IPOs command market capitalizations that would have been unimaginable a few decades ago. From Facebook, with a pricing of $104 billion, in 2012 to SpaceX, going public in June 2026 at $1.8 trillion, the trend lines are pointing upwards, especially if Anthropic and OpenAI deliver on their trillion-dollar plus pricing promise. Implications By itself, the trend towards private companies scaling up more, while public, and going public at eye-popping market capitalizations may be understandable and explainable, but there are implications that we need to consider both from an investing and governance standpoint. Corporate governance: One of the reasons that private companies often delay going public is because governance requirements, from board composition to top management compensation, are more stringent at public than private businesses. While Sarbanes-Oxley, which wrote into law many of the current governance rules for public companies, is often toothless and ineffective, it still forces disclosures about governance (on conflicts of interest and board member relationships) at public companies. In addition, public market investors can pressure public companies to change governance practices or top management, if companies underperform in the market place. One of the perils of letting companies scale up more before these governance questions get raised is that the top management in these companies may have few checks on their actions. It is true that venture capitalists could operate as a disciplinary mechanism, but in an age of founder worship and where VCs can be divided and conquered, you can have companies with market pricing of a billion, hundreds of billions or even trillions run by people who are ill-suited for the task. Delayed business model building: If the first imperative for a private business is to scale up, because scaling pushed up pricing both in private and public markets, the challenge of business building will get deferred to a later stage. The problem with scaling up first, and building a business model later, is that it may be too late, since the choices made to allow for scaling up may impede the pathway to profitability. Again, if your response is that VCs will work on fixing this problem, they have little incentive to do so, since they benefit from scaling up and exiting these businesses, before the business problems become too big to ignore. Scaling stories: If you believe, as I do, that valuation is a bridge between stories and numbers, and that the balance between the two shifts over the life cycle, with stories dominating early in the life cycle and the numbers taking center stage in the later stages, it is understandable that VCs and founders, when marketing their companies are primarily story tellers. I don't have a problem with that, but as I noted in my last post on AI as a business, the stories that are being told for these companies are often incomplete, and almost entirely focused on the scaling question. Thus, in the Anthropic sales pitch it is the growth in the annualized revenue run rate (ARR) and the size of the AI market (huge, but with no specifics) that comprises the bulk of the story, with little or no mention of business models or profitability. Disruption without replacement: Disruption has been a key component of the stories that underlie many of the largest companies that have gone public in this century. Accepting the premise that a healthy economy needs a shaking up of the status quo, and that disruption can lead to economic growth and better practices, it is still legitimate to look at disruption's debris. One of the perils of supplying capital in almost endless quantities to private businesses that aim to disrupt, without challenging them on business models, is that you may succeed at disrupting the status quo (driving existing players out of business) but your disruptor may not be able to build a business that can be self-sustaining in the long term. Conclusion I am sure that you are already aware of the core message of this post, which is that notwithstanding the current emphasis on scaling up businesses, not all businesses are meant to scale up, and that scaling up comes with challenges that founders may be ill-equipped to meet. That said, ambitious founders will feel the urge to make their businesses bigger, and if they raise capital (from venture capitalists) to make this happen, the incentives to scale up will increase, even if it makes little or no business sense to do so, with all parties hoping to exit by selling to others (public or private) who will price based on scale. While this has always been the case, changes in private and public capital markets have tilted the scale even further in favor of scaling, and it is possible that companies, both public and private, with sky-high pricing have been built on bad business models that are irredeemable. YouTube Video Blog posts on Venture Capital and Scaling Blood in the Shark Tank: Pre-money, Post-money and Play-money Valuations (February 2015) Billion-dollar Tech Babies: A Blessing of Unicorns or a Parcel of Hogs (June 2015) Venture Capital: It is a pricing, not a value game! (October 2016) Risk Capital in Markets: A Temporary Retreat or a Long-term Pullback (July 2022)
The only "reform" that changes our lives in a fundamentally positive way is radical decentralization via distributing centralized power. Presidents like to deal with the CEOs of corporate monopolies for self-evident reasons: Rather than engage in the tedious, contentious herding-of-cats in nimble, dynamic, competitive sectors, the Prez makes a deal with the monopoly CEO and the deal is imposed on everyone down the political, corporate, workplace hierarchy. Centralized power makes a coup--a forced swap of leadership--meet the new boss, same as the old boss--easy. Financial coups are easier, too, with one central bank and one cartel of "too big to fail, too big to care" banks. Centralized power offers many other Irresistible Temptations. Reformers love centralized power because if they can grab control of it, they can force-feed their glorious reforms (or profit-maximizing schemes) down everyone's throats whether they agree or not: it is against the law to complain about corporate/state monopolies controlling our lives, everyone must install a Flock camera in their bedroom, no one can criticize the Supreme Leader in private, everyone must wear approved Silly Hats in public, etc. Oops, those reforms sound like an authoritarian, totalitarian state gone mad. Yes, precisely. All centralized power arrangements end up manifesting authoritarian, totalitarian extremes of madness, because that's the only possible outcome of centralizing power: petty dictators are soon running the asylum, and loving every minute of it. The patients, not so much. We see this everywhere now, as monopolies are manifestations of centralized power. This is why I call the status quo Privatized Totalitarianism as privately owned and operated monopolies / cartels have the same headlock on us as state monopolies, and the two work together, as this serves the interests of both: you make the Silly Hats, and we mandate their use, and penalize anyone attempting to modify your software, app, device or Silly Hat to evade your monopoly chokehold. We both get rich exploiting the powerless peasantry, so what's not to like? Politics now boils down to a Silly Hats slugfest over who gets control of the Privatized Totalitarianism casino. The only meaningful reform is to decentralize power by demolishing every monopoly and cartel and banning the aggregation of power. But what about "efficiency"? Yes, Privatized Totalitarianism is very "efficient"-- efficient at extraction, exploitation, surveillance, repression, propaganda, PR and social control mechanisms. If the public can "vote with their feet" by moving to a different physical location but they're still living in the same cartel-monopoly economy wherever they move, their "liberty" is illusory. It's like changing cabins in the gulag: maybe this hut has fewer leaks and fewer fleas, but it's still in the gulag. Just as what we're losing by using AI is invisible because we've lost the capacity to even see what's been lost, we've lost the capacity to see the systemic decay of the quality of our lives in the invisible gulag of Privatized Totalitarianism. So even as we thrill to some new novelty or tiny discount, we've lost the capacity to see what's been lost in the slow destruction of decentralized, competitive dynamism in favor of the profit-maximizing, sclerotic gulag we're all trapped in without even being aware that we're trapped, for the key to maintaining the kingdom is to foster the illusions of choice, liberty and competition while distracting us with ceaseless hype about new technologies, novelties and meaningless discounts as "competition" and "choice." It's like looking at a row of different brand products and then reading the fine print to discover that they're all owned by the same corporation. That's Privatized Totalitarianism, well cloaked behind carefully maintained illusions of choice, liberty and competition. And if you protest, it might get worse: "I am altering the deal, pray I don't alter it any further." The only "reform" that changes our lives in a fundamentally positive way is radical decentralization via distributing centralized power. Everything else is just changing huts in the gulag and being delighted with the steady stream of absurd parodies of novelty: "New gruel, new taste, now with micro-plastics!" New podcast: Charles Hugh Smith on the End Game of Repressed Interest Rates: Stagflationary Inflation followed by "Cold Turkey" (29:25 min) New collection of five intriguing stories: Jumble Bin Stories (Kindle $6, print $12) read samples for free (PDF) My book Investing In Revolution is available ($18 for the paperback, $24 for the hardcover and $8.95 for the ebook edition). Introduction (free) Subscribe to my Substack for free NOTE: Contributions/subscriptions are acknowledged in the order received. Your name and email remain confidential and will not be given to any other individual, company or agency. Thank you, Frank M. 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September is here, and there’s actually quite a bit going on.