The Large Language Model Bubble: 'Snake Oil' for the 21st Century

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The technology industry, having exhausted its capacity for genuine innovation, has seized upon "artificial intelligence" as its latest speculative vehicle. What we are witnessing is not a revolution in human capability but the largest economic bubble in history, one that will make the dot-com crash and the housing crisis look like minor corrections when it inevitably collapses.

The Emperor Has No Clothes: What "AI" Actually Is

The term "artificial intelligence" represents perhaps the most successful marketing campaign in corporate history. It conjures images of thinking machines, of silicon consciousness, of digital minds 'learning' that rival or surpass human cognition. This is pure fiction, puffery, a carefully constructed illusion, misinformation designed to extract investment capital and justify obscene multi-trillion dollar valuations.

What the industry peddles as "AI" consists primarily of large language models: sophisticated statistical systems trained on vast corpuses of text to predict probable word sequences, patterns. These systems do not think, do not understand, do not reason, do not learn, do not 'remember.' They pattern-match and interpolate, producing outputs bland simulacrum that mimic human language, human emotions, without any underlying comprehension of meaning, causation, causality, logic, knowledge, skill or experience.

The dangers become immediately apparent when these systems encounter tasks requiring actual reasoning. Basic logic puzzles that children can solve through deductive reasoning routinely confound large language models. Simple mathematical word problems produce confident but completely incorrect answers. The systems hallucinate facts, invent citations, and generate plausible-sounding nonsense with the same confidence they display when producing accurate information. 

It's becoming apparant that the use of LLM's may not have any marginal utility after prolonged exposure, users self report diminishing returns the longer an interaction takes place. In many cases the use of these 'services' and the long term effects associated with exposure to them are deleterious to mental health outcomes for the vast majority of users. It's always important to remember when using 'free' services, there is no such thing as a 'free service.' When you are not required to pay for a service, it's because YOU, more specifically, your attention, is the commodity being traded; as you assist in 'training,' the models, through your unremunerated labor, in effect this 'training' is than used to better manipulate you and everyone else using these, so called, 'services,' which in turn may command more of your attention, creating a vicious cycle.    

Consider Arthur C. Clarke's observation that "any sufficiently advanced technology is indistinguishable from magic." The AI industry has inverted this insight, using it as a con: present statistical parlor tricks with sufficient marketing polish and technological mystification, and investors and customers will perceive magic where only clever binary code, algorithms exist.

The Resource Catastrophe

The environmental cost of this speculative bubble may prove to be its most devastating legacy. Training and operating large language models consumes staggering quantities of resources at precisely the historical moment when humanity faces existential climate crisis.

Energy consumption has reached obscene levels. Individual model training runs can consume more electricity than tens of thousands of homes use in a year. Gigawatts (GW) of power. The inference operations required to generate responses to user queries, multiplied across scores of millions of interactions, demand continuous power draw equivalent to small cities. Data centers supporting these systems operate around the clock, their cooling systems and server farms burning through electricity at rates that strain regional power grids and drive up costs to the average household and small business.

Water usage for cooling these facilities diverts millions of gallons from already stressed water systems. Communities facing drought and water scarcity watch as tech companies siphon aquifers to keep server banks from overheating, all to power services of dubious utility.

Land consumption accelerates as the industry constructs massive data center complexes across the globe. Agricultural land, ecosystems, and communities face displacement to accommodate the physical infrastructure required to maintain the illusion of machine intelligence.

The bitter irony cuts deep: just as renewable energy adoption was beginning to bend the curve on emissions, just as solar and wind power were approaching true base load viability, the AI bubble has triggered an energy demand spike that threatens to erase years of progress. Power prices climb as utilities struggle to meet the sudden surge in consumption. Planned retirements of fossil fuel plants face delays as AI companies demand more capacity than renewable buildout can immediately provide in hyper critical power generation plants using methan gas as the primary fuel source. 

The technofuedalist monopolistic, dominant, accumulation, rulling class promises that AI will solve climate change, optimize energy systems, and unlock sustainable futures. Meanwhile, their products actively accelerate environmental destruction, consuming the resources we desperately need to deploy actual climate solutions.

The Economics of Mass Delusion

The financial structure underlying the AI bubble rests on foundations even more precarious than its technical capabilities. A handful of technology monopolists have convinced markets that large language models represent a multi-trillion-dollar opportunity, despite scant evidence of sustainable business models or genuine customer demand.

The conversion problem exposes the weakness at the bubble's core. Scores of tens of millions of users may interact with free versions of these services, curious about the novelty or finding occasional spectacle utility in their outputs. But conversion to paying customers remains dismal. The vast majority of users, once the novelty wears off, recognize these tools as marginally useful at best, certainly not worth substantial subscription fees. The services provide another form of diversion, entertainment and occasionally convenient summarization, but fail to deliver the transformative productivity gains that would justify the industry's valuations.

Companies like Nvidia have positioned themselves brilliantly, selling the proverbial shovels during the gold rush. Their GPU sales to AI companies have driven spectacular stock appreciation. But shovel sellers in a bubble face the same ultimate fate as the prospectors: when the rush ends and the claims prove worthless, demand for shovels collapses too.

Nvidia: The Shovel Seller's Reckoning

Nvidia has become the poster child for AI bubble wealth creation, its market capitalization soaring into the trillions as data centers scramble to acquire the GPUs necessary for training and running large language models. The company has successfully positioned itself as the essential infrastructure provider for the AI revolution, the indispensable supplier without whom the future cannot be built.

Yet Nvidia's meteoric rise rests entirely on the continued viability of the AI bubble itself. The company produces hardware optimized for the specific computational demands of large language models and similar systems. When organizations stop building new data centers, when the speculative frenzy subsides, when companies recognize that their AI investments generate no return, in fact, burn billions of dollars, Nvidia's order books will crater.

The historical parallel to previous bubble dynamics holds perfectly. During the dot-com boom, Cisco and other networking equipment manufacturers saw their valuations explode as companies raced to build internet infrastructure. Cisco was considered the safest play in the technology sector—selling the picks and shovels while others speculated on internet business models. Then the bubble burst. Demand for networking equipment collapsed. Cisco's stock price fell over 80% and never recovered to its bubble-era highs, even as the internet genuinely did transform commerce and communication.

Nvidia faces an even more precarious position because the underlying technology it enables generates far less genuine value than the internet did. When the AI bubble bursts, organizations will not merely slow their infrastructure buildout—they will mothball existing capacity and liquidate equipment. The secondary market will flood with used GPUs, which may or may not have utility for other applications, as failed AI startups liquidate assets and established companies write down their AI investments.

The company has already found itself in the awkward position of having to debunk claims that its customers are not actually using the massive quantities of GPUs they've purchased. Reports emerged suggesting that significant portions of deployed AI hardware sit idle or drastically underutilized, purchased as speculative bets or for appearances rather than actual productive use. Nvidia's defensive responses to these reports betray anxiety about a market recognizing that the emperor has no clothes—that the demand driving their growth represents bubble speculation rather than genuine need, after all the mention of 'AI' buble has increased 897% in the preceding 6 months.

The Enron Parallels and Vendor Financing Schemes

More troubling still, Nvidia has been forced to publicly address comparisons to Enron, the energy company whose fraudulent accounting practices became synonymous with corporate malfeasance and outright fraud. That such comparisons have emerged at all speaks volumes about the concerns percolating among serious market observers.

The parallels become apparent when examining Nvidia's business practices revealed in material public filings. The company has increasingly relied on vendor financing arrangements—essentially lending money to customers so they can purchase Nvidia's products. This accounting maneuver allows Nvidia to book revenue from sales that customers might not otherwise be able to afford or might not genuinely need.

Vendor financing represents a classic warning sign of demand weakness masked by financial engineering. When a company must finance its own sales, it suggests that organic market demand cannot sustain the revenue growth that valuations require. Real customers with genuine needs and viable business models secure their own financing. Customers who require vendor financing are often marginal operators making speculative bets, exactly the kind of demand that evaporates when bubble dynamics reverse.

The practice also creates significant credit risk concentrated on Nvidia's balance sheet. When the AI bubble bursts and customers default on these financing arrangements, Nvidia will face not only collapsing demand but also massive write-downs on the loans it extended. The company becomes both the manufacturer suffering from demand collapse and the creditor absorbing defaults—a double exposure that will amplify losses.

The Circular Firing Squad: Deals Built on Nothing

Perhaps nothing illustrates the absurdity of the AI bubble more clearly than the increasingly grandiose deal announcements that dominate tech 'hype' news cycles. Every week brings new proclamations: massive investments, historic partnerships, unprecedented orders for AI infrastructure. The numbers grow ever larger, the rhetoric ever more breathless, the promises ever more fantastic.

Yet examination of these deals reveals them as a circular firing squad—companies using capital raised on AI hype to purchase AI infrastructure from other companies whose valuations depend on AI hype, all funded by investors whose returns depend on AI hype continuing. No actual value gets created. Money simply circulates within a closed ecosystem of true believers and opportunists, each transaction cited as evidence of genuine market demand when it actually represents mutual delusion.

Company A raises billions from investors excited about AI, then announces a massive deal to purchase GPUs from Nvidia, boosting Nvidia's revenue and stock price. Nvidia's rising valuation helps Company B raise more capital, which it uses to purchase cloud computing credits from Company C, which itself recently raised billions to build data centers filled with Nvidia GPUs, purchased using vendor financing from Nvidia. Company C announces record revenue, its stock soars, and investors pour more capital into Company D, which announces an even larger AI infrastructure deal.

The entire structure resembles not a functioning market but an elaborate shell game. Each transaction gets trumpeted as proof of concept, each deal announcement drives further speculation, each rising stock price validates the premises on which all the other deals rest. But strip away the circular transactions, the vendor financing, the speculative capital inflows, and the underlying customer demand supporting the structure barely exists.

Real businesses operate differently. Real demand comes from customers who have actual problems that products genuinely solve, who generate revenue from real economic activity, who purchase goods and services because those purchases create value exceeding their cost. The AI bubble deals represent speculation compounding upon speculation, each layer dependent on layers below it, the entire structure resting on a foundation of collective delusion about AI capabilities and market potential.

When the music stops, when one significant player cannot meet its obligations or admits that AI investments generated no return, the cascade begins. Deals predicated on other deals unravel. Vendor financing defaults accelerate. Stock prices collapse as the circular logic sustaining valuations breaks. Nvidia, OpenAI, Et al. sitting at the center of this web, will discover that being everyone's counterparty means absorbing losses from every direction simultaneously.

The Enron comparisons, initially dismissed as hyperbolic, may prove generous. Enron at least operated in energy markets where real demand existed and real products changed hands, even if the accounting was fraudulent. The AI bubble deals increasingly resemble pure vapor—transactions that exist primarily to justify other transactions, revenue that exists primarily on paper, demand that exists primarily in press releases.

When correction comes, Nvidia will discover that manufacturing capacity optimized for a speculative bubble becomes a liability the moment speculation ceases. The company cannot easily pivot to other markets, cannot simply retool to produce different chips for different applications. It has bet everything on AI remaining a growth sector indefinitely. That bet will fail.

The parallel to tulip mania in 17th century Holland proves instructive. Sophisticated merchants convinced themselves and others that tulip bulbs represented genuine stores of value, that prices would continue rising indefinitely because everyone understood tulips were the future, some 'superb' examples selling for more than a grand townhouse on a prime canal in Amsterdam. The fundamental absurdity became apparent only in retrospect, after fortunes had been destroyed and the mania's grip finally broke.

AI valuations rest on similar collective delusion. Markets have convinced themselves that companies burning billions annually with no clear path to profitability somehow represent sound investments because "AI changes everything." The transformation promised never quite materializes, but the hype cycle grinds forward, feeding on itself.

The GDP Mirage: Deutsche Bank's Damning Analysis

Perhaps no single data point exposes the hollowness of the AI bubble more starkly than Deutsche Bank's analysis of recent US economic growth. Their research revealed a startling conclusion: the only positive GDP growth seen over the last six quarters has come directly from data center construction and related infrastructure buildout. Strip away the AI investment boom, and the United States economy has been in negative growth trajectory—a functional recession masked by speculative bubble activity.

This finding demolishes any pretense that the current economic picture reflects genuine health or sustainable expansion. The data center construction represents pure bubble economics: building infrastructure to support services that generate minimal real value, sustained entirely by the momentum of speculative investment rather than actual customer demand or productive output.

The implications prove profound. When economists and policymakers tout GDP growth, when politicians claim economic success, when market analysts justify continued investment, they point to figures propped up entirely by one sector engaged in speculative overbuilding. The rest of the economy—manufacturing, retail, services, the productive sectors where actual value gets created and real goods and services change hands—has contracted or stagnated.

This is not economic growth in any meaningful sense. It is the equivalent of counting the construction of empty office towers during a real estate bubble as evidence of economic vitality. The buildings get erected, the concrete gets poured, the equipment gets installed, and GDP ticks upward. But the moment the bubble bursts, those buildings sit vacant, the construction stops, and the illusory growth evaporates, revealing the underlying recession it had temporarily obscured.

This represents perhaps the purest expression of bubble dynamics: economic activity that exists only to support speculation, that will evaporate the moment the speculative premise collapses. When the AI bubble bursts, and it will burst, the data center construction that temporarily propped up GDP will cease. Partially completed facilities will sit abandoned. Finished centers will operate far below capacity or shutter entirely as AI companies fold or drastically scale back operations.

The resulting market correction will dwarf previous tech bubbles. Trillions of dollars in market capitalization will evaporate. Pension funds, retail investors, and institutions that bought into the AI narrative will absorb catastrophic losses. The economic ripples will cascade through supply chains, employment, and regional economies that became dependent on data center development.

The Surveillance State Endgame

When the bubble bursts and the private sector cannot maintain the AI infrastructure it has constructed, the state likely steps in. Governments cannot allow such massive capital investments to simply vanish, particularly when those investments include sophisticated computing infrastructure with clear surveillance and control applications.

The data centers built ostensibly to power chatbots and image generators will find new purpose as tools of population monitoring and social control. The surveillance state apparatus, increasingly totalitarian in character, will inherit the physical infrastructure and technical capabilities developed during the AI bubble. What was sold as consumer convenience becomes the foundation for pervasive monitoring, predictive policing, and automated suppression of dissent.

This transition serves the inegalitarian regime perfectly. The computational power marshaled to predict the next word in a sentence can just as easily track individual movements, analyze social networks for potential resistance, and automate the apparatus of oppression. The bubble's burst becomes an opportunity for the state to acquire, likely through bailouts and subsidies, a surveillance infrastructure that civil liberties concerns might otherwise have prevented from being constructed.

The Technofuedalist Con

The AI bubble represents the final gambit of a technology industry that has run out of genuine innovations to monetize. Having exhausted the possibilities of social media, smartphones, and cloud services, having failed to deliver on promises of cryptocurrency revolution and metaverse transformation, the technofuedalist robber barons have placed their bet on AI.

These are not visionaries pioneering new frontiers of human capability. They are carnival barkers selling snake oil, using sophisticated marketing and deliberately obscure technical jargon to disguise the fundamental emptiness of their offerings. They have convinced investors, governments, and segments of the public that statistical text prediction represents a paradigm shift in human technological development, that systems which cannot solve basic logic puzzles will somehow revolutionize every industry and replace human cognitive, white collar, labor.

The public remains largely unimpressed. Outside of investor circles and technology enthusiast communities, skepticism prevails. People interact with these systems, recognize their limitations, danger and return to existing tools and methods that actually work. The technology gets shoved down throats through aggressive marketing, through mandatory integration into existing software, through breathless media coverage of every incremental development. But organic adoption that would indicate genuine value creation largely fails to materialize.

The Reckoning Approaches

Economic bubbles follow predictable trajectories. Initial innovation or speculation attracts investment. Success stories multiply. FOMO (fear of missing out) drives more capital into the sector. Valuations detach from fundamentals or objective reality. Warning signs appear but get dismissed. True believers insist "this time is different." Then something breaks, the spell shatters, and the collapse begins, short sellers make fortunes.

The AI bubble has reached the stage where warning signs multiply but true believers still dominate the discourse. The reckoning approaches. When it arrives, the consequences will extend far beyond financial markets. Years of resources that could have addressed genuine challenges—climate breakdown, infrastructure decay, healthcare access, educational improvement, housing—will have been squandered on generating plausible text strings and creating simulacrum images or moving images and sounds through statistical interpolation.

The environmental damage will persist long after the bubble bursts. The surveillance infrastructure will remain operational even as the consumer applications fail. The concentration of wealth from cloud capital and power among technology monopolists will have increased exponentially even as their bubble-inflated valuations collapse.

The AI bubble represents late-stage capitalism in microcosm: spectacular resource misallocation, predatory extractive institutions disguised as innovation, environmental destruction in pursuit of paper profits, and the cannibalization of genuine productive capacity, human labor to feed speculative mania. It is the rot economy producing rot, the final desperate gambit of a system that can imagine its own end more easily than it can imagine creating actual value for actual people.

The revolution struggling to be born must reckon with the wreckage this bubble will leave behind. The alternative is to let the technofuedalists, imperialism capitalists, monopolists and their state collaborators convert the ruins into infrastructure for intensified control and extraction. The choice, as always, is ours to make. Such a choice, may need to be fueled by caffeine.

 

 

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