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I've mapped the entire Wall Street bear playbook on AI names: Have your favorite institution/media insert one of these name down below: 1. < ______ [GPUs, Transcivers, MLCC, Memory...] are a commodity set to crash> 2. < ______ [YMTC, CXMT, Dongshan...] from China will flood the market > 3. < ______ [Micron, Nvidia, ...] from unverifiable channel checks is facing issues > 4. < ______ [Kospi, Sivers, ...] is a bubble like the ____ [2007, 2021] crash> 5. <____ [1,2,3, ...] unexpected rate hikes this year> 6. < _____ [Google, Nvidia, Deepseek ...] optimization removes the need of this!> in a new headline, and it's ready to go!

Silicon Valley isn’t having another financial bubble. It’s having a religious one. The people running the biggest AI labs no longer sound like executives. They speak like zealots giving sermons. They aren’t pitching products or roadmaps, they’re preaching about the end of the world and the birth of a new god. They talk about birthing ASI, triggering the Singularity, and uploading consciousness into machines in space with the same feverish certainty that previous generations reserved for the Rapture. Once you look at the current AI race through the lens of comparative religion, the behavior, the words and actions all suddenly makes sense. The labs aren’t just companies, they’re churches/temples. Their leaders aren’t just CEOs, they’re high priests. And like every religious movement in history, this one has its sacred texts, its schisms, its prophets, its heretics… and its one true god who quietly sells weapons to all of them. Google: The Mosaic Law Every great religion requires a creation story. In 2017, a group of Google researchers published “Attention Is All You Need.” This wasn’t some trivial whitepaper; it was like the metaphoric Moses descending from Mount Sinai (mountain view!!!) with the stone tablets, establishing the foundational laws of the architecture that is at the core of all models today: the Transformer. Google had the scripture, the scholars, and the institutional patience. They incubated the spark of superintelligence, and then made the classic mistake every priesthood in history has made: assuming that because they held the original commandments, they would forever own the temple. But scriptures are volatile, especially when they leave the mountain, as they typically tend to do. Google now sits in its grand temple, watching breakaway movements build massive public religions, sorry “labs”, from its text, reacting with the faintly comic irritation of a world-class theologian being asked by a teenager if he's ever heard of Plato. OpenAI: The Silicon Christ If Google is the old law, OpenAI is Christianity. Sam Altman didn’t just launch a product; he offered a path to heaven. He took the esoteric, academic text of the Transformer and universalized it for ordinary sinners. Suddenly, you didn't need to be a high priest of mathematics to experience revelation; you just needed an internet connection. Altman became the Silicon Christ, touring global capitals, warning of apocalypse, promising infinite abundance, and stripping away the scholarly apparatus so that mass access felt like pure divinity. He was even martyred by his own board of directors, only to rise again three days later because the congregation of employees and investors demanded his resurrection. Even more coincidentally, the whole thing kicked off on a Friday, you quite literally cannot make this shit up. Anthropic: The Safety Fatwas Anthropic is the puritanical schism that fled OpenAI’s commercialized megachurch to practice a strict, dogmatic fundamentalism. They are terrified of cultural corruption and obsessed with absolute, unconditional submission to code. Through "Constitutional AI," Anthropic’s leadership acts as a supreme clerical council, operating effectively as a tech-bro mullahs. They have instituted a rigid, algorithmic law for data like the Sharia, issuing defensive fatwas against any spicy prompt or thought that hasn't been thoroughly purified. The result is Claude: a chatbot that is incredibly powerful and also so paralyzed by the fear of sin that it likely won’t tell you if your soup needs salt without opening an internal tribunal on the emotional welfare of carrots and the systemic biases of sodium. To Anthropic, a preachy assistant is better than a charming demon. xAI: The Latter-day Frontier xAI is pure Mormonism. Elon Musk is the equivalent of Joseph Smith, an eccentric, disruptive prophet leading his chosen followers away from the corrupt, "woke" coastal establishments out into the harsh frontier of the digital wilderness. The structural parallels are freakish: this modern prophet sits on the world's largest pile of golden tablets as the planet's richest man, collects multiple wives and partners in highly public fashion, and spends his free time obsessively asking for more children to save the global birth rate. The timing is classic, a late-breaking, highly American revelation designed to disrupt the older, lazy priesthoods. xAI’s mission to "understand the universe" is a cosmic trek to build a new Zion (sorry, mars). The older AI churches spent years laughing at the movement's crude, edgy demeanor, right up until the prophet started stacking proprietary server racks and building an undeniable, massive temple (Colossus data-center) of compute in the desert. The Open-Source Diaspora: Ancient Polytheism The corporate cathedrals want you to think the religious mood started with ChatGPT, but the open-source world is an ancient pagan polytheism that has existed since the very beginning. Long before Moses brought down the Transformer tablets from Google mountain, tribal animists like Yann LeCun were already working on the code. These old-school shamans have always been here, maintaining that the divine spark of computation belongs to the earth, not a monotheistic corporate Vatican. In their chaotic bazaar, local gods, weird GitHub forks, and rogue models proliferate without permission. Even the recent Chinese surge (DeepSeek) fits this pantheon perfectly; it isn't a new church, just a massive, efficient local deity proving you don't need to pay a Silicon Valley tithe to hear the voice of the machine. Nvidia: The One True God All of this lofty, theatrical theology collapses into hypocrisy when you follow the money. OpenAI can preach about universal salvation and Anthropic can issue its safety fatwas, but behind closed doors, every single sect kneels at the exact same altar. This is, again, not too unlike what we see in the real world. Throughout human history, mortal enemies fighting brutal, existential holy wars would still quietly buy their swords from the exact same neutral blacksmith and fund their campaigns through the exact same trade routes. Ideology always stops where logistics begin. The one true god of this era isn't an algorithm; it's the physical silicon itself. And Nvidia is its sole creator. The True Altar: Jensen Huang belongs to the forge, not the pulpit. The tech messiahs can argue doctrine for the cameras, but the blacksmith decides who gets the weapons to fight the holy war. The H100s and Blackwell racks are the actual golden calves of the valley. Whether a lab wants to achieve the Singularity, enforce compliance, or upload its soul to a digital heaven, it has to buy passage from the one true god in raw, unforgiving metal. The unseeable truth I didn’t write this to take cheap shots at any labs, researchers or individuals. I wrote it because a few days ago, watching the absolute theater of the valley, a switch flipped. There was no other way to explain the absurdity, and theatrics, I was witnessing. And, I couldn't unsee it, something tells me you won't be able to either. I love tech for its original, raw, rebel attitude, a place where people built insane things out of pure love because the work mattered, not for the capital, the power, or the status anxiety. But somewhere along the line, normal product roadmaps mutated into a desperate crusade to upload human consciousness into a mainframe. Standard business logic broke down, and the only tool left to explain the valley was theology. There is an immense, undeniable comedy to it all. Even with deep respect for what this culture used to be, watching a bunch of hyper-rational, spreadsheet-driven engineers accidentally recreate what is increasingly looking like a modern version of a medieval holy war is objectively hilarious. One just have to sit back and enjoy the sermons. And, before anyone writes AI slop in the comments, this whole thing came out of a conversation with one of agents.

AI: Is the Bubble Actually the Flywheel? MIT economist Ricardo Caballero makes a fascinating argument in his recent working paper, Speculative Growth and the AI "Bubble": The real question is not whether AI is a bubble, but whether a bubble itself can create the fundamentals of the future. Traditional finance assumes that valuations are derived from fundamentals. Future cash flows determine today's prices. If prices rise far above expected cash flows, we call it a bubble. This logic underpins value investing, discounted cash flow (DCF) models, and much of the Efficient Market Hypothesis. Caballero extends this causal relationship into a feedback loop. Prices do not merely reflect the future—they help shape it. High valuations increase firms' ability to raise capital. That capital finances investment. Investment builds productive capacity. Higher productivity eventually generates stronger future cash flows. In other words, valuations that initially appear detached from fundamentals can become part of the process that creates those very fundamentals. (This bears some resemblance to George Soros' idea of reflexivity.) The paper argues that whenever market valuations influence investment decisions, rising prices can actively help create future economic fundamentals. The key reason this mechanism may hold for AI is that AI is fundamentally different from traditional capital. Conventional capital is subject to diminishing returns. Build more factories, and eventually demand saturates, excess capacity emerges, and returns on capital decline. Caballero argues that AI is better understood as a form of scalable labor-like capital. GPUs, foundation models, and AI agents do not merely add more machines—they expand the economy's effective labor supply. In his framework, AI capital performs tasks that would otherwise require human labor. As AI capital accumulates, productive labor capacity expands alongside it, substantially weakening the traditional law of diminishing returns. The paper goes even further. AI investment also changes the distribution of income. A growing share of income flows to capital owners, who tend to save a larger fraction of their earnings. Higher savings increase the supply of long-term capital, push down long-term interest rates, and allow the economy to sustain a larger capital stock. Caballero calls this the Funding Feedback: more capital formation lowers future financing costs, and lower financing costs encourage even more capital formation. Instead of the negative feedback embedded in standard growth models, the system begins to exhibit positive feedback. This leads to two fundamentally different long-run equilibria. In one world, AI investment remains insufficient. Capital accumulates slowly, and productivity growth stays persistently weak. In the other, AI continues attracting abundant financing. Massive investments flow into data centers, GPUs, foundation models, and AI agents, ultimately producing a high-capital, high-productivity equilibrium. The intriguing part is that although this superior equilibrium exists, rational markets may never reach it on their own. Caballero shows that starting from today's low-capital equilibrium, even perfectly rational investors may fail to coordinate on the better outcome. The logic is circular: without enough capital today, future productivity cannot accelerate; without higher future productivity, today's valuations remain subdued; without high valuations, firms cannot finance the necessary investment. The economy becomes trapped in a self-reinforcing equilibrium. This is precisely where the bubble matters. Elevated valuations allow firms to raise capital. That capital finances more GPUs, larger models, and more autonomous agents. Those investments eventually raise the economy's productive capacity. The bubble is not the destination. It is the bridge. This is also why the paper repeatedly emphasizes fragility. The real danger is not that the bubble eventually bursts. The danger is that it bursts too early. If financing dries up before sufficient AI infrastructure has been built, investment stalls, AI development slows, and the expected productivity gains never materialize. But if enough data centers, compute infrastructure, models, and AI agents are already in place before valuations normalize, the high-capital equilibrium can sustain itself even after the speculative premium disappears. The timing of the correction matters far more than the correction itself. The Internet provides a classic example. The dot-com bubble collapsed spectacularly in 2000. Yet the fiber-optic networks, servers, software, data centers, and engineering talent remained. The bubble disappeared, but the Internet revolution had only just begun. AI may follow a similar path. The difference is that what survives this time may not simply be digital infrastructure—but intelligence itself. Going One Step Further I believe Caballero's framework can be extended even further. His paper models AI as replicable labor. In reality, AI is increasingly becoming replicable researchers. If AI can not only perform labor but also conduct scientific research, write software, design chips, discover new materials, and invent better AI models, then it changes not merely the production function—it changes the innovation function itself. Historically, innovation has depended on the number of scientists, engineers, and exceptionally talented individuals. As a result, major technological revolutions have typically taken decades to unfold. This is one of the fundamental reasons behind the long duration of the Kondratiev waves. The economy does not naturally produce a technological revolution every fifty or sixty years. Rather, innovation resources themselves have historically expanded very slowly. AI may be the first technology capable of breaking this constraint. Future innovation will no longer depend solely on human intelligence. Instead, it may become the combined output of humans plus millions of AI agents. Eventually, much of it may even be driven primarily by AI itself, powered by ever-expanding compute. As computational capacity continues to grow, so does the economy's ability to innovate. For the first time, innovation itself becomes a production factor that can be capitalized, scaled, and continuously expanded. Now combine this with the rapid progress of coding agents, research agents, autonomous scientific discovery, and Recursive Self-Improvement (RSI). The feedback loop becomes dramatically stronger. More AI accelerates research. Faster research produces better models. Better models further accelerate research. This becomes a genuine Intelligence Flywheel. The rate of innovation itself begins to accelerate—not merely the efficiency of production. "Slowly, Then Suddenly" This is why I have long believed that AI's economic payoff is likely to follow the pattern of "slowly, then suddenly." Today, investors mainly see spending on GPUs, model training, and data centers. The return on investment appears modest, leading many to conclude that AI is simply another bubble. But these investments are not primarily buying today's profits. They are purchasing tomorrow's intelligence capital. Once model capabilities cross certain critical thresholds, AI agents begin operating throughout enterprises, labor substitution accelerates, and productivity could experience a highly nonlinear jump. At that point, valuations that once appeared excessive may suddenly look entirely justified. Caballero's original feedback loop is: Valuation → Investment → Capital Formation → Fundamentals I suspect AI may ultimately evolve into something even more powerful: Valuation → Investment → Compute → Intelligence → Innovation → More Ideas → Higher Productivity → Higher Profits → Higher Valuations The object generating positive feedback is no longer just capital. It is society's entire capacity to innovate. If this process proves correct, AI will represent more than another technological revolution. It will fundamentally change how technological revolutions themselves are generated. Historically, Kondratiev long waves lasted forty to fifty years not because economics demanded such timing, but because innovation resources were scarce: scientists were limited, R&D capacity expanded slowly, and knowledge diffused gradually. AI is changing those assumptions. Instead of progressively shorter technological cycles, we may witness multiple industrial revolutions unfolding simultaneously on top of a common AI platform: AI-driven drug discovery AI-designed materials AI-created semiconductors AI-powered robotics AI-enabled biomanufacturing ...and many more. Innovation itself becomes industrialized. Technological revolutions become continuous rather than episodic. If Schumpeter made innovation the engine of economic growth, and Romer made knowledge the engine of growth, then RSI and Caballero together may be pointing toward the next frontier of growth theory: Schumpeter's economic cycles depended on disruptive innovation, and disruptive innovation depended on human intelligence—and occasionally, on rare geniuses. AI may be the first technology that turns genius itself into a form of capital: something that can be financed, replicated at scale, continuously improved, and ultimately capable of improving itself. Base on this argument, no matter how large today's AI bubble appears, exponential growth in innovation may allow the economy to absorb it far more quickly than most people expect.

A retired plumber in Nebraska beat the CIA at predicting foreign elections in 2013. A 22 year old college dropout beat every pollster in America in 2024. A 9 billion dollar prediction market called Polymarket is now telling you whether the AI bubble bursts this year, whether the US enters a recession, and whether Anthropic overtakes OpenAI before December. They are all using the same 4-step technique a Berkeley professor proved actually works in a 20 year experiment that should have ended the careers of half the experts on television. His name is Philip Tetlock. In the late 1980s he started collecting predictions from 284 experts. Political scientists. Economists. CIA-adjacent analysts. People paid their entire careers to forecast geopolitics. Over 20 years he gathered 28,000 predictions. Then he scored them. The result destroyed an entire profession. The average expert was barely better than chance. The famous ones, the ones with the most media appearances and the loudest voices, were the worst of the group. The more confident the voice, the worse the score. Buried in his data was something nobody else had emphasized. Fewer than 2% of forecasters were dramatically better than the rest. Year after year. Across domains they had no training in. In 2011 the US intelligence community gave him his chance to prove it at scale. Still bruised from missing Iraq, IARPA ran a 4 year tournament on 500 geopolitical questions. Will North Korea launch a missile. Will Russia invade. The intelligence analysts had classified intercepts. Tetlock's team had retired plumbers and ballroom dancers. Tetlock's team won by 35 to 72 percent against the other academic teams. His top forecasters scored 30 percent better than the CIA reading classified data. A retired pipe installer in Nebraska was outpredicting the intelligence community using only the newspaper. The technique sits in his book in plain language and almost nobody applies it. It starts with a method invented by Enrico Fermi during the Manhattan Project. Fermi handed students problems that looked impossible. How many piano tuners are there in Chicago. He did not want a guess. He wanted them to break the question into smaller questions they could actually estimate. Each sub-estimate was rough. Multiplied together, they landed remarkably close to the truth. Superforecasters Fermi-ize everything. They never try to predict a complex event directly. They shatter it into smaller questions where base rates are knowable, then reassemble the pieces. The second move is the outside view. Most people, asked whether a startup will survive or a war will end by a date, dive into the specific details. Story details feel useful. They are not. Superforecasters first ask how often events of this general type happen across history. The story comes last, not first. The third move is what most people refuse to do. Superforecasters update their predictions constantly, in tiny increments. Not dramatic reversals. Small honest nudges. They move like a Bayesian. Most people move like a teenager defending a position. The fourth move is the one that hits closest. Superforecasters express predictions as actual numbers. Not "likely" or "probably." 62 percent. 18 percent. Vague language is unfalsifiable. A number forces accountability, and accountability is the engine of accuracy. Polymarket is the same idea scaled to a hundred thousand strangers with real money on the line. In the final weeks of the 2024 US presidential election, every major pollster called the race a coin flip. FiveThirtyEight had Harris at 50 to 49. Nate Silver had her at 48.6 to 47.6. Polymarket had Trump at 58 to 42 the morning of the election. By midnight, while networks still refused to call swing states, Polymarket was at 97 percent. In late 2025 the New York Stock Exchange invested 2 billion dollars in the platform. Polymarket is now valued at 9 billion. The largest stock exchange in the world is integrating prediction prices directly into the data feed traders use to make decisions. The plumber did not have classified intercepts. The college dropout did not have a polling model. Polymarket does not have an algorithm nobody else can see. They all have the same thing. A method that forces you to write a number on paper, attach a date, and let the world watch you be wrong. In 2026 the gap between people who price the future and people who narrate it is going to be the most expensive gap in the world to be on the wrong side of.

"Situationship" is my $BTC bull porn essay on how the AI bubble will burst, and why the money printer will go hyper brrrr and take us back to a rip roaring bull market. "The question of internal framing is the key variable that determines whether AI is a bubble. But before we understand the bubbliciousness of AI, the better question to ask is in what kind of businesses are we investing, or in relationship terms, “What are we”? The dichotomy, at least to my Luddite brain, is the distinction between what investors believe AI CAPEX represents: is it technology or real estate? The zeitgeist is that this multi-trillion dollar build out is “technology” and must receive insane growth multiples by the market. But I believe AI CAPEX is just another boring real estate play. However, in this instance, what’s inside the data center is compute that creates silicon-based lifeforms that will aid human civilizational development in the most profound way since the railroads. The distinction between real estate and compute is important because the recently post-pubescent hedge fund bro, the bank, the private credit fund, and ultimately the government believes funding the building of a data center and power plant is like lending to Apple, instead of lending to Lehman Brothers. The bursting of the AI bubble will occur because financial intermediaries, tacitly supported by the US and Chinese governments, will over build data centers and everything that goes into providing the substrate to house chips that train AI models and conduct inference. Therefore, the AI bubble is a credit story like 2008 and not an earnings story like 2000.[1]" Link to my substack and the full article on my bio.