4 Warning Signs the Biggest Crash in Decades Is About to Begin

I want you to imagine that you are in the late 90s. You open a brokerage account. You see all these new internet companies and everyone around you is saying the same thing. This is the future. So you do what most people would do. You take your savings and put it into a few dot com stocks that everyone is excited about. For a while, it feels amazing. Your money keeps going up. You check your account and see double digit gains. Friends tell you how smart you are. You start to feel like you finally get investing. Then the year 2000 hits. The bubble bursts and those same stocks start falling. 10%. 20%. 50%. Some of them never come back. You watch your account lose value so fast that it doesn't even feel real. And the strange thing is, the internet does go on to change everything. Just not fast enough to match the prices people were paying back then. Now, fast forward to you today. You open your investing app and this time, the big story isn't the internet. It's artificial intelligence. The stocks in your index fund are hitting new highs. The companies leading the charge are worth trillions. Everyone is saying this is a once in a lifetime opportunity. It might be. But just like 2000, there are early signs that the story and reality might be starting to drift apart again. Before you decide whether to stay invested, take profits, or tune out the noise, you need to understand the four signs shaping today's market. Because those same signs may be building the biggest crash in decades and could determine where your money goes next. Even if you only check your portfolio once in a while, there's a truth shaping almost everything you own. A tiny group of tech giants now carries the weight of the entire market. These companies dominate headlines, lead the biggest indexes, and power many of the apps most investors use. You might not notice, but if just 7 companies make up over 35% of the S&P 500, their gains or losses carry far more weight than you think. Without realizing it, you're already tied to them because when a handful of stocks make up a huge slice of the S&P 500, their fate becomes your fate too. That's what makes this moment feel so tense. Artificial intelligence has become the new internet story, limitless, urgent, and fueled by fear of missing out. The biggest tech companies are pouring staggering amounts of money into this race. Some are spending tens of billions each year on new data centers. Others have committed $50, $60, even $80 billion annually to chips, cloud infrastructure, and the power required to train ever-larger models. And this isn't slowing down. Recent forecasts show global AI spending reaching roughly $1.5 trillion this year-end, with total compute infrastructure investment projected to approach $7 trillion by 2030. To many people, this looks like progress. Some even call it the next industrial revolution. But to Michael Burry, it looks like a system leaning heavily on expectations that may never meet reality. And that's where everything shifts. Because behind the excitement are four forces steering the entire market. You won't hear much about them on the news, yet they influence every dollar you're investing, whether you buy individual stocks or simply hold an index fund. If you want to understand where your returns are truly coming from, and what could threaten them, these four forces are the map. And the first warning begins with something almost invisible. The moment artificial intelligence starts running out of the fuel that made it powerful in the first place. Artificial intelligence looks unstoppable from the outside. But every breakthrough you've seen so far depended on one simple ingredient. Data. Mountains of it. The internet became a giant buffet that models could eat from without limits. That's what made the early years of artificial intelligence feel so explosive. But here's the part almost no one talks about. We're running out of that buffet. That supply is drying up. Think of artificial intelligence as a student who has read nearly every book in their library. At first, they learn quickly because everything is new. But once they've gone through almost all the good material, each new lesson teaches them less and costs more effort. That's the data shield. The invisible point where models stop improving at the pace investors expect. Not because the technology is broken, but because the fuel behind it is drying out. Most of the high-quality text, images, code, and conversations online have already been scraped, filtered, and fed into the largest models. What remains is messy. Duplicated posts, low-value content, private data behind paywalls, and information companies cannot legally touch. To keep improving, artificial intelligence giants now need access to datasets that are locked away, heavily regulated, or extremely expensive. Only the biggest firms can afford them. Everyone else gets pushed to the sidelines. But even for the giants, the costs explode. Buying exclusive datasets, licensing private archives, partnering with publishers, or creating synthetic training material demands enormous spending. And synthetic data has a limit, too. Models trained mostly on their own output start to degrade, like a photocopy that loses sharpness each time it's copied. If artificial intelligence progress slows, investors don't just lose a little excitement. They lose the story that justifies today's valuations. Markets price these companies as if improvements will keep speeding up forever. But Burry knows what happens when the story and the math fall out of sync. That's when bubbles become fragile, because expectations stretch far beyond what the fundamentals can support. There's another layer here. When progress slows, companies often try to mask it by pushing for larger models, more compute and faster training cycles. But scaling without fresh, diverse data is like turning up the volume on a speaker playing the same song. It gets louder, not better. And yet the market keeps rewarding size instead of substance. So artificial intelligence companies face a simple dilemma. Spend far more to gain far less, or accept slower improvement. Neither fits the narrative of pulling their stock prices upward. And that pressure feeds the next warning. The algorithm arms race. When companies feel themselves slowing down, they do the same thing athletes do when they sense someone gaining on them. They sprint. That's the energy driving this arms race. It's not just competition anymore. It's fear. Fear of falling behind. Fear of missing the next breakthrough. Fear of losing the story that investors are pouring billions into. And that fear has pushed the biggest companies in the world into a race where the price of admission keeps rising. Imagine 7 marathon runners deciding they need to turn the race into a sprint halfway through. They're burning through their energy, hoping the others burn out first. That's what the magnificent 7 are doing with artificial intelligence spending. They're buying more chips, building more data centers, and signing massive long-term deals for power and compute, as if slowing down simply isn't an option. The problem is simple. These companies are investing in infrastructure and spending at a pace that assumes profits will arrive fast enough to justify it. But profits depend on real businesses paying real money for artificial intelligence services. Not just curiosity, hype, or experimentation. So the uncomfortable question becomes unavoidable. Will these companies ever make enough to justify their valuations? And if they do, where will that money actually come from? Right now, investors are acting as if artificial intelligence will instantly replace entire industries, create entirely new markets, and unlock cash streams large enough to support trillions in value. But reality is far more cautious. Many artificial intelligence products still lose money. Many require massive computational resources. And many don't yet solve problems customers will pay premium prices month after month. Yet the spending continues. NVIDIA sells the shovels in this gold rush. And Big Tech keeps buying whatever they can. Data center power demand is exploding. Costs are surging. And now governments have stepped into the race as well. The United States and China are each treating artificial intelligence like strategic infrastructure pouring billions into development because they see it as a contest for global influence. When nations treat something like a weapon, cost becomes secondary. But you can't outrun economics forever. If revenue growth doesn't eventually rise to meet the spending curve, even the strongest companies can feel pressure. And when margins tighten, investors begin to question the story they once accepted without hesitation. That's the real danger of the algorithm arms race. It's not who wins. It's how long they can afford to keep running. And when companies fear losing momentum, they turn to structures that look strong on the surface but fragile underneath. That's where the third warning takes over. Automated Wealth Engine. Let's explain this with a simple example. Imagine three friends who each decide to boost their business by buying gift cards from one another. One friend spends $100 on the second friend's gift card. The second friend immediately spends that same $100 on the third friend's card. And the third friend spends $100 right back on the first friend's card. Each of them now claims they made $100 in sales, even though no new money entered the group. Their reported revenue jumps from $0 to $300. But their actual wealth hasn't changed at all. It looks productive on paper. It feels like progress. But nothing real has happened. That same pattern is what the Automated Wealth Engine mirrors, except the numbers aren't in the hundreds. They're in the tens and hundreds of billions. Instead of three friends swapping gift cards, it's companies like OpenAI, NVIDIA, Oracle, AMD, and major cloud providers moving enormous amounts of money back and forth through chip purchases, compute contracts, and strategic investments. Each transaction gets recorded as revenue. Each deal looks like growth. But much of the money is still the same capital looping inside the same circle, creating numbers that rise far faster than the underlying economic reality. To understand it, look at what's happening between the biggest players in artificial intelligence. It's easier to see it as a loop rather than a normal market. Start with OpenAI. It's valued at around $500 billion, and it needs an enormous amount of computing power. So OpenAI signs a massive cloud deal with Oracle, reportedly in the hundreds of billions. That's huge revenue for Oracle. But Oracle can't serve that demand without buying even more chips, so it turns around and spends tens of billions on NVIDIA GPUs. Now look at NVIDIA. After selling those chips, NVIDIA agrees to invest back into OpenAI, up to $100 billion. That money flows back into the same ecosystem that bought the chips in the first place. OpenAI uses that capital to expand further, which pushes more demand back toward Oracle, NVIDIA, AMD, and other partners. It keeps going. AMD gives OpenAI special access to huge amounts of GPUs and even offers the option to buy 160 million AMD shares. XAI, Mistral, Figure, Harvey, Ambience, and others plug into the same cycle, all feeding off the same pool of money and hardware. Microsoft, worth over $3 trillion, sits on top of the whole system, funding it, providing cloud services, and relying on the very chips produced by the companies it indirectly supports. Every arrow in this diagram looks like growth. Every deal looks like revenue. Every investment looks like momentum. But a lot of the money isn't coming from real customers yet. It's circulating inside a closed loop. It's a feedback loop. Higher spending. Bigger narrative. Higher valuation. Incentive. To spend more. That's what Burry would pay attention to. Not the headlines, but the mechanics. When the same capital touches the same group of companies again and again, it creates the appearance of explosive demand. Elevated levels of revenues. Stock prices move. Narratives strengthen. But the underlying question remains untouched. Is this real economic activity? Or is it the same dollar doing laps around the track? The problem is that when an ecosystem begins depending on circular money to stay impressive, it becomes vulnerable to something far bigger. What happens when the world supplying that money starts to tighten? That's where the fourth warning appears. And it reaches beyond artificial intelligence itself. It's the one force that affects every stock, every index fund, and every investor who thinks they're safely diversified. It's the liquidity mirage. Liquidity is one of those financial ideas that feels distant until the moment it disappears. The easiest way to picture it is like water in a swimming pool. When the water is high, everyone floats without effort. But when the water drains even a little, you feel the floor beneath you. The liquidity mirage works the same way. It makes markets feel stable, valuations feel justified, and risk feels small right up until the moment the waterline drops. For years, the world lived on cheap money. Interest rates were near zero, central banks kept injecting cash, and stocks rose as if gravity didn't apply. That lifted everything, especially the biggest companies. Even after rates climbed, confidence stayed unusually high, as if investors believed liquidity would magically return whenever needed. That belief is the mirage. Passive investing intensifies it. Millions put money into index funds automatically. Those index funds then buy the same giant companies simply because they occupy a larger slice of the index. It's an automatic demand, steady, mechanical, and completely detached from fundamentals. Burry has warned for years that when money flows blindly, prices detach from reality. Now add artificial intelligence optimism on top of that. The Magnificent Seven already dominate the index, and artificial intelligence hype has made them even heavier. So when passive flows pour in, most of the money ends up in the same places, pushing valuations higher, whether earnings justify it or not. It creates an upward drift that feels safe, but depends entirely on liquidity staying abundant. Here's the risk. Liquidity can shrink slowly. Higher interest rates slow borrowing. Consumers pull back. Companies hesitate to raise capital. Governments face rising costs. As liquidity tightens, markets become more sensitive to shocks. Even the strongest companies can wobble when their valuations are built on assumptions instead of durable cash flow. This is why Burry's approach matters. Underneath today's rally is a system increasingly reliant on passive flows, generous valuations, and the expectation that central banks will always cushion the fall. But if liquidity tightens through the middle of this decade, the story supporting today's prices could shift quickly. The danger isn't a sudden collapse. It's the slow fade, the moment investors notice the water has been dropping for a long time. When that realization hits, repricing can be fast and unforgiving. And that raises the next question. Is this the same setup that led to the 2000 bubble, or the start of something even larger? When you look at what's happening today, it's hard not to see the similarities to the late 1990s. Investors back then were convinced the internet would transform everything. Valuations exploded. Caution faded. And people believed they were witnessing a once-in-a-generation shift. The mood surrounding artificial intelligence today feels strikingly familiar. The promises are bigger. The spending is louder. And the belief that this time is different is everywhere. But here's the part you should also focus on. The companies leading artificial intelligence today actually build useful products. They generate real revenue. They aren't worthless ideas with catchy domain names. That's the key difference from the 2000 bubble. Still, the uncomfortable parallels remain. Expectations push to extremes, capital spending accelerating, and valuations built on a future far smoother and faster than reality usually delivers. Burry cares about what holds the story together. In 2000, it was faith that the internet would change everything overnight. Today, it's faith that artificial intelligence will accelerate endlessly, and that liquidity will always be there to support the companies racing toward that future. So no, this isn't a carbon copy of the dot-com bubble. But the resemblance is hard to ignore. And seeing that resemblance is what prepares you for what comes next. When you strip everything back, the four warnings point to one simple truth. Your returns depend on forces most investors never even realize are shaping their portfolios. The data shield shows how artificial intelligence progress could slow as high-quality data runs thin. The algorithm arms race shows how spending can race ahead of real demand. The automated wealth engine shows how money can circulate inside a small group of companies and appear stronger than it is. And the liquidity mirage shows how quickly confidence can fade when the flow of money tightens. None of this is a reason to panic. But it is a reason to prepare. Preparation for a regular investor doesn't require complex strategies. It means avoiding anything that only works if the world stays perfect. You don't need leverage. You don't need speculative bets. You don't need to chase whatever stock is trending today. What you need is consistency, steady contributions, a diversified portfolio, and enough cash so you never have to sell in a downturn. Market drops feel awful while they're happening, but they're also where long-term wealth is built. If prices fall, your automatic investments buy more. If markets wobble, your plan stays intact. You benefit by staying disciplined while others react emotionally. Even though the market felt a wobble, smart money is still buying. Click the video on screen to find out the six stocks smart money is buying right now.