शैडोइंग अभ्यास: The AI Bubble… We Need to Talk - वीडियो के साथ अंग्रेजी बोलना सीखें
पाठ बनाया जा रहा है...
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Right now, half of the internet's screaming that AI is the biggest bubble in history.
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The other half's screaming that it's the biggest breakthrough since sliced bread.
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And honestly, I avoided making this video for a long time, mostly because AI is the only thing anyone talks about anymore.
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But the longer this debate spiraled, the clearer it got to me that both sides were arguing about the wrong thing entirely.
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It's reached a point where I felt like I couldn't just sit back and watch anymore, so here we are.
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right.
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Both of them are watching the wrong thing entirely.
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They're missing what's hiding in plain sight.
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Everyone in this fight is arguing about the same thing: user growth, adoption, revenue, how many people are using this stuff.
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They're all just arguing about demand.
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But demand is also the side of the coin that can lie to you.
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It's noisy, it's narrative driven, and it's a story that changes depending on who's telling it and what they're trying to sell you.
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But every coin has two sides.
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And the other side, the one nobody's talking about, is the side that paints a much better picture of what's actually unfolding.
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It's what the world's best investors have quietly used to track financial bubbles for more than 200 years.
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And it starts by looking at the other side of the coin: supply.
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Because demand can be faked, and we'll get more into exactly how that happens a bit later.
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But you can't fake supply.
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Supply takes real capital, committed up front, and it tells the real story of what's happening.
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This year alone, the biggest tech companies in the world are on track to spend around $725 billion on artificial intelligence.
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And when most people hear a number like that, they usually just scroll right past it, without ever stopping to picture how much money that actually is.
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Because $725 billion isn't just a big number, it's an almost incomprehensible amount of money.
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To put it into perspective, if you spent $1 million every single day starting from the day Jesus was born, all the way through the fall of the Roman Empire,
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the Middle Ages, the Renaissance, both world wars, and all the way up to today, you still wouldn't have spent as much as Big Tech is projected to spend in AI in just this year.
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It breaks down to roughly $2 billion a day,
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$83 million an hour, or about $23 ,000 every second. on artificial intelligence.
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We've never seen a bubble of this size before.
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But as Mark Twain put it, "History doesn't repeat itself, but it often rhymes.
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And what we're seeing right now is very familiar." The real
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question isn't whether AI is going to change the world or not.
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That part doesn't actually really matter.
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Financial bubbles aren't required to be built on bad technology.
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In fact, most of the time, it's the opposite.
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ResearchGate looked at 175 years of major breakthroughs, from 1825 to 2000, that included innovations like the television, the automobile, and the internet.
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And they found that 37 of the 51 major breakthroughs in technology triggered a massive speculative financial bubble.
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As billionaire investor George Soros said, "Stock market bubbles don't grow out of thin air.
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They have a solid basis in reality, but reality is distorted by a misconception." If you look throughout history, it's the same pattern repeating itself over and over,
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and it always ends the same way.
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Which is why the best investors have stopped watching the demand side and started watching the supply side.
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Because when you follow the paper trail, you get to the truth.
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It starts with a framework that is rooted in some of the oldest ideas in economics, going all the way back to Adam Smith's in the 1700s.
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The increase of stock, which raises wages, tends to lower profit.
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When the stocks of many rich merchants are turned into the same trade, their mutual competition naturally tends to lower its profit.
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And when there is a like increase of stock in all the different trades carried on in the same society, the same competition must produce the same effect in them all.
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When Adam Smith says "stock," he is referring to capital and productive assets.
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So translate this out of 1700's powdered wig talk, and what Adam Smith is really saying is that money naturally flows into industries where there are perceived high returns,
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which means more competition enters, which ultimately drives returns down for everyone in the industry.
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And this idea has been modernized and expanded upon into a framework with a new name, the capital cycle theory.
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It ignores the noise involved with demand
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and just focuses on what actually matters: the capital cycle theory flowing through the supply side of an industry.
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And after I explain how it works, you'll start to notice the same patterns repeating across stock market bubbles throughout history.
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The capital cycle moves through an industry in four steps.
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First, the flood.
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A new technology shows up, the returns look sexy, and capital comes pouring in to chase them.
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Step two is the boom.
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Money keeps pouring in, competitors pile in faster than the demand can grow, and the more everyone builds, the more the returns start to slip.
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Step three: the collapse.
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All that new supply finally crushes the very returns that attracted it in the first place.
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Prices fall, companies fail, the industry consolidates, and a lot of people lose a lot of money.
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And finally, the inheritance.
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This is where the real money gets made.
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Capital flees the industry, the builders go bankrupt, and somebody walks into the wreckage, buys the assets for pennies on the dollar,
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and collects the value the original builders in the industry paid for.
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That's the capital cycle The best opportunities show up where capital is fleeing, the worst where it's flooding in.
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And if you go back through history, you'll see the capital cycle showing up again and again.
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But before I show you, here's Oaktree Capital co -founder and billionaire investor Howard Marks, breaking down how history repeats itself in the markets.
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Most people ignore history.
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Most booms, and I, you know, I've probably lived through about half a dozen real booms in my 50 years in the business.
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um...
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are usually about something new.
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And The Internet in '99, the Nifty Fifty, Xerox in '69, whatever it might be, subprime mortgage securities.
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The people who get excited about it,
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who caught into it, who are intoxicated by the positives and willing to ignore the negatives um If you say to them,
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"Well, that happened 20 and 40 years ago
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and it ended badly," what they say is they use the four worst words in the world.
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It's different this time.
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The rules of the past don't apply.
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Yes, the average P -E ratio historically has been 16, but now it's 32, and that's okay, because the Internet has changed the world.
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So let's rewind to one of the earliest examples of the capital cycle theory playing out from start to finish.
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Take yourself back to the 1830s.
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The first industrial revolution is in full swing in Britain.
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Mines and factories need to move coal and iron faster than horse -drawn wagons allow them to.
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Then new railway technology shows up and changes everything.
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And where excitement is, the money follows.
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This is the flood.
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Capital poured into anything with the word "railway" attached to it.
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And not just from bankers, shopkeepers, clergymen women, widows, all of them piled into railway stocks, because nobody wanted to be the one who missed out on getting rich.
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At its peak, railway investment hit around 7 % of the entire British economy.
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In a single year, parliament passed 272 separate acts just to authorize new lines.
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Then came the boom.
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Companies stopped building the railways the country needed and started building whatever they thought would push their share price up.
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Routes got duplicated, lines got run out to villages that would never fill a single train.
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Then a reality check came
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when the new lines were bringing in only about a quarter of the revenue investors had been promised.
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And once that was undeniable, the math caught up all at once.
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By 1850 the collapse was in full effect, and the British Railway Index saw its shares being worth less than half their peak.
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Fortunes that had felt permanent were simply gone.
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And then the last step: the inheritance.
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The original investors got wiped out, themselves laying were still sitting in the ground.
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Over the following decades, that network became the backbone of the entire British industrial economy.
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And the people who got rich were the ones who scooped those assets up for pennies after the bubble burst.
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That's the capital cycle from start to finish: flood, boom, collapse, inheritance.
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Money flooded toward the shiniest new industry, certain that's where the best returns were, until all the competition led to oversupply,
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returns getting crushed, and the people who supplied the boom were destroyed by the very supply they created
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while the value they paid for was left for whoever showed up after the crash to snag for a great price.
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But while the top hats and mutton chops make it easy to dismiss all this as a Victorian problem, we would never fall victim to now.
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That couldn't be farther from the truth.
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Jump all the way to the late 90s, and we can see the same cycle play out again.
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After the US rewrote its telecom laws in 1996,
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the flood poured into fiber optic cable. and under the oceans.
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But then the hype started to fade, and reality caught up.
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Around 85 % of all that fiber sat in the ground completely unused.
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Bandwidth prices fell about 90%.
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The Nasdaq fell more than 70%.
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And Corning, one of the largest fiber makers in the world, watched its stock go from over $100 a share to about a dollar.
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And funny enough, Corning over the past year has become one of the hottest names in AI right now, and their stock chart looks awfully similar today, as it did back in the early 2000s.
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If we fast forward another decade, we see the pattern play out again in oil.
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Innovations in fracking unlocked massive, previously inaccessible reserves.
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Capital flooded in, drillers came chasing all at once, US oil production roughly doubled, and the US became the largest oil producer on earth.
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the price of oil collapsed from over $100 a barrel to about $30.
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More than 200 North American oil and gas companies went bankrupt, the borrowers drowned, and the cheap energy they left behind got picked up at a steep discount.
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So railway mania in the 1840s, fiber optic cable in the late 90s, and shale just over a decade ago.
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Three different centuries, three different technologies, three completely different industries, nothing in common except one thing: the exact same cycle playing out underneath all of it.
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Which brings us to the biggest one yet, the one our entire economy is currently clinging onto for dear life: artificial intelligence.
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This is the one that matters the most, because when the other three collapsed, they took down some investors and a couple of industries.
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But artificial intelligence right now is holding up the entire economy.
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Before we get into just how big this AI thing has actually become, a quick pause.
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Because a lot of what I've covered so far comes down to one pattern: the people building countries,
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the infrastructure, the capital, they spend first, and hope demand shows up later.
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If you're interested, you can check out Printful using the link below or in the pinned comment.
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Thank you to Printful for sponsoring this video.
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And now back to how the entire United States economy has quietly become dependent on one massive bet.
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A Harvard economist named Jason Furman ran the numbers, and in the first half of 2025, he found that investment in information processing equipment and software,
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which is the AI build -out, was only about 4 % of GDP, but it accounted for roughly 92 % of all the growth in the US economy.
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So if you strip the AI build -out from the equation, the US economy only grew at about 1 /10th of 1%.
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So where does AI sit inside the capital cycle?
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Well, the money is still pouring in, but if you look closely, There are cracks starting to show.
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Remember when I said you can fake demand and that we'd come back to it?
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This is us coming back to it.
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In the 90s, they faked demand with a story.
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WorldCom stood up and said internet traffic was doubling every hundred days
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and the whole industry ran with a number that was never real.
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This time, they're not faking it with a story.
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They're faking it with money.
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You've probably seen this chart that looks like a circuit board.
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Every name on it is one of the biggest companies in AI.
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And every arrow is money moving between them.
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A purchase here, an investment there, a loan over there.
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And if you follow almost any arrow, it loops back to one place.
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NVIDIA funds OpenAI.
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OpenAI pays Oracle.
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Oracle buys NVIDIA chips.
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The money makes its rounds throughout the industry and eventually comes home to NVIDIA.
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And every time it passes a hand, it gets counted again as somebody's brand new demand.
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market is really just the same money going around in a circle.
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If this sounds familiar, it should.
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The fiber bubble ran the same trick.
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Equipment makers like Lucent and Nortel lent their own customers the money to buy their gear, then booked it as revenue.
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Activist hedge fund and alternative asset manager Buxton Helmsley clocked this immediately.
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They noted that: "Vendor financing did not merely add risk at the margin.
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It manufactured the appearance of end demand.
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Equipment sold to customers who could pay only because the seller had financed them was recognized as revenue, validated growth narratives, and supported valuations,
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until the moment the sellers could no longer extend credit." So that circle can do a lot.
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It can manufacture demand, prop up revenue, and keep the chips moving.
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But there's one thing it can't do: it can't escape the ideas Adam Smith laid out about money chasing high returns, competition entering, and returns getting compressed for everyone.
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And the best place to look for returns slipping is in the eye of the storm: OpenAI.
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Two years ago, OpenAI basically was AI.
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ChatGPT had no real competition.
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Then everyone showed up: Google, Anthropic, Meta, XAI, a dozen others, all selling more or less the same thing.
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It's the boom arriving, right on schedule.
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AI's financials leaked, for the first time we could see what that competition did to the hottest company in the world.
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And it's ugly.
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OpenAI lost $38 .5 billion in 2025, nearly eight times its loss the year before.
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But the real story for OpenAI isn't just in the headline number, it's buried a few lines down.
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When you look at what OpenAI had to spend on sales and marketing, it jumped more than 400 % in a single year to $5 .7 billion.
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That's using the product.
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That's the cost of the boom.
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The returns are falling, exactly the way they fell for railways, for fiber, and for shale,
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which means you already know what comes next and we're already seeing early signals of it throughout the industry.
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In early June, a chipmaker called Broadcom beat its earnings, but guided its AI sales for the next quarter a few hundred million dollars light.
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For a company worth nearly $2 trillion, that should barely move the needle.
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But following that news, about $300 billion of its market value vanished in a single session.
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One of the biggest single -day losses in Wall Street history
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over what amounts to a rounding error for a company of Broadcom's size.
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and soft guidance can erase $300 billion in a day, you're not looking at a healthy market.
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You're looking at one priced for perfection, where being even slightly less than perfect costs you more than a quarter of a trillion dollars.
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There's an old line in business: the pioneers get the arrows, and the settlers get the land.
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It means the first companies into a new market take the hits: the risk, the losses, and the resistance.
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Then the later arrivals walk in after everything has stabilized, the dust has settled, and quietly take the prize without ever
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You've watched this happen in front of your eyes for your whole life, whether you realize it or not.
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Take search.
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In the mid -90s, AltaVista, Lycos, and Excite were the pioneers.
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They spent millions building the first web crawlers and teaching the world what a search engine even was.
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Then they buried their pages in news, email, and banner ads chasing revenue to pay for it all and let the actual search product decay.
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Then Google walked in, didn't repeat their mistakes, and took the land or rideshare.
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Before Uber and Lyft, a company called Sidecar spent its life getting sued by cities and blocked by taxi commissions, fighting the legal battles that made the model possible.
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And while it was stuck in court bleeding cash, Uber and Lyft copied the model, raised billions, and took over the world on the foundation Sidecar had built.
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There's countless other examples of this, it's the Capital Cycle Theory.
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The first movers flood in, prove the market, and pay for it with everything they have, then eventually get taken down by the arrows.
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The supply they built gets left behind for the settlers, where the second movers walk in and inherit it for pennies on the dollar.
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Which brings us all the way back to where we started.
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Everyone is staring at one side of the coin, the demand side, screaming about whether it's real.
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And it's easy to see why.
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The demand side is loud, it's a story, and a story is what gets clicked.
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But in finance, the side that actually decides what happens is never the loud one.
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It's the quiet mechanics underneath that drive everything.
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If you want to track that side instead of just following the noise in traditional media, hit subscribe.
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Once you understand the cycle of how capital flows through industries, the whole fight everyone is having online looks like the wrong fight.
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Bubble or not was never the right question.
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The question was always who floods in, who drowns, and who inherits.
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Once you start asking the real questions, you start getting to the real answers.
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इस पाठ की शब्दावली और बोलने के सुझाव
यह C1 स्तर का स्पीकिंग पाठ वीडियो “The AI Bubble… We Need to Talk” पर आधारित है। इस वीडियो में शैडोइंग के लिए 228 वाक्य और 3237 शब्द हैं। बोला गया हिस्सा 17:56 लंबा है। वक्ता तेज़ बोलता है, लगभग 181 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। 83% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| investor संज्ञा | /ɪnˈvɛs.tə(ɹ)/ | विनिधानकर्ता, निवेशक |
| inheritance संज्ञा | /ɪnˈhɛɹɪtəns/ | उत्तराधिकार |
| penny संज्ञा | /ˈpɛni/ | पेनी, पेन्स |
| optic विशेषण | /ˈɑp.tɪk/ | प्रकाशीय |
| bankrupt विशेषण | /ˈbæŋk.ɹəpt/ | दिवालिया, दिवलीया |
| builder संज्ञा | /ˈbɪl.dəɹ/ | निर्माता, राजगीर |
| bury क्रिया | /ˈbɛɹi/ | गाड़ना, दफ़नाना |
| pioneer संज्ञा | /ˌpaɪ.əˈnɪɹ/ | अग्रणी |
| cellar संज्ञा | /ˈsɛləɹ/ | तहख़ाना, सुरागार |
| bleed क्रिया | /ˈbliːd/ | रक्तस्राव |
| mechanic संज्ञा | /mɪˈkænɪk/ | मिस्त्री, मेकेनिक |
| competitor संज्ञा | /kəmˈpɛt.ɪ.tɚ/ | प्रतियोगी, प्रतिस्पर्धी |
| decay संज्ञा | /diˈkeɪ/ | अपघटन |
| scroll संज्ञा | /skɹoʊl/ | पुस्तक |
| translate क्रिया | /tɹænzˈleɪt/ | अनुवाद करना, भाषान्तर करना |
वीडियो में आने वाली फ़्रेज़ल वर्ब
| शब्द | अर्थ |
|---|---|
| take down क्रिया | उतारना |
| stand up क्रिया | उठना |
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | We've never seen · have stopped · has been modernized |
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | can be faked · was born · is projected |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | people who get · one who missed · people who got |
ध्यान देने योग्य उच्चारण
वक्ता 35 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे they're, can't, isn't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: breakthrough /ˈbɹeɪk.θɹuː/, underneath /ˌʌndɚˈniθ/
- “sh” और “zh” की ध्वनियाँ: shale /ʃeɪl/, perfection /pɚˈfɛk.ʃən/, equation /ɪˈkweɪ.ʒən/
- लंबे शब्द — बलाघात सही रखें: inheritance /ɪnˈhɛɹɪtəns/, competitor /kəmˈpɛt.ɪ.tɚ/, economist /iˈkɑ.nə.mɪst/, automobile /ˈɔː.tə.məˌbil/, historically /hɪˈstɔɹɪkli/
हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:
- /v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: investor /ɪnˈvɛs.tə(ɹ)/, mover /ˈmuːvə/, perceive /pɚˈsiv/, wipe /waɪp/, equation /ɪˈkweɪ.ʒən/
- /z/ — इसे “ज” (/dʒ/) न बनाएँ: translate /tɹænzˈleɪt/, pause /pɔːz/
- शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: scroll /skɹoʊl/, stare /stɛɚ/, steep /stiːp/, sponsor /ˈspɒn.səː/
इस वीडियो से अभ्यास कैसे करें
- पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
- 0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
- अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर investor, inheritance, penny जैसे शब्दों पर ध्यान दें।
शैडोइंग तकनीक क्या है?
शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।