शैडोइंग अभ्यास: it’s boring, but it’s what bursts the ai bubble - वीडियो के साथ अंग्रेजी बोलना सीखें
अभ्यास मोड चुनें
हर मोड पूरी स्क्रीन का अभ्यास कक्ष खोलता है।
252 वाक्य
यदि वाक्य बहुत छोटे या लंबे हों, तो समायोजित करने के लिए Edit पर क्लिक करें।
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The single biggest threat to America isn't the national debt, it's not the Strait of Hormuz, and it's not even the AI bubble itself.
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It's energy.
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But not energy in the way you're probably thinking.
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It's not blackouts or your monthly utility bill.
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It's that America has pushed all its chips to the center of the table and bet the entire economy on one wager, that AI arrives on schedule.
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And energy is the thing that decides the schedule.
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Because right now, AI feels invincible.
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The valuations keep growing, the spending isn't slowing down, and the stock market keeps pumping.
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But if something's going to pop the AI bubble, it's energy.
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And by the end of this video, I can prove it to you.
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But first, the size of the wager.
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Because betting the economy isn't an exaggeration.
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In 2024, the biggest tech companies spent a little over $200 billion building AI infrastructure.
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In 2025, that number hit almost $400 billion.
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This year, their own estimates say more than $700 billion.
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So the spending isn't just growing, it's nearly doubling every year, and it's that spending that's carrying the economy.
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The level of concentration we're seeing in these tech stocks isn't subtle, it's practically screaming, it's a bubble.
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Last quarter AI accounted for more than half of America's real GDP growth.
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George Saravellos, head of FX research at Deutsche Bank, wrote in a message to clients, AI machines appear to be saving the US economy right now.
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In the absence of tech-related spending, the US would be close to, or in, a recession this year.
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But you probably already know all this.
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You've likely heard AI is a bubble a million times by now.
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And here's the thing about all those bubble takes.
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Every single one of them is arguing about the wrong question.
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They're arguing about whether AI is here to stay, whether it's truly going to revolutionize every industry, and whether a chatbot that can't count the letters in strawberry is maybe about to take your job.
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But that was never the right question.
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The real bet isn't whether AI works, it's actually about whether we can even physically deliver it on time.
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Whether the AI future can arrive fast enough to justify the insane prices we're paying for it today.
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Because right now, money is disappearing into AI faster than Thanos can snap his fingers.
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It's the fastest and largest build-out in history, yet nobody's actually making any money on it.
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Which is fine, for now.
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Investors will happily fund losses as long as they think a payoff is on its way.
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But eventually, the investors, the companies, and the lenders, funding all of this, need that payoff to actually show up.
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Eventually the capital has to come home with a return.
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That's how the AI bubble pops.
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It's not when it fails, but when it's late.
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When the capital starts to get sick of waiting around and decides to go somewhere else.
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So what decides whether AI shows up on time?
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And what determines whether these trillions of dollars from big tech will ever see a return on investment?
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Well, before I get into that, let's take a quick pause.
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As you've probably heard this a thousand times by now, learn AI or you'll get left behind, but nobody mentions the hard part.
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ChatGPT is good at one thing, Claude at another, Gemini something else.
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So you end up subscribed to all of them.
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At roughly $20 a month each, that's $60 a month total, or $720 a year, all just to try and keep up.
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Which brings us to the sponsor of today's video, ChatLLM by Abacus AI.
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They've solved that problem.
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Chat LLM by Abacus AI puts every top model in one place.
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GPT 5.6, Claude Fable 5, Gemini 3.1, even Grok and DeepSeek, and when a new model drops,
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it's instantly available, which means no waiting around and no new subscriptions.
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You can pick which model to use yourself, or let the Route LLM feature read your prompt and automatically pick the best model for you.
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And Chat LLM by Abacus AI goes past just chat.
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It can generate images, video, and even build full apps and websites, with hosting included, no code needed.
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The price for all this is just $10 a month, less than any single subscription it replaces.
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If you're interested, you can check out Chat LLM by Abacus AI using the link below, or in the pinned comment.
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Thank you to Abacus AI for sponsoring this video.
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Now back to the single wager our entire economy's riding on.
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This entire bet is trapped inside a race between two clocks.
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Clock one decides how fast the AI future can actually arrive, how quickly we can build it, Plug it in and turn it on.
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Clock 2 decides how long we can afford to wait for it.
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We'll get to that later because it's the scarier of the two.
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So, let's start with the first one.
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Whenever a technological breakthrough like AI shows up, Wall Street loves to hand out the same Mark Twain quote as advice.
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Don't bet on the miners.
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Invest in the picks and shovels.
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The phrase is more overused than the man in the arena
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speech in a one-bedroom San Francisco apartment with four founders vibe coding an AI app.
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What Wall Street's saying is, don't try to pick the winning chatbot.
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Just buy the stuff underneath the boom, the data centers, the power, the infrastructure.
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But here's what nobody mentions when they hand you that advice.
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America's running out of picks and shovels, because you can't just build a data center.
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A data center doesn't get electricity by plugging into the grid and asking nicely.
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It has to join something called an interconnection queue.
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That's the waitlist to get access to the power grid.
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Think of it as the DMV, but for electricity.
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Except instead of measuring the weight in bad attitudes and hours, the interconnection queue measures it in regulatory headaches and years.
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In America's largest power grid, that weight has stretched from under two years in 2008 to over 8 years today.
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And the waitlist is only where the problems start, because AI data centers don't use electricity the way an office tower does.
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Busy during the day, quiet at night, asleep on weekends.
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Data centers run full blast around the clock every day of the year.
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They already eat more than 4% of America's electricity.
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And by 2035, this number is expected to reach a fifth of the country's electricity consumption.
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And the main workhorse feeding this entire build-out is this alien-looking device, the gas turbine, which is where the problems continue.
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It's the machine behind roughly 40% of America's electricity, and they're among the most complex pieces of machinery on Earth.
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Only a handful of companies can actually build one.
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Three manufacturers control over three-quarters of the global market.
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One of them is GE Vernova.
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Its order backlog has ballooned from roughly 80 gigawatts to 116, and its own CEO says the company will be largely sold out through the end of 2028.
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And in 2024 alone, developers ordered nearly three times more turbine capacity than every factory on Earth could physically produce in a single year.
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Which means it doesn't matter how many zeros Big Tech puts on the check, the manufacturers are structurally constrained.
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But it's not just the gas turbines either.
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Transformers, which are the machines that step power down from the grid, now take over two and a half years to get with an estimated 30% supply shortage.
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And for new transmission lines, they take 6.5 years on average to complete due to permitting delays, backlogs, and a mountain of other BS.
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So it's not just a single waitlist that's backed up.
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It's the entire American energy supply chain.
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We're seeing tech move at Netflix speeds, and the infrastructure underneath it moving at the speed of blockbuster.
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Which is why the companies trapped between these two speeds are starting to act desperate.
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Two developers reportedly paid $25 million, for a turbine, just to reserve a place in line for one.
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Others are rebuilding retired jet engines to makeshift the power they need.
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We're seeing grown, profitable companies bolting old aircraft engines to the ground because they're desperate for power.
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So the obvious fix is the one you're already thinking.
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Just build more.
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Big tech is great at throwing money at problems.
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Why not throw money at the source until the clock speeds up?
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Well, we already ran that exact experiment.
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And money lost.
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Remember Remember that 8 year wait just to plug into the grid?
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It belongs to a power grid called PJM Interconnection.
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PJM is the largest grid operator in America, serving 67 million people across 13 states from Chicago to Washington DC, roughly 1 in 5 Americans.
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If you live anywhere in that footprint, your lights are on because of decisions they make,
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and PJM's biggest decision of the year happens at an event nobody watches, the capacity auction.
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Here's how it works.
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PJM doesn't just buy electricity, it buys guarantees.
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Once a year, PJM holds an auction to pay power plants to promise they'll be ready with electricity on the worst days of the year.
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It's essentially a billion dollar pinky promise that there's going to be electricity when there's a January freeze, or an August heat wave.
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You can think of it like insurance.
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They pay a premium so the grid doesn't fail when it's needed most.
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For most of this century, that insurance was dirt cheap, because American electricity demand flatlined for 20 years.
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It made the capacity auction the single most boring event in American finance.
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In the auction covering 2024, a promise of power cleared at about $29 per megawatt day.
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Then, the data centers arrived.
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Not one at a time, by the dozen.
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Buildings
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that each swallow as much electricity as a small city walking into a system designed for a world where demand barely grew.
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And the data centers don't want power eventually.
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They want it now, guaranteed, forever.
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The next auction cleared at just under $270, a more than 830% spike.
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The regional bill went from $2.2 billion to $14.7.
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The same promise, seven times the price, the biggest leap since the capacity market was invented in 2007.
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That got attention.
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Governors across the region leaned on PJM, federal regulators signed off, and the auction got a ceiling, a legal maximum on what the grid was allowed to pay.
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The very next auction hit that ceiling.
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So did the one after it, and the one after that.
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What looked like a spike turned out to be the new floor.
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Then came December 2025, and something happened that had never happened in PJM's history.
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The auction came up short.
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Each auction is built around a target reserve margin.
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It's the extra safety cushion of power supply required above expected
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peak demand to guarantee the grid can handle those worst days of the year.
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It's basically the backups in case of an emergency.
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For the first time in history, PJM went shopping with more money than ever before, and paid the maximum price allowed under federal law,
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but there still wasn't enough power supply to go around.
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And that's the physical reality of the grid.
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All that money can't summon a gas turbine, a transformer, or a transmission line out of thin air when the world's manufacturers are booked years out in advance.
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Which means the grid failed to buy what it needed and then paid full price for what it got.
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And the bill for all this doesn't just get sent to big tech, it gets sent to you.
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It flows straight into your electric bills.
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The average household in Maryland saw their electric bill rise by $21 a month, or nearly $250 per year.
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Which may not sound like a lot at first, until you remember that's just the average, for one household, in one state.
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When you remember that PJM covers 13 states and 67 million people, it's a lot.
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PJM pays an independent watchdog called Monitoring Analytics.
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They ran the numbers on the December auction and found that of the $16.4 billion the auction will cost,
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roughly $6.5 billion can be traced directly to data centers.
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Forty cents of every capacity dollar traced to data centers.
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Across the last three auctions, the number is more than $23 billion.
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So that's clock one, the power clock.
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The infrastructure can't keep up with the demand, money can't speed it up, and there's no clear path to fixing it.
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These companies just have to wait.
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Which sounds survivable.
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Annoying and expensive, but survivable.
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Because late is better than never.
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Unless the thing doing the waiting has an expiration date.
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And spoiler, it does.
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Which brings us to clock two.
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The chip clock.
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The one that determines how long we can afford to wait.
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Because those data centers are really just massive warehouses for a single product,
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semiconductor chips, specialized processors that train AI and the most expensive piece of the entire bet.
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Of the more than $700 billion a year, the biggest chunk is spent on the chips and computing hardware.
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And there are two problems with these chips.
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The first is, they don't age well.
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Think of that one grandma of yours who hasn't worn sunscreen since the 70s.
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A cutting edge AI chip stays cutting edge for somewhere between two to six years, depending on who you ask.
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Newer, faster chips arrive, and the old ones slide toward worthless.
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But the infrastructure those chips are living on lasts for decades.
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So the chips age like a smartphone, and the infrastructure ages like a bridge.
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That mismatch has a price tag.
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If a data center scales back, or its chips go obsolete after a few years, the multi-billion dollar grid equipment built to serve it stays on the utilities' books for decades,
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and a chunk of that cost gets shifted to the utilities' customers, meaning you.
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And then there's the second problem, the bigger one.
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Right now, tech companies are reporting record earnings.
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On the surface, you could maybe even say, what AI bubble?
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But Michael Burry, the investor from the movie The Big Short, thinks those record earnings are an illusion.
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He believes big tech's cooking the books, and like most things in finance, he believes it comes down to sneaky accounting.
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When a company buys a machine, it spreads the cost across the years the machine stays useful, called the useful life.
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That schedule is called depreciation.
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Stretch the useful life out, and your annual depreciation drops, making profits look bigger today.
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Shrink the years, and your annual depreciation expense rises, lowering your reported profits.
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Which means one boring accounting guess, how long does a chip stay useful, quietly determines billions in big tech earnings.
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And the problems arise when nobody agrees on the answer.
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The tech giants started extending the official useful life of their servers from a 3 or four-year standard to six years.
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Burry says the real number is much lower.
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If he's right, the industry is understating depreciation by about $176 billion between 2026 and 2028.
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By Burry's math, this would overstate Oracle's earnings by nearly 27% by 2028, and Meta's by almost 21%.
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NVIDIA fired back immediately at this.
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They said that their customers get four to six years out of these chips just fine.
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What we're watching is a classic kindergarten game of he said, she said.
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That's also what makes this interesting, because whether you side with Burry or NVIDIA doesn't actually matter, because the tech companies themselves can't agree on the answer.
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In January 2024, Amazon decided its servers would last six years instead of five.
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That single decision reduced its depreciation expense by $3.2 billion and added $2.5 billion to its profit for the year.
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Twelve months later, Amazon changed its mind.
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They walked the estimate from six years back down to five for subsets of their servers and networking equipment.
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The reason Amazon gave in the filing was the increased pace of technology development, particularly in artificial intelligence.
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And while Amazon was walking some things back, Meta went the opposite direction.
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Meta extended the life of its servers and network assets to five and a half years.
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That single change reduced its 2025 depreciation costs by $2.9 billion, and added roughly a dollar to Meta's earnings per share.
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So, same technology, same time period, two accounting departments, looking at the same silicon, and reaching opposite conclusions.
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And that's the race we have.
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There are two clocks ticking.
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One says new power infrastructure can take 5 to 10 years to build.
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The other says today's AI chips may only be economically cutting edge for 3 to 6 years, depending on who you ask, of course.
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If those clocks fall out of sync, billions of dollars of infrastructure could arrive for a very different AI market than the one companies are planning for today,
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and the race is already being timed.
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Goldman Sachs looked at how often data centers actually open on schedule.
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Historically, only about 72% of projects due within the next year showed up on time.
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When you adjust for today's supply chain and labor problems, Goldman expects about 60% of next year's promised capacity to arrive when promised look two years out,
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and it drops to 50%.
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Flip a coin, that's the current odds the AI future arrives on schedule.
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And Bloomberg's energy research arm found something even uglier.
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They ran two forecasts side by side.
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The first asks a simple question.
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Based on how many AI chips are actually being sold, how much data center capacity should America be building?
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The second asks a harder one.
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How much can America actually power?
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They found that by 2033, the gap will be 63 gigawatts apart.
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For context, every operational data center in America adds up to a little over 40 gigawatts.
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So, the gap Bloomberg found isn't a small rounding error.
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To deliver the future these companies have already paid for, America would have to find a way to power a gap half as large as every data center the country's already built.
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That took decades to build, on a grid that now has an 8-year waitlist.
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The math doesn't just look bad, the math looks impossible.
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Which brings us to the age-old question.
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How did we get here?
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How does the entire energy supply chain get this backed up?
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Well, here's the uncomfortable answer.
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It's not because AI is failing, it's because it isn't.
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The demand is painfully real.
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Everyone wants in.
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Data centers drove roughly half of America's electricity demand growth last year.
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The line for power is measured in years because everyone showed up at once.
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If you've seen my previous videos, it's the flood stage of the capital cycle.
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Success is what broke the grid.
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And the chips?
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How does the most expensive hardware on Earth expire so fast?
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Again, not because AI stalled, because it didn't.
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Every new generation of chips buries the one before it.
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The technology is moving so fast that its own products can't hold their value.
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Success is what kills the chips.
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So the better AI does, the more demand floods the grid and the longer clock one takes.
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The faster AI improves, the quicker the hardware goes stale and the shorter clock two gets.
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Success winds both clocks against each other.
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Now if you'd rather get your financial news from people yelling about earnings beats, No hard feelings.
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But if you want the deeper stuff, the stuff buried in government reports and depreciation schedules, hit subscribe.
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Because it's those boring documents where the real stories actually live.
ऐप डाउनलोड करें
धाराप्रवाह बोलने के लिए ज़रूरी सब कुछ
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इस पाठ की शब्दावली और बोलने के सुझाव
यह B2 स्तर का स्पीकिंग पाठ वीडियो “it’s boring, but it’s what bursts the ai bubble” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: chip, center, grid, billion, clock। इस वीडियो में शैडोइंग के लिए 252 वाक्य और 3242 शब्द हैं। बोला गया हिस्सा 17:51 लंबा है। वक्ता तेज़ बोलता है, लगभग 182 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 80% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।
इस वीडियो की मुख्य शब्दावली
वीडियो के 15 सबसे कठिन शब्द, उच्चारण और अर्थ के साथ:
| शब्द | उच्चारण | अर्थ |
|---|---|---|
| grid संज्ञा | /ɡɹɪd/ | झर्झर, जाली |
| depreciation संज्ञा | /dɪˌpɹiːʃɪˈeɪʃən/ | अवक्षयण |
| bubble संज्ञा | /ˈbʌbəl/ | बुलबुला, बुल्ला |
| turbine संज्ञा | /ˈtɜː.baɪn/ | टर्बाइन |
| investor संज्ञा | /ɪnˈvɛs.tə(ɹ)/ | विनिधानकर्ता, निवेशक |
| mathematics संज्ञा | /mæθ(.ə)ˈmæt.ɪks/ | गणित, गणित-विधा |
| shovel संज्ञा | /ˈʃʌv.əl/ | बेलचा, खनित्र |
| queue संज्ञा | /kju/ | पंक्ति, क़तार |
| spike संज्ञा | /spaɪk/ | कीला |
| ceiling संज्ञा | /ˈsilɪŋ/ | छत |
| trace संज्ञा | /tɹeɪs/ | चिह्न |
| developer संज्ञा | /dɪˈvɛləpɚ/ | विकासक |
| yell क्रिया | /jɛl/ | चिल्लाना |
| recession संज्ञा | /ɹɪˈsɛʃn̩/ | मंदी |
| swallow क्रिया | /ˈswɑ.loʊ/ | निगलना |
इस वीडियो का व्याकरण
वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:
| संरचना | वीडियो में |
|---|---|
| Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है | is trapped · is expected · will be largely sold |
| Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है | has pushed · You've likely heard · you've probably heard |
| Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी | available, which means · turbine, which is · Transformers, which are |
ध्यान देने योग्य उच्चारण
वक्ता 49 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे isn't, can't, doesn't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।
- “th” की ध्वनियाँ: mathematics /mæθ(.ə)ˈmæt.ɪks/, underneath /ˌʌndɚˈniθ/, breakthrough /ˈbɹeɪk.θɹuː/
- “sh” और “zh” की ध्वनियाँ: depreciation /dɪˌpɹiːʃɪˈeɪʃən/, interconnection /ˌɪn.tɚ.kəˈnɛk.ʃən/, shovel /ˈʃʌv.əl/, specialize /ˈspɛʃəˌlaɪz/, recession /ɹɪˈsɛʃn̩/
- लंबे शब्द — बलाघात सही रखें: depreciation /dɪˌpɹiːʃɪˈeɪʃən/, interconnection /ˌɪn.tɚ.kəˈnɛk.ʃən/, mathematics /mæθ(.ə)ˈmæt.ɪks/, developer /dɪˈvɛləpɚ/, machinery /məˈʃiːnəɹi/
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: waitlist /ˈweɪtˌlɪst/, gigawatt /ˈɡɪɡəˌwɒt/, investor /ɪnˈvɛs.tə(ɹ)/, shovel /ˈʃʌv.əl/, developer /dɪˈvɛləpɚ/
- /z/ — इसे “ज” (/dʒ/) न बनाएँ: specialize /ˈspɛʃəˌlaɪz/, arise /əˈɹaɪz/, pause /pɔːz/, freeze /ˈfɹiːz/
- शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: spike /spaɪk/, specialize /ˈspɛʃəˌlaɪz/, swallow /ˈswɑ.loʊ/, sponsor /ˈspɒn.səː/, smartphone /ˈsmɑɹtfoʊn/
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 grid, depreciation, bubble 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।