跟读练习: The AI Take Over Has Completely Backfired (Here's the Proof) - 通过视频学习英语口语
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AI was supposed to change everything, replace millions of jobs, transform every industry, and become the biggest technological shift in history.
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But there's just one problem.
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The numbers don't seem to add up.
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Big tech is expected to spend around $725 billion on AI this year.
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Meanwhile, the entire global market for AI services generated only around $25 billion last year.
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That means for every $10 these companies are pouring into AI infrastructure, they're getting back only about 25 cents in revenue.
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And now the cracks are becoming harder to ignore.
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Products are being shut down.
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The cost of running AI keeps exploding.
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Lawsuits are piling up.
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And investors are starting to ask the question nobody wanted to ask two years ago.
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Where's the money?
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So is AI actually the next technological revolution?
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Or are we watching one of the biggest tech bubbles in history start to burst?
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Hi friends, I'm Maddie.
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I'm a senior software engineer who previously worked Google, Internet, other bictict companies like Amazon, IBM, and Microsoft.
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For two years now, the story I've been told, that you've been told, is that AI was about to replace every engineer alive.
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We are not far from the world.
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I think we'll be there in three to six months where AI is writing 90% of the code.
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And then in 12 months, we may be in a world where AI is writing essentially all of the code.
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But as of now, the money flow doesn't exactly match up to this story.
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So in In this video, I'll break down why the AI hype is cracking in 2026, the spending method doesn't close, the AI products quietly getting switched off,
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and the legal problem no one thought about until too late.
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Let's get into it.
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We're going to start with the amount of money companies are spending on AI.
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The five companies building this infrastructure, Microsoft, Amazon, Alphabet, Meta, and Oracle,
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are on track for roughly $725 billion of capital expenditure in 2026.
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That's up 77% in a single year, and it was around $162 billion back in 2022.
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Goldman Sachs predicts that the total spend will be past $7.6 trillion through 2031. To put
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that into perspective, $725 billion a year is more than the entire
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annual GDP of Switzerland being spent by a handful of companies on hardware
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that loses most of its value in three or four years.
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And so here's the problem.
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That money isn't coming from spare cash anymore.
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Rather, it's eating companies alive.
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JP Morgan estimates AI capital expenditure went from around a third
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of hyperscaler operating cash flow in 2023 to roughly 93% in 2026.
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Their combined free cash flow, the actual money left over, fell to about $7 billion in the second quarter of 2026,
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down from $60 billion at the end of 2025.
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And analysts expect it to go negative this quarter.
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Amazon is looking at deeply negative free cash flow this year.
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Microsoft has told investors that its spare cash is shrinking, and it left a $60 billion buyback basically sitting there, unused.
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So the richest companies on earth are borrowing hard against a payoff that hasn't arrived yet.
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And the moment that made everyone realize how dangerous this could be is when DeepSeek, a Chinese startup that in early 2025 trained a competitive model for a few million dollars,
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erased something like $600 billion of NVIDIA's value in one trading day, the single largest day wipeout any US stock has ever taken.
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DeepSeek's success forced every investor to ask this very important question, what are we actually getting for all this spend?
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And even if that money were all there, the electricity and the infrared might not be.
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That part often doesn't get talked about as much because it's much less exciting than those stock charts.
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But there is a hard physical limit.
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Training and running these models eat staggering amounts of power.
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The International Energy Agency says electricity demand from data centers jumped 17% in 2025, and the AI-specific slice grew closer to 50%.
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In the US, data centers are now driving about half of all new electricity demand growth.
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The grid just simply can't keep pace
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because capital moves at the speed of markets and power plants move at the speed of permits and construction.
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Gartner is already projecting that power shortages could constrain 40% of AI data centers by 2027.
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So you have companies committing hundreds of billions of dollars to build-outs that physically cannot happen yet,
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and quietly signing deals for their own nuclear reactors to get around this limitation.
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That is not the behavior of an industry that has everything figured out.
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In addition to the massive cost of AI, it might seem as though projects keep on failing even with the help of AI.
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An MIT report found that roughly 95% of enterprise-generative AI pilots delivered no measurable return on investment.
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However, there's a bit more to that number.
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It does measure whether a pilot moved the bottom line within about six months, and a lot of them were in marketing, which is a hard place to show fast returns.
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Also, internally built AI tools succeeded about a third of the time, while vendor-based ones succeeded about two thirds.
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So most companies seem as though they're using it to solve problems that don't return much.
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This is the phenomenon even in large companies.
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For example, Amazon set a target of getting 80% of its developers using AI tools every week
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and built an internal leaderboard called KiroRank that scored engineers on how much AI they used.
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So then naturally, as you can imagine, engineers started pointing AI agents at pointless, made-up tasks purely to run up their token counts and climb the board,
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a move the industry has coined token taxing.
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It drove up Amazon's own compute bill for basically nothing.
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So in May of 2026, they officially killed the leaderboard, and a senior VP had to send around a note saying, please don't use AI just for the sake of AI.
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Also, a recent survey of more than 2,500 enterprises found
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that around 74% of companies that put a live AI customer service agent into production had to roll it back.
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So while the demo looks incredible, productionization is harder, especially in legacy systems full of edge cases the demo never saw or trained on.
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The reason why these AI agents are getting switched off isn't only because they're wrong.
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It's because that when they're wrong, the company can get into liability and big legal trouble.
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Late last year, the Australian government paid Deloitte around $440,000 for a report on a welfare system.
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A researcher started reading it and noticed something off.
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The report cited academic papers that just didn't exist and quoted a federal court judge saying something that the judge never said.
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It turns out that Deloitte had actually been using Gen.ai to help write it, and the AI had just hallucinated its sources.
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Deloitte ended up issuing a corrected version and refunded part of the fee.
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The lesson here is pretty stark.
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If your AI puts something in front of a client, you are liable for what it outputs.
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You don't get to blame it on the software.
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Now, stack that on top of hallucinations would turn out to be really stubborn.
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One 2026 benchmark that tested 37 different models clocked made-up answer rates anywhere from 15 to 52 percent.
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And oddly, several of the newer reasoning models actually did it more often than the older ones, not less.
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On legal questions in particular, Stanford put the range of hallucination at somewhere between 58 percent and 88 percent.
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So put yourself in a Fortune 500 boardroom.
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You've got a tool that that will confidently make things up some meaningful fraction of the time, a core precedent saying you own every word it says,
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and insurers who've started writing outright AI exclusions into their policies because they can't price the risk.
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What do you do?
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You quietly kill a lot of those customer-facing agents.
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So we have now a grim picture of the AI ecosystem.
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The spending is unsustainable, the pilots mostly fail, and the agents are getting retracted.
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So what should you do with this information if you're building a career?
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There are three things.
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Number one, don't confuse the bubble with the technology.
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For example, the dot-com crash didn't mean that the internet was fake, but it meant the money had run way ahead of the value
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and the correction wiped out everyone with no real business underneath.
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The same sort of principle can be applied here.
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You can think about it like this.
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Would a tool still be worth using if AI got 10 times more expensive to run?
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Real models with real products underneath will survive that.
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Number two, become the person who owns the output, not the person who produces it.
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Every failure we talked about in this video, for example, the dead pilots, the agents getting rolled back, Deloitte handing the government a faulty report, is caused
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because of the lack of type of this person someone who can catch what the AI got wrong
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and take responsibility for it.
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That role is growing while the just generate code role shrinks.
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So you should use AI every day, ship fast with it, and stay accountable for what it produces.
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And build robust eval and verification systems.
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And number three, last but not least, go where the deflation sends the money.
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So when a bubble sorts itself out, the money that was pumped into it doesn't just disappear, but instead it gets deflected toward products that can actually prove their worth.
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AI security is a good example.
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Every one of these systems getting shipped is an attack surface almost nobody can defend yet.
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Same with the people who can integrate AI into real systems, evaluate it, and make it reliable instead of just impressive on stage in a controlled environment.
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So you should specialize into the part that needs human judgment.
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So in conclusion, here's the real story of whether AI is all hype or not in 2006.
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Yes, AI stocks are getting hammered.
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Yes, 95% of those enterprise pilots went nowhere.
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And yes, the AI replaces everyone mantra is getting walked back in boardrooms across the industry.
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But none of that means AI itself is finished.
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The internet survived the crash and came back bigger.
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So AI will be fine.
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The only question here is whether you'll come out of this as someone who chased the bubble
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or if you'll become someone who understood what was actually valuable underneath it.
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And that's all I have for you in this video.
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If this perspective was helpful, hit that like button, hype the video, and subscribe for more videos on software engineering, AI, and tech.
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本片段的口语练习目标
这段视频非常适合提升英语影子跟读的流畅度和信息抓取能力。内容围绕AI行业的热点话题展开，包含大量经济数据和行业术语，语速适中且逻辑清晰，能帮助你在跟读时训练对长句的理解和复述能力，尤其适合雅思口语练习中“科技与社会”类话题的表达积累。通过模仿视频中的语调变化（如疑问、强调的语气），还能提升口语的自然度，突破“背书式”表达的瓶颈。
实用短语库
- the numbers don't seem to add up（数据似乎对不上）
- cracks are becoming harder to ignore（问题越来越难以忽视）
- eating companies alive（让企业不堪重负）
- free cash flow（自由现金流）
- hard physical limit（物理上限）
这些短语既可以用于日常讨论，也能为英语口语练习增添专业感。比如在描述某个行业困境时，“cracks are becoming harder to ignore”比“there are more and more problems”更生动。
突破你的薄弱点
视频中频繁出现长句嵌套（如“JP Morgan estimates AI capital expenditure went from... to...”），这正是很多学习者在看视频学英语时容易卡壳的地方。建议采用“影子跟读”法：播放一句，暂停并立即重复，重点关注“from... to...”“that means...”等逻辑连接词的节奏。此外，单词“infrastructure”（基础设施）和“expenditure”（支出）的重音位置容易出错，记得前者重音在“fra”，后者在“pen”。多练习几遍，你会发现自己处理复杂句式的能力明显提升！
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | is expected · being shut · I've been told |
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | I've been told · has told · hasn't arrived |
| 定语从句 who / which + 从句 — 补充说明人或事物 | marketing, which is · person who owns · someone who can |
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。