Pratique du Shadowing: The AI Take Over Has Completely Backfired (Here's the Proof) - Apprendre l'anglais à l'oral avec la vidéo
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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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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « The AI Take Over Has Completely Backfired ». Cette vidéo contient 128 phrases et 1772 mots à répéter en shadowing. La partie parlée dure 9:41. Le locuteur parle vite, environ 183 mots par minute : attendez-vous à des liaisons et à des sons réduits. Seuls 81 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.
Vocabulaire clé de cette vidéo
Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| investor nom | /ɪnˈvɛs.tə(ɹ)/ | investisseur, investisseuse |
| hype nom | /haɪp/ | matraquage publicitaire, promotion |
| underneath adverbe | /ˌʌndɚˈniθ/ | dessous |
| hallucination nom | /həˌluːsɪˈneɪʃən/ | hallucination, illusion |
| leaderboard nom | classement | |
| expenditure nom | /ɛkˈspɛndɪt͡ʃə/ | dépense |
| token nom | /ˈtoʊkən/ | symbole |
| technological adjectif | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | technologique |
| demo nom | /ˈdɛm.oʊ/ | démo, démonstration |
| buyback nom | /ˈbaɪˌbæk/ | rétrocession |
| compute verbe | /kəmˈpjuːt/ | computer, calculer |
| confidently adverbe | /ˈkɑnfɪdəntli/ | candidement, confidemment |
| constrain verbe | /kənˈstɹeɪn/ | astreindre, contraindre |
| deflation nom | /dɪˈfleɪʃən/ | déflation |
| deflect verbe | /dɪˈflɛkt/ | détourner |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| make up verbe | /ˌmeɪk ˈʌp/ | constituer, composer |
| switch off verbe | éteindre | |
| add up verbe | additionner | |
| break down verbe | tomber en panne | |
| come back verbe | revenir | |
| end up verbe | se terminer, se finir | |
| figure out verbe | cerner | |
| get back verbe | retourner |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | is expected · being shut · I've been told |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | I've been told · has told · hasn't arrived |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | marketing, which is · person who owns · someone who can |
Prononciation à surveiller
Le locuteur utilise 32 contractions et formes réduites, comme don't, doesn't, they're. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: hallucination /həˌluːsɪˈneɪʃən/, deflation /dɪˈfleɪʃən/, measurable /ˈmɛʒəɹəbəl/, specialize /ˈspɛʃəˌlaɪz/, insurer /ɪnˈʃʊɹ.ɚ/
- Mots longs — placez bien l’accent: hallucination /həˌluːsɪˈneɪʃən/, expenditure /ɛkˈspɛndɪt͡ʃə/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, confidently /ˈkɑnfɪdəntli/, generative /ˈd͡ʒɛnəɹətɪv/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: hype /haɪp/, hallucination /həˌluːsɪˈneɪʃən/, hallucinate /həˈl(j)uːsɪneɪt/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: expenditure /ɛkˈspɛndɪt͡ʃə/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, generative /ˈd͡ʒɛnəɹətɪv/, shortage /ˈʃɔː(ɹ)tɪd͡ʒ/, researcher /ˈɹiˌsɝ.t͡ʃɚ/
- /r/ anglais — langue recourbée, sans frotter la gorge: investor /ɪnˈvɛs.tə(ɹ)/, boardroom /ˈbɔɹdˌɹuːm/, constrain /kənˈstɹeɪn/, generative /ˈd͡ʒɛnəɹətɪv/, measurable /ˈmɛʒəɹəbəl/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme investor, hype, underneath.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.