Pratique du Shadowing: Did the AI bubble just pop? - Apprendre l'anglais à l'oral avec la vidéo
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71 phrases
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just three weeks ago, it felt like nothing could go wrong.
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SpaceX was up 7 % in a day,
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every tech CEO in America was on stage explaining why they needed another $100 billion to decimate farmland in rural America,
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and Jensen Wong was telling anyone who'd listen that demand for AI chips was insatiable.
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But since then, the market took a dump.
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Meta created an entirely new class of non -voting stock just to pay for GPUs,
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while Microsoft offered a quarter of its employees money to leave so it can afford more of them.
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Elon predicted that 200 NeoClouds are about to experience Guppy -level death, and CoreWeave, a former Ethereum mining operation
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that became the poster boy of reckless AI spending after they pivoted into renting NVIDIA GPUs back to NVIDIA,
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is now worth a third of what it was at the beginning of the month.
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And then on Monday, Anthropic filed to go public, which showed that between January and when the government banned Fable back in June,
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they lost more money than the annual GDP of over half the countries on Earth.
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So naturally, the question on everyone's mind is, are we finally cooked?
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In today's video, we'll break down the roughest three weeks in the history of AI,
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find out what a neocloud is and why Elon wants them dead, and we'll analyze Anthropics financials in their Trust Me Bro S1.
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It is October 28th, 2026, and you're watching the Code Report.
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Before we can talk about the AI bubble, you need to understand how a GPU actually gets paid for.
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And to do that, here's me in a bubble bath to explain.
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First, NVIDIA makes a GPU that they need to sell.
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The problem is the GPU is expensive and you don't have enough money to buy it.
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So NVIDIA says, hey, we have money
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and we'll give it to you as long as you give us a portion of your company
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and use the money we give you to buy our GPUs.
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But NVIDIA's money only covers a fraction of what you need.
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So you go to Wall Street and borrow the rest by putting up the GPU you haven't bought yet as collateral.
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Now normally, a bank wouldn't lend to you with a depreciating pile of chips as collateral,
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but NVIDIA has promised that if nobody rents the GPUs they give you, they'll rent them themselves.
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So NVIDIA's money gets used to buy NVIDIA GPUs that eventually get rented back to NVIDIA, which makes NVIDIA's stock go up,
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which it uses to invest in more companies, which borrow more money to buy more NVIDIA GPUs, which keeps the good times going.
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On Wall Street, they called this a circular financing arrangement, and back in college, we'd call it a s*.
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The most popular company eating this forbidden fruit is CoreWeave, but NVIDIA has done it with a few other companies as well.
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And to be honest, it works pretty well as long as the chips rent for more than they depreciate.
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The labs keep buying, and someone keeps lending you money to buy more chips.
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But three weeks ago, the third one stopped being true when it was discovered that CoreWeave, after it borrowed $52 billion to buy chips,
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was now paying 15 % interest on that debt.
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Then on Sunday night, Elon chimed in, saying that debt markets have almost entirely shut off for any company whose only revenue is AI data centers,
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and that he expects 200 plus of these neocloud type companies to die.
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But conveniently, SpaceX is fine, because it also happens to sell rockets.
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And right in the middle of all this, Anthropic decided to ask the public for $100 billion at a valuation
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that would make it worth more than 492 out of the 500 companies in the S &P 500,
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with 4 out of those 7 remaining companies being where they rent their compute from
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and happen to owe $265 billion to through 2045.
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The S1 itself is 330 pages long, with 90 of those being risk factors to the company, which is the fun part.
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In it, Anthropic admits that everything we've already covered over the last few months is true, including Mythos being allergic to its sandbox,
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Claude Haiku filing a fake homicide tip through the Philadelphia Police Department's website,
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and a Chinese state -sponsored group using Claude code to attack banks and government agencies.
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The filing also says that Claude now uses itself to improve itself,
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and that they might not notice their models getting significantly smarter until after it's already happened.
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Their guiding principle, according to the foreword, is to under -promise and over -deliver,
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which is a little worrisome for a company whose head of alignment was quoted just last month,
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saying he thinks there's a greater than 10 % chance that AI kills all humans within the next decade.
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So are we cooked?
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Well, as always, it depends.
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It's not looking great
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if your whole business model is borrowing money from one company just to give it right back to them.
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But it's hard to call AI a bubble the day after
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OpenAI claims one of its models disproved a 27 -year -old conjecture that half of complexity theory is built on.
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But thankfully, as developers, things are actually looking pretty decent right now, since every one of these companies are fighting to sell us subsidized tokens.
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Akimi just dropped an open model within a point of Opus at half the price that we talked about yesterday,
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and the GPU score we financed should be on eBay any week now.
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And if you'd like to be one of the people profiting from the exit liquidity instead of providing it,
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you can put those subsidized tokens to work with HyperAgent, the sponsor of today's video.
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If you're already paying for ChatGPT, you can now use that plan to sign into HyperAgent
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and hire a team of agents without setting up yet another AI subscription.
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Right now, I'm using it to work on a v4 release of a date library I'm contributing to.
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So I set up a HyperAgent room, which is basically a Slack channel where all my agents and human co -maintainers can work together.
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I've got one agent triaging new issues as they come in another one drafting pull requests for new features, and a third one writing the changelog.
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They all work in parallel and hand things off to each other, and only ping a human when something needs approval.
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And anyone on a team can jump in to ask questions or make changes whenever they want, instead of routing everything through me.
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Sign in with your existing ChatGPT plan to try it out at the link below.
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This has been The Code Report, thanks for watching, and I will see you in the next one.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale s’appuie sur la vidéo « Did the AI bubble just pop? ». Les mots qui reviennent le plus souvent : Gpu, Nvidia, companies, chips. Cette vidéo contient 71 phrases et 1128 mots à répéter en shadowing. La partie parlée dure 5:22. Le locuteur parle vite, environ 210 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
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| chip nom | /t͡ʃɪp/ | brisure, fragment |
| bubble nom | /ˈbʌbəl/ | bulle |
| borrow verbe | /ˈbɑɹ.oʊ/ | emprunter |
| sponsor nom | /ˈspɒn.səː/ | commanditaire, sponsor |
| lend verbe | /ˈlɛnd/ | prêter |
| token nom | /ˈtoʊkən/ | symbole |
| collateral nom | /kəˈlætəɹəl/ | nantissement, gage |
| naturally adverbe | /ˈnæt͡ʃ.(ə.)ɹə.li/ | naturellement |
| bath nom | /bɑːθ/ | bain, baignoire |
| decent adjectif | /ˈdiː.sənt/ | intègre |
| hire verbe | /ˈhaɪɹ/ | louer |
| discover verbe | /dɪˈskʌvə/ | découvrir |
| exit nom | /ˈɛɡzət/ | sortie |
| contribute verbe | /kənˈtɹɪb.jut/ | contribuer |
| principle nom | /ˈpɹɪn.sɪ.pəl/ | principe |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| set up verbe | /ˌsɛt ˈʌp/ | mettre en place, installer |
| break down verbe | tomber en panne | |
| find out verbe | découvrir, savoir | |
| go up verbe | monter, s'élever | |
| put up verbe | placer |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | haven't bought · has promised · has done |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | was discovered · was quoted · is built |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | NVIDIA, which makes · company, which is · room, which is |
Prononciation à surveiller
Le locuteur utilise 16 contractions et formes réduites, comme we'll, I'm, you're. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: subscription /səbˈskɹɪpʃən/, fraction /ˈfɹæk.ʃən/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, depreciate /dəˈpriʃiˌeɪt/, insatiable /ɪnˈseɪʃ(j)əbəl/
- Mots longs — placez bien l’accent: collateral /kəˈlætəɹəl/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, developer /dɪˈvɛləpɚ/, complexity /kəmˈplɛk.sɪ.ti/, valuation /ˌvæ.ljuːˈeɪ.ʃən/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: hire /ˈhaɪɹ/, homicide /ˈhɑ.məˌsaɪ̯d/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: chip /t͡ʃɪp/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, arrangement /əˈɹeɪnd͡ʒmənt/, allergic /əˈlɝ.d͡ʒɪk/, changelog /ˈtʃeɪndʒˌlɒɡ/
- /r/ anglais — langue recourbée, sans frotter la gorge: borrow /ˈbɑɹ.oʊ/, collateral /kəˈlætəɹəl/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, hire /ˈhaɪɹ/, contribute /kənˈtɹɪb.jut/
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 chip, bubble, borrow.
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.