Shadowing Practice: Did the AI bubble just pop? - Learn English Speaking with Video
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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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Vocabulary and speaking notes for this lesson
This speaking lesson is built on the video “Did the AI bubble just pop?”. The speaker keeps coming back to these words: Gpu, Nvidia, companies, chips. This video has 71 sentences and 1128 words to shadow. The speech runs for 5:22. The speaker talks fast, about 210 words per minute, so expect linked and reduced sounds. Only 81% of the words are among the 3,000 most common in English, so the vocabulary is demanding.
Key vocabulary in this video
15 words from the video worth learning, with pronunciation and meaning:
| Word | Pronunciation | Meaning |
|---|---|---|
| chip noun | /t͡ʃɪp/ | A small piece broken from a larger piece of solid material. |
| bubble noun | /ˈbʌbəl/ | A spherically contained volume of air or other gas in a liquid, commonly a soapy liquid. |
| borrow verb | /ˈbɑɹ.oʊ/ | To receive (something) from somebody temporarily, expecting to return it. |
| sponsor noun | /ˈspɒn.səː/ | A person or organization with some sort of responsibility for another person or organization, especially where the responsibility has a religious, legal, or… |
| lend verb | /ˈlɛnd/ | To allow to be used by someone temporarily, on condition that it or its equivalent will be returned. |
| token noun | /ˈtoʊkən/ | Something serving as an expression of something else. |
| collateral noun | /kəˈlætəɹəl/ | A security or guarantee (usually an asset) pledged for the repayment of a loan if one cannot procure enough funds to repay. |
| naturally adverb | /ˈnæt͡ʃ.(ə.)ɹə.li/ | In a natural manner. |
| bath noun | /bɑːθ/ | A tub or pool which is used for bathing: bathtub. |
| lab noun | /læːb/ | A Labrador retriever. |
| decent adjective | /ˈdiː.sənt/ | Appropriate; suitable for the circumstances. |
| hire verb | /ˈhaɪɹ/ | To obtain the services of in return for fixed payment. |
| discover verb | /dɪˈskʌvə/ | To find or learn something for the first time. |
| exit noun | /ˈɛɡzət/ | An act of going out or going away, or leaving; a departure. |
| contribute verb | /kənˈtɹɪb.jut/ | To give something that is or becomes part of a larger whole. |
Phrasal verbs you will hear
| Word | Pronunciation | Meaning |
|---|---|---|
| set up verb | /ˌsɛt ˈʌp/ | To make ready for use. |
| break down verb | To stop functioning. | |
| find out verb | To discover, as by asking or investigating. | |
| go up verb | To move upwards. | |
| put up verb | To place in a high location. | |
| work together verb | To be coworkers with someone; to share a workspace with someone. |
Grammar in this video
The structures the speaker uses most, with the exact words from the video:
| Structure | In the video |
|---|---|
| Present perfect have/has + past participle — a past action that still matters now | haven't bought · has promised · has done |
| Passive voice be + past participle — the focus is on what happens, not who does it | was discovered · was quoted · is built |
| Relative clauses who / which + clause — extra information about a person or thing | NVIDIA, which makes · company, which is · room, which is |
Pronunciation to watch
The speaker uses 16 contractions and reduced forms, such as we'll, I'm, you're. Say them the short way, as you hear them.
- The “sh” and “zh” sounds: subscription /səbˈskɹɪpʃən/, fraction /ˈfɹæk.ʃən/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, depreciate /dəˈpriʃiˌeɪt/, insatiable /ɪnˈseɪʃ(j)əbəl/
- Long words — get the stress right: collateral /kəˈlætəɹəl/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, developer /dɪˈvɛləpɚ/, complexity /kəmˈplɛk.sɪ.ti/, valuation /ˌvæ.ljuːˈeɪ.ʃən/
How to practise with this video
- Listen to the whole video once without speaking and note the words you do not know.
- Start at 0.75× speed, shadow it sentence by sentence, then go back to normal speed once it feels easy.
- Record yourself and compare with the original, paying attention to words like chip, bubble, borrow.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.