跟读练习: Did the AI bubble just pop? - 通过视频学习英语口语

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just three weeks ago, it felt like nothing could go wrong.

SpaceX was up 7 % in a day,

every tech CEO in America was on stage explaining why they needed another $100 billion to decimate farmland in rural America,

and Jensen Wong was telling anyone who'd listen that demand for AI chips was insatiable.

But since then, the market took a dump.

Meta created an entirely new class of non -voting stock just to pay for GPUs,

while Microsoft offered a quarter of its employees money to leave so it can afford more of them.

Elon predicted that 200 NeoClouds are about to experience Guppy -level death, and CoreWeave, a former Ethereum mining operation

that became the poster boy of reckless AI spending after they pivoted into renting NVIDIA GPUs back to NVIDIA,

is now worth a third of what it was at the beginning of the month.

And then on Monday, Anthropic filed to go public, which showed that between January and when the government banned Fable back in June,

they lost more money than the annual GDP of over half the countries on Earth.

So naturally, the question on everyone's mind is, are we finally cooked?

In today's video, we'll break down the roughest three weeks in the history of AI,

find out what a neocloud is and why Elon wants them dead, and we'll analyze Anthropics financials in their Trust Me Bro S1.

It is October 28th, 2026, and you're watching the Code Report.

Before we can talk about the AI bubble, you need to understand how a GPU actually gets paid for.

And to do that, here's me in a bubble bath to explain.

First, NVIDIA makes a GPU that they need to sell.

The problem is the GPU is expensive and you don't have enough money to buy it.

So NVIDIA says, hey, we have money

and we'll give it to you as long as you give us a portion of your company

and use the money we give you to buy our GPUs.

But NVIDIA's money only covers a fraction of what you need.

So you go to Wall Street and borrow the rest by putting up the GPU you haven't bought yet as collateral.

Now normally, a bank wouldn't lend to you with a depreciating pile of chips as collateral,

but NVIDIA has promised that if nobody rents the GPUs they give you, they'll rent them themselves.

So NVIDIA's money gets used to buy NVIDIA GPUs that eventually get rented back to NVIDIA, which makes NVIDIA's stock go up,

which it uses to invest in more companies, which borrow more money to buy more NVIDIA GPUs, which keeps the good times going.

On Wall Street, they called this a circular financing arrangement, and back in college, we'd call it a s*.

The most popular company eating this forbidden fruit is CoreWeave, but NVIDIA has done it with a few other companies as well.

And to be honest, it works pretty well as long as the chips rent for more than they depreciate.

The labs keep buying, and someone keeps lending you money to buy more chips.

But three weeks ago, the third one stopped being true when it was discovered that CoreWeave, after it borrowed $52 billion to buy chips,

was now paying 15 % interest on that debt.

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,

and that he expects 200 plus of these neocloud type companies to die.

But conveniently, SpaceX is fine, because it also happens to sell rockets.

And right in the middle of all this, Anthropic decided to ask the public for $100 billion at a valuation

that would make it worth more than 492 out of the 500 companies in the S &P 500,

with 4 out of those 7 remaining companies being where they rent their compute from

and happen to owe $265 billion to through 2045.

The S1 itself is 330 pages long, with 90 of those being risk factors to the company, which is the fun part.

In it, Anthropic admits that everything we've already covered over the last few months is true, including Mythos being allergic to its sandbox,

Claude Haiku filing a fake homicide tip through the Philadelphia Police Department's website,

and a Chinese state -sponsored group using Claude code to attack banks and government agencies.

The filing also says that Claude now uses itself to improve itself,

and that they might not notice their models getting significantly smarter until after it's already happened.

Their guiding principle, according to the foreword, is to under -promise and over -deliver,

which is a little worrisome for a company whose head of alignment was quoted just last month,

saying he thinks there's a greater than 10 % chance that AI kills all humans within the next decade.

So are we cooked?

Well, as always, it depends.

It's not looking great

if your whole business model is borrowing money from one company just to give it right back to them.

But it's hard to call AI a bubble the day after

OpenAI claims one of its models disproved a 27 -year -old conjecture that half of complexity theory is built on.

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.

Akimi just dropped an open model within a point of Opus at half the price that we talked about yesterday,

and the GPU score we financed should be on eBay any week now.

And if you'd like to be one of the people profiting from the exit liquidity instead of providing it,

you can put those subsidized tokens to work with HyperAgent, the sponsor of today's video.

If you're already paying for ChatGPT, you can now use that plan to sign into HyperAgent

and hire a team of agents without setting up yet another AI subscription.

Right now, I'm using it to work on a v4 release of a date library I'm contributing to.

So I set up a HyperAgent room, which is basically a Slack channel where all my agents and human co -maintainers can work together.

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.

They all work in parallel and hand things off to each other, and only ping a human when something needs approval.

And anyone on a team can jump in to ask questions or make changes whenever they want, instead of routing everything through me.

This has been The Code Report, thanks for watching, and I will see you in the next one.

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本课的词汇与口语要点

这节口语课以视频“Did the AI bubble just pop?”为素材。视频中反复出现的词有：Gpu, Nvidia, companies, chips。这段视频共有 71 个句子、1128 个单词可供跟读。讲话部分时长为 5:22。说话人语速较快，每分钟约 210 个词，会出现较多连读和弱读。只有 81% 的单词属于英语最常用的 3,000 词，词汇难度较高。

视频中的重点词汇

视频中 15 个值得学习的单词，附发音和释义：

单词	发音	释义
chip 名词	/t͡ʃɪp/	筹码
bubble 名词	/ˈbʌbəl/	泡, 氣泡 /气泡
borrow 动词	/ˈbɑɹ.oʊ/	借, 貸 /贷
sponsor 名词	/ˈspɒn.səː/	保證人 /保证人
lend 动词	/ˈlɛnd/	借出, 借
token 名词	/ˈtoʊkən/	代幣 /代币, 籌碼 /筹码
collateral 名词	/kəˈlætəɹəl/	擔保 /担保, 抵押品
bath 名词	/bɑːθ/	浴缸, 浴盆
decent 形容词	/ˈdiː.sənt/	體面 /体面, 正派
hire 动词	/ˈhaɪɹ/	租借, 出租
discover 动词	/dɪˈskʌvə/	發現 /发现, 發覺 /发觉
exit 名词	/ˈɛɡzət/	離開 /离开, 退場 /退场
contribute 动词	/kənˈtɹɪb.jut/	貢獻 /贡献
principle 名词	/ˈpɹɪn.sɪ.pəl/	原理, 原則 /原则
invest 动词	/ɪnˈvɛst/	耗費 /耗费, 投資 /投资

视频中出现的短语动词

单词	释义
break down 动词	壞了 /坏了, 出故障
find out 动词	找出, 查明
go up 动词	上昇 /上升, 上去

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
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	haven't bought · has promised · has done
被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的	was discovered · was quoted · is built
定语从句 who / which + 从句 — 补充说明人或事物	NVIDIA, which makes · company, which is · room, which is

需要注意的发音

说话人用了 16 次缩略和弱读形式，例如 we'll, I'm, you're。请按听到的简短形式来说。

“sh” 和 “zh” 音: subscription /səbˈskɹɪpʃən/, fraction /ˈfɹæk.ʃən/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, depreciate /dəˈpriʃiˌeɪt/, insatiable /ɪnˈseɪʃ(j)əbəl/
长单词——注意重音位置: collateral /kəˈlætəɹəl/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, developer /dɪˈvɛləpɚ/, complexity /kəmˈplɛk.sɪ.ti/, valuation /ˌvæ.ljuːˈeɪ.ʃən/

中文母语者容易读错的音：

/v/ — 不要读成 /w/，上齿轻触下唇: discover /dɪˈskʌvə/, invest /ɪnˈvɛst/, developer /dɪˈvɛləpɚ/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, conveniently /kənˈvinjəntli/
词尾辅音 — 要读清楚，后面不要加元音: chip /t͡ʃɪp/, lend /ˈlɛnd/, bath /bɑːθ/, lab /læːb/, decent /ˈdiː.sənt/
/r/ 和 /l/ 的区别: collateral /kəˈlætəɹəl/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, principle /ˈpɹɪn.sɪ.pəl/, parallel /ˈpæ.ɹəˌlɛl/, circular /ˈsɜɹ.kjə.ləɹ/

如何用这段视频练习

先完整听一遍视频，不要开口，记下不认识的单词。
先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
录下自己的声音并与原声对比，特别注意 chip, bubble, borrow 这类单词。

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。

影子跟读法: 阅读完整分步指南 →

跟读练习: Did the AI bubble just pop? - 通过视频学习英语口语

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