शैडोइंग अभ्यास: Did the AI bubble just pop? - वीडियो के साथ अंग्रेजी बोलना सीखें

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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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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “Did the AI bubble just pop?” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: Gpu, Nvidia, companies, chips। इस वीडियो में शैडोइंग के लिए 71 वाक्य और 1128 शब्द हैं। बोला गया हिस्सा 5:22 लंबा है। वक्ता तेज़ बोलता है, लगभग 210 शब्द प्रति मिनट, इसलिए जुड़ी हुई और कमज़ोर ध्वनियाँ ज़्यादा मिलेंगी। केवल 81% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
bubble संज्ञा	/ˈbʌbəl/	बुलबुला, बुल्ला
borrow क्रिया	/ˈbɑɹ.oʊ/	उधार लेना
lend क्रिया	/ˈlɛnd/	उधार देना
bath संज्ञा	/bɑːθ/	स्नानागार
decent विशेषण	/ˈdiː.sənt/	शोभनीय, उचित
hire क्रिया	/ˈhaɪɹ/	नियुक्त करना
discover क्रिया	/dɪˈskʌvə/	खोज निकालना, खोज करना
exit संज्ञा	/ˈɛɡzət/	प्रस्थान, निकासी
principle संज्ञा	/ˈpɹɪn.sɪ.pəl/	जड़सूत्र, असूल
rocket संज्ञा	/ˈɹɑk.ɪt/	राकेट, प्रक्षेपास्त्र
parallel विशेषण	/ˈpæ.ɹəˌlɛl/	समानान्तर
arrangement संज्ञा	/əˈɹeɪnd͡ʒmənt/	प्रबंधन, इंतज़ाम
developer संज्ञा	/dɪˈvɛləpɚ/	विकासक
poster संज्ञा	/ˈpoʊstɚ/	पोस्टर, इश्तहार
subscription संज्ञा	/səbˈskɹɪpʃən/	सदस्यता

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	haven't bought · has promised · has done
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	was discovered · was quoted · is built
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	NVIDIA, which makes · company, which is · room, which is

ध्यान देने योग्य उच्चारण

वक्ता 16 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे we'll, I'm, you're। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: subscription /səbˈskɹɪpʃən/, fraction /ˈfɹæk.ʃən/, valuation /ˌvæ.ljuːˈeɪ.ʃən/, depreciate /dəˈpriʃiˌeɪt/, insatiable /ɪnˈseɪʃ(j)əbəl/
लंबे शब्द — बलाघात सही रखें: collateral /kəˈlætəɹəl/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, developer /dɪˈvɛləpɚ/, complexity /kəmˈplɛk.sɪ.ti/, valuation /ˌvæ.ljuːˈeɪ.ʃən/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: discover /dɪˈskʌvə/, invest /ɪnˈvɛst/, developer /dɪˈvɛləpɚ/, borrowing /ˈbɔrəwɪŋ/, valuation /ˌvæ.ljuːˈeɪ.ʃən/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: exit /ˈɛɡzət/, analyze /ˈæn.əˌlaɪz/, subsidize /ˈsʌbsɪdaɪz/
/æ/ — “ऐ” से ज़्यादा खुला: collateral /kəˈlætəɹəl/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, lab /læːb/, parallel /ˈpæ.ɹəˌlɛl/, fraction /ˈfɹæk.ʃən/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर bubble, borrow, lend जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: Did the AI bubble just pop? - वीडियो के साथ अंग्रेजी बोलना सीखें

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?