शैडोइंग अभ्यास: OpenAI: A Bubble Bigger Than Dotcom - वीडियो के साथ अंग्रेजी बोलना सीखें

We have no current plans to make revenue.

We have no idea how we may one day generate revenue.

We have made a soft promise to investors that once we've built this sort of generally intelligent system,

basically, we will ask it to figure out a way to generate an investment return for you.

Sam Altman, his plan for paying back OpenAI's investors was to ask AI.

They've now got this AI bubble so big that even the US Treasury is now quietly sounding the alarm.

Back in June, OpenAI confidentially filed to become a public company.

This came a week after Anthropix's same announcement.

And while the company claimed they were keeping their options open on the timeline, other sources reported Sam Altman was pushing for as early as September.

That was until an independent reporter, Ed Zittron, got his hands on OpenAI's financials, Which is one of those things where we knew it was bad,

we just didn't know how bad.

Last year, OpenAI burned around $38 billion.

Now, much of these costs came from turning the company from a non-profit into a for-profit.

But as Ed Citrin so concisely put it, despite these changes, they've remained profitless.

They turned from a non-profit to a for-profit, though they've remained profitless.

A few days after these numbers leaked, reports came out that OpenAI was delaying going public,

exciting SpaceX's volatile stock, which if you haven't seen looks more like a crypto pump and dump than a legitimate company stock.

But it's more likely that the reason was simply the cat was out of the bag.

OpenAI was wanting to go public at a 1 trillion valuation

despite spending 21 billion dollars to make just 13 billion in revenue.

Earlier this year, it was reported that Sarah Fryer, OpenAI's own chief financial officer, warned Sam Altman against going public this year.

Her reasons were quite simple.

Sora being a failed project, executives leaving the company, at the time the Elon Musk trial,

and of course the $1.4 trillion commitment that they couldn't pay.

Seems reasonable.

But as a result of her pushback, it was reported that Sam Altman excluded her from joining investor meetings to discuss spending with OpenAI investors,

which is literally what she's in charge of.

So the woman with the most information warned privately while Sam Altman continue to publicly praise AI.

This plays out across the entire industry, but maybe the CFO is wrong.

Maybe the demand is there and the losses are temporary.

So let's check, who is actually paying for this?

OpenAI currently has the largest market share at 53.9%, with Gemini at 27.9%, Claude 9.2%, and Grok at 2.4%.

Despite this, only around 5% of ChatGPT users actually pay for a subscription

and notably more users are switching to Claude or Gemini.

So I just switched from ChatGBT to Claude as I'm sure many of you have.

I can't lie Claude is a game changer man like that ChatGBT stuff that's in the past.

As someone who literally went viral for teaching people how to

study with ChatGBT I fear I might be switching over to Claude because she is that girl.

So even after spending six billion dollars on sales

and marketing they couldn't convince their users to upgrade to the 20 paid version it's almost as

if people don't find it that valuable which is even more concerning

when you consider

that 20 a month isn't anywhere near what these ai companies need to charge to be profitable

so if users aren't willing to pay who is the obvious answer is businesses, but they're not seeing the value either in may this year

ibm survey of ceos revealed only 25 of ai initiatives have delivered expected return on investment

and only 16 have scaled enterprise wide

and where gains do exist they're narrow the gain concentrates in code generation customer support assistance

and drafting most other roles show no measurable payback yet

which makes sense chat gpt claude gemini these are all large language models

so language-based tasks like coding is where we see some utility

but these companies are pushing these models to be something they're not they've overhyped them

and as a result users corporations investors they're not getting the value they were promised

and at the same time companies like meta are announcing they

will sell their ai data center compute to other companies despite saying this just last year even with the capacity

that we are bringing online in 2025 we are having a hard time meeting the demand

that teams have for compute resources across the company they went

from not having enough compute for their own internal use at meta to now needing to sell that compute

because they have too much this comes after xai did the

same thing they're now leasing their data center compute to Anthropic and Google.

So let me get this straight.

Users don't want to pay $20 a month.

Enterprise companies are not seeing a return.

Meta and XAI, who are supposedly leaders in AI, and yet they don't even have a use case for all these data centers they've been building,

so they're leasing them to their competitors.

And the cherry on top is that costs have exploded.

OpenAI, Anthropic, Microsoft, they've all moved away from a flat rate subscription model to a usage-based billing for their enterprise customers.

With AI now being billed by usage, one company accidentally spent $500 million in a single month using Claude.

They told their developers to code as much as possible with AI, so they did.

We went from use AI for everything to let's measure our usage.

But the result is companies are now avoiding American AI models and opting instead for Chinese models like GLM,

DeepSeek, and Kimi, which are either free or very cheap.

Coinbase announced they cut their AI spend 50% by switching to GLM and Kimi.

Cursor admitted they built their Frontier coding model on top of Kimi, and even Microsoft is testing DeepSeek and Copilot as a cheaper option.

Chinese models grew from about 1% of token usage on OpenRouter in late 2024 to over 60% by early 2026,

surpassing US models for the first time.

But it gets worse.

It's not just users who are moving away from ChatGPT, it's OpenAI's own partners.

You may remember Apple's Siri was supposed to be powered by ChatGPT, but fast forward to June this year, it's now powered by Google's Gemini.

And last year, OpenAI acquired the AI hardware company IO for $6.4 billion.

They wanted physical devices running chat GPT like from the movie Her, devices that always listen and continuously respond.

Key detail, IO's founder is Johnny Ive, the man who designed the iPhone.

And what do we learn this month?

Apple is suing OpenAI.

Alleging OpenAI asked job candidates from Apple to share details about secret projects

and to to bring device components and prototypes to their interviews.

But it doesn't stop there.

Microsoft is no longer OpenAI's exclusive cloud provider and will no longer pay a revenue share to OpenAI.

Every few months, Microsoft and OpenAI have to release some joint statement about their strained relationship, which is exactly what companies do when everything is fine, right?

Elon Musk has sued them, now Apple is suing them, and if that's not enough,

then there is the extremely high turnover with key leaders leaving on a monthly basis.

Turns out OpenAI is a dumpster fire of a company, but zoom out and they represent the entire AI industry.

And this isn't just me saying this.

The people who regulate the U.S economy are now quietly saying the same thing.

A leaked draft report from the U.S.

Treasury Department outlines the risk that AI firms pose to the economy.

The analysts found that AI firms are more deeply entrenched in the U.S economy than their dot-com predecessors.

A downturn in the AI market would send shockwaves throughout the entire economic ecosystem.

Stock markets, private credit markets, companies financing data center buildouts, cloud providers, chip manufacturers, and utilities would all feel the effects.

What I find most concerning about this leak is that the report was put together by Treasury analysts for Secretary Scott Besant,

100

Fed Chair Kevin Walsh, and various other federal financial regulators, the very people who are supposed to be regulating the U.S economy.

101

So while Scott Besant's analysts were working on this report, here's what he said in New York.

102

And we are seeing that the hyperscalers are going to spend at least $750 billion this year.

103

Obviously, a lot of that's import, so it won't go directly into GDP.

104

But we are seeing the here and now, whether it's in the construction jobs, in the capital markets, the things that are happening.

105

And I think that we all just have to have an open mind in terms of where this can take us.

106

The Treasury Secretary seemingly went into this interview knowing his own

107

analyst had warned the AI bubble is now bigger than the dot-com bubble.

108

And yet he sat there and praised hyperscalers spending more than $750 billion on data centers alone this year.

109

And then had the audacity to say we all just need to have an open mind about where this can take us.

110

The people with the raw numbers, the data, the truth about how big this bubble really is are privately discussing how bad it is

111

while publicly lying to our faces.

112

Telling us to have an open mind when it's our retirements on the line.

113

It's our tax dollars partially funding these AI data centers, it's our electricity bills increasing to power them.

114

When this AI bubble bursts all the wealthy people sitting in that room will be just fine.

115

In fact, they'll buy up more houses when everything crashes.

116

But it's us, the everyday people, who will have fewer jobs, higher grocery prices, a wrecked retirement, and less stability overall.

117

In the report, the analysts argue that AI firms pose significant risk to the entire system if financial conditions change,

118

productivity goals are missed, or various choke points stymie growth.

119

We have already checked all of these boxes.

120

First condition, missed productivity goals.

121

We've covered that one.

122

Only a quarter of enterprise AI initiatives deliver the expected return.

123

A perfect example of this, Pizza Hut franchisees are suing after they claim they were forced to use an AI delivery management platform for increased productivity,

124

but instead resulted in delivery delays and unhappy customers, costing more than $100 million in losses.

125

Second condition we've already met, choke points, stymie, and growth.

126

Oracle borrowed $43 billion this year to build data centers.

127

But there's a problem with this.

128

Oracle is using these billions to build data centers to then lease back to other AI companies.

129

This unlocks $638 billion in future revenue, and half of that is supposed to come from OpenAI alone,

130

who we know is also burning through billions.

131

And let's not forget, this is the same industry where two of the five leading AI companies, Meta and xAI, are already leasing their data centers.

132

Breaking news on the Bloomberg terminal, Oracle and OpenAI are apparently ending plans to expand that Texas data center site.

133

This is Stargate.

134

Remember, it was just yesterday where Brody Ford here at Bloomberg reported

135

that Oracle was planning to cut thousands of jobs as it

136

was dealing with the cash crunch from a massive AI data center expansion effort.

137

And now a new report out saying that Oracle

138

and OpenAI have scrapped plans to expand that flagship AI data center in Texas.

139

And that's before we factor in the growing backlash against AI data centers.

140

$64 billion worth of data center projects have been blocked or delayed amid local opposition.

141

Choke points everywhere you look.

142

Which brings us to the third condition, which we've already met.

143

Financial conditions changing.

144

The money is drying up.

145

Back in March, Reuters reported that OpenAI was offering private equity firms preferred equity stakes with a guaranteed minimum return of 17.5%.

146

OpenAI isn't just running out of money, they're running out of financing options.

147

And let's consider if the current investors were truly confident in this company,

148

they would put more of their money in instead of stepping aside to allow private equity to extract 17.5%.

149

So how can Sam Altman guarantee such a high return?

150

Well, despite what he said at the beginning of the video, it's quite clear that the plan was always to go public.

151

But why would we, the public, want to own stock in a company that has no profits?

152

No money?

153

They're broke.

154

But OpenAI wanting public funds really shouldn't come as a surprise.

155

Last year, Sarah Fryer, the CFO, made comments about a federal backstop, Meaning the government would pay for OpenAI's debts if OpenAI couldn't.

156

She later walked those comments back, but fast forward to this month, we learned that OpenAI has offered the US government a 5% stake in the company.

157

This is yet another example of billionaires privatizing the earnings and socializing the losses.

158

If the government takes an ownership stake in OpenAI, the company officially becomes too big to fail.

159

And who prevents it from failing?

160

The taxpayers.

161

And if you think the government taking an ownership stake in a company is unlikely,

162

well, since January 2025, the U.S government has announced investments worth $26.7 billion across 30 deals involving direct ownership of companies.

163

The U.S government is now apparently a private equity firm.

164

Sam Altman planned to ask ChatGPT how investors would get their money back.

165

Maybe someone should ask it, who's left holding the bag?

166

Reportedly that the administration and potentially some sort of sovereign wealth fund would take a 5% stake in open AI.

167

I mean, is that a situation that you would like to see happen or do you think that's more disruptive?

168

No, I don't think there's any need to.

169

They talk about, I think Sam Orman said, well, we can share the benefits of AI and the profits of AI.

170

What profits?

171

What returns?

172

That 5% will have to get congressional approval.

173

The sovereign wealth fund is still an idea at this point.

174

But fundamentally, large language models are not the future.

175

The only reason big tech is investing in this is that they've run out of hyper-growth ideas.

176

They don't have a next iPhone.

177

They don't have a new Google search.

178

So they've put over a trillion dollars with trillions more to come into a kind of a dead-end industry

179

because when that ends, they'll have to admit that they don't have anything else.

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इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो में शैडोइंग के लिए 179 वाक्य और 2429 शब्द हैं। बोला गया हिस्सा 14:53 लंबा है। वक्ता लगभग 163 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। केवल 79% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं, इसलिए शब्दावली कठिन है।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 कम प्रचलित शब्द, उच्चारण और अर्थ के साथ:

investor /ɪnˈvɛs.tə(ɹ)/ (संज्ञा) — विनिधानकर्ता, निवेशक. A person who invests money in order to make a profit.
bubble /ˈbʌbəl/ (संज्ञा) — बुलबुला, बुल्ला. A spherically contained volume of air or other gas in a liquid, commonly a soapy liquid.
enterprise /ˈɛntəˌpɹaɪz/ (संज्ञा) — उद्यम, उपक्रम. A company, business, organization, or other purposeful endeavor.
usage /ˈjuːsɪd͡ʒ/ (संज्ञा) — लौकिकता. A custom or established practice.
delay /dɪˈleɪ̯/ (संज्ञा) — देर, विलंब. A period of time before an event occurs; the act of delaying; procrastination; lingering inactivity.
ownership /ˈoʊnɚʃɪp/ (संज्ञा) — मल्कियत, स्वामित्व. The state of having complete legal control of something; possession; proprietorship.
leak /liːk/ (संज्ञा) — रिसाव. A crack, crevice, fissure, or hole which admits water or other fluid, or lets it escape.
warn /woɹn/ (क्रिया) — चेताना. To make (someone) aware of impending danger, evil, etc.
cloud /ˈklaʊ̯d/ (संज्ञा) — मेघ, बादल. A visible mass of water droplets suspended in the air.
guarantee /ˌɡæɹ.ənˈtiː/ (संज्ञा) — प्रतिभूति. Anything that assures a certain outcome.
initiative /ɪˈnɪʃ.ə.tɪv/ (संज्ञा) — पहल. A beginning; a first move.
praise /pɹeɪz/ (क्रिया) — सराहना करना, तारीफ़ करना. To give praise to; to commend, glorify, or worship.
publicly /ˈpʌb.lɪ.kli/ (क्रिया विशेषण) — सरेआम, खुलेआम. In public, openly, in an open and public manner.
generate /ˈd͡ʒɛn.ə.ɹeɪt/ (क्रिया) — उपजाना. To bring into being; give rise to.
quietly /ˈkwaɪ.ɪt.li/ (क्रिया विशेषण) — चुपके. Producing little or no noise.

ध्यान देने योग्य उच्चारण

वक्ता 43 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे they're, they've, don't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“sh” और “zh” की ध्वनियाँ: ownership /ˈoʊnɚʃɪp/, initiative /ɪˈnɪʃ.ə.tɪv/, subscription /səbˈskɹɪpʃən/, potentially /pəˈtɛnʃ(ə)li/, congressional /kəŋˈɡɹɛ.ʃə.nəl/
लंबे शब्द — बलाघात सही रखें: productivity /ˌpɹɒdʌkˈtɪvəti/, initiative /ɪˈnɪʃ.ə.tɪv/, utility /juːˈtɪlɪti/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, electricity /ɪˌlɛkˈtɹɪsɪti/

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 investor, bubble, enterprise जैसे शब्दों पर ध्यान दें।

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	have made · they've remained · haven't seen
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	was reported · were promised · being billed
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	financials, Which is · someone who literally · valuable which is

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।

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इस वीडियो का व्याकरण

शैडोइंग तकनीक क्या है?