Shadowing Practice: OpenAI: A Bubble Bigger Than Dotcom - Learn English Speaking with Video
Creating lesson...
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We have no current plans to make revenue.
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We have no idea how we may one day generate revenue.
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We have made a soft promise to investors that once we've built this sort of generally intelligent system,
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basically, we will ask it to figure out a way to generate an investment return for you.
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Sam Altman, his plan for paying back OpenAI's investors was to ask AI.
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They've now got this AI bubble so big that even the US Treasury is now quietly sounding the alarm.
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Back in June, OpenAI confidentially filed to become a public company.
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This came a week after Anthropix's same announcement.
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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.
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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,
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we just didn't know how bad.
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Last year, OpenAI burned around $38 billion.
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Now, much of these costs came from turning the company from a non-profit into a for-profit.
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But as Ed Citrin so concisely put it, despite these changes, they've remained profitless.
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They turned from a non-profit to a for-profit, though they've remained profitless.
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A few days after these numbers leaked, reports came out that OpenAI was delaying going public,
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exciting SpaceX's volatile stock, which if you haven't seen looks more like a crypto pump and dump than a legitimate company stock.
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But it's more likely that the reason was simply the cat was out of the bag.
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OpenAI was wanting to go public at a 1 trillion valuation
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despite spending 21 billion dollars to make just 13 billion in revenue.
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Earlier this year, it was reported that Sarah Fryer, OpenAI's own chief financial officer, warned Sam Altman against going public this year.
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Her reasons were quite simple.
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Sora being a failed project, executives leaving the company, at the time the Elon Musk trial,
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and of course the $1.4 trillion commitment that they couldn't pay.
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Seems reasonable.
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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,
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which is literally what she's in charge of.
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So the woman with the most information warned privately while Sam Altman continue to publicly praise AI.
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This plays out across the entire industry, but maybe the CFO is wrong.
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Maybe the demand is there and the losses are temporary.
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So let's check, who is actually paying for this?
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OpenAI currently has the largest market share at 53.9%, with Gemini at 27.9%, Claude 9.2%, and Grok at 2.4%.
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Despite this, only around 5% of ChatGPT users actually pay for a subscription
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and notably more users are switching to Claude or Gemini.
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So I just switched from ChatGBT to Claude as I'm sure many of you have.
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I can't lie Claude is a game changer man like that ChatGBT stuff that's in the past.
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As someone who literally went viral for teaching people how to
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study with ChatGBT I fear I might be switching over to Claude because she is that girl.
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So even after spending six billion dollars on sales
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and marketing they couldn't convince their users to upgrade to the 20 paid version it's almost as
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if people don't find it that valuable which is even more concerning
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when you consider
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that 20 a month isn't anywhere near what these ai companies need to charge to be profitable
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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
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ibm survey of ceos revealed only 25 of ai initiatives have delivered expected return on investment
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and only 16 have scaled enterprise wide
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and where gains do exist they're narrow the gain concentrates in code generation customer support assistance
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and drafting most other roles show no measurable payback yet
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which makes sense chat gpt claude gemini these are all large language models
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so language-based tasks like coding is where we see some utility
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but these companies are pushing these models to be something they're not they've overhyped them
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and as a result users corporations investors they're not getting the value they were promised
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and at the same time companies like meta are announcing they
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will sell their ai data center compute to other companies despite saying this just last year even with the capacity
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that we are bringing online in 2025 we are having a hard time meeting the demand
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that teams have for compute resources across the company they went
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from not having enough compute for their own internal use at meta to now needing to sell that compute
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because they have too much this comes after xai did the
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same thing they're now leasing their data center compute to Anthropic and Google.
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So let me get this straight.
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Users don't want to pay $20 a month.
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Enterprise companies are not seeing a return.
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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,
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so they're leasing them to their competitors.
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And the cherry on top is that costs have exploded.
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OpenAI, Anthropic, Microsoft, they've all moved away from a flat rate subscription model to a usage-based billing for their enterprise customers.
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With AI now being billed by usage, one company accidentally spent $500 million in a single month using Claude.
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They told their developers to code as much as possible with AI, so they did.
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We went from use AI for everything to let's measure our usage.
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But the result is companies are now avoiding American AI models and opting instead for Chinese models like GLM,
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DeepSeek, and Kimi, which are either free or very cheap.
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Coinbase announced they cut their AI spend 50% by switching to GLM and Kimi.
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Cursor admitted they built their Frontier coding model on top of Kimi, and even Microsoft is testing DeepSeek and Copilot as a cheaper option.
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Chinese models grew from about 1% of token usage on OpenRouter in late 2024 to over 60% by early 2026,
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surpassing US models for the first time.
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But it gets worse.
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It's not just users who are moving away from ChatGPT, it's OpenAI's own partners.
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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.
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And last year, OpenAI acquired the AI hardware company IO for $6.4 billion.
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They wanted physical devices running chat GPT like from the movie Her, devices that always listen and continuously respond.
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Key detail, IO's founder is Johnny Ive, the man who designed the iPhone.
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And what do we learn this month?
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Apple is suing OpenAI.
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Alleging OpenAI asked job candidates from Apple to share details about secret projects
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and to to bring device components and prototypes to their interviews.
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But it doesn't stop there.
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Microsoft is no longer OpenAI's exclusive cloud provider and will no longer pay a revenue share to OpenAI.
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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?
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Elon Musk has sued them, now Apple is suing them, and if that's not enough,
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then there is the extremely high turnover with key leaders leaving on a monthly basis.
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Turns out OpenAI is a dumpster fire of a company, but zoom out and they represent the entire AI industry.
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And this isn't just me saying this.
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The people who regulate the U.S economy are now quietly saying the same thing.
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A leaked draft report from the U.S.
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Treasury Department outlines the risk that AI firms pose to the economy.
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The analysts found that AI firms are more deeply entrenched in the U.S economy than their dot-com predecessors.
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A downturn in the AI market would send shockwaves throughout the entire economic ecosystem.
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Stock markets, private credit markets, companies financing data center buildouts, cloud providers, chip manufacturers, and utilities would all feel the effects.
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What I find most concerning about this leak is that the report was put together by Treasury analysts for Secretary Scott Besant,
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Fed Chair Kevin Walsh, and various other federal financial regulators, the very people who are supposed to be regulating the U.S economy.
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So while Scott Besant's analysts were working on this report, here's what he said in New York.
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And we are seeing that the hyperscalers are going to spend at least $750 billion this year.
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Obviously, a lot of that's import, so it won't go directly into GDP.
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But we are seeing the here and now, whether it's in the construction jobs, in the capital markets, the things that are happening.
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And I think that we all just have to have an open mind in terms of where this can take us.
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The Treasury Secretary seemingly went into this interview knowing his own
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analyst had warned the AI bubble is now bigger than the dot-com bubble.
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And yet he sat there and praised hyperscalers spending more than $750 billion on data centers alone this year.
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And then had the audacity to say we all just need to have an open mind about where this can take us.
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The people with the raw numbers, the data, the truth about how big this bubble really is are privately discussing how bad it is
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while publicly lying to our faces.
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Telling us to have an open mind when it's our retirements on the line.
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It's our tax dollars partially funding these AI data centers, it's our electricity bills increasing to power them.
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When this AI bubble bursts all the wealthy people sitting in that room will be just fine.
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In fact, they'll buy up more houses when everything crashes.
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But it's us, the everyday people, who will have fewer jobs, higher grocery prices, a wrecked retirement, and less stability overall.
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In the report, the analysts argue that AI firms pose significant risk to the entire system if financial conditions change,
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productivity goals are missed, or various choke points stymie growth.
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We have already checked all of these boxes.
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First condition, missed productivity goals.
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We've covered that one.
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Only a quarter of enterprise AI initiatives deliver the expected return.
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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,
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but instead resulted in delivery delays and unhappy customers, costing more than $100 million in losses.
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Second condition we've already met, choke points, stymie, and growth.
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Oracle borrowed $43 billion this year to build data centers.
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But there's a problem with this.
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Oracle is using these billions to build data centers to then lease back to other AI companies.
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This unlocks $638 billion in future revenue, and half of that is supposed to come from OpenAI alone,
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who we know is also burning through billions.
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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.
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Breaking news on the Bloomberg terminal, Oracle and OpenAI are apparently ending plans to expand that Texas data center site.
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This is Stargate.
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Remember, it was just yesterday where Brody Ford here at Bloomberg reported
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that Oracle was planning to cut thousands of jobs as it
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was dealing with the cash crunch from a massive AI data center expansion effort.
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And now a new report out saying that Oracle
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and OpenAI have scrapped plans to expand that flagship AI data center in Texas.
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And that's before we factor in the growing backlash against AI data centers.
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$64 billion worth of data center projects have been blocked or delayed amid local opposition.
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Choke points everywhere you look.
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Which brings us to the third condition, which we've already met.
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Financial conditions changing.
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The money is drying up.
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Back in March, Reuters reported that OpenAI was offering private equity firms preferred equity stakes with a guaranteed minimum return of 17.5%.
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OpenAI isn't just running out of money, they're running out of financing options.
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And let's consider if the current investors were truly confident in this company,
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they would put more of their money in instead of stepping aside to allow private equity to extract 17.5%.
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So how can Sam Altman guarantee such a high return?
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Well, despite what he said at the beginning of the video, it's quite clear that the plan was always to go public.
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But why would we, the public, want to own stock in a company that has no profits?
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No money?
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They're broke.
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But OpenAI wanting public funds really shouldn't come as a surprise.
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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.
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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.
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This is yet another example of billionaires privatizing the earnings and socializing the losses.
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If the government takes an ownership stake in OpenAI, the company officially becomes too big to fail.
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And who prevents it from failing?
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The taxpayers.
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And if you think the government taking an ownership stake in a company is unlikely,
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well, since January 2025, the U.S government has announced investments worth $26.7 billion across 30 deals involving direct ownership of companies.
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The U.S government is now apparently a private equity firm.
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Sam Altman planned to ask ChatGPT how investors would get their money back.
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Maybe someone should ask it, who's left holding the bag?
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Reportedly that the administration and potentially some sort of sovereign wealth fund would take a 5% stake in open AI.
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I mean, is that a situation that you would like to see happen or do you think that's more disruptive?
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No, I don't think there's any need to.
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They talk about, I think Sam Orman said, well, we can share the benefits of AI and the profits of AI.
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What profits?
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What returns?
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That 5% will have to get congressional approval.
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The sovereign wealth fund is still an idea at this point.
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But fundamentally, large language models are not the future.
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The only reason big tech is investing in this is that they've run out of hyper-growth ideas.
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They don't have a next iPhone.
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They don't have a new Google search.
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So they've put over a trillion dollars with trillions more to come into a kind of a dead-end industry
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because when that ends, they'll have to admit that they don't have anything else.
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English Speaking Practice: Analyzing the AI Industry's Hype and Realities
Why Practice Speaking with This Video?
This video offers a dynamic context for english speaking practice, blending industry insights with casual commentary. The speaker uses conversational language, personal anecdotes, and technical terms, making it ideal for practicing fluency in both formal and informal settings. Discussing topics like market share, financial challenges, and user behavior helps build vocabulary for business, technology, and everyday discussions. Plus, the natural flow of ideas encourages shadowing—repeating phrases aloud to mimic pronunciation and rhythm—an effective technique for improving speaking skills. Whether preparing for IELTS speaking practice or real-world conversations, this content trains you to articulate complex ideas clearly.Grammar & Expressions in Context
The video is rich with practical language structures. Here are key examples:- Contrast clauses: "Despite this, only around 5% of ChatGPT users actually pay for a subscription" – Learn to use "despite" to link opposing ideas.
- Hypothetical questions: "Maybe the demand is there and the losses are temporary. So let's check, who is actually paying for this?" – Practice using rhetorical questions to engage listeners.
- Colloquial phrases: "Claude is a game changer" and "that ChatGPT stuff that's in the past" – Master informal expressions to sound natural in casual conversations.
- Conditional structures: "If users aren't willing to pay, who is?" – Improve conditional sentences for discussing possibilities.
Common Pronunciation Traps
The speaker’s tone and word choice present opportunities to refine improve english pronunciation. Watch for:- Volatile (pronounced /ˈvɒlətaɪl/) – Avoid misstressing the second syllable; stress the first.
- Enterprise (pronounced /ˈentəpraɪz/) – Don’t confuse with "entertain"; focus on the "praɪz" ending.
- Colloquial contractions: "I'm sure many of you have" – Practice reducing "have" to /v/ in fast speech.
- Intonation in anecdotes: "I can't lie, Claude is that girl" – Use rising intonation for emphasis in personal stories.
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 | have made · they've remained · haven't seen |
| Passive voice be + past participle — the focus is on what happens, not who does it | was reported · were promised · being billed |
| Relative clauses who / which + clause — extra information about a person or thing | financials, Which is · someone who literally · valuable which is |
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.