ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: OpenAI: A Bubble Bigger Than Dotcom
กำลังสร้างบทเรียน...
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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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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
วิดีโอนี้มี 179 ประโยค และ 2429 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 14:53 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 163 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน มีเพียง 79% ของคำที่อยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ คำศัพท์จึงค่อนข้างยาก
คำศัพท์สำคัญในวิดีโอนี้
คำที่พบไม่บ่อย 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:
- investor /ɪnˈvɛs.tə(ɹ)/ (คำนาม) — ผู้ลงทุน. A person who invests money in order to make a profit.
- bubble /ˈbʌbəl/ (คำนาม) — ฟอง. A spherically contained volume of air or other gas in a liquid, commonly a soapy liquid.
- enterprise /ˈɛntəˌpɹaɪz/ (คำนาม) — ธุรกิจ. A company, business, organization, or other purposeful endeavor.
- sue /sjuː/ (คำกริยา) — ฟ้อง, ฟ้องร้อง. To file a legal action against someone, generally a non-criminal (civil) action.
- delay /dɪˈleɪ̯/ (คำนาม) — ดีเลย์. A period of time before an event occurs; the act of delaying; procrastination; lingering inactivity.
- productivity /ˌpɹɒdʌkˈtɪvəti/ (คำนาม) — ผลิตภาพ. The state of being productive, fertile, or efficient.
- warn /woɹn/ (คำกริยา) — เตือน. To make (someone) aware of impending danger, evil, etc.
- choke /t͡ʃoʊk/ (คำกริยา) — สำลัก, หายใจติดขัด. To be unable to breathe because of obstruction of the windpipe (for instance food or other objects that go down the wrong way, or fumes or particles in the air…
- cloud /ˈklaʊ̯d/ (คำนาม) — เมฆ. A visible mass of water droplets suspended in the air.
- guarantee /ˌɡæɹ.ənˈtiː/ (คำนาม) — การรับประกัน. Anything that assures a certain outcome.
- expand /ɪkˈspænd/ (คำกริยา) — ขยาย. To change (something) from a smaller form or size to a larger one; to spread out or lay open.
- praise /pɹeɪz/ (คำกริยา) — ยกย่อง, สรรเสริญ. To give praise to; to commend, glorify, or worship.
- argue /ˈɑɹ.ɡju/ (คำกริยา) — เถียง, ถก. To show grounds for concluding (that); to indicate, imply.
- founder /ˈfaʊ̯n.dəː/ (คำนาม) — ผู้สร้าง, ผู้ก่อตั้ง. One who founds or establishes (a company, project, organisation, state, etc.).
- narrow /ˈnæɹəʊ/ (คำคุณศัพท์) — แคบ. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 43 ครั้ง เช่น they're, they've, don't ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “sh” และ “zh”: ownership /ˈoʊnɚʃɪp/, initiative /ɪˈnɪʃ.ə.tɪv/, subscription /səbˈskɹɪpʃən/, potentially /pəˈtɛnʃ(ə)li/, congressional /kəŋˈɡɹɛ.ʃə.nəl/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: productivity /ˌpɹɒdʌkˈtɪvəti/, initiative /ɪˈnɪʃ.ə.tɪv/, utility /juːˈtɪlɪti/, temporary /ˈtɛmp(əˌɹ)ɛɹi/, electricity /ɪˌlɛkˈtɹɪsɪti/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง investor, bubble, enterprise เป็นพิเศษ
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | have made · they've remained · haven't seen |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | was reported · were promised · being billed |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | financials, Which is · someone who literally · valuable which is |
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ