Shadowing Practice: Elon Musk Explains How the AI Bubble Will Burst. - Learn English Speaking with Video

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196 משפטים

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Are you worried about the Chinese catching up and getting ahead?

For digital intelligence, it comes down to how many chips do you have and how much power do you have?

China has more electricity than the United States, Europe and India combined.

And at some point, they probably will have a lot of compute.

Ergo, they will be the leaders.

There's a good chance that they are the leaders at some point.

Well, I don't know if you guys saw this one recently, but Elon Musk has just done a very spicy interview with The Economist.

He was fired up.

I would like to just admonish you and the media for the absurd characterisation of the far right.

Um...

Zero.

People.

I hear you loud and clear.

Zero.

Do you realise the media is despised?

It is false and misleading and nonsense.

Please keep this part in.

Yeah, he was ready to rant, that's for sure.

But earlier in the interview, one of the really big talking points that they covered was, of course, AI.

Of course it's AI, it's everywhere, it's driving my car, it's writing my emails, it's even telling me how many calories are in a glass of milk.

I really did look that one up.

But more importantly than all of that is that AI is powering a really high percentage of the stock market's returns.

And thanks to these seven companies, it's made the stock market look like this, with a valuation like this.

And that's why I found Elon's thoughts in this interview really interesting

because it kind of lines up exactly with what Steve Eisman has been saying recently too.

A guy that's pretty good at predicting stock market bubbles.

So in this video I want to take you through the three elements to the problem

that Elon discusses in this interview and what we should be looking out for as investors.

The first problem is arguably the biggest structural problem of this AI boom in America one that's really, really difficult to fix, and that is power.

But before we get into where the power is actually running out, listen to how Elon frames this whole AI race.

Because to be honest, the rest of this video is kind of centered around this one sentence.

You can think of like the constraints on AI, essentially electricity and AI chips.

And really, it's just those two inputs, power and chips.

But the scary thing is that for one of them, electricity, Elon says the US just isn't competitive in the long term and that's a problem.

And if you look at electricity production AI is a function of whatever the limiting factor is

if it's chips

or electricity China has far more electricity than the United States

in fact China has more electricity than the United States Europe

and India combined already my guess is China gets to four times the electricity production of the US

which is roughly proportionate to the population.

Now, in the long run, this is where AI's true bottlenecks starts to appear.

We know that China already outproduces the US in raw generation, but check out this Bloomberg article.

They note that China added the equivalent of 40% of the entire US grid's capacity in a single year

and has built more generation capacity in the last four years than the whole US grid combined.

The power gap is not closing.

In fact, by almost all projections, it is widening.

China is getting further ahead.

And this is a massive problem because AI eats electricity for breakfast.

We need stacks of it for the future of this technology.

And that raises the obvious question, which is why can't the US just build more power?

To be fair, the US certainly isn't ignoring its problems they are trying to build out the power infrastructure

and they recognize these power-hungry data centers are becoming a problem.

For example in February Trump announced the rate payer protection pledge

basically telling tech companies build your own power for your data centers

but even with this push the annoying thing is getting the new power generation needed up and online.

As of this year, there's roughly 2,000 gigawatts of proposed power generation sitting in US interconnection queues,

and the median wait to actually get switched on is roughly five years.

In Texas alone, ERCOT is managing a 474 gigawatt queue of large loads, and data centers make up 90% of it.

Some projects are facing waits of up to 12 years.

So the reality years, getting the new power generation online is a slow slog.

And this is why so many of these AI hyperscalers are turning to these things, these backup gas turbines.

I spoke about this in a video earlier this year, but even Elon can't wait for the grid.

He bought like 35 of these gas turbines for XAI's Colossus supercomputer in Memphis.

With these turbines, normally you'd find like one or two of them as backup generators for like a hospital.

But XAI had literally 35 of them humming as the data center's primary energy source.

So even Elon brought his own portable power plant rather than waiting for the grid.

That should tell you everything you need to know about how stretched the system is.

For digital intelligence, it comes down to how much AI compute do you have?

Well, how many chips do you have?

And how much power do you have?

How much electricity do you have?

The constraint right now, I think, is actually slightly more on power and cooling than it is on AI chips

because the power demands of the AI chips are very, very high.

We'll talk about water demands, but really the water usage of AI is negligible.

The electrical demand is very high.

And it's not just Elon either.

Everyone's trying to get these gas turbines now.

So much so, it's broken the supply chain.

Wait times for large turbines have stretched to five years.

GE Vinova's backlog is up to 116 gigawatts.

Siemens Energy's backlog sits around 69 gigawatts.

Turbine prices for plants coming online in 2030, 2031 are skyrocketing.

They're up 75% percent.

I mean, talk about a picks and shovels play.

Overall, really, it's GE Vinova, Siemens Energy, and Mitsubishi Heavy.

They are the three big dogs selling these turbines.

And you can see on InvestingPro, despite by normal metrics, these companies are all sitting reasonably overvalued.

The analysts are still seeing them as fair or undervalued because of that AI runway ahead.

Energy infrastructure is in hot demand.

When you ask Warren AI what's going on, you can see it's just two factors.

The data center boom has begun and thus gas turbines, which are the bridge, supply dispatchable power as renewables and the grid scales up.

And because of this trend, GE Venover is up 68% in the past year.

Siemens Energy is up 65%.

And to follow the energy infrastructure theme even more closely, InvestingPro also has ProPix AI, which is a model built on over 50 financial signals that surfaces those outperformers.

For example, in this tab, we can see a strategy called Energy Elite.

So if you're looking for ideas on this specific theme, being the energy infrastructure boom.

You can look here

and see how some of those businesses have compared to say market metrics like the S&P 500 energy index.

So it's a really fun feature.

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and thanks always to investing.com for sponsoring these videos

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but back to it

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so that's the power story that's elon's first point in the ai bubble debate the us is failing

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and china is winning and that brings us right to elon's second argument

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which is good old chip starved china and since the us has banned the export of the latest ai chips to China.

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China is somewhat chip starved.

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So China is chip starved but that might actually be a blessing in disguise for them.

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So for those that don't know

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because Nvidia makes the best AI chips in the world

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and the US controls what Nvidia is allowed to sell to

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China we've seen restrictions come into place banning the export of the most powerful chips to China.

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Now the rules have actually loosened a little bit this year.

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Trump approved limited H200 sales to China in December That started in small amounts in February, but there was a catch.

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The US is imposing a 25% tariff on these chips destined for China So it's also worth noting Nvidia's newest chips,

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which is Blackwell and the upcoming Rubin architecture They're still completely off limits to China

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But here's why this is actually a problem for America

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because the US has applied these constraints to China It has actually spurred China on to do their own thing

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and now they are more competitive than they've ever been Now China's homegrown deep ultraviolet lithography machines,

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the machines that print incredibly tiny circuit patterns onto the silicon wafers, they are still a fair way behind ASML, which is the gold standard,

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particularly when it comes to performance and reliability, but SMIC, which is essentially China's answer to TSMC, they have found ways to work around some limitations.

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So they've already demonstrated 7nm production using older DUV technology

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and it's now starting to get there with 5nm production too

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and right across China there's a massive effort underway to close this technology gap So

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while the export restrictions have undoubtedly hurt China in the short

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term they've also given the country an enormous incentive to build its own semiconductor ecosystem You know,

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maybe China is chip starved today but in a few years from now

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That could be a different story and that brings us to Elon's third

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and final argument as to why the AI narrative in the US might be getting a little bit stretched

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because despite China being chip starved right now and potentially remaining

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that way for years to come it's pumping out AI models that are already really good and really cheap.

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How do you think about the Chinese models and the geopolitics of it?

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Are you worried about the Chinese catching up and getting ahead?

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Given that the Chinese AI companies are doing as well as they are with a relatively small amount of compute, it seems that if they had a lot of compute,

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there's a good chance that they would be the leaders.

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And at some point, they probably will have a lot of compute.

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So they will, ergo, they will be the leaders.

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There's a good chance that they are the leaders at some point.

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Listen to that.

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Elon is confident it's only a matter of time before China is the world leader in AI.

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And the reason for it is despite China's chip-starved AI ecosystem, they're still producing models that are very, very good.

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I think realistically, Fable is still clearly the smartest model.

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I think anyone realistically would say that that's still the case.

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But the Kimi model is getting quite close to Fable.

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So even Elon, who has every incentive to talk his own book, is saying the gap between the best US model and China's best is closing.

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And this is exactly where what I was talking about in my Steve Eisman video comes back in

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because Steve isn't so much worried about the technical capabilities of these Chinese models like Elon is,

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he's worried about what happens to US LLM pricing once they are competitive.

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AKA he's predicting a price war that will squeeze OpenAI and Anthropic.

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And now we actually have numbers to prove this point.

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This is from AI research firm artificial analysis.

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From their testing, they found that DeepSeek's v4 Flash cost $0.03 per test, compared to $0.86 for Moonshot AI's Kimi K3,

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versus $1.86 for OpenAI's ChatGPT 5.6 Sol, and $3.15 for Claude's Fable 5.

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Now, Steve Eisman estimates OpenAI and Anthropic together account for roughly 70% of all AI-related revenue flowing into the big hyperscalers,

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Microsoft, Amazon, Google.

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So take the time to think about it.

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If a cheaper, good enough Chinese model starts pulling customers away on price, that's really bad for OpenAI and Anthropics margins.

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And if that means OpenAI

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and Anthropics have to pull back on the hundreds of billions in future spending that they've already committed to the hyperscalers, spending that has been baked into share prices,

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well, that could just be a catalyst for an AI correction.

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So let's step back, let's connect the dots.

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There's been a lot in this video, power, chips, pricing models, etc. But really, Elon's talking about three elements in one system.

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Right now, America has the chips, but it's running out of power.

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China has the power, but it's short on chips.

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That's the current standoff.

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But the problem for America is the future.

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China's chip shortage is a solvable in years problem.

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You know, lithography, yields, manufacturing.

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However, America's power shortage is more of a solvable in decades problem, because he can't just build a gas turbine or a transmission line

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or a new power plant on just a snappy short timeline.

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So you get a power bottleneck in the US, plus China solving its chip problem, plus the models being good for a fraction of the price.

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That is a recipe for hurt, particularly for OpenAI and Anthropic.

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And if they can't spend the hundreds of billions they've committed to spend at the hyperscalers, then the hyperscalers suffer and the whole AI narrative starts to crack.

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that's the story

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and interestingly that's also why Elon is trying to build his

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data centers in space solar is much more effective in space plus Elon's also got pre-established launch capacity through SpaceX

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so might not be as implausible as it sounds but with

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that said they are Elon Musk's thoughts on the AI bubble

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that we're in I hope

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that all made sense I hope you enjoyed it big thanks

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Apart from that, guys, thanks for watching, and I'll see you in the next one.

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אוצר מילים והערות הגייה לשיעור הזה

שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “Elon Musk Explains How the AI Bubble Will Burst”. המילים שחוזרות הכי הרבה: china, chip, Elon, model, electricity. בסרטון הזה יש 196 משפטים ו-2546 מילים לתרגול Shadowing. הדיבור נמשך 13:38. הדובר מדבר מהר, כ-187 מילים בדקה, אז צפו לצלילים מחוברים ומקוצרים. רק 81% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית, כך שאוצר המילים מאתגר.

אוצר מילים מרכזי בסרטון הזה

15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:

מילה	הגייה	משמעות
turbine שם עצם	/ˈtɜː.baɪn/	טוּרְבִּינָה
starve פועל	/stɑɹv/	גָּוַע
lithography שם עצם	/lɪθˈɒɡɹəfi/	הדפס אבן
solvable שם תואר	/ˈsɑlvəbəl/	פתיר
restriction שם עצם	/ɹɪˈstɹɪkʃən/	הַגְבָּלָה, סְיָג
queue שם עצם	/kju/	תּוֹר
metric שם תואר	/ˈmɛt.ɹɪk/	מֶטְרִי
shortage שם עצם	/ˈʃɔː(ɹ)tɪd͡ʒ/	מַחְסוֹר
incentive שם עצם	/ɪnˈsɛntɪv/	תמריץ
geopolitics שם עצם	/ˌdʒioʊˈpɑlətɪks/	גֵּאוֹפּוֹלִיטִיקָה
mislead פועל	/mɪsˈliːd/	הִטְעָה
shovel שם עצם	/ˈʃʌv.əl/	אֵת
tariff שם עצם	/ˈtæɹəf/	תעריף
semiconductor שם עצם	/ˌsɛmikənˈdʌktə/	מוֹלִיךְ לְמֶחֱצָה
hum פועל	/ˈhʌm/	זִמְזֵם

Phrasal verbs שתשמעו

מילה	משמעות
check out פועל	בדק

דקדוק בסרטון הזה

המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:

מבנה	בסרטון
הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו	has just done · has built · have stretched
סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה	was fired · is despised · is allowed
משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר	question, which is · turbines, which are · ASML, which is

הגייה ששווה לשים לב אליה

הדובר משתמש ב-25 קיצורים וצורות מקוצרות, כמו they've, can't, isn't. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.

הצלילים של “sh” ו-“zh”: restriction /ɹɪˈstɹɪkʃən/, shortage /ˈʃɔː(ɹ)tɪd͡ʒ/, admonish /ædˈmɑn.ɪʃ/, interconnection /ˌɪn.tɚ.kəˈnɛk.ʃən/, proportionate /pɹəˈpɔɹʃənət/
מילים ארוכות — שימו לב להטעמה: lithography /lɪθˈɒɡɹəfi/, ecosystem /ˈikoʊˌsɪstəm/, geopolitics /ˌdʒioʊˈpɑlətɪks/, interconnection /ˌɪn.tɚ.kəˈnɛk.ʃən/, negligible /ˈnɛɡlɪd͡ʒɪbəl/

צלילים שדוברי עברית מתקשים בהם:

/w/ — שפתיים מעוגלות, לא /v/: gigawatt /ˈɡɪɡəˌwɒt/, squeeze /skwiːz/, underway /ʌndəɹˈweɪ/, runway /ˈɹʌnweɪ/
/æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: backlog /ˈbæk(ˌ)lɔɡ/, admonish /ædˈmɑn.ɪʃ/, snappy /ˈsnæpi/, standoff /ˈstændɔf/, tariff /ˈtæɹəf/
/r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: starve /stɑɹv/, constraint /kənˈstɹeɪnt/, lithography /lɪθˈɒɡɹəfi/, restriction /ɹɪˈstɹɪkʃən/, metric /ˈmɛt.ɹɪk/

איך לתרגל עם הסרטון הזה

הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו turbine, starve, lithography.

מהי טכניקת השאדואינג?

שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.

טכניקת Shadowing: קרא את המדריך המלא שלב אחר שלב →