Pratique du Shadowing: Is the AI Boom Just Another Bubble? - Apprendre l'anglais à l'oral avec la vidéo
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Hey there! I believe most of you here have heard of AI or even used it, like ChatGPT, Gemini, and others. Maybe you use it to help with your tasks, ask questions, make pictures, create videos, and some people are even getting married to it. Everything feels AI-hyped right now. Many companies are starting to build their own AI too. Google, Microsoft, Elon, and many others are making it, using it, and pouring billions of dollars into it. You might also see AI-related stocks like Nvidia going crazy over the past few years. Investors are happy, so they pour in even more money. And then, suddenly, you stop and think… wait, hold on. Isn’t this starting to feel like a bubble? Everyone seems way too excited and is throwing billions into AI almost blindly. People have been saying this for a long time, and now you’re probably wondering, is it really a bubble? Well, in this video, we’re going to talk about how bubbles really happen, why so many people say AI is a bubble, the infinite circular money glitch, whether this is the dot-com bubble 2.0, and finally, whether AI will really burst and crash. Alright, let’s get started.
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Section 1. How bubbles really happen. So, when everyone says AI is in a bubble, some of you might ask, what is a bubble? A bubble is like this. Everyone starts buying the same thing because they think it will be super valuable later. At first, only a few people buy it. Then more people copy. Soon, everyone is buying it just because everyone else is buying it. Nobody really stops to ask if it’s actually worth that much right now. So, the price goes higher and higher, even though the real value hasn’t changed much yet. And when people finally realize it’s not as amazing as they imagined, they stop buying. That’s when the bubble pops.
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And bubbles have happened many times before. In fact, many major economic crises were also linked to bubbles. For example, the 2008 housing bubble, when everyone rushed to buy houses and it eventually popped, played a major role in causing a global crisis. There was also Japan’s economic bubble, which is one reason its economy stayed weak for decades. And of course, the dot-com bubble in the early 2000s, which was driven by internet hype. So, bubbles have happened many times, in many countries, and with many different technologies.
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Section 2. Why so many people say AI is a bubble? Mostly because it feels very familiar. People remember what happened during the dot-com era, when the internet was new and exciting. Back then, prices went up fast, excitement went crazy, and everyone believed the future had already arrived. That memory never really disappeared. And because both are transformative technologies with similar hype stories, people connect AI to the dot-com bubble. For example, ChatGPT needed only 2 months to reach 100 million users, much faster than platforms like TikTok, which needed about 9 months, or Instagram, which needed 2.5 years, or Facebook, which needed more than 4 years. AI tools are being used by professionals and regular people across many industries. So, you might think, if platforms like Facebook, Instagram, and Google made tons of money, then AI tools like ChatGPT should make even more money, right?
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Well, that’s the problem. Many AI companies are still not clearly profitable, especially OpenAI, the company behind ChatGPT. AI is extremely expensive to run. Training models, buying chips, running servers, paying engineers, and doing constant research all cost huge amounts of money. AI needs GPUs, which are special machines that handle the heavy calculations, and that is super expensive. Some estimates suggest training GPT-4 may have cost tens of millions of dollars, and possibly over $100 million. And that’s not even the end of the cost. Every time you use ChatGPT online, all the calculations happen on OpenAI’s servers.
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That means electricity, cooling systems (often water-based), and maintenance all add to costs.
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At the same time, users expect AI to be cheap or even free. For example. When many people saying something simple in ChatGPT like “thank you” actually cost millions for OpenAI. Of course, one “thank you” costs almost nothing, but billions of them add up to millions of dollars. Some reports and estimates suggest OpenAI may have lost around $11.5 billion in a single quarter in 2025! With this situation, it starts to make sense why so many people call this an AI bubble.
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Section 3. The infinite circular money glitch. One of the biggest reasons people worry about an AI bubble is something called circular investment. The idea is simple. Big AI companies invest in each other, then buy from each other, which makes growth look much bigger than it really is.
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Here’s an over-simplified example with numbers so it’s easy to see. Remember, this is an over-simplified version. So, a chip company like Nvidia designs the most important chips for AI. An AI company like OpenAI needs those chips to run ChatGPT and train new models.
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So, let’s say Nvidia invests $10 billion into OpenAI, either directly or through long-term deals and partnerships. OpenAI now has more money and more computing power, so it spends a large part of it on cloud computing for more powerful AI.
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Now look at where that money goes. OpenAI spends $8 billion on cloud services from companies like Oracle or other AI cloud providers. Those cloud companies use that $8 billion to buy GPUs and hardware, mostly from Nvidia. So, a big part of the original $10 billion flows back to Nvidia as chip sales. So, Nvidia sees higher revenue. OpenAI shows growth. Cloud companies see higher sales. On paper, everyone looks like they’re winning.
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And again, this is an over-simplified version, because the real scenario looks like this. Yeah, quite different and more complicated but the idea is same.
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From the outside, valuations go up and revenues look strong. But the uncomfortable question is this: how much of this money comes from real users and businesses paying from outside the system, and how much is just the same tens of billions of dollars moving in a circle? If one part of that loop slows down or stops, the pressure spreads quickly to everyone else. Even though big companies like Nvidia, Oracle, and Microsoft are quite diversified beyond AI, the idea of money just moving around still makes people worry. Section 4. Is this dot-com bubble 2.0? So, as I mentioned before, in the early 2000s there was a bubble called the dot-com bubble. The internet was the new hype, and investors threw money at almost any company with “.com” in its name, even if it had no profits and no real business model. At its peak, over 400 internet companies went public, and the Nasdaq, which is heavily focused on technology stocks, jumped by over 600% in total.
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So, is this just the same scenario, with a different mask? The short answer is, there are indeed similarities. Because of that history, people today look at AI and feel uneasy.
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Like the internet in the past, AI is also a new technology. AI-related stocks have pushed tech markets sharply higher again in recent years. Excitement is high, investment is massive, and expectations are huge. While many AI-leading companies like Nvidia, Microsoft, and Google are making huge profits and are strong, cash-rich businesses, very different from dot-com companies, where many didn’t even make a single penny, but other companies like OpenAI are still burning tons of money with unclear profit potential. This makes people wonder: how long will this trend survive?
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Section 5. Will the AI bubble burst, and what happens next? So, the big question is, will the AI bubble actually burst? The honest answer is, it might. There’s a real possibility it will happen.
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But that doesn’t mean AI will suddenly disappear. When bubbles burst, what usually breaks first is not the technology itself, but the hype around it. If AI does burst, the first thing that happens is that money slows down. Investors become more careful. Companies stop throwing money at every AI project just because it sounds cool. AI companies that don’t make real profit will struggle, while the ones that survive will rise again. But here’s the important part. AI itself doesn’t go away. Just like after the dot-com bubble, the internet didn’t disappear. I mean, you’re literally watching this video on the internet right now. What disappeared were unrealistic business models, like Pets.com, a company selling pet products that earned hundreds of thousands but spent millions. That didn’t last. But companies that proved their businesses could survive didn’t die. Many people forgot that Amazon, eBay, and Cisco are all dot-com bubble survivors.
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Are they dead today? No. They’re giants. And if you’re investing in tech stocks, this is why it’s important to understand what you’re actually investing in. Always do your own research. If you want to learn more about market crashes, you can check out my other video on what happens when the stock market crashes. The link is in the description.
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So, in conclusion. AI is a real and revolutionary technology that’s already changing how we work and live. But when a powerful new technology shows up, money often moves faster than reality.
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Some AI companies may fail and some hype may disappear, but AI itself won’t vanish. Just like after the dot-com bubble, the companies that actually make sense are the ones that survive. So, don’t panic, and don’t blindly hype it either. If you want to invest in tech, always do your own research and don’t just follow the crowd or FOMO. Help us keep making videos like this by becoming a member and unlock some exclusive perks too. If you want me to make other videos explaining these topics, please like and subscribe. Thanks for watching.
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Vocabulaire et conseils d’expression pour cette leçon
Cette vidéo contient 20 phrases et 1648 mots à répéter en shadowing. La partie parlée dure 14:04. Le locuteur parle à un rythme régulier d’environ 117 mots par minute, confortable pour le shadowing. Seuls 81 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| bubble nom | /ˈbʌbəl/ | bulle |
| hype nom | /haɪp/ | matraquage publicitaire, promotion |
| cloud nom | /ˈklaʊ̯d/ | nuage |
| invest verbe | /ɪnˈvɛst/ | investir, consacrer |
| chip nom | /t͡ʃɪp/ | brisure, fragment |
| burst verbe | /bɜːst/ | éclater |
| disappear verbe | /dɪsəˈpɪɹ/ | disparaître |
| circular adjectif | /ˈsɜɹ.kjə.ləɹ/ | circulaire |
| investor nom | /ɪnˈvɛs.tə(ɹ)/ | investisseur, investisseuse |
| server nom | /ˈsɝvɚ/ | serveur |
| estimate nom | /ˈɛs.tɪ.mət/ | estimation |
| ton nom | /tʌn/ | tonne |
| excitement nom | /ɪkˈsaɪtmənt/ | excitation |
| scenario nom | /sɪˈnɛəɹioʊ/ | scénario |
| infinite adjectif | /ˈɪnfɪnɪt/ | infini |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| go up verbe | monter, s'élever |
| slow down verbe | ralentir, décélérer |
| check out verbe | mater, regarder |
| go away verbe | partir, s'en aller |
| show up verbe | apparaître, se montrer se pointer |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have heard · hasn't changed · have happened |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | bubble, which is · GPUs, which are · other, which makes |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | were also linked · are indeed |
Prononciation à surveiller
Le locuteur utilise 20 contractions et formes réduites, comme didn't, don't, you're. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « sh » et « zh »: calculation /ˌkælkjuˈleɪʃn̩/, partnership /ˈpɑɹtnɚʃɪp/, rush /ɹʌʃ/, conclusion /kənˈkluːʒən/, revolutionary /ˌɹɛvəˈluʃənɛɹi/
- Mots longs — placez bien l’accent: complicated /ˈkɑm.plɪˌkeɪ.tɪd/, electricity /ɪˌlɛkˈtɹɪsɪti/, uncomfortable /ʌnˈkʌm.fɚ.tə.bl̩/, revolutionary /ˌɹɛvəˈluʃənɛɹi/, expectation /ˌɛk.spɛkˈteɪ.ʃən/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Répétez phrase par phrase à vitesse normale, en reprenant chacune jusqu’à ce que votre rythme corresponde à celui du locuteur.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme bubble, hype, cloud.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.