Shadowing Practice: AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society - Learn English Speaking with Video
בחר מצב תרגול
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74 משפטים
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My name is Dharan Acemoglu.
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I'm an institute professor at MIT.
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I am an economist and I study inequality, political economy, and technology.
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And my book, Power and Progress, Our Thousand Year Struggle Over Technology and Prosperity with Simon Johnson, is really at the center of these topics,
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but especially in the context of new digital technologies and AI and who will benefit from them.
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You know, AI is completely unique.
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Its effects on the economy are going to be unique.
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But on the other hand, we have so much knowledge from history about how similar breakthroughs in technology have worked out,
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under what conditions they have been foundations for shared prosperity, when they have been tools in the hands of a narrow elite to exploit the rest of the population.
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So there's a lot we can learn.
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And in fact, the British Industrial Revolution,
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where our prosperity and great technological improvements originate provides ample evidence on how sometimes things went very wrong
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and poverty and inequality was created out of new technological breakthroughs
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and how sometimes new technologies together with new institutions became the basis of shared prosperity.
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When people hear my book, my general arguments from the tech industry, I get a love and hate message.
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There are some people who think, you know, questioning what the tech industry is doing is sacrilege,
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that, you know, we are killing the golden goose that's going to deliver prosperity to the United States.
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And other people are clearly worried about what their industry their own firms are doing
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and they're grappling towards a framework to make sense of it and that's that's what i'm hoping that we contribute to
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ai machine learning
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and all sorts of other really impressive digital technologies are going
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to be as good as what we make of them
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so it's wrong to ask the question what will ai do
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to labor The right question is what we will decide to do with AI and how this will impact inequality.
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There is tremendous potential to use AI for good, but that's not the path we're going.
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And on our current path, I think AI and ML will be used for monitoring workers, reducing their autonomy.
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They will be used for automation, sidelining labor, and perhaps disempowering the already weaker members of our society.
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And if you look at over the last 40 years, we see many digital technologies deepen inequality because they automated work and displaced workers,
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and they weren't used for creating new jobs for the same workers who were displaced from their jobs.
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So I think those are the pitfalls that we should try to avoid with AI, and I think, again, we are not likely to avoid them unless we do a course correction.
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I think today we have a more open conversation about how we can use new technologies for workers.
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Labor unions have become more awake to the issues of digital technologies and AI.
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And I think there's a broader openness in the U .S society
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that was absent just four years ago about regulation and reducing the power of big tech.
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I think all of those are useful developments.
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I mean, I think it's impossible to predict what these amazingly impressive technologies are going to bring
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but I would venture the following guesses.
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I think we're going to see a lot of investment in AI and related technologies
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and I think that they are going to be rolled out very quickly in some businesses
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and there's going to be a lot of disappointment.
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There's going to be a lot of disappointment
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because in the middle of the hype many businesses are going to try to implement the technologies too fast.
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In many cases, I think they're going to underappreciate what humans were doing
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and overestimate what AI can do which is going to lead to disappointing results.
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That doesn't mean that we're not going to see some impressive applications, we will, but I think on the whole,
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we're not going to see the sort of amazing productivity promises become realized.
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I am a big believer in the market process.
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I don't think there's any alternative but the market to bring more innovation and better prosperity.
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Central planning is not going to be our future, but that doesn't mean that the market is going to get everything right.
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And one of the things that it doesn't get right is exactly distributional issues and issues related to the direction of technology.
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If we leave it to the market, whether we're going to use AI for automating work or monitoring workers or creating new tasks, what does that mean?
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It's not the market.
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It's Google and Facebook and Amazon and Microsoft and OpenAI.
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That's not the market.
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We already live in a world in which there are very, very big corporations.
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that have an enormous influence on our society, on the future of work, and they're not the market, and they have their own ideology, they have their own priorities,
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they have their own profit motives, they have their own business models.
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I don't think we can just think that there is a miraculous market that's going to solve all problems.
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When you talk about inequality and worker control, people may get the impression that it's just businesses versus workers.
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There's an element of that.
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We live in a society in which there is conflict.
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It's inevitable.
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But I also think that there's a path both in the tech sector and in the broader corporate sector where
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businesses can flourish while doing things that are good for their workers.
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And I would say a key distinction is whether you see your workers as a cost or as a resource.
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If they are a cost, you're going to just introduce more and more conflict, you're going to try to cut those costs.
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If they are a resource, which I think they are, then businesses can try to flourish while increasing the productivity of their businesses, providing better tools to their workers,
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improving their trainings, their skills.
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Every political regime depends on information.
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Every political regime depends on some degree of who controls information, how information is presented, who manipulates whom what agendas are encouraged
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and AI is changing that completely and it's changing
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that again in a very lopsided way
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because AI is very much concentrated in the hands of a few people
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and a few companies and that raises a big threat to democracy.
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About This Lesson
Ready to boost your English speaking skills while exploring a fascinating topic? In this lesson, you’ll practice shadowing speech with a thought-provoking video featuring economist Daron Acemoglu. You’ll listen to complex ideas about AI, inequality, and technology, then repeat phrases to improve your pronunciation, fluency, and understanding of academic language. It’s a great way to learn English with videos—combining real-world content with practical speaking practice!
Key Vocabulary & Phrases
- Sacrilege: Something that offends a person’s beliefs. Example: "Questioning the tech industry was seen as sacrilege."
- Pitfalls: Hidden dangers or mistakes. Example: "We must avoid the pitfalls of AI misuse."
- Course correction: Changing direction to fix a problem. Example: "A course correction is needed to ensure shared prosperity."
- Disempowering: Taking away power from someone. Example: "AI could risk disempowering vulnerable workers."
- Distributional issues: Problems related to how resources are shared. Example: "Markets often fail to address distributional issues."
Practice Tips
Shadowing this video? Here’s how to succeed! The speaker’s tone is calm and academic, so focus on matching his pace—he pauses slightly to emphasize key points. Start by shadowing short chunks: listen to 5-10 seconds, then repeat. Pay attention to words like "sacrilege" and "distributional"—break them into syllables if needed. Use a shadowing site to record yourself and compare. Remember, shadow speak isn’t about perfection—it’s about progress! Even 5 minutes a day will help you sound more natural. Keep going—you’re mastering real English while learning about critical global issues.
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | was created · were displaced · be rolled |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | have worked · have been · have become |
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.