쉐도잉 연습: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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Here's a list of the top five companies that cut their way to the top.
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Yes, these are the ones that figured out a way to move into the number one spot by shrinking rather than growing.
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Well, The list is empty, you can see.
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Certainly an organization can improve operational efficiency and cost by reducing fat and overhead.
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In some cases, it's an essential step to prevent going out of business.
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But if you want to win big, You won't do it by getting small.
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You get there.
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Bye.
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Investing. by innovating. by growing.
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These are the things that smart organizations already know.
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They look at technology as an enabler, a way to achieve strategic competitive advantage.
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The winners of tomorrow are the ones
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that are figuring out how to leverage AI today to make their employees more capable and creative.
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They see AI as augmented intelligence, rather than artificial.
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Let's see why this is the case.
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But before we do that, let's take a look at what seems to be the prevailing view of the impact of AI by many.
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Jeffrey Hinton, often called the godfather of AI, is a truly brilliant man.
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His work laid the foundation for so much of what we have today.
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In 2016, he said, and I quote, "People should stop training radiologists now.
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It's just completely obvious that in five years, deep learning is going to be better than radiologists." Hmm.
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So that proximal humerus fracture looks familiar, but I digress.
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All this sounds pretty dire, especially if you're a radiologist.
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Your whole profession is going away.
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So let's check in and see how that statement held up.
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Well, it turns out that 10 years later, not only have radiologists not gone extinct,
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but we actually are training more of them than we were when Hinton made that statement.
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So is that because AI failed?
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No, AI is actually doing amazing things now that we couldn't imagine a decade ago.
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Part of the answer lies in this thing called Jevons paradox.
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Let's take a look at that and see what it is.
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Way back in 1865, while studying coal use in England,
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a guy named William Stanley Jevons observed that more efficient steam engines reduced the amount of coal needed to power them.
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So you might think demand for coal would go down.
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But the opposite occurred.
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More industries turned to using this more efficient energy source to get more things done.
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and total coal consumption actually rose instead of falling.
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In other words, As efficiency went up, Costs went down.
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and demand increased.
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That's Jevons' paradox in a nutshell.
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We all learned in Econ 101 that as costs go down, Demand goes up.
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That all makes sense, right?
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Well, cheap energy meant they could start to do things that would have been cost -prohibited before.
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It allowed them to dream up some new use cases that no one ever considered because it would have been impractical previously.
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All of that just increases demand even more.
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But we're talking about AI here, not coal.
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So where's the connection with all of this?
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Just hang in here with me.
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Let's take a look at spreadsheet software.
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Automated arithmetic reduced accounting effort per task.
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So now we have spreadsheets who needs accountants.
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Nobody needs to just sit around and do all that arithmetic.
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Jevons teaches us that as efficiency goes up, Cost goes down demand increases, and that's exactly what happened again.
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Demand for financial analysis exploded.
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All of these accountants that used to labor over their arithmetic were now freed up to do higher -level thinking.
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Entirely new categories of work emerged.
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In the end, we needed more, not less, of these people, but with a higher level of skill.
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Now how about AI?
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What kind of smarter work could we do if we made use of AI?
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Well, one area is entirely new categories of work.
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Things that we never did before.
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Jobs like AI product managers, safety engineers, prompt engineers.
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We didn't need those in the past because we didn't have the AI that needed all of those things.
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So it's actually creating new jobs in support of the AI itself.
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Expansion of long -tail services.
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Things that were previously too expensive.
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Think of stuff like custom tutoring, niche legal analysis, personalized healthcare support.
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That's something that really only a few people could afford, but now would be available potentially to everyone.
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And then we're going to raise our expectations for speed.
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quality, and availability, creating more work around integration, oversight, compliance, and trust.
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Rather than fewer jobs overall, Jevons' paradox would indicate that what we would have would be fewer of these routine,
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low discretion roles and more of the high context, high accountability roles such as problem framing and goal setting,
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things where we need a human in the loop to do the decision making, AI supervision, evaluation and governance, customer facing roles where trust matters,
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and we need something with an emotional intelligence as well, and then the ability to do cross -functional coordination.
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So think of it this way: AI reduces the labor required per task, but dramatically expanding what is economically feasible to do.
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It can increase the total amount of work humans are employed to support.
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In other words, Efficiency goes up.
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Cost goes down.
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and demand increases. for people.
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And we need more people then, not less, because of AI.
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Smart companies understand Jevons paradox and they realize they're going to need more
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and smarter employees to run their operations that leverage AI.
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And smart employees will be the ones who are able to do these kinds of AI era skills.
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I think one of the most important is adaptability and flexibility.
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We don't know where all of this is going to go, so the people who are light on their feet and able to adjust, will be some of the winners.
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You need to be lifelong learners as well.
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You've got to enjoy this technology, enjoy the ride, soak it all up like a sponge
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because the information is coming at us faster and faster and the ones who can learn and assimilate
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and adjust will be again the winners.
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They need to be able to think critically.
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AI doesn't always get everything right.
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And even when it does, it doesn't mean that's what we wanted it to do.
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So we have to think and we have to be the ones that are in control.
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The human in the loop ultimately makes the decisions.
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We need to decide what the system should do and why it should do it.
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We apply meaning to it.
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And the system then does whatever we ask and does it at speed.
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And don't forget creativity.
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Because again, we don't know what the possibilities are here.
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So people who can think outside the box, you're actually going to have more time to do that because AI is going to automate much of the mundane stuff.
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So you have more time to think the big picture thoughts.
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The not so smart organizations will be
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so busy trying to cut their way to the top that they'll miss this train that's already pulling away from the station.
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ways to innovate and branch into new use cases and business opportunities where AI provides a competitive advantage.
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And the smart employees will be the ones who are skilling up to take advantage of this new dynamic.
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실생활 시나리오: AI와 일자리의 미래
이 비디오는 AI가 기업과 일자리에 미치는 영향에 대해 논의합니다. "제본스 패러독스"라는 경제학 원리를 통해 AI가 단순히 인력을 대체하는 것이 아니라, 새로운 일자리와 수요를 창출할 수 있다는 관점을 제시합니다. 라디오로지스트의 예나 스프레드시트 소프트웨어의 사례를 통해, 기술의 발전이 효율성을 높이면서도 전체 수요를 증가시키는 과정을 설명합니다. 이는 영어 회화 연습에서도 자주 다루는 "기술과 사회" 주제의 대화에 유용한 내용입니다.
유용한 구문과 콜로케이션
- strategic competitive advantage: 전략적 경쟁 우위 (기업이 시장에서 선점할 수 있는 전략적인 이점을 나타냅니다.)
- augmented intelligence: 증강 지능 (AI가 인간의 능력을 보완하는 개념으로, "artificial intelligence"와 대비됩니다.)
- Jevons Paradox: 제본스 패러독스 (효율성이 높아져도 자원 소비가 증가하는 현상을 설명하는 용어입니다.)
- cost-prohibitive: 비용상 불가능한 (비용이 너무 높아서 실행할 수 없는 것을 의미합니다.)
- higher-level thinking: 고차원적인 사고 (단순한 작업이 아닌 분석이나 창의적인 사고를 뜻합니다.)
쉐도잉 챌린지: 자연스러운 발음과 리듬 연습
영어 쉐도잉은 IELTS 스피킹 준비나 일상 회화 연습에 매우 효과적인 방법입니다. 이번 챌린지에서는 비디오에서 "Jevons Paradox"를 설명하는 부분을 선택해 연습해 보세요. 다음 단계를 따라 하세요:
- 비디오에서 "Way back in 1865... that's Jevons Paradox in a nutshell." 부분을 찾아 듣습니다.
- 한 문장씩 멈추고, 즉시 따라 말합니다. 발음 뿐만 아니라 억양과 리듬도 최대한 비슷하게 따라하세요.
- 3-5번 반복한 후, 원본 없이 혼자 말해 보세요. 자신의 발음과 원본을 비교해 차이점을 찾습니다.
쉐도잉 사이트를 이용해 더 다양한 자료로 연습하면 효과가 더 좋습니다. shadow speech를 통해 영어 회화 실력을 빠르게 향상시켜 보세요!
이 영상의 문법
화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.
| 문형 | 영상 속 표현 |
|---|---|
| 관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보 | ones who are · people who are · ones who can |
| 수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점 | were now freed · are employed |
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.