跟讀練習: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - 通過影片學習英語口語

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Here's a list of the top five companies that cut their way to the top.

Yes, these are the ones that figured out a way to move into the number one spot by shrinking rather than growing.

Well, The list is empty, you can see.

Certainly an organization can improve operational efficiency and cost by reducing fat and overhead.

In some cases, it's an essential step to prevent going out of business.

But if you want to win big, You won't do it by getting small.

You get there.

Bye.

Investing. by innovating. by growing.

These are the things that smart organizations already know.

They look at technology as an enabler, a way to achieve strategic competitive advantage.

The winners of tomorrow are the ones

that are figuring out how to leverage AI today to make their employees more capable and creative.

They see AI as augmented intelligence, rather than artificial.

Let's see why this is the case.

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.

Jeffrey Hinton, often called the godfather of AI, is a truly brilliant man.

His work laid the foundation for so much of what we have today.

In 2016, he said, and I quote, "People should stop training radiologists now.

It's just completely obvious that in five years, deep learning is going to be better than radiologists." Hmm.

So that proximal humerus fracture looks familiar, but I digress.

All this sounds pretty dire, especially if you're a radiologist.

Your whole profession is going away.

So let's check in and see how that statement held up.

Well, it turns out that 10 years later, not only have radiologists not gone extinct,

but we actually are training more of them than we were when Hinton made that statement.

So is that because AI failed?

No, AI is actually doing amazing things now that we couldn't imagine a decade ago.

Part of the answer lies in this thing called Jevons paradox.

Let's take a look at that and see what it is.

Way back in 1865, while studying coal use in England,

a guy named William Stanley Jevons observed that more efficient steam engines reduced the amount of coal needed to power them.

So you might think demand for coal would go down.

But the opposite occurred.

More industries turned to using this more efficient energy source to get more things done.

and total coal consumption actually rose instead of falling.

In other words, As efficiency went up, Costs went down.

and demand increased.

That's Jevons' paradox in a nutshell.

We all learned in Econ 101 that as costs go down, Demand goes up.

That all makes sense, right?

Well, cheap energy meant they could start to do things that would have been cost -prohibited before.

It allowed them to dream up some new use cases that no one ever considered because it would have been impractical previously.

All of that just increases demand even more.

But we're talking about AI here, not coal.

So where's the connection with all of this?

Just hang in here with me.

Let's take a look at spreadsheet software.

Automated arithmetic reduced accounting effort per task.

So now we have spreadsheets who needs accountants.

Nobody needs to just sit around and do all that arithmetic.

Jevons teaches us that as efficiency goes up, Cost goes down demand increases, and that's exactly what happened again.

Demand for financial analysis exploded.

All of these accountants that used to labor over their arithmetic were now freed up to do higher -level thinking.

Entirely new categories of work emerged.

In the end, we needed more, not less, of these people, but with a higher level of skill.

Now how about AI?

What kind of smarter work could we do if we made use of AI?

Well, one area is entirely new categories of work.

Things that we never did before.

Jobs like AI product managers, safety engineers, prompt engineers.

We didn't need those in the past because we didn't have the AI that needed all of those things.

So it's actually creating new jobs in support of the AI itself.

Expansion of long -tail services.

Things that were previously too expensive.

Think of stuff like custom tutoring, niche legal analysis, personalized healthcare support.

That's something that really only a few people could afford, but now would be available potentially to everyone.

And then we're going to raise our expectations for speed.

quality, and availability, creating more work around integration, oversight, compliance, and trust.

Rather than fewer jobs overall, Jevons' paradox would indicate that what we would have would be fewer of these routine,

low discretion roles and more of the high context, high accountability roles such as problem framing and goal setting,

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,

and we need something with an emotional intelligence as well, and then the ability to do cross -functional coordination.

So think of it this way: AI reduces the labor required per task, but dramatically expanding what is economically feasible to do.

It can increase the total amount of work humans are employed to support.

In other words, Efficiency goes up.

Cost goes down.

and demand increases. for people.

And we need more people then, not less, because of AI.

Smart companies understand Jevons paradox and they realize they're going to need more

and smarter employees to run their operations that leverage AI.

And smart employees will be the ones who are able to do these kinds of AI era skills.

I think one of the most important is adaptability and flexibility.

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.

You need to be lifelong learners as well.

You've got to enjoy this technology, enjoy the ride, soak it all up like a sponge

because the information is coming at us faster and faster and the ones who can learn and assimilate

and adjust will be again the winners.

They need to be able to think critically.

AI doesn't always get everything right.

And even when it does, it doesn't mean that's what we wanted it to do.

So we have to think and we have to be the ones that are in control.

The human in the loop ultimately makes the decisions.

We need to decide what the system should do and why it should do it.

We apply meaning to it.

And the system then does whatever we ask and does it at speed.

And don't forget creativity.

Because again, we don't know what the possibilities are here.

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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為什麼用這個影片練習口語？

這個影片討論AI對職場的影響，話題前沿且貼近現實，適合練習商務英語和觀點表達。內容包含邏輯推理、案例分析，能幫你掌握如何清晰闡述複雜概念。通過影子跟讀（shadow speak），你不僅能提升口語流暢度，還能學會在正式場合中自然表達觀點，無論是工作彙報還是日常討論都很實用。

語境中的語法與表達

“...the list is empty, you can see.” 這裡用逗號連線兩個句子，是口語中常見的省略結構，相當於“as you can see”。練習時注意語調的自然停頓，讓表達更口語化。
“It's just completely obvious that...” “it作形式主語+that從句”的結構，用於強調觀點。模仿時要注意“it's obvious that”的連讀，以及“completely”的重音位置。
“...freed up to do higher-level thinking” “freed up”表示“被解放，有時間做某事”，是職場英語中的高頻短語。結合影片語境，可練習用“be freed up to + 動詞原形”描述工作內容的轉變。

發音難點解析

影片中部分單詞的發音容易出錯，適合用影子跟讀（英語影子跟讀）練習：

proximal：發音為/prɒk'sɪməl/，注意“x”發/ks/音，而非/ɡz/。
humerus：發音為/'hjuːmərəs/，重音在第一個音節，“u”發/juː/音。
Jevons：人名發音為/'dʒevənz/，“ev”發/e/音，末尾的“s”發/z/音。

通過影子跟讀網站（shadowing site）反覆模仿這些單詞的發音，能有效提高英語發音的準確性。

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
定語從句 who / which + 從句 — 補充說明人或事物	ones who are · people who are · ones who can
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	have been cost · were now freed · are employed

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - 通過影片學習英語口語

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為什麼用這個影片練習口語？

語境中的語法與表達

發音難點解析

影片中的語法

什麼是跟讀法？