跟讀練習: What Happens When Capitalism Doesn't Need Workers Anymore? - 通過影片學習英語口語

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Everybody has some level of anxiety over what our AI future will look like.

Somewhere between Skynet and a post -scarcity utopia, the most immediate concern for most people is that this technology will end up doing their job better than they can.

So far, one side of the argument points out that big new technologies in the past have only ever made economies wealthier, and whatever jobs they replace, they end up making more, better jobs somewhere else.

The other side argues that, yeah, sure, when we replaced our muscles with machinery in the past,

invested most of our evolutionary traits into. But...

If machines replace that, what else do we have left to offer?

Now, Nobody can predict the future, least of all economists.

But we don't really need to, because there are certain economies that are going to see the widespread impacts of these changes well before most others.

In fact, they kind of already are.

In places like the Philippines and Bangladesh, the threat of AI is much more imminent.

A threat to jobs, to entire industries, and to the economic growth they've spent decades building.

These economies have spent the last 30 years constructing entire industries around outsourced service work.

Things like call centers, data entry, transcription, and basic software support.

These jobs were once considered safe from automation because they required language skills, context, and that special human touch that machines just couldn't replace.

Well, it turns out, Machines got a lot better at replicating that human touch.

Tools like LLMs can now handle those tasks in seconds at a fraction of the cost, and these jobs, which make up a big share of GDP in many developing countries, are looking like they might be the first dominoes to fall.

In the Philippines, the IMF estimates

that a staggering 89 % of outsourced service jobs are at high risk of being automated by AI.

That's over a million people whose jobs could disappear in just a few years.

In other words, AI is already making the world's richest countries even richer, and is making it harder for everyone else to catch up.

And that's just the beginning of the story.

Even in rich countries, AI is starting to divide the economy into those who can leverage it

and those who are going to get replaced by it.

The US Bureau of Labor Statistics predicts that roles like cashiers, bank tellers, postal staff and customer service representatives are all on track to shrink.

One estimate suggests 7 .1 million jobs could disappear in the next five years, with up to 47 % of current roles at risk have been replaced by AI.

Of course, it's also worth remembering that companies and their investors have now ploughed trillions of dollars into developing this technology, so they want to eventually see a return.

As heartless as it is, cutting millions of workers off payroll is probably the most immediate way to start seeing those returns.

So there is an incentive to play up the scare campaign

because what sounds horrifying to most people sounds like opportunity to those actually writing the checks.

But even still, the trend lines are clear, AI is already reshaping who gets ahead, who falls behind and most importantly how fast the gap is widening.

So as always, we've got some important questions to answer.

Why is AI supercharging growth in rich countries while simultaneously threatening the economic survival of others?

In a world where one person armed with AI can replace five people, what exactly happens to the other four?

And perhaps most importantly, can workers or even entire economies adapt fast enough to survive the shift?

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According to the Center for Economic Policy Research, the US could see a 5 .4 % boost to GDP over the next decade thanks to AI -driven productivity gains.

The UK, Germany and South Korea aren't far behind with similar projections.

Meanwhile lower income countries are looking at much more modest gains, closer to 2 .7 -3 .5%,

which would be a departure from the expectations about developing countries, well, developing faster.

The Philippines is a good example.

For years it has been one of the world's premier destinations for business process outsourcing.

A $37 billion industry that includes customer service, billing, transcription and tech support.

Bye.

But here's the economic nightmare scenario.

Most of these jobs are exactly the kind of repetitive, text -based tasks that large language models like ChatGBT are rapidly learning to automate.

Jobs in the Philippines are at high risk of being replaced by AI, and it's already happening.

Roughly two -thirds of outsourcing companies in the country are now using AI tools to cut costs and speed up workflows.

Major US companies like AT &T, Google and Accenture outsource work to the Philippines, but if AI can perform the same task faster and cheaper and without requiring health insurance,

vacation days or human resource departments, those jobs will be amongst the first casualties.

Bangladesh is in a similar boat.

Its outsourcing sector has grown to 400 firms employing over 80 ,000 people,

Which again, is precisely the kind of job that AI is becoming increasingly capable of automating.

If AI can deliver the same quality of work at a better speed for significantly less money, there's simply no compelling economic reason to continue outsourcing.

A single slot in a server rack could soon replace an entire core centre in Manila or Dakar.

And that means companies could start to reshore, bringing jobs back to wealthy nations where local automation can rival offshore labour on price.

That completely flips the script on the entire outsourcing model

that emerging economies have built their entire growth strategies around for the past three decades, and it's a big reason why the gap between wealthy

and poorer nations is set to widen after a few decades of these economies actually slowly catching up.

AI also rewards exactly the kind of specialised skills that are hardest to scale globally.

Building and training large AI models requires advanced education, reliable municipal infrastructure, and access to advanced technologies.

Those resources are overwhelmingly concentrated in wealthy nations.

That means the most valuable AI jobs are also the least accessible to workers in emerging markets.

And when workers in those countries do manage to gain access to those highly sought after skills, they often don't stick around.

Talented engineers are being recruited by global tech companies or relocating entirely to hubs like San Francisco, London, Berlin, or even centers within China.

with fewer startups, fewer teachers and researchers, and dramatically fewer chances to catch up in the global AI race.

And it's clear which countries are leading that race.

In short, the countries least equipped to absorb disruption are the ones getting hit first and hardest, while the countries best positioned to benefit from AI are already pulling ahead because they control the capital,

infrastructure, talent and resources shaping the future of AI.

But AI isn't just dividing countries along economic lines, it's also creating stark divisions between the people within those same countries.

This technology is not impacting all people in the same way, it's making some workers nearly obsolete while making others far more valuable.

That's because AI represents a very specific kind of capital, and understanding this distinction is crucial for predicting its economic impact.

In the past, most new technologies functioned as what economists call "complementary capital", meaning these were machines and technologies that made human workers more productive.

For example, a combine harvester didn't eliminate farm workers, instead it made each individual worker dramatically more efficient.

Before mechanization, harvesting a single field might require 20 people working for several days, while with a harvester one person could do the same job in a fraction of the time.

capital worked together and as productivity increased so did wages and living standards.

Workers remained essential to the process, they just became much more productive.

For many high skilled roles, AI will become more complementary capital, boosting productivity without replacing the human worker.

A financial analyst using AI to scan reports on spot anomalies can get insights faster

and can focus more time on strategic thinking.

A doctor leveraging AI for diagnostics can spend more time on direct patient care.

In these cases, AI multiplies what skilled professionals can do and makes their expertise more valuable in the marketplace.

But for more routine, process -driven work, AI increasingly acts as what economists call substitutive capital, replacing human labour altogether instead of enhancing it.

An AI -powered chatbot doesn't make a customer support agent faster, it replaces them.

A sophisticated code generator doesn't assist a junior developer, it replaces them.

actually needs human labor to function.

And in the AI economy, capital ownership is more concentrated than it ever has been in modern history.

Most of the major breakthroughs in artificial intelligence are coming from a handful of elite firms in the US and China.

Since 2017 the US has produced 135 large scale AI systems.

China is not far behind with 110, but the gap widens quickly.

From there the UK has managed 25 and France 24.

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And the companies leading the charge with these breakthroughs are experiencing

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exponential growth thanks to what is known as the data network effect.

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The more data they collect, the better their AI model performs, the better their model the more users they attract, and the more users they attract the more data they generate.

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This creates a powerful feedback loop where market power and profits concentrate in just a few dominant companies.

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PwC estimated that AI could add $15 .7 trillion to global GDP by 2030, but 70 % of that wealth is projected to go to just two countries,

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the USA and China.

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Because they own AI.

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In 2024 alone, over 1100 US based AI companies raised major funding rounds.

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That's more than double all of Europe combined.

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IBM and Microsoft alone hold thousands of AI related patents, giving them long term control over everything from enterprise tools to foundational models.

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Smaller firms, even those in wealthy countries, are becoming increasingly dependent on licensing tools and models that they didn't build and don't control and that extends beyond software.

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The physical machines that power AI, CPUs and GPUs are overwhelmingly designed and manufactured in just 5 countries.

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More than 90 % of that hardware comes from the US, Taiwan, China, South Korea and Japan, and that means a tiny handful of countries don't just run AI systems,

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but also manufacture the foundational components that make AI possible in the first place.

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That's the reality of AI as capital.

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It primarily benefits those who already own the assets, while replacing those who don't.

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The more you can leverage AI as a productivity multiplier, the more economically valuable you become in the marketplace.

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But for workers in routine roles, especially those without access to retraining programs, the future is looking far less promising.

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Now even if you weren't aware of these exact figures, they probably aren't surprising.

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And that's exactly the point.

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This is a reality that people are noticing.

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Nearly one third of Americans in a recent survey said they're fairly or very worried about losing their jobs to automation.

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This isn't some hypothetical scenario we're speculating about.

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We've witnessed similar disruptions before.

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When industrial automation and large -scale outsourcing ramped up in the 1980s and 1990s, it hit manufacturing hard, especially in places like the US and Western Europe.

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In America alone more than 7 million factory jobs disappeared between 1980 and 2010 and most of them didn't come back.

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These factory jobs may have been replacing US workers with Chinese workers, but there is no critical reason why human workers couldn't be replaced with clankers.

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The midwest bore the brunt of this economic transformation.

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Cities like Detroit, Cleveland and Youngstown were once packed with well -paying jobs in steel, cars and textiles, but then came robotic welders, computer -run assembly lines and cheaper labor overseas.

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and entire local economies started to fall apart.

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The consequences extended far beyond simple job loss.

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A lot of these towns saw life expectancy drop, opioid addiction rise, and schools struggled to keep up.

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The jobs that eventually did return often paid less and didn't offer the stability or benefits that had previously supported entire communities.

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The UK experienced something similar.

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Coal mining, shipbuilding and steel plants across northern England and Scotland shut down as automation and privatisation took coal.

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Even today, places like Sheffield and Sunderland still lag behind the rest of the country

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when it comes to income and social mobility.

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The lesson is clear.

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Even when the long -term picture improves, the short -term impact of technological disruption can be devastating.

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And once inequality takes root in an economy, it becomes extremely difficult to reverse.

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So, What can we actually do about this looming challenge?

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Because at this point it's clear that AI is already transforming the global economy, but whether it deepens existing inequality or helps us solve it, depends on the actions that countries and individuals take in the coming years.

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First, the good news is, we can already see what's coming our way.

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In lower income countries like the Philippines and Bangladesh, the frontline effects of AI are unfolding in real time.

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These economies show us which jobs go first, where the risk is the highest, and what happens when governments act or don't. For example,

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the government of the Philippines has launched a national AI strategy with the goal of retraining over a million workers by 2028.

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Bangladesh meanwhile has released a draft policy framework focused on developing AI talent, modernizing its education system, and supporting tech startups.

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The goal is to position Bangladesh as a competitive player in the AI -enabled services market, while safeguarding jobs through upskilling and digital inclusion programs.

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Whether those efforts will prove sufficient remains to be seen, but they offer a clear warning and a playbook for wealthier nations to follow.

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Two distinct sides of our economies need to do two things simultaneously: invest heavily into AI infrastructure

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and invest just as heavily into their people.

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This includes educational investments into computer science, yes, but also the kind of skills AI struggles to automate. and creative decision making.

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A recent analysis of 12 million job postings in the US found

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that AI adoption tends to increase demand for these distinctly human skills far more often than it eliminates jobs entirely.

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Building an accessible digital economy is equally important

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because right now nearly 2 .6 billion people worldwide still don't have access to the internet.

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Without that basic connectivity there is simply no opportunity to compete or even participate in the emerging AI economy.

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The World Bank estimates

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that every 10 % increase in broadband access can boost GDP growth in developing countries by up to 1 .4%, and that's before factoring in the additional benefits that AI capabilities could provide.

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So along with retraining, countries need policies that expand broadband access, reduce the cost of devices, and give more people the digital skills they need to benefit from AI.

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Social safety nets matter too.

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They function as economic buffers that give displaced workers the time and resources they need to adapt, retrain, and re -enter the labour market from a position of strength.

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But if AI allows businesses to grow while workers lose their income, the economy starts to hollow out.

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Productivity rises but consumption falls.

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Innovation continues but inequality grows and it becomes a serious drag on overall economic growth.

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If we want AI to boost productivity broadly, not just corporate profits, we'll need to rethink how we design and share the value it creates.

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And that includes fundamental questions about who gets to build AI, who governs its development and deployment, and who ultimately benefits from the massive productivity gains it generates.

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If you want to see just how far this could go, what happens if AI keeps getting better and most people end up with nothing of value to trade, we made an entire video about that thought experiment two years ago.

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You should be able to click to that on your screen now.

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Thanks for watching, mate.

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Bye.

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本課介紹

在本課中，學習者將通過對一段關於人工智慧對當前經濟影響的講座進行跟讀，練習英語發音和口語表達。通過探討人工智慧對就業市場的影響，學習者將提升他們的聽說能力，理解複雜的經濟概念。此外，學習者還將學習如何用更流利的英語表達觀點，以及如何在與他人交流時自信地分享所學內容。

關鍵詞彙與短語

人工智慧（Artificial Intelligence）
經濟增長（Economic Growth）
外包（Outsourcing）
自動化（Automation）
技術技能（Technical Skills）
失業（Unemployment）
勞動市場（Labor Market）
全球經濟（Global Economy）

練習技巧

在進行影子跟讀時，請注意這段影片的語速和語調。以下是一些練習的建議：

每次播放影片時，儘量跟隨說話者的節奏，不要急於發音，確保每個單詞都清晰可見。
在模仿說話者的語調時，注意語音的重音與語調變化，這將有助於提升您的表達能力。
嘗試在影子跟讀時錄下自己的聲音，回放以比較你的發音和語音，找出需要改進的地方。
運用英語影子跟讀的方法，將材料分成短句反覆練習，直到您能自然流暢地表達。
利用網路資源如shadowspeaks，幫助提高英語發音，使自己在口語交流中更加自信。

通過不斷的練習和對話，您將能夠在與他人的交流中更加自信，增強您的英語口語能力。

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
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	have only ever made · have left · they've spent
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	being automated · have been replaced · was brought
定語從句 who / which + 從句 — 補充說明人或事物	those who can · those who are · those who already

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: What Happens When Capitalism Doesn't Need Workers Anymore? - 通過影片學習英語口語

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本課介紹

關鍵詞彙與短語

練習技巧

影片中的語法

什麼是跟讀法？