Prática de Shadowing: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - Aprenda a falar inglês com vídeo
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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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O Cenário: Inteligência Artificial e o Futuro do Trabalho
No vídeo, exploramos como organizações inteligentes usam a IA não como substituto, mas como aliado. Um exemplo intrigante é a afirmação do "padrinho da IA", Jeffrey Hinton, de que radiologistas seriam substituídos em cinco anos — mas, 10 anos depois, a profissão não só persistiu como cresceu. Isso se liga ao Paradoxo de Jevons, um conceito econômico que explica por que maior eficiência (como com máquinas a vapor ou planilhas eletrônicas) não reduz a demanda, mas a aumenta, criando novos tipos de trabalho. Essa discussão nos mostra como a IA pode transformar o mercado, mas não eliminá-lo.
Trechos Úteis e Colocações para Memorizar
- Leverage AI: "Figurar out how to leverage AI today" (Descobrir como usar a IA hoje) — expressão chave para falar de uso estratégico de tecnologia.
- Augmented intelligence: "AI as augmented intelligence" (IA como inteligência aumentada) — contrasta com "artificial intelligence" e destaca a colaboração humana-máquina.
- Cut their way to the top: "Companies that cut their way to the top" (Empresas que subiram ao topo reduzindo custos) — coloquial para descrever crescimento por economia.
- Jevons Paradox: "O Paradoxo de Jevons" — termo técnico importante para discutir eficiência e demanda.
- Cost-prohibitive: "Things that would have been cost-prohibitive" (Coisas que seriam proibitivas em custo) — útil para falar de limitações financeiras.
Seu Desafio de Shadowing
O shadowing é uma técnica poderosa para melhorar a pronúncia em inglês e a fluência. Aqui está sua tarefa: ouça o trecho do vídeo onde o narrador explica o Paradoxo de Jevons (começando com "Way back in 1865..." e terminando com "total coal consumption actually rose instead of falling"). Repita cada frase imediatamente após o narrador, imitando a entonação, ritmo e destaque. Foque em:
- A pronúncia de "Jevons" (dʒɛvənz) e "coal" (koʊl).
- A pausa após "In other words" para enfatizar a explicação.
- O tom de curiosidade na frase "So you might think demand for coal would go down, but the opposite occurred".
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | ones who are · people who are · ones who can |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | were now freed · are employed |
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.