Shadowing Practice: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - Learn English Speaking with Video
Creando lección...
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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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Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación de nivel C1 se basa en el vídeo “How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact”. Las palabras que más se repiten: demand, Jevons, cost, coal, smart. Este vídeo tiene 104 frases y 1240 palabras para practicar shadowing. La parte hablada dura 7:45. El hablante habla a un ritmo natural de unas 160 palabras por minuto, cercano a una conversación cotidiana. El 85 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.
Vocabulario clave de este vídeo
Las 15 palabras más avanzadas del vídeo, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| radiologist sustantivo | /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/ | radiólogo |
| paradox sustantivo | /ˈpæ.ɹəˌdɒks/ | paradoja |
| arithmetic sustantivo | /æɹɪθˈmɛtɪk/ | aritmética, arismética |
| automate verbo | /ˈɔ.təˌmeɪt/ | automatizar |
| innovate verbo | /ˈɪn.ə.veɪt/ | innovar |
| spreadsheet sustantivo | /ˈspɹɛd.ʃit/ | hoja de cálculo |
| accountant sustantivo | /əˈkaʊn.tənt/ | contador, contadora |
| leverage sustantivo | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | palanca, palanqueo |
| adjust verbo | /əˈd͡ʒʌst/ | ajustar, arreglar |
| adaptability sustantivo | /ədæptəˈbɪlɪti/ | adaptabilidad, flexibilidad |
| assimilate verbo | /əˈsɪm.ɪ.leɪt/ | asimilar |
| augment verbo | /ɔɡˈmɛnt/ | aumentar, augmentar |
| digress verbo | /daɪˈɡɹɛs/ | irse por una digresión/disquisición, divagar |
| enabler sustantivo | facilitador, habilitador | |
| godfather sustantivo | /ˈɡɑdfɑðɚ/ | padrino |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| go up verbo | subir |
| figure out verbo | averiguar, captar |
| go away verbo | irse, marcharse |
| hold up verbo | aguaitar, esperar |
| turn out verbo | resultar |
| work around verbo | baipasear, circunvalar |
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Oraciones de relativo who / which + oración — información extra sobre una persona o cosa | ones who are · people who are · ones who can |
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | were now freed · are employed |
Pronunciación a tener en cuenta
El hablante usa 16 contracciones y formas reducidas, como don't, didn't, doesn't. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “th”: arithmetic /æɹɪθˈmɛtɪk/, godfather /ˈɡɑdfɑðɚ/
- Los sonidos de “sh” y “zh”: spreadsheet /ˈspɹɛd.ʃit/, nutshell /ˈnʌt.ʃɛl/, discretion /dɪˈskɹɛʃən/, coordination /kəʊˌɔːdɪˈneɪʃən/, supervision /ˌs(j)uːpəˈvɪʒən/
- Palabras largas — cuida el acento: radiologist /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/, arithmetic /æɹɪθˈmɛtɪk/, adaptability /ədæptəˈbɪlɪti/, assimilate /əˈsɪm.ɪ.leɪt/, impractical /ɪmˈpɹæktɪkəl/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: spreadsheet /ˈspɹɛd.ʃit/, sponge /spʌnd͡ʒ/
- /v/ — no es /b/: los dientes tocan el labio inferior: innovate /ˈɪn.ə.veɪt/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, prevail /pɹɪˈveɪl/, oversight /ˈəʊvəˌsaɪt/, overhead /ˈəʊvəˌhɛd/
- /z/ — sonora, no /s/: personalize /ˈpɜː.sə.nə.laɪz/, feasible /ˈfiː.zɪ.bəl/, observe /əbˈzɜːv/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como radiologist, paradox, arithmetic.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.