Pratique du Shadowing: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - Apprendre l'anglais à l'oral avec la vidéo
Création de la leçon...
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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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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale de niveau C1 s’appuie sur la vidéo « How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact ». Les mots qui reviennent le plus souvent : demand, Jevons, cost, coal, smart. Cette vidéo contient 104 phrases et 1240 mots à répéter en shadowing. La partie parlée dure 7:45. Le locuteur parle à un rythme naturel d’environ 160 mots par minute, proche d’une conversation courante. 85 % des mots font partie des 3 000 mots les plus courants en anglais ; le reste mérite d’être vu avant de commencer.
Vocabulaire clé de cette vidéo
Les 15 mots les plus avancés de la vidéo, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| radiologist nom | /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/ | radiologue, radiologiste |
| paradox nom | /ˈpæ.ɹəˌdɒks/ | paradoxe, antinomie |
| arithmetic nom | /æɹɪθˈmɛtɪk/ | arithmétique |
| automate verbe | /ˈɔ.təˌmeɪt/ | automatiser |
| innovate verbe | /ˈɪn.ə.veɪt/ | innover |
| spreadsheet nom | /ˈspɹɛd.ʃit/ | tableur, chiffrier |
| accountant nom | /əˈkaʊn.tənt/ | comptable |
| leverage nom | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | effet de levier |
| adjust verbe | /əˈd͡ʒʌst/ | ajuster |
| adaptability nom | /ədæptəˈbɪlɪti/ | adaptabilité |
| assimilate verbe | /əˈsɪm.ɪ.leɪt/ | assimiler, absorber |
| augment verbe | /ɔɡˈmɛnt/ | augmenter |
| digress verbe | /daɪˈɡɹɛs/ | s'écarter, dévier |
| enabler nom | facilitateur | |
| godfather nom | /ˈɡɑdfɑðɚ/ | parrain |
Les verbes à particule que vous entendrez
| Mot | Sens |
|---|---|
| go down verbe | descendre |
| go up verbe | monter, s'élever |
| figure out verbe | cerner |
| go away verbe | partir, s'en aller |
| hold up verbe | retenir |
| turn out verbe | se finir, se terminer |
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | ones who are · people who are · ones who can |
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | were now freed · are employed |
Prononciation à surveiller
Le locuteur utilise 16 contractions et formes réduites, comme don't, didn't, doesn't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: arithmetic /æɹɪθˈmɛtɪk/, godfather /ˈɡɑdfɑðɚ/
- Les sons « sh » et « zh »: spreadsheet /ˈspɹɛd.ʃit/, nutshell /ˈnʌt.ʃɛl/, discretion /dɪˈskɹɛʃən/, coordination /kəʊˌɔːdɪˈneɪʃən/, supervision /ˌs(j)uːpəˈvɪʒən/
- Mots longs — placez bien l’accent: radiologist /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/, arithmetic /æɹɪθˈmɛtɪk/, adaptability /ədæptəˈbɪlɪti/, assimilate /əˈsɪm.ɪ.leɪt/, impractical /ɪmˈpɹæktɪkəl/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: humerus /ˈhjuːməɹəs/, prohibit /pɹəˈhɪbɪt/, overhead /ˈəʊvəˌhɛd/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: radiologist /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, adjust /əˈd͡ʒʌst/, sponge /spʌnd͡ʒ/, fracture /ˈfɹæk.t͡ʃʰəː/
- /r/ anglais — langue recourbée, sans frotter la gorge: radiologist /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/, paradox /ˈpæ.ɹəˌdɒks/, arithmetic /æɹɪθˈmɛtɪk/, spreadsheet /ˈspɹɛd.ʃit/, leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme radiologist, paradox, arithmetic.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.