Shadowing Practice: How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact - Learn English Speaking with Video
Les maken...
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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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Woordenschat en spreektips bij deze les
Deze spreekles op niveau C1 is gebaseerd op de video “How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact”. Deze woorden komen het vaakst terug: demand, Jevons, cost, coal, smart. Deze video bevat 104 zinnen en 1240 woorden om na te spreken. Het gesproken deel duurt 7:45. De spreker praat in een natuurlijk tempo van ongeveer 160 woorden per minuut, dicht bij een alledaags gesprek. 85% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden; de rest kun je beter vooraf bekijken.
Belangrijke woorden in deze video
De 15 moeilijkste woorden uit de video, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| radiologist zelfstandig naamwoord | /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/ | radioloog |
| paradox zelfstandig naamwoord | /ˈpæ.ɹəˌdɒks/ | paradox, tegenspraak |
| arithmetic zelfstandig naamwoord | /æɹɪθˈmɛtɪk/ | aritmetica, rekenkunde |
| automate werkwoord | /ˈɔ.təˌmeɪt/ | automatiseren |
| innovate werkwoord | /ˈɪn.ə.veɪt/ | vernieuwen, innoveren |
| spreadsheet zelfstandig naamwoord | /ˈspɹɛd.ʃit/ | spreadsheet, rekenblad |
| accountant zelfstandig naamwoord | /əˈkaʊn.tənt/ | boekhouder, accountant |
| leverage zelfstandig naamwoord | /ˈlɛv.(ə.)ɹɪd͡ʒ/ | hefboom, hefboomkracht |
| adjust werkwoord | /əˈd͡ʒʌst/ | aanpassen, verstellen |
| assimilate werkwoord | /əˈsɪm.ɪ.leɪt/ | assimileren, integreren |
| augment werkwoord | /ɔɡˈmɛnt/ | vergroten, verhogen |
| digress werkwoord | /daɪˈɡɹɛs/ | afdwalen |
| godfather zelfstandig naamwoord | /ˈɡɑdfɑðɚ/ | peter, gevader |
| humerus zelfstandig naamwoord | /ˈhjuːməɹəs/ | opperarmbeen |
| impractical bijvoeglijk naamwoord | /ɪmˈpɹæktɪkəl/ | onpraktisch, onhandig |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| go down werkwoord | afdalen |
| figure out werkwoord | uitvogelen, er achter komen |
| go away werkwoord | weggaan |
| turn out werkwoord | uitvallen, resulteren |
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Betrekkelijke bijzinnen who / which + zin — extra informatie over een persoon of ding | ones who are · people who are · ones who can |
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | were now freed · are employed |
Uitspraak om op te letten
De spreker gebruikt 16 samentrekkingen en verkorte vormen, zoals don't, didn't, doesn't. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: arithmetic /æɹɪθˈmɛtɪk/, godfather /ˈɡɑdfɑðɚ/
- De klanken “sh” en “zh”: spreadsheet /ˈspɹɛd.ʃit/, nutshell /ˈnʌt.ʃɛl/, discretion /dɪˈskɹɛʃən/, coordination /kəʊˌɔːdɪˈneɪʃən/, supervision /ˌs(j)uːpəˈvɪʒən/
- Lange woorden — let op de klemtoon: radiologist /ˌɹeɪ.diːˈɒl.ə.d͡ʒɪst/, arithmetic /æɹɪθˈmɛtɪk/, adaptability /ədæptəˈbɪlɪti/, assimilate /əˈsɪm.ɪ.leɪt/, impractical /ɪmˈpɹæktɪkəl/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: leverage /ˈlɛv.(ə.)ɹɪd͡ʒ/, personalize /ˈpɜː.sə.nə.laɪz/, sponge /spʌnd͡ʒ/, explode /ɪkˈsploʊd/, emerge /ɪˈmɝd͡ʒ/
- /æ/ — opener dan de Nederlandse “e”: paradox /ˈpæ.ɹəˌdɒks/, arithmetic /æɹɪθˈmɛtɪk/, adaptability /ədæptəˈbɪlɪti/, impractical /ɪmˈpɹæktɪkəl/, fracture /ˈfɹæk.t͡ʃʰəː/
- /g/ — een harde plofklank, geen Nederlandse “g”: augment /ɔɡˈmɛnt/, digress /daɪˈɡɹɛs/, godfather /ˈɡɑdfɑðɚ/, governance /ˈɡʌvəɹnəns/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als radiologist, paradox, arithmetic.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.