Shadowing-Übung: Every Generation Thinks This Time Is Different: The AI Job Myth - Englisch Sprechen Lernen mit Video
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If you're young and you're trying to figure out what to do with your career right now, you're probably hearing this.
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AI is going to take your job.
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And even if you aren't so young, you're probably hearing the same thing loudly from everyone.
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And the honest response to that is every generation has heard some version of it about the machine of the moment.
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Cars, electricity, the PC, the internet, smart phones, every single one of these was predicted to permanently destroy work.
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Yep, and every single one created more total jobs than it destroyed.
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Just different jobs.
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Yes, so Jeff and I want to walk through the pattern because the pattern is real and it's consistent.
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But from the inside the moment, it never looks like a pattern.
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It always looks like the exception.
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It always looks like this time is different.
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Which is where I want to start, because this time has been said a lot of times.
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All right, wave one, cars.
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The turn of the 20th century, one of the biggest employers in most cities was the horse economy.
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Oh, this is going to be my favorite segment.
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You're right.
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You're a barrel racer, so I can see why you would care about this.
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I love me some fast horses, which is very inconvenient for a video about the technology that replaced them.
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Sad.
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But the good news is, nobody has built an AI that can turn a barrel yet.
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And AI better never take the thrill of turning barrels away from me.
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Sorry, go on.
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The horse economy.
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Don't worry, Bree.
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No one wants to watch a robot rodeo.
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So yeah, we've got stable hands, blacksmiths, carriage makers, farriers, grooms, feed suppliers, manure haulers.
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Yeah, that was a real job title, believe it or not, and sometimes sounds a lot like my job at times.
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New York City, around 1900, had roughly 200 ,000 working horses in it, producing millions of pounds of,
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yeah, you guessed it, manure every day.
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Which sounds like a job you'd want a machine to take.
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Yeah, it turns out that you definitely want a machine to do that cars arrived, and within about 30 years, the horse economy had mostly gone.
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Hundreds of thousands of jobs vanished.
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And that's the version of the story that gets told.
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The jobs that disappeared.
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Right.
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The part that doesn't get told, though, is about what happened next.
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We had jobs that started to appear.
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The auto industry created assembly line work, mechanics, gas station attendance, road construction, motels, drive -in restaurants, traffic engineers,
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auto insurance, driver's ed teachers, an entire economy that just didn't exist before.
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More jobs than the ones that were lost.
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Way more and in fact none of them were predictable in 1900.
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Nobody in 1899 said, you know what the future is?
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Motel clerks.
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Because nobody had thought of a motel yet.
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Nobody had thought of a motel yet.
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That's because they lacked the vision to see what was on the other side of the mountain.
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They just saw the mountain.
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Fast forward.
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The same pattern shows up over and over.
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Electricity kills the gaslighting industry.
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I think you mean street lamp lighting industry because gas lighting is a different problem, right?
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I mean the street lamp lighting industry and the ice cutting business more icebreakers That's right,
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but it creates the appliance industry the entertainment industry
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and eventually the entire computer Industry all of which were much bigger than what actually got replaced.
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Yep The PC in the 1980s kills the typing tool and the filing clerk but creates software developers,
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systems analysts, IT support, the entire tech sector.
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The internet in the 90s kills travel agents, video rental stores, and encyclopedia salesmen.
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Well, those encyclopedias were always a little bit cumbersome to carry around after all.
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That's right.
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But it creates e -commerce, digital marketing, UX designers, community managers,
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and roughly every job that ends with the word engineer and did not exist in 1995.
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Then smartphones after 2007 kill the standalone GPS unit and the MP3 player, but create the app economy,
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the gig economy, and the creator economy.
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It was millions of jobs.
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Oh yeah.
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And Jeff, here's the stat I want people to sit with.
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David Otter at MIT has been doing this analysis for years.
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His 2024 paper on new work found that more than 60 % of the jobs Americans do today didn't exist in 1940.
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Hold on.
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According to my calculations, that's more than half.
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That's right.
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More than half.
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Those jobs weren't sitting there waiting to be discovered.
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Humans invented them on top of the tools they were handed.
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Again, these were all waiting just on the other side of the mountain.
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What brings us to the next part, then, is what surprises people the most.
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Bree, when ATMs first got installed everywhere in the 70s and 80s, what were bank tellers supposed to do?
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Disappear, I guess?
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Well, that's what a lot of people were predicting at that time.
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You know, you've got a machine that dispenses cash, and that should be the end of tellers.
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Well, what actually happened was the number of U .S bank tellers went up from around 300 ,000 in the mid -70s,
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closer to 600 ,000 by 2010.
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So, economist James Besson has the numbers on this one. Because?
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Well, because the ATM that made a branch cheaper to run meant fewer tellers per branch, so banks opened more branches is.
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And the job of the teller itself actually shifted from just counting cash to helping customers with more complicated stuff.
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Same title, different work, and actually more of them.
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Right.
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This is a version of a very old economic principle, and this is your territory, Jeff.
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Yeah, the very old stuff is always my territory, thanks.
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Yeah, I'm referring specifically to a thing called Jevons paradox, which I've touched on in a previous video of mine.
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It's wrote a book back in 1865, and no, I was not there when it was written, that was called The Coal Question.
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His observation was simple.
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When steam engines got more efficient, you didn't need as much coal to run one.
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So the conventional wisdom held that coal consumption would go down as a result.
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Plot twist, that's not what happened.
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It went up.
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You've seen this movie, haven't you, Bree?
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Well, no spoilers for the folks at home.
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Yes, it went way up.
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And because the more efficient the machine, the engine, well, then the cheaper it got to operate and it could do more work for you.
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And that meant more usage,
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more people found more uses for these machines and therefore coal consumption actually increased.
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That's right.
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And the same pattern shows up with software.
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That's right.
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Same pattern.
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AI makes writing software faster and cheaper, the naive prediction is great.
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Now we need fewer developers.
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But remember, what the Jevons pattern actually predicts is that more software gets written,
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more things get built, that were too expensive or impractical to build before.
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And demand for developers who can now do more actually goes up.
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Which is not what the headlines usually say.
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No, it turns out headlines don't do 160 -year -old economics
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and headline writers don't often look over the other side of the mountain to see might be waiting there.
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Okay, so here's the part I want people to hear.
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Every generation has this exact conversation.
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And it's with the exact same confidence.
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Oh, yeah.
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In 1930, John Maynard Keynes wrote an essay called Economic Possibilities for Our Grandchildren.
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And in it, he coined the phrase technological unemployment.
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His argument, machines are getting so productive that within 100 years,
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we'd all be working 15 -hour weeks and running out of things to do.
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Yeah, we were supposed to all have personal jetpacks by now, too, though.
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But tell me, how's that working out for you, that 15 -hour work week?
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Not so great, if you ask me.
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I'm doing at least three of them every week.
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Well, more is better, then.
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So tell me about it.
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On second thought, don't.
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But you know what no company ever says?
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I think we're finished.
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We're done.
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Nothing else left to do.
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Nope, they don't say that.
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Nature abhors a vacuum, and so do companies.
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They will always fill the void with, wait for it, more work.
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Right.
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But we keep forgetting that.
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In the 1960s, there was a presidential commission on automation because everyone was convinced the machines were about to make labor obsolete.
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In the 1980s, it was Japanese robotics.
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In the 2000s, it was offshoring.
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Every wave had a very smart person saying, this time is different.
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And each time the tools got adopted and humans reorganized around them.
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Because the pattern is in tech eliminates jobs.
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The pattern is tech eliminates tasks and humans invent new jobs around what's left.
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I like your angle here Bree.
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A job is a bundle of tasks.
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So you've got some tasks in the bundle that get automated.
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Well, the bundle regroups.
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Sometimes new bundles form, sometimes people just get to do more interesting parts.
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The Bay Teller got to spend less time counting 20s and start actually helping people.
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The developer today gets to stop writing boilerplate code and start solving the actual problem.
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And from inside the moment this always looks like the exception
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because you can see the tasks getting eliminated in real time and you cannot see the jobs that haven't been invented yet.
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That's because they're not on the other side of the mountain.
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In 1918 -99 nobody saw the motel clerk over here.
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And nobody in 2019 saw the prompt engineer.
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Which is a six figure job now.
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Which is a six figure job now.
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That's exactly the point.
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Somebody watching this video is going to have a job in 2040 with a title
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that would sound completely made up if we said it out loud right now.
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And they're going to be very good at it.
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So if you're early in your career and the headlines are telling you your future is cooked, I'm going to let you in on a little secret.
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You're actually not that special because we've done this before.
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Right.
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They're telling you what everybody's grandparents were told.
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The tools change what the work looks like.
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They don't change the fact that there's still more work to do than we have actual humans to do it.
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Yep.
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So learn the tools.
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Get good at the parts of the work the tools can't do.
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Get comfortable inventing the jobs that don't exist yet, because that's what every generation has actually done.
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Even the ones that we're sure this time is going to be different.
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Especially those.
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In fact, despite what the prophets of doom have been saying,
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the unemployment rate both in the US and globally has remained largely unchanged since the admin of generative AI.
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So what that means is for every horror story of AI caused layoffs, many of
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which were really just a way of hiding poor hiring practices
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there must be a relatively equal number of hirings otherwise the rate would change
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so jeff what's on the other side of this ai mountain well nobody knows for sure
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but just remember this curious people don't get replaced they get liberated
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Wortschatz und Sprechhinweise zu dieser Lektion
Diese Sprechübung auf Niveau B2 basiert auf dem Video „Every Generation Thinks This Time Is Different: The AI Job Myth“. Dieses Video enthält 182 Sätze und 1877 Wörter zum Nachsprechen. Der gesprochene Teil dauert 12:33. Der Sprecher spricht in natürlichem Tempo, etwa 150 Wörter pro Minute, ähnlich wie im Alltagsgespräch. 84 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen.
Wichtiger Wortschatz in diesem Video
Die 15 anspruchsvollsten Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| teller Substantiv | /ˈtɛləɹ/ | Erzähler, Erzählerin |
| motel Substantiv | /moʊˈtɛl/ | Motel |
| invent Verb | /ɪnˈvɛnt/ | erfinden |
| bundle Substantiv | /ˈbʌn.dl̩/ | Bündel |
| headline Substantiv | /ˈhɛd.laɪn/ | Schlagzeile, Überschrift |
| developer Substantiv | /dɪˈvɛləpɚ/ | Entwickler, Entwicklerin |
| clerk Substantiv | /klɝk/ | Büroangestellte, Angestellte |
| predict Verb | /pɹɪˈdɪkt/ | prophezeien |
| eliminate Verb | /ɪˈlɪm.ɪ.neɪt/ | eliminieren, zerstören |
| barrel Substantiv | /ˈbæɹəl/ | Fass, Tonne |
| manure Substantiv | /məˈnʊɹ/ | Mist, Dung |
| encyclopedia Substantiv | /ɪnˌsaɪkləˈpidi.ə/ | Enzyklopädie, Lexikon |
| automobile Substantiv | /ˈɔː.tə.məˌbil/ | Automobil, Auto |
| lamp Substantiv | /ˈlæ̝mp/ | Lampe |
| disappear Verb | /dɪsəˈpɪɹ/ | verschwinden, entschwinden |
Phrasal Verbs, die du hören wirst
| Wort | Aussprache | Bedeutung |
|---|---|---|
| go up Verb | hinaufgehen, steigen | |
| turn out Verb | sich herausstellen als/dass, sich erweisen als | |
| figure out Verb | herausfinden | |
| go down Verb | absteigen, hinabsteigen | |
| make up Verb | /ˌmeɪk ˈʌp/ | wiedergutmachen, kompensieren |
Sätze zum Wiederholen
Kurze, vollständige Sätze aus dem Video, die du im Alltag wiederverwenden kannst:
- According to my calculations, that's more than half.
- I'm doing at least three of them every week.
- They're telling you what everybody's grandparents were told.
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Present Perfect Continuous have/has been + -ing – eine Handlung, die früher begann und noch andauert | has been doing · have been saying |
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | was predicted · has been said · were lost |
| Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist | has heard · has been said · has built |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 42 Kurzformen und abgeschwächte Formen, zum Beispiel don't, you're, didn't. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die „th“-Laute: blacksmith /ˈblæk.smɪθ/, thrill /θɹɪl/
- Die Laute „sh“ und „zh“: vanish /ˈvænɪʃ/, calculation /ˌkælkjuˈleɪʃn̩/, automation /ˌɔ.təˈmeɪ.ʃən/, offshore /ɒfˈʃɔː(ɹ)/, prediction /pɹɪˈdɪkʃən/
- Lange Wörter – auf die Betonung achten: developer /dɪˈvɛləpɚ/, eliminate /ɪˈlɪm.ɪ.neɪt/, encyclopedia /ɪnˌsaɪkləˈpidi.ə/, automobile /ˈɔː.tə.məˌbil/, generative /ˈd͡ʒɛnəɹətɪv/
Laute, die Deutschsprachigen schwerfallen:
- Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: generative /ˈd͡ʒɛnəɹətɪv/, unchanged /ʌnˈt͡ʃeɪnd͡ʒd/, naive /naɪˈiv/, carriage /ˈkæɹɪd͡ʒ/, void /vɔɪd/
- /æ/ — offener als „ä“: barrel /ˈbæɹəl/, lamp /ˈlæ̝mp/, blacksmith /ˈblæk.smɪθ/, grandparent /ˈɡɹæn(d)pɛəɹənt/, impractical /ɪmˈpɹæktɪkəl/
- /dʒ/ — stimmhaft, nicht „tsch“: generative /ˈd͡ʒɛnəɹətɪv/, unchanged /ʌnˈt͡ʃeɪnd͡ʒd/, carriage /ˈkæɹɪd͡ʒ/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie teller, motel, invent.
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.