跟讀練習: AI Destroyed My Profession - 通過影片學習英語口語
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It's December 2013 and the President of the United States is looking straight into a camera talking to kids all over America.
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He tells them to stop playing video games and instead to start making them.
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And then he says a sentence
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that ends up on classroom posters all over the country
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and you know what a whole ass generation listened coding boot
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camps popped up everywhere computer science became one of the most popular majors
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and the phrase learn to code turned into the safest career advice on the planet it got
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so popular that in 2019
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when a bunch of journalists lost their jobs people online mocked them with just three words learn to code
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now fast forward to 2026 in the first half of this
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year alone tech companies announced almost 140 000 job cuts
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and most of them say the reason is ai so is that it is the best job of the 21st century dead
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Let me keep it real with you folk, I'm asking that question sincerely,
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I'm a software engineer myself with a master's degree and everything so for me this isn't just another story from the news,
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it's my industry and honestly that makes this video a little personal.
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Here's the thing, companies thought that AI would be a cheap replacement for programmers
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And now that they've actually tried it, three bills are starting to show up.
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One for the AI itself, one for the code it writes, and a third one that almost nobody is talking about yet.
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So let's open them up, one by one.
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Okay, let's start with the layoffs, because they're very much real.
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In January, Amazon cut 16 ,000 corporate jobs.
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Oracle let go of around 21 ,000 people over the year,
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Meta cut 8 ,000, and a payments company called Block laid off half of its entire workforce in one go.
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And the people building AI didn't really help calm anyone down.
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Back in 2025, the head of Anthropic, one of the biggest AI companies in the world said AI
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could wipe out half of all entry -level office jobs within a few years.
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So it's easy to look at all of this and think that it's over, that we lost.
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But not everyone believes the companies are telling the whole truth.
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Even Sam Altman, the guy who runs OpenAI, said that some companies are AI washing their layoffs.
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In other words, They blame AI for cuts they were going to make anyway.
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And you have to admit, it's a pretty convenient excuse, because saying we fired 4 ,000 people just sounds terrible.
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But saying that AI changed our industry sounds like the future.
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AI is the perfect excuse, because it makes the decisions look like it came from the technology and not from the company.
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There's also one more thing a lot of headlines leave out.
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Many of these companies are spending insane amounts of money on data centers and AI chips right now.
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So sometimes when a company says AI took your job,
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what it really means is we needed your salary to pay for AI.
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But is programming actually dying?
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Before we open any of those bills, let's check that first, because the answer is not exactly what the headlines say.
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In 2025, one headline went all over the internet.
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The number of programmers in the US had dropped to its lowest level since 1980.
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That's a time when most people didn't even know what the internet was.
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The numbers are correct, but there's a catch.
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You see, in the official US statistics, a computer programmer is a pretty small category.
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It's around 110 ,000 people whose job is mostly writing code based on someone else's plans.
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Most of the people you'd call programmers are actually counted as software developers and that group has around 1 .9 million people.
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The official forecast says it will grow by 10 % over the next 10 years.
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So the headline wasn't exactly lying, it just compared one small room to the whole building.
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It's the same story with the chart saying computer science graduates have one of the highest unemployment rates out of any major.
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The sample behind it was very small, and when economists took a closer look,
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it turned out that around 90 % of young people with that degree were actually working.
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So no, programming is not dead.
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But something is definitely wrong.
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And you can see it the moment you follow the money.
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Time to open the first envelope.
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The bill for the AI itself.
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First, a quick explanation.
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AI tools charge by something called tokens.
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A token is basically a small piece of a word
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and you pay for every token the AI reads and every token it writes.
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So, the more work you give it, the more it costs.
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Now, here's the funny part.
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Over the last few years the price per token has dropped like a rock
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and the bills they actually went up how is
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that possible AI coding tools don't just finish your line of code anymore
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now they work on their own code for hours reading through huge amounts of code
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and trying things again and again so even though every single token is cheap, they burn through an unbelievable amount of them.
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It's like gas getting 10 times cheaper and then everyone going out and buying a private yacht.
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And in 2026 companies started getting the bills.
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In May, Uber's CEO admitted
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that the entire company had burned through its entire yearly budget for AI coding tools in just four months.
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He even said that the link between all that spending and any real benefit for a customer is not there yet.
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Soon after, Uber put limits on how much each employee could spend.
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Then in August it was Microsoft's turn.
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It came out that one engineer had spent 28 ,000 dollars on AI in just 28 days.
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That's a thousand dollars a day.
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At that pace, one engineer's AI use would cost more throughout the year than paying three American software developers.
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So Microsoft sent out an internal email with a sentence
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that's honestly peak email said token maxing is not what we are optimizing for token maxing yep
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that's an actual word in a real email in a trillion dollar company
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and when your company has to put a word like
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that in an official email just to tell you to stop spending money you know something went wrong
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and it's not just uber
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and microsoft gartner one of the biggest research firms in tech predicts
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that by 2028 ai coding costs will be higher than the average developer's salary think about
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that for a second the whole point was to replace expensive programmers with cheap AI,
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and now the AI is on track to cost more than the programmer.
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But hey, at least all that money bought a lot of code, right?
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Well, that's exactly where the second bill comes in.
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Envelope number two, the code.
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To understand this one, you need to know a term programmers use all the time and that's technical debt.
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It works exactly like a credit card.
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When you write code very quickly it gets a bit messy.
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You get something that works today and it feels amazing just like swiping a credit card.
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But every change you make after that gets slower and more expensive because you keep paying interest on that mess.
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And eventually, someone has to pay off the whole thing.
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So what happens to that debt, when AI writes a big part of the code?
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A company called GitClear has been tracking exactly that.
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They looked at more than 600 million code changes from 2023 to 2026, and they found two things moving in the opposite directions.
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Copying and pasting code went way up.
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Meanwhile, the work of cleaning up and reorganizing old code,
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which programmers call refactoring has collapsed in 2022
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it made about 21 % of all changed code by 2026
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it was under 4 % in other words AI is really good at adding new code
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and really bad at tidying up the code that's already there
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so the mess keeps growing and then there's security A company called Veracode tested more than 150 AI models,
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and when nobody specifically told them to be careful,
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about 45 % of the code they wrote had a known security hole hiding in there.
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And you know what's the worst part?
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That number has barely moved in two years.
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The models got much better at writing code that works, but they didn't improve much at writing code that's safe.
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Another big study looked at more than 300 ,000 changes made by AI tools on GitHub.
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Almost a quarter of the problems the AI introduced were still sitting there in the latest version of the code.
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Nobody had gone back to fix them.
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AI really does make a lot of programmers faster, and that's a big deal.
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And nobody has a reliable estimation of how much all of this debt will cost in dollars.
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The bill simply hasn't arrived yet.
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So yeah, AI is writing code faster than ever, but a lot of it is the kind of code
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that someone some day has to come back and fix
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which raises another question who's gonna fix it because that's the third bill
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and it's the one no one is ready to pay
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welcome to the third envelope and this one isn't about money or code it's about people.
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In 2025 a team from Stanford started tracking payroll data for millions of American workers and they've kept updating it ever since.
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What they found is
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that young people aged 22 to 25 in jobs where AI can do a lot of the work
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now have about 19 % fewer jobs than they should have compared to young people in other jobs.
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But get this, experienced worker in the exact same jobs are doing fine.
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Companies are not firing their seniors, they've actually quietly stopped hiring juniors.
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And if you think about it, That makes sense.
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The tasks companies used to give to juniors, for example, building simple features, writing tests, and fixing small bugs,
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are exactly the tasks that the AI is best used for.
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Researchers in South Korea talked to developers about this, and one senior engineer said something pretty chilling.
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Their company let a lot of junior engineers go,
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and there was, quote, no impact whatsoever.
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Then the researchers talked to juniors who are still learning, and one of them said this.
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I don't know what not to do, I've only seen the good examples.
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See that right there?
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That's the problem.
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Juniors used to learn by writing bad code, breaking things and then getting roasted in code reviews.
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Now the AI hands them the code that works and they never find out why it works.
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Anthropic, one of the companies that actually makes these AI tools, tested this in early 2026.
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They had 52, mostly junior developers, learn a new coding tool.
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Half of them could use AI and the other half had to figure it out on their own.
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Then everyone took a test to see how much they actually understood.
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The group with AI scored 17 points lower and they weren't even noticeably faster.
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Now think about where this goes.
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Every senior developer on the planet started as a junior, who at first broke something, he made a mistake.
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That's how you learn and right now we're building a system where nobody gets to break anything.
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It's like a restaurant where the head chef gets an amazing machine that does all the chopping,
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so the restaurant stops hiring kitchen staff.
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It works great, right up until the day the head chef retires
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and nobody in the building knows how to cook
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so in 10 years who's gonna be the senior developer who
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checks what the AI wrote who's gonna find those security holes
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and clean up all the copied code from the bill number two put it all together
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and you get something like this the AI writes the code then the code needs fixing
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but the people who could fix it are never getting hired.
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You can stop thinking of this third thing as a bill, instead think of it as an actual time bomb.
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You know, there are people at the very top that can see it coming.
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Amazon cut 16 ,000 corporate jobs in January 2026.
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But that same year, the boss of Amazon's cloud business said that Amazon is hiring 11 ,000 interns and new graduates.
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And when people asked him about replacing juniors with AI, he called it one of the dumbest things I've ever heard.
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Say what you want about Amazon, but here, that's a good call.
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So, what does all of this mean for you?
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If you're a student, or you're thinking about learning to code, that same antropic study has some good news.
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Not everyone in the AI group did badly.
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The people who asked AI why something works, instead of just asking to do all the work for them
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learned almost as well as the people who didn't use AI at all.
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AI isn't your enemy, it's just a really bad teacher if you only ask it for answers.
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Instead you should question everything and the most important question to ask is why?
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Why is it making those specific decisions?
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Use it for brainstorming and understanding
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And suddenly, it might become one of the best teachers you'll ever have
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The job itself is changing also
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The time of manually typing out all of your code line by line is over You shouldn't do that
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Unless you want to look like a Neanderthal to your own colleagues
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Instead, you should focus a lot more on understanding what needs to be built, and checking whether the AI actually built it correctly,
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which, if you've been paying attention, is exactly the skill that companies are running out of.
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I can't promise anyone a job, and honestly, nobody can right now.
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but if the data is right the companies
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that fired everyone to save money are gonna need people who really understand code
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and there's a real chance those people will be very very expensive okay remember
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that sentence from 2013 back then it was the best career advice in the world
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In 2026 it might need a small update.
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Don't just let AI write your code.
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Understand it.
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Because programming is not dead.
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But somebody is gonna have to pay these three bills.
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And my guess is that it won't be the AI.
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Unless of course the AI gets so good that it can clean up its own mess.
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And to be fair, that's the one big question nobody can really answer just yet.
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AI is getting better at coding pretty much every month.
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Maybe one day it really will be able to do all of this on its own or maybe
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today's AI is gonna hit a ceiling and will end up needing people who understand code more than ever.
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Honestly, nobody knows, but that's a story for another video.
FOR YOU
推薦影片
本課的詞彙與口語要點
這節口語課以影片“AI Destroyed My Profession”為素材。 影片中反覆出現的詞有:code, companies, bill。 這段影片共有 195 個句子、2476 個單詞可供跟讀。 講話部分時長為 20:17。 說話人語速平穩,每分鐘約 122 個詞,很適合跟讀。 87% 的單詞屬於英語最常用的 3,000 詞,詞彙容易理解。
影片中的重點詞彙
影片中 15 個值得學習的單詞,附發音和釋義:
| 單詞 | 發音 | 釋義 |
|---|---|---|
| programmer 名詞 | /ˈpɹoʊɡɹæmɚ/ | 程式設計師 /程式設計師, 程式員 /程式員 |
| token 名詞 | /ˈtoʊkən/ | 代幣 /代幣, 籌碼 /籌碼 |
| hire 動詞 | /ˈhaɪɹ/ | 租借, 出租 |
| headline 名詞 | /ˈhɛd.laɪn/ | 標題 /標題 |
| envelope 名詞 | /ˈɛnvə(ˌ)loʊp/ | 信封 |
| salary 名詞 | /ˈsæləɹi/ | 薪金, 薪水 |
| chef 名詞 | /ʃɛf/ | 廚師 /廚師 |
| researcher 名詞 | /ˈɹiˌsɝ.t͡ʃɚ/ | 研究員 /研究員, 研究者 |
| meanwhile 副詞 | /ˈmiːnwaɪl/ | 同時 /同時 |
| chart 名詞 | /t͡ʃɑɹt/ | 圖表 /圖表 |
| cloud 名詞 | /ˈklaʊ̯d/ | йүн, 雲 /雲 |
| wash 動詞 | /wɑʃ/ | 洗, 洗滌 /洗滌 |
| explanation 名詞 | /ˌɛkspləˈneɪʃən/ | 解釋 /解釋, 說明 /說明 |
| worker 名詞 | /ˈwɝ.kɚ/ | 工人, 工作者 |
| skill 名詞 | /skɪl/ | 技巧, 技能 |
影片中出現的短語動詞
| 單詞 | 發音 | 釋義 |
|---|---|---|
| come back 動詞 | 回來 /回來, 返回 | |
| come out 動詞 | /ˌkʌm ˈaʊt/ | 出, 出來 /出來 |
| end up 動詞 | 到達 | |
| find out 動詞 | 找出, 查明 | |
| go out 動詞 | 出, 出口 | |
| go up 動詞 | 上昇 /上升, 上去 | |
| pop up 動詞 | 彈出 | |
| show up 動詞 | 露面 |
影片中的語法
說話人最常用的結構,並附上影片中的原話:
| 結構 | 影片中的用法 |
|---|---|
| 現在完成進行時 have/has been + -ing — 以前開始、現在仍在繼續的動作 | has been tracking · you've been paying |
| “used to” used to + 動詞 — 過去的習慣或狀態,現在已不再如此 | used to give · used to learn |
| 現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事 | they've actually tried · has dropped · has collapsed |
| 定語從句 who / which + 從句 — 補充說明人或事物 | guy who runs · people who could · people who asked |
需要注意的發音
說話人用了 36 次縮略和弱讀形式,例如 gonna, didn't, don't。請按聽到的簡短形式來說。
- “sh” 和 “zh” 音: chef /ʃɛf/, wash /wɑʃ/, explanation /ˌɛkspləˈneɪʃən/, estimation /ˌɛs.təˈmeɪ.ʃən/
- 長單詞——注意重音位置: developer /dɪˈvɛləpɚ/, explanation /ˌɛkspləˈneɪʃən/, unemployment /ʌn.ɪmˈplɔɪ.mənt/, unbelievable /ˌʌnbɪˈliːvəbl̩/, whatsoever /ˌ(h)wʌtsoʊˈɛvɚ/
中文母語者容易讀錯的音:
- /v/ — 不要讀成 /w/,上齒輕觸下唇: developer /dɪˈvɛləpɚ/, envelope /ˈɛnvə(ˌ)loʊp/, convenient /kənˈviː.niənt/, unbelievable /ˌʌnbɪˈliːvəbl̩/, whatsoever /ˌ(h)wʌtsoʊˈɛvɚ/
- 詞尾子音 — 要讀清楚,後面不要加母音: envelope /ˈɛnvə(ˌ)loʊp/, chef /ʃɛf/, chart /t͡ʃɑɹt/, cloud /ˈklaʊ̯d/, wash /wɑʃ/
- /r/ 和 /l/ 的區別: salary /ˈsæləɹi/, replacement /ɹɪˈpleɪsmənt/, reliable /ɹɪˈlaɪəbl̩/, correctly /kəˈɹɛk(t).li/, classroom /ˈklɑːsɹʊm/
如何用這段影片練習
- 先完整聽一遍影片,不要開口,記下不認識的單詞。
- 用正常速度逐句跟讀,每句重複到你的節奏與說話人一致為止。
- 錄下自己的聲音並與原聲對比,特別注意 programmer, token, hire 這類單詞。
什麼是跟讀法?
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧,最初開發用於專業口譯員的培訓,並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大:您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同,跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性,語調,節奏,連讀,聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。












