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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.

He tells them to stop playing video games and instead to start making them.

And then he says a sentence

that ends up on classroom posters all over the country

and you know what a whole ass generation listened coding boot

camps popped up everywhere computer science became one of the most popular majors

and the phrase learn to code turned into the safest career advice on the planet it got

so popular that in 2019

when a bunch of journalists lost their jobs people online mocked them with just three words learn to code

now fast forward to 2026 in the first half of this

year alone tech companies announced almost 140 000 job cuts

and most of them say the reason is ai so is that it is the best job of the 21st century dead

Let me keep it real with you folk, I'm asking that question sincerely,

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,

it's my industry and honestly that makes this video a little personal.

Here's the thing, companies thought that AI would be a cheap replacement for programmers

And now that they've actually tried it, three bills are starting to show up.

One for the AI itself, one for the code it writes, and a third one that almost nobody is talking about yet.

So let's open them up, one by one.

Okay, let's start with the layoffs, because they're very much real.

In January, Amazon cut 16 ,000 corporate jobs.

Oracle let go of around 21 ,000 people over the year,

Meta cut 8 ,000, and a payments company called Block laid off half of its entire workforce in one go.

And the people building AI didn't really help calm anyone down.

Back in 2025, the head of Anthropic, one of the biggest AI companies in the world said AI

could wipe out half of all entry -level office jobs within a few years.

So it's easy to look at all of this and think that it's over, that we lost.

But not everyone believes the companies are telling the whole truth.

Even Sam Altman, the guy who runs OpenAI, said that some companies are AI washing their layoffs.

In other words, They blame AI for cuts they were going to make anyway.

And you have to admit, it's a pretty convenient excuse, because saying we fired 4 ,000 people just sounds terrible.

But saying that AI changed our industry sounds like the future.

AI is the perfect excuse, because it makes the decisions look like it came from the technology and not from the company.

There's also one more thing a lot of headlines leave out.

Many of these companies are spending insane amounts of money on data centers and AI chips right now.

So sometimes when a company says AI took your job,

what it really means is we needed your salary to pay for AI.

But is programming actually dying?

Before we open any of those bills, let's check that first, because the answer is not exactly what the headlines say.

In 2025, one headline went all over the internet.

The number of programmers in the US had dropped to its lowest level since 1980.

That's a time when most people didn't even know what the internet was.

The numbers are correct, but there's a catch.

You see, in the official US statistics, a computer programmer is a pretty small category.

It's around 110 ,000 people whose job is mostly writing code based on someone else's plans.

Most of the people you'd call programmers are actually counted as software developers and that group has around 1 .9 million people.

The official forecast says it will grow by 10 % over the next 10 years.

So the headline wasn't exactly lying, it just compared one small room to the whole building.

It's the same story with the chart saying computer science graduates have one of the highest unemployment rates out of any major.

The sample behind it was very small, and when economists took a closer look,

it turned out that around 90 % of young people with that degree were actually working.

So no, programming is not dead.

But something is definitely wrong.

And you can see it the moment you follow the money.

Time to open the first envelope.

The bill for the AI itself.

First, a quick explanation.

AI tools charge by something called tokens.

A token is basically a small piece of a word

and you pay for every token the AI reads and every token it writes.

So, the more work you give it, the more it costs.

Now, here's the funny part.

Over the last few years the price per token has dropped like a rock

and the bills they actually went up how is

that possible AI coding tools don't just finish your line of code anymore

now they work on their own code for hours reading through huge amounts of code

and trying things again and again so even though every single token is cheap, they burn through an unbelievable amount of them.

It's like gas getting 10 times cheaper and then everyone going out and buying a private yacht.

And in 2026 companies started getting the bills.

In May, Uber's CEO admitted

that the entire company had burned through its entire yearly budget for AI coding tools in just four months.

He even said that the link between all that spending and any real benefit for a customer is not there yet.

Soon after, Uber put limits on how much each employee could spend.

Then in August it was Microsoft's turn.

It came out that one engineer had spent 28 ,000 dollars on AI in just 28 days.

That's a thousand dollars a day.

At that pace, one engineer's AI use would cost more throughout the year than paying three American software developers.

So Microsoft sent out an internal email with a sentence

that's honestly peak email said token maxing is not what we are optimizing for token maxing yep

that's an actual word in a real email in a trillion dollar company

and when your company has to put a word like

that in an official email just to tell you to stop spending money you know something went wrong

and it's not just uber

and microsoft gartner one of the biggest research firms in tech predicts

that by 2028 ai coding costs will be higher than the average developer's salary think about

that for a second the whole point was to replace expensive programmers with cheap AI,

and now the AI is on track to cost more than the programmer.

But hey, at least all that money bought a lot of code, right?

Well, that's exactly where the second bill comes in.

Envelope number two, the code.

To understand this one, you need to know a term programmers use all the time and that's technical debt.

It works exactly like a credit card.

When you write code very quickly it gets a bit messy.

You get something that works today and it feels amazing just like swiping a credit card.

But every change you make after that gets slower and more expensive because you keep paying interest on that mess.

And eventually, someone has to pay off the whole thing.

So what happens to that debt, when AI writes a big part of the code?

A company called GitClear has been tracking exactly that.

They looked at more than 600 million code changes from 2023 to 2026, and they found two things moving in the opposite directions.

100

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.

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Vocabolario e note di pronuncia per questa lezione

Questa lezione di conversazione si basa sul video “AI Destroyed My Profession”. Le parole che ritornano più spesso: code, companies, bill. Questo video contiene 195 frasi e 2476 parole da ripetere con lo shadowing. Il parlato dura 20:17. Chi parla mantiene un ritmo costante di circa 122 parole al minuto, comodo per lo shadowing. Il 87% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è facile da seguire.

Vocaboli chiave di questo video

15 parole del video che vale la pena imparare, con pronuncia e significato:

Parola	Pronuncia	Significato
programmer sostantivo	/ˈpɹoʊɡɹæmɚ/	programmatore, programmatrice
token sostantivo	/ˈtoʊkən/	simbolo, segno
developer sostantivo	/dɪˈvɛləpɚ/	sviluppatore, sviluppatrice
hire verbo	/ˈhaɪɹ/	assoldare, noleggiare
headline sostantivo	/ˈhɛd.laɪn/	titolo
envelope sostantivo	/ˈɛnvə(ˌ)loʊp/	busta
salary sostantivo	/ˈsæləɹi/	stipendio, salario
chef sostantivo	/ʃɛf/	chef, capocuoco
researcher sostantivo	/ˈɹiˌsɝ.t͡ʃɚ/	ricercatore, ricercatrice
meanwhile avverbio	/ˈmiːnwaɪl/	intanto, nel frattempo
chart sostantivo	/t͡ʃɑɹt/	carta, carta nautica
cloud sostantivo	/ˈklaʊ̯d/	nuvola, nube
wash verbo	/wɑʃ/	lavare
badly avverbio	/ˈbæd.li/	male
explanation sostantivo	/ˌɛkspləˈneɪʃən/	spiegazione, esplicazione

I phrasal verb che sentirai

Parola	Significato
clean up verbo	ripulire
come back verbo	tornare
end up verbo	andare a finire, finire
find out verbo	scoprire
go out verbo	uscire, andare fuori
go up verbo	salire
lay off verbo	licenziare
pay off verbo	corrompere

La grammatica di questo video

Le strutture che chi parla usa di più, con le parole esatte del video:

Struttura	Nel video
Present perfect continuous have/has been + -ing — un’azione iniziata prima e ancora in corso	has been tracking · you've been paying
“Used to” used to + verbo — un’abitudine o uno stato passato che non è più vero	used to give · used to learn
Present perfect have/has + participio passato — un’azione passata che conta ancora adesso	they've actually tried · has dropped · has collapsed
Frasi relative who / which + frase — un’informazione in più su una persona o una cosa	guy who runs · people who could · people who asked

Pronuncia a cui fare attenzione

Chi parla usa 36 contrazioni e forme ridotte, come gonna, didn't, don't. Pronunciale nella forma breve, così come le senti.

I suoni “sh” e “zh”: chef /ʃɛf/, wash /wɑʃ/, explanation /ˌɛkspləˈneɪʃən/, estimation /ˌɛs.təˈmeɪ.ʃən/
Parole lunghe — attenzione all’accento: developer /dɪˈvɛləpɚ/, explanation /ˌɛkspləˈneɪʃən/, unemployment /ʌn.ɪmˈplɔɪ.mənt/, unbelievable /ˌʌnbɪˈliːvəbl̩/, whatsoever /ˌ(h)wʌtsoʊˈɛvɚ/

I suoni difficili per chi parla italiano:

/h/ — va soffiata, non è muta: hire /ˈhaɪɹ/, headline /ˈhɛd.laɪn/, whatsoever /ˌ(h)wʌtsoʊˈɛvɚ/
Consonante finale — senza aggiungere una vocale dopo: envelope /ˈɛnvə(ˌ)loʊp/, chef /ʃɛf/, chart /t͡ʃɑɹt/, cloud /ˈklaʊ̯d/, wash /wɑʃ/
/æ/ — più aperta della “e”: programmer /ˈpɹoʊɡɹæmɚ/, salary /ˈsæləɹi/, badly /ˈbæd.li/, collapse /kəˈlæps/, manually /ˈmænj(u)əliː/

Come esercitarsi con questo video

Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
Fai shadowing frase per frase a velocità normale, ripetendo ognuna finché il tuo ritmo coincide con quello di chi parla.
Registrati e confronta con l’originale, facendo attenzione a parole come programmer, token, developer.

Cos'è la tecnica dello Shadowing?

Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.

Tecnica dello shadowing: leggi la guida completa passo dopo passo →

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