Shadowing Practice: The AI Take Over Has Completely Backfired (Here's the Proof) - Learn English Speaking with Video
Elige un modo de práctica
Cada modo abre una sala de práctica a pantalla completa.
128 oraciones
Si las oraciones son demasiado cortas o largas, haz clic en Editar para ajustarlas.
1
AI was supposed to change everything, replace millions of jobs, transform every industry, and become the biggest technological shift in history.
2
But there's just one problem.
3
The numbers don't seem to add up.
4
Big tech is expected to spend around $725 billion on AI this year.
5
Meanwhile, the entire global market for AI services generated only around $25 billion last year.
6
That means for every $10 these companies are pouring into AI infrastructure, they're getting back only about 25 cents in revenue.
7
And now the cracks are becoming harder to ignore.
8
Products are being shut down.
9
The cost of running AI keeps exploding.
10
Lawsuits are piling up.
11
And investors are starting to ask the question nobody wanted to ask two years ago.
12
Where's the money?
13
So is AI actually the next technological revolution?
14
Or are we watching one of the biggest tech bubbles in history start to burst?
15
Hi friends, I'm Maddie.
16
I'm a senior software engineer who previously worked Google, Internet, other bictict companies like Amazon, IBM, and Microsoft.
17
For two years now, the story I've been told, that you've been told, is that AI was about to replace every engineer alive.
18
We are not far from the world.
19
I think we'll be there in three to six months where AI is writing 90% of the code.
20
And then in 12 months, we may be in a world where AI is writing essentially all of the code.
21
But as of now, the money flow doesn't exactly match up to this story.
22
So in In this video, I'll break down why the AI hype is cracking in 2026, the spending method doesn't close, the AI products quietly getting switched off,
23
and the legal problem no one thought about until too late.
24
Let's get into it.
25
We're going to start with the amount of money companies are spending on AI.
26
The five companies building this infrastructure, Microsoft, Amazon, Alphabet, Meta, and Oracle,
27
are on track for roughly $725 billion of capital expenditure in 2026.
28
That's up 77% in a single year, and it was around $162 billion back in 2022.
29
Goldman Sachs predicts that the total spend will be past $7.6 trillion through 2031. To put
30
that into perspective, $725 billion a year is more than the entire
31
annual GDP of Switzerland being spent by a handful of companies on hardware
32
that loses most of its value in three or four years.
33
And so here's the problem.
34
That money isn't coming from spare cash anymore.
35
Rather, it's eating companies alive.
36
JP Morgan estimates AI capital expenditure went from around a third
37
of hyperscaler operating cash flow in 2023 to roughly 93% in 2026.
38
Their combined free cash flow, the actual money left over, fell to about $7 billion in the second quarter of 2026,
39
down from $60 billion at the end of 2025.
40
And analysts expect it to go negative this quarter.
41
Amazon is looking at deeply negative free cash flow this year.
42
Microsoft has told investors that its spare cash is shrinking, and it left a $60 billion buyback basically sitting there, unused.
43
So the richest companies on earth are borrowing hard against a payoff that hasn't arrived yet.
44
And the moment that made everyone realize how dangerous this could be is when DeepSeek, a Chinese startup that in early 2025 trained a competitive model for a few million dollars,
45
erased something like $600 billion of NVIDIA's value in one trading day, the single largest day wipeout any US stock has ever taken.
46
DeepSeek's success forced every investor to ask this very important question, what are we actually getting for all this spend?
47
And even if that money were all there, the electricity and the infrared might not be.
48
That part often doesn't get talked about as much because it's much less exciting than those stock charts.
49
But there is a hard physical limit.
50
Training and running these models eat staggering amounts of power.
51
The International Energy Agency says electricity demand from data centers jumped 17% in 2025, and the AI-specific slice grew closer to 50%.
52
In the US, data centers are now driving about half of all new electricity demand growth.
53
The grid just simply can't keep pace
54
because capital moves at the speed of markets and power plants move at the speed of permits and construction.
55
Gartner is already projecting that power shortages could constrain 40% of AI data centers by 2027.
56
So you have companies committing hundreds of billions of dollars to build-outs that physically cannot happen yet,
57
and quietly signing deals for their own nuclear reactors to get around this limitation.
58
That is not the behavior of an industry that has everything figured out.
59
In addition to the massive cost of AI, it might seem as though projects keep on failing even with the help of AI.
60
An MIT report found that roughly 95% of enterprise-generative AI pilots delivered no measurable return on investment.
61
However, there's a bit more to that number.
62
It does measure whether a pilot moved the bottom line within about six months, and a lot of them were in marketing, which is a hard place to show fast returns.
63
Also, internally built AI tools succeeded about a third of the time, while vendor-based ones succeeded about two thirds.
64
So most companies seem as though they're using it to solve problems that don't return much.
65
This is the phenomenon even in large companies.
66
For example, Amazon set a target of getting 80% of its developers using AI tools every week
67
and built an internal leaderboard called KiroRank that scored engineers on how much AI they used.
68
So then naturally, as you can imagine, engineers started pointing AI agents at pointless, made-up tasks purely to run up their token counts and climb the board,
69
a move the industry has coined token taxing.
70
It drove up Amazon's own compute bill for basically nothing.
71
So in May of 2026, they officially killed the leaderboard, and a senior VP had to send around a note saying, please don't use AI just for the sake of AI.
72
Also, a recent survey of more than 2,500 enterprises found
73
that around 74% of companies that put a live AI customer service agent into production had to roll it back.
74
So while the demo looks incredible, productionization is harder, especially in legacy systems full of edge cases the demo never saw or trained on.
75
The reason why these AI agents are getting switched off isn't only because they're wrong.
76
It's because that when they're wrong, the company can get into liability and big legal trouble.
77
Late last year, the Australian government paid Deloitte around $440,000 for a report on a welfare system.
78
A researcher started reading it and noticed something off.
79
The report cited academic papers that just didn't exist and quoted a federal court judge saying something that the judge never said.
80
It turns out that Deloitte had actually been using Gen.ai to help write it, and the AI had just hallucinated its sources.
81
Deloitte ended up issuing a corrected version and refunded part of the fee.
82
The lesson here is pretty stark.
83
If your AI puts something in front of a client, you are liable for what it outputs.
84
You don't get to blame it on the software.
85
Now, stack that on top of hallucinations would turn out to be really stubborn.
86
One 2026 benchmark that tested 37 different models clocked made-up answer rates anywhere from 15 to 52 percent.
87
And oddly, several of the newer reasoning models actually did it more often than the older ones, not less.
88
On legal questions in particular, Stanford put the range of hallucination at somewhere between 58 percent and 88 percent.
89
So put yourself in a Fortune 500 boardroom.
90
You've got a tool that that will confidently make things up some meaningful fraction of the time, a core precedent saying you own every word it says,
91
and insurers who've started writing outright AI exclusions into their policies because they can't price the risk.
92
What do you do?
93
You quietly kill a lot of those customer-facing agents.
94
So we have now a grim picture of the AI ecosystem.
95
The spending is unsustainable, the pilots mostly fail, and the agents are getting retracted.
96
So what should you do with this information if you're building a career?
97
There are three things.
98
Number one, don't confuse the bubble with the technology.
99
For example, the dot-com crash didn't mean that the internet was fake, but it meant the money had run way ahead of the value
100
and the correction wiped out everyone with no real business underneath.
101
The same sort of principle can be applied here.
102
You can think about it like this.
103
Would a tool still be worth using if AI got 10 times more expensive to run?
104
Real models with real products underneath will survive that.
105
Number two, become the person who owns the output, not the person who produces it.
106
Every failure we talked about in this video, for example, the dead pilots, the agents getting rolled back, Deloitte handing the government a faulty report, is caused
107
because of the lack of type of this person someone who can catch what the AI got wrong
108
and take responsibility for it.
109
That role is growing while the just generate code role shrinks.
110
So you should use AI every day, ship fast with it, and stay accountable for what it produces.
111
And build robust eval and verification systems.
112
And number three, last but not least, go where the deflation sends the money.
113
So when a bubble sorts itself out, the money that was pumped into it doesn't just disappear, but instead it gets deflected toward products that can actually prove their worth.
114
AI security is a good example.
115
Every one of these systems getting shipped is an attack surface almost nobody can defend yet.
116
Same with the people who can integrate AI into real systems, evaluate it, and make it reliable instead of just impressive on stage in a controlled environment.
117
So you should specialize into the part that needs human judgment.
118
So in conclusion, here's the real story of whether AI is all hype or not in 2006.
119
Yes, AI stocks are getting hammered.
120
Yes, 95% of those enterprise pilots went nowhere.
121
And yes, the AI replaces everyone mantra is getting walked back in boardrooms across the industry.
122
But none of that means AI itself is finished.
123
The internet survived the crash and came back bigger.
124
So AI will be fine.
125
The only question here is whether you'll come out of this as someone who chased the bubble
126
or if you'll become someone who understood what was actually valuable underneath it.
127
And that's all I have for you in this video.
128
If this perspective was helpful, hit that like button, hype the video, and subscribe for more videos on software engineering, AI, and tech.
Descargar app
Todo lo que necesitas para hablar con fluidez
Pronunciación con IAEvalúa cada oración
Práctica de IPADomina cada sonido
VocabularioConstruye tu banco de palabras
Juego de vocabularioAprende mientras juegas
Mismo canal
FOR YOU
Recomendar Video
C14
76 oraciones
The Limitless AI Lie. The Bubble Is Slowly BURSTING.
Business EnglishDaily ConversationUniversity Students
B22
132 oraciones
Something Feels Off About 2026...
Business EnglishUniversity StudentsWorking Adults
C14
261 oraciones
Stanford CS Professor: AI Can Code. That’s Why You Should Learn Programming | Chris Piech
Business EnglishUniversity StudentsWorking Adults
C12
196 oraciones
Elon Musk Explains How the AI Bubble Will Burst.
IELTS PrepBusiness EnglishUniversity Students
C12
43 oraciones
Meta’s Llama 4 is mindblowing… but did it cheat?
American AccentBusiness EnglishUniversity StudentsWorking Adults
C14
228 oraciones
The AI Bubble… We Need to Talk
Business EnglishUniversity StudentsWorking Adults
C11
127 oraciones
The Alignment Problem Explained: Crash Course Futures of AI #4
Business EnglishDaily ConversationUniversity Students
C11
83 oraciones
Leading AI CEOs Warn UN Security Council of Existential Risks | Sam Altman On AI Controls | N18G
Business EnglishDaily ConversationUniversity Students
C118
104 oraciones
How Smart Organizations Will Use AI: Jevons Paradox & Workforce Impact
Business EnglishUniversity StudentsWorking Adults
C11
42 oraciones
What 50% Unemployment Will Actually Do to the Economy
IELTS PrepBusiness EnglishUniversity Students
C111
145 oraciones
Which Commercial property sectors will benefit from AI? | The Market in Minutes
Business EnglishUniversity StudentsWorking Adults
B22
124 oraciones
How AI Became More Expensive Than The Workers It Replaced
Business EnglishUniversity StudentsWorking Adults
Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación de nivel C1 se basa en el vídeo “The AI Take Over Has Completely Backfired”. Este vídeo tiene 128 frases y 1772 palabras para practicar shadowing. La parte hablada dura 9:41. El hablante habla rápido, unas 183 palabras por minuto, así que espera sonidos enlazados y reducidos. Solo el 81 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.
Vocabulario clave de este vídeo
Las 15 palabras más avanzadas del vídeo, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| investor sustantivo | /ɪnˈvɛs.tə(ɹ)/ | inversionista, inversor |
| hype sustantivo | /haɪp/ | furor, propaganda exagerada |
| underneath adverbio | /ˌʌndɚˈniθ/ | abajo |
| hallucination sustantivo | /həˌluːsɪˈneɪʃən/ | alucinación, ilusión |
| expenditure sustantivo | /ɛkˈspɛndɪt͡ʃə/ | gasto, desembolso |
| token sustantivo | /ˈtoʊkən/ | seña |
| technological adjetivo | /ˌtɛk.nəˈlɑ.dʒɪ.kəl/ | tecnológico |
| buyback sustantivo | /ˈbaɪˌbæk/ | recompra, recompra accionaria |
| compute verbo | /kəmˈpjuːt/ | computar, calcular |
| confidently adverbio | /ˈkɑnfɪdəntli/ | confidentemente |
| constrain verbo | /kənˈstɹeɪn/ | constreñir |
| deflation sustantivo | /dɪˈfleɪʃən/ | deflación |
| deflect verbo | /dɪˈflɛkt/ | desviar, rebatir |
| generative adjetivo | /ˈd͡ʒɛnəɹətɪv/ | generativo |
| hallucinate verbo | /həˈl(j)uːsɪneɪt/ | alucinar |
Phrasal verbs que vas a escuchar
| Palabra | Pronunciación | Significado |
|---|---|---|
| make up verbo | /ˌmeɪk ˈʌp/ | reponer, suplir |
| switch off verbo | apagar | |
| add up verbo | adicionar | |
| break down verbo | descomponerse, averiarse | |
| end up verbo | terminar, ir a parar | |
| figure out verbo | averiguar, captar | |
| get back verbo | regresar | |
| pile up verbo | arrumar, apilar |
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | is expected · being shut · I've been told |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | I've been told · has told · hasn't arrived |
| Oraciones de relativo who / which + oración — información extra sobre una persona o cosa | marketing, which is · person who owns · someone who can |
Pronunciación a tener en cuenta
El hablante usa 32 contracciones y formas reducidas, como don't, doesn't, they're. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: hallucination /həˌluːsɪˈneɪʃən/, deflation /dɪˈfleɪʃən/, measurable /ˈmɛʒəɹəbəl/, specialize /ˈspɛʃəˌlaɪz/, insurer /ɪnˈʃʊɹ.ɚ/
- Palabras largas — cuida el acento: hallucination /həˌluːsɪˈneɪʃən/, expenditure /ɛkˈspɛndɪt͡ʃə/, technological /ˌtɛk.nəˈlɑ.dʒɪ.kəl/, confidently /ˈkɑnfɪdəntli/, generative /ˈd͡ʒɛnəɹətɪv/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: specialize /ˈspɛʃəˌlaɪz/, stagger /ˈstæɡɚ/, stubborn /ˈstʌbɚn/, startup /ˈstɑɹtˌʌp/, slice /slaɪs/
- /v/ — no es /b/: los dientes tocan el labio inferior: investor /ɪnˈvɛs.tə(ɹ)/, generative /ˈd͡ʒɛnəɹətɪv/, verification /ˌvɛ.ɹə.fɪˈkeɪ.ʃən/, vendor /ˈvɛn.dɚ/, evaluate /ɪˈvaljʊeɪt/
- /z/ — sonora, no /s/: specialize /ˈspɛʃəˌlaɪz/, unused /ʌnˈjuzd/, confuse /kənˈfjuːz/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como investor, hype, underneath.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.