Shadowing Practice: Is AI the next dot-com crash? | Business Beyond - Learn English Speaking with YouTube
C1
Billions are being poured into data centers to make two magical letters work.
298 sentences
If sentences are too short or too long, click Edit to adjust them.
1
Billions are being poured into data centers to make two magical letters work.
2
AI.
3
AI.
4
AI.
5
AI.
6
AI.
7
AI.
8
Artificial intelligence.
9
The one technology to save us all.
10
You shouldn't be asking what it can do,
11
but what it can do if you buy into the hype.
12
This technology has extremely high narratability.
13
We might have AI that is smarter than any human by the end of this year.
14
I have engineers within Anthropoc who say I don't write any code anymore.
15
But at the same time,
16
headlines like 95% of companies don't report profit increases connected to AI make you wonder,
17
is the hype actually legit or are we really close to an inflated bubble?
18
To get to the limits and problems of AI
19
and its bubbly character
20
that I'm going to explore with these two we first need to get what companies can actually use AI for
21
and that's why I went to BMW.
22
It's not that BMW builds entire cars with AI yet
23
but they're using AI to manage their production equipment which they hand over to production partners.
24
That can be presses, welding machines,
25
lifting cranes, you name it and thousands of them.
26
AI helps to keep track of these 250 000 tools that BMW needs to manage.
27
Our AI is like small little helpers which do specific tasks.
28
So they are creating the inventory order,
29
they are sending the tasks to the supplier,
30
they are checking the results and at the end of the day if everything is green,
31
so green light, you say okay this task can go to our asset accounting.
32
What BMW is using here is called an agentic AI,
33
even multiple of those agents between BMW and its suppliers.
34
First, a task agent asks the supplier to perform an inventory task.
35
The supplier takes a photo of the QR code
36
and this data runs through a validation agent that checks if everything looks correct.
37
If not, a human gets alerted.
38
If everything looks good, a documentation agent takes care of the paperwork.
39
The agents are based on large language models like ChatGPT,
40
Gemini and Claude, but they work a bit differently.
41
We say, hey chat GPT,
42
please give me three recipes that I can cook this week for myself.
43
And then it will do that from whatever information it has in its system.
44
All right, let's get cooking.
45
Quick, colorful and full of flavor.
46
An agentic AI, however, would ask for my taste preferences,
47
my caloric needs, habits, scrape supermarkets for cheaper offers,
48
check how potential items would be ranked against WHO health recommendations,
49
verify its own decisions and create a shopping list tailored to the layout of the supermarket that I'll be going to.
50
Simplifying it, an agentic AI does more with less human input and makes decisions on its own within set limits.
51
You can automate more and need less workers.
52
In narrow scenarios, it can even operate without ongoing human input.
53
In BMW's AI automation process,
54
QR codes have an important role.
55
This QR code is in the middle of everything.
56
The supplier gets the inventory order,
57
then he goes to the tool and scan the QR code.
58
Ah, so you literally just take the phone and then you take a photo and then you have all the information.
59
And we get all the geolocation and metadata through the photo and have the inventory number with the QR code.
60
That's it.
61
By using AI, BMW wants employees to be able to focus on cases outside the norm,
62
but not spend time with the ones where everything is clear.
63
But such an integration into an existing workflow takes time.
64
For BMW, it took one and a half years to get kick-started on Agentic AI.
65
And this integration challenge is often underestimated in the hype of things.
66
This technology relies, for it to be potent,
67
it relies on redesigning the way those employees do their work.
68
And that ends up being a business problem, not a technology problem.
69
This is David Crawford.
70
He helps enterprises integrate AI into their workflows.
71
Unlike some more recent technology disruptions,
72
you can't just deploy the technology and expect to get efficiency.
73
You actually have to do the hard work of mapping out your business process,
74
changing how you want to do it differently,
75
etc. If successful, Crawford says,
76
companies can see a rise in revenue of up to 10 to 25% before taxes.
77
But during my research...
78
I came across a lot of companies that promised the use of AI or agentic AI.
79
But when I asked them about showcasing it,
80
many were like, hmm, we're still testing,
81
product isn't ready, all sorts of excuses.
82
Which makes me wonder again is all of this AI stuff a bit of a*****.
83
And it's not just me,
84
there are also some well-known over promises of AI.
85
Do Not Pay claimed that it could automate anything a lawyer does.
86
The founder even offered $1 million to any lawyer
87
that would argue in front of the Supreme Court with the help of Do Not Pay.
88
Eventually, they got a fine from the Federal Trade Commission for False Promises.
89
In other lawyer replacing AIs,
90
Stanford University scientists who double-checked these AIs found severe hallucinations in their answers.
91
Sometimes the hallucination was so bad that it reversed the meaning of the laws.
92
And even benchmarks don't seem to match real-world performance.
93
Cognition's agent Devon, a software engineering AI,
94
had great benchmarking scores, but in real world they found
95
that Devon was only successful at three of the 20 tasks it was built for.
96
This GPT-5 for example and even Gemini 3 can do well on
97
or actually do better than humans on international math olympiads
98
but then they can fail on extremely silly questions that has never been in any benchmark.
99
This is Rao Kambamputi.
100
He works on fundamental problems in planning and and decision-making by AI systems.
101
There is such a thing as studying to the test.
102
And when you study to the test,
103
will you generalize beyond that?
104
If you actually happen to see specific exams,
105
the questions that will come.
106
The general, the entire premise of education has been that if you somehow do well in the test,
107
then the capabilities that you got in doing well on the test will help you generalize and handle other situations.
108
This notion of generalization is something that still remains like an Achilles heel for LLMs.
109
So it has become difficult to make a connection of how
110
good an AI performs in benchmarking to what it actually can do in a reliable way in the real world.
111
One of the biggest concerns around AI is always hallucinations.
112
How do you deal with them in these kind of automated workflows if anything goes wrong?
113
So the important part to control that is to have really strong guidelines
114
and our agents are fixed to one process step in the workflows.
115
So they have clear rules so that we can ensure no hallucination is happening
116
and we have always these testing possibilities in every single process step.
117
Wow, AI and no hallucinations and 100% reliable.
118
Sounds like a dream but I'm going to check with Rao Kambambutti later.
119
But it's certainly a fit for David Kirsch and Brent Goldfarb's analysis of 58 historical technological innovations like electricity,
120
aviation, railroad, dot-com boom.
121
They checked why some of them created bubbles and others didn't.
122
And they ended up with four reoccurring factors.
123
a narrative to buy into like we're we might have ai
124
that is smarter than any human by the end of this year um
125
and i would say no later than next year um
126
and then probably by 2030
127
or 2031 call it five years from now uh ai will
128
be smarter than uh all of humanity collectively i have engineers
129
within anthropo who say i don't write any code anymore i just i just let the model write the code
130
I edit it, I do the things around it.
131
I think, I don't know,
132
we might be 6 to 12 months away.
133
Second, uncertainty around how to use the new thing.
134
And if 95% of companies don't see a return on investment on AI,
135
that's a lot of uncertainty.
136
Third, the presence of pure-play companies,
137
the ones that do nothing but AI.
138
And fourth, novice investors.
139
So what we did is we took this set of technologies and said,
140
well, you know, and for each one,
141
we did a kind of deep dive into the history of that technology.
142
Where did it come from?
143
Who were the promoters?
144
What were the opportunities associated with it?
145
How did it change economic activity?
146
and these four pillars emerged as associated with increasing the likelihood of a bubble.
147
It's not a deterministic model.
148
We can't say, oh, there are narratives,
149
technological uncertainty, pure plays, novice investors,
150
therefore there will be a bubble.
151
And the first pointer for a bubble is narrative.
152
You and I are living through this once-in-human-history transition where humans go from
153
being the smartest thing on planet Earth to not the smartest thing on planet Earth.
154
These narratives or stories ultra-simplify complex processes,
155
and part of the reason why everyone is like,
156
yeah, that might be true,
157
is because we've been surrounded by these narratives for quite some time.
158
For example, by Hollywood movies like Star Trek,
159
Her, Space Odyssey, Axe Machina,
160
The Matrix, where technology can do almost anything.
161
This technology has extremely high narratability.
162
It can support so many narratives that,
163
let's say I don't buy the one about replacing the need for human labor,
164
but there's some other narrative that AI does kind of plug into that connects to my preferences.
165
And then all of a sudden I'm like,
166
okay, I'll write a check for that.
167
So check for the narrative factor on the AI bubble.
168
BMW also uses Agentic AI for a different task.
169
Automated offers for companies that want to buy,
170
lease a lot of cars.
171
So there are all sorts of questions about car models,
172
specifications, engine sizes, lease length,
173
rates that usually would all need to be read and categorized and answered by a human employee.
174
But at BMW's subsidiary Alphabet,
175
all these emails, inquiries get fed into an AI
176
that lays out a rough answer and creates quotes that a human double checks.
177
So what was a difficult thing to work with
178
when you need an AI to access all these kind of different data points?
179
Well, the challenge was to make the system work for the most cases,
180
most of the times.
181
So it's easy enough to make the AI make a perfect job just for one configuration.
182
But trying to do this for all of them,
183
adjusting the prompts and the settings and the configuration of the systems,
184
and then you have run it against all different documents again to see how it performs against them.
185
In AI lingo, that is called the reliability of a model.
186
The holy grail here are the five nines,
187
meaning a reliability of 99.999%.
188
Even top models today have failure rates of at least 3 but up to 10%.
189
So a lot of hallucinations when it comes to summarizing short documents.
190
And many experts even argue that these hallucinations are here to stay.
191
You might think 90% doesn't sound too bad,
192
but let's transfer that to a real world example.
193
Flights.
194
What if 10% of flights in the US would crash?
195
That would have been about 1.7 million crashes in 2025.
196
Absolutely horrific.
197
And so there is nothing that anybody can see that can theoretically remove hallucinations from such a model.
198
You can try to improve it with training,
199
but even actually there was just a recent paper,
200
I believe in nature machine intelligence,
201
talking about the fact that,
202
you know, LLMs can't reliably distinguish between fact and belief.
203
And the belief basically is,
204
if they hear somebody saying something, that becomes a belief.
205
And so there is always this question of,
206
you can try to make the data more clean,
207
or you can try to do additional sorts of training,
208
but if you just let the machine,
209
let the model come up with a long completion of a prompt,
210
there is very little reason to believe that it will have 100% accuracy.
211
But this makes AI harder to utilize for companies because there's always the looming danger of something going wrong,
212
also known as AI slop.
213
That's when an AI does a bad job and humans need to spend more time fixing their mistakes
214
than it would have taken them to do the task originally.
215
David Kirsch and Brent Goldfarb described this factor as uncertainty.
216
How will a new technology translate into real business?
217
Something that also happened when radio came along.
218
Why would we want to broadcast from one to many?
219
And the thought was, well,
220
we do that in church.
221
So therefore, it'll be used for religious services.
222
But then the question is how to pay for it.
223
Because how do you know if somebody is tuning in?
224
And this became a huge problem for early radio.
225
So big that there even was a public contest for ideas on how to make money with radio broadcasts.
226
And AI could be running into similar issues money-making wise because to scale AI,
227
you need loads of money.
228
Loads.
229
To build the necessary infrastructure, data centers.
230
Until 2030, McKinsey says, data centers alone will need $5.2 trillion in investments.
231
That's 15 times the amount of the Apollo space project costs.
232
17 space missions.
233
Just for comparison, in 2025,
234
AI tech is expected to generate around 60 billion dollars in revenue by one estimate.
235
I'm just getting dizzy from these numbers.
236
We need to find uses for this technology that transcend employee productivity
237
and actually do some of those really novel things accelerate drug discovery,
238
accelerate, you know, health and wellness, accelerate healthcare.
239
And the AI industry needs to find new revenue streams ASAP because they might be living on borrowed time as...
240
Even companies like Google, Microsoft,
241
Amazon and Meta, who've got a lot of cash sitting around that they can spend on data centers,
242
even they are taking on debt to finance their data centers.
243
In one of the largest private debt deals on record,
244
Meta received 72 billion US dollars to finance its gigantic data center Hyperion,
245
which is going to be built in Louisiana.
246
Also, Oracle took on 18 billion in fresh debt for its AI infrastructure.
247
Until now, it was one of the main things that separated the AI boom from what it's often compared to,
248
the dotcom boom and bust.
249
Back then many companies racked up debt as they raced to lay fiber optic cables into the ground
250
that years later would become today's high-speed internet.
251
But we all know what happened before.
252
The dot-com crash.
253
And not everybody is turning a blind eye to this.
254
In October 2025 the UK's central bank,
255
Bank of England,
256
put out a warning about the increasingly debt-financed AI infrastructure
257
and AI stocks valued at close levels seen at the peak of the dot-com bubble.
258
And there's a really interesting graph in this report, this one.
259
It shows the exact moment what happened to AI stocks
260
when the news broke that the Chinese model DeepSeq can achieve similar results like ChetGPT,
261
Claw, Gemini, with a fraction of the computing power.
262
And as you can see here,
263
everything took a bit of a nosedive.
264
The stocks recovered after some time,
265
but a breakthrough like that could render these gigantic data centers and the expected revenue from them somewhat useless.
266
So that's definitely a check for uncertainty.
267
The last bubble sign from Brent and David has something to do with all of us.
268
Because people, while not really having a clue what's going on,
269
still throw money into AI stocks.
270
In 2024, 30 billion US dollars was poured into Nvidia by retail investors and made it the most popular retail stock.
271
But Brent and David even go beyond this definition of novice investors.
272
We're all novices.
273
That's the take-home lesson.
274
There was a saying on Wall Street that you're a novice until you go through a bear market.
275
Just to recognize that that can really happen.
276
Nobody's done AI before.
277
I don't know exactly how useful it's going to be in each application.
278
I don't know how expensive it's going to be over time.
279
I don't know how, we don't know actually what business model's gonna be the most effective way for AI.
280
So where does this all leave us on the bubble thing?
281
Undeniably, AI has an impact.
282
David Crawford said some results from Bain & Company clients range from 10 to 25% uplift in their earnings before taxes.
283
And BMW says...
284
We see this percentage also within the inventory workflow and
285
when we think about how we can scale now over all of our processes,
286
I think Gen.ai will be able to bring that potential.
287
But you don't get these results by throwing ChatGPT or Co-Pilot into a company.
288
Also undeniable, there is some trash talk going around,
289
some lofty company valuations and we checked all the bubble boxes from David Kirsch and Brent Goldfarb.
290
But is it March 2000,
291
just before the burst of the dotcom bubble?
292
We don't know.
293
And according to many experts, no one really does.
294
I'm using AI models as much as I can to see what they are capable of,
295
and the advancements in the last years have been
296
but I'd say that we're missing a breakthrough in reliability to match the crazy data center spending and devaluations.
297
Microsoft's stock just took a 12% nosedive because it became clear how much they spend on AI and data centers.
298
The whole AI house of cards seems to be pretty fragile and maybe we're just one deep-seek moment away from everything going...
Download App
AI scoring for every sentence you speak
TRENDING
Popular
Context & Background
In the recent video titled "Is AI the next dot-com crash? | Business Beyond," the speaker discusses the profound impact of artificial intelligence (AI) in today's business landscape. With billions invested in the technology, AI is portrayed as a potential game-changer for companies across various sectors. However, despite the excitement surrounding AI's possibilities, there are mixed reports regarding its profitability and practicality in real-world applications. The discussion sheds light on how companies like BMW are utilizing AI not just to automate tasks but to enhance efficiency and decision-making in production processes. This exploration offers valuable insights into the evolving role of AI in business, making it an engaging topic for English learners focused on contemporary technological discussions.
Top 5 Phrases for Daily Communication
- “AI helps to keep track of…” - A useful phrase for discussing technology's role in organization and management.
- “If everything looks good…” - Handy when talking about assessments and approvals in various situations.
- “Our AI is like small little helpers…” - This simile can be adapted to describe assistance in different contexts.
- “We say, hey, please give me…” - A casual way to make requests, perfect for everyday conversation.
- “Making decisions on its own…” - Great for conversations about automation and independence in tasks.
Step-by-step Shadowing Guide
To effectively utilize the shadow speech technique for enhancing your english speaking practice, follow this step-by-step guide based on the video content:
- Watch and Listen: Begin by watching the video carefully. Pay attention to the speaker's intonation, rhythm, and pronunciation. It’s crucial to listen for context and vocabulary used, particularly the phrases highlighted earlier.
- Pause and Repeat: Use a shadowing app or simply pause the video after each sentence. Repeat what the speaker says aloud. Focus on mimicking their pronunciation and emotion to improve your pronunciation.
- Break It Down: If certain phrases are difficult, break them down into smaller parts. For instance, separate the line “AI helps to keep track of…” into manageable segments and practice them individually before rejoining them for fluidity.
- Engage with the Content: After shadowing, take some time to write down your understanding of the discussed AI applications. This reinforces your learning and helps in constructing your own sentences using the phrases learned.
- Practice Consistently: Incorporate these phrases into your daily conversations. Try talking to friends or even practicing in front of a mirror to build confidence.
By engaging with these strategies, you not only enhance your english speaking practice but also become adept at discussing contemporary topics like AI, making your learning experience relevant and valuable.
What is the Shadowing Technique?
Shadowing is a science-backed language learning technique originally developed for professional interpreter training and popularized by polyglot Dr. Alexander Arguelles. The method is simple but powerful: you listen to native English audio and immediately repeat it out loud — like a shadow following the speaker with just a 1–2 second delay. Unlike passive listening or grammar drills, shadowing forces your brain and mouth muscles to simultaneously process and reproduce real speech patterns. Research shows it significantly improves pronunciation accuracy, intonation, rhythm, connected speech, listening comprehension, and speaking fluency — making it one of the most effective methods for IELTS Speaking preparation and real-world English communication.
☕ Buy us a coffee
ShadowingEnglish remains 100% free thanks to your support. Server and AI costs are high — your coffee keeps us going! 🙏