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AI Trust Is Collapsing. The Industry Is DELUSIONAL. - Video học tiếng Anh
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AI Trust Is Collapsing. The Industry Is DELUSIONAL.
AI Trust Is Collapsing. The Industry Is DELUSIONAL.
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0:00
We just spent 285 billion dollars building what people are calling a digital god that can solve
0:06
the International Math Olympiad problems in seconds. And yet, show it a wall clock,
0:10
and it gets it wrong about half the time. That should worry you. Not in a “this is
0:15
interesting” way… but in a “we might not actually understand what we just
0:20
created” way. Because right now, there’s a delusion spreading through Silicon Valley.
0:25
73% percent of insiders think AI will change everything. Only 10% of the public wants it.
0:31
So which is it? The greatest breakthrough in human history… or a multi-billion dollar
0:37
misunderstanding of what people actually need? This is why AI trust is collapsing.
0:42
Ever since AI burst onto the scene with bold promises, it’s been a strange and
0:47
uneven journey for early adopters. It works as a highly advanced Mechanical Turk, programmed
0:52
to accomplish specific tasks - but whether it passes muster depends specifically on the success
0:57
conditions. In tasks with a highly specific and objective condition, it’s incredibly good.
1:03
The software has been shown to pass the bar exam - an incredibly challenging legal quiz
1:08
that would-be lawyers study for months for - with ease, simply scanning all its accessible
1:13
data for the answers. But for lawyers who use it to create depositions and legal papers,
1:18
they’ve often been in for unpleasant surprises. AI has been found to hallucinate fictional cases
1:23
to make the case for its side - leading more than one lawyer to be reprimanded by
1:28
the judge or even threatened with disbarment for trying to present a fraudulent case based on AI.
1:34
So why does AI get so many things right - and so many wrong?
1:39
AI experts have studied the system’s ups and downs, and discovered some shocking shortfalls.
1:43
It fails to identify simple facts like counting the number of “r”s in the word “strawberry”,
1:48
or incorrectly identifies the time shown on an analog clock - a common error made 50% of
1:54
the time by models. And sometimes, these mistakes can be dangerous/ Google’s AI summaries responded
2:00
to a joke query of “How many rocks should I eat?” by telling people to eat one rock a day,
2:05
based on an article from satire website The Onion. AI gets the big things right,
2:09
but often gets tripped up on the little things.
2:12
It’s called the “Jagged Frontier” and it calls everything into question.
2:16
As of February 2026, $285.9 billion had been invested in AI in the US alone,
2:23
seeking to enhance the technology and compete in what’s essentially becoming
2:27
a tech arms race. A yearly investment of $25.2 billion in Generative AI is
2:33
the cost of building 17 Burj Khalifa’s. But the progress for AI hasn’t been smooth.
2:39
Huge gains are countered with sudden setbacks. Ethan Mollick,
2:42
the writer of the paper identifying this problem, argued that AI simply doesn’t
2:46
learn the same way humans do. It makes the same mistake repeatedly. And as people
2:51
charge full-speed ahead into an AI-driven future, those unexpected shortfalls could be dangerous.
2:57
If the tech powers have their way, it’ll be in every single part of our lives.
3:01
But now, people are saying “enough”.
3:03
Talk to anyone about AI and you’ll hear a divided story. On one side are the early adopters,
3:08
the people who can’t stop talking about how it’s transformed their work,
3:12
their creativity, even their daily lives. On the other,
3:15
a quieter but rising group is beginning to push back. People who are proud to have never used it,
3:20
accusing every query of stealing art and poisoning the environment. Like
3:24
every controversial issue, both sides have vocal activists and it seems evenly split.
3:29
But the reality of the data paints a very different picture.
3:33
The Stanford AI Index has been tracking people’s views of artificial intelligence
3:37
both in and out of the industry since 2017. Every year a more dramatic shift emerges.
3:43
Right now, there’s one group that believes fully that AI is on the upswing,
3:47
and the future only means good things. The problem is… those are the people involved in AI. 73% of
3:54
experts expect a positive impact from further investment in AI. They have been consistently
3:59
pitching it to companies both in and out of the tech sector as the solution to their problems.
4:04
But the general public paints a very different picture.
4:07
When you head into the larger audience, AI support doesn’t just fall, it drops
4:12
off a cliff. Only 23% of Americans see a positive impact from AI,
4:16
with most worried about a massive loss of jobs as AI automates one industry after
4:21
another. Only 10% say they’re more excited than worried. That’s lower than the number of people
4:26
who think the moon landing is fake. This isn’t just a difference of opinion. It’s the biggest
4:32
disconnect between the public and financial elites since 2008, when blind confidence helped trigger
4:38
the worst crash since the Great Depression. And the experts still aren’t worried. They
4:43
believe everything will fix itself… once AI hits its next milestone.
4:48
But this isn’t just optimism. It’s an echo chamber.
4:51
When you spend enough time looking at stories about the economy, markets,
4:54
and where everything might be headed, it’s easy to get stuck in this mindset where you
4:58
feel like you need to have everything figured out immediately. Like if you can’t solve the
5:02
whole picture at once, you just stay stressed, overthink it, and don’t know where to start.
5:06
And I think that’s something a lot of people can relate to, because life is like
5:10
that too. Sometimes the pressure isn’t one huge dramatic thing, it’s just the constant weight of
5:15
uncertainty, stress, and feeling like you’re supposed to have all the answers right now.
5:20
That’s one reason therapy can be so valuable. It gives you space to slow down,
5:23
sort through what you’re feeling, and take one step at a time instead of treating
5:27
everything like it has to be all-or-nothing. Therapy has helped me take what feels like
5:31
a huge problem and break it down into something a lot more manageable. It’s
5:34
helped me focus on what I can do right now and make things less overwhelming.
5:38
And that’s why I’m glad BetterHelp is sponsoring this video.
5:41
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5:56
Sometimes progress isn’t about fixing everything at once. Sometimes it’s just
5:59
about having the support and perspective to move forward a little more clearly.
6:03
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6:07
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6:12
AI experts point to impressive feats like AI ripping its way through a math olympiad,
6:17
solving PHD-level science questions, or analyzing statistical data. And in these areas,
6:23
AI has been showing incredible progress. AI agents handling cybersecurity issues
6:28
showed a 93% success rate in 2025, up from only 15% the previous year.
6:34
But just because AI is performing, doesn’t mean it’s thinking.
6:38
Tech experts love to say the doubters are just stubborn. Resistance is just
6:43
temporary and everyone falls in line once it becomes unavoidable.
6:46
But what if that assumption is wrong? What if this time… is different?
6:51
From day one, AI companies have been selling a vision. Not just better tools,
6:55
but a quantum leap in intelligence. They tap into something people already recognize… the version
7:00
of AI we’ve seen in movies. An all-powerful system. Fully autonomous. Capable of reasoning,
7:06
deciding, even running the world better than humans. And at the center of all this is one
7:11
idea: the singularity. The moment AI surpasses human intelligence entirely.
7:17
And everything hinges on it. The believers say we have to push harder,
7:21
invest more, because on the other side is a utopia. But the skeptics see something else.
7:26
They see all the moments AI fails. But what if neither is right?
7:31
What if AI is simply an illusion of intelligence?
7:33
A June 2025 Apple study sent ripples through the tech world when it cast doubt on the entire
7:39
driving argument for AI. Apple was in a strange position during the AI boom. It was still one
7:44
of the most powerful tech companies in the world, but its own AI efforts weren’t really
7:48
delivering results. There were several false starts, and after pressure from investors,
7:53
it shifted direction. Instead of trying to lead the race alone, it began partnering
7:56
with other companies to bring AI into its devices. And that gave it something rare in the tech world…
8:02
Impartiality. Apple researchers
8:05
dug into the primary current AI models and experimented to see which problems it could
8:09
solve and which it ran into trouble with. Their studies confirmed what most people
8:13
already knew. AI is amazing at determining the right answer from a collection of data,
8:18
and is able to sort through it faster than any human. But it relies on statistical analysis, and
8:24
when there isn’t a preponderance of information, its ability to determine truth decreases.
8:29
And the more steps, the more trouble it finds.
8:32
One technique the researchers used was to present familiar mathematical problems in new
8:37
formulations. They didn’t give the AI the chance to use models that were already out there. There
8:42
was a significant decrease in the AI’s ability to determine the next step, indicating that it
8:47
relied heavily on pattern recognition rather than on actual knowledge and thinking. And the
8:52
more steps it had to take to solve a problem, the harder it became for it to stay on track.
8:57
This became clear when it was tested on the Tower of Hanoi puzzle, a task where you move a stack of
9:02
discs from one rod to another under strict rules. As the number of discs increased, the AI started
9:09
to struggle. It hit repeated stumbling blocks… and eventually, it seemed to give up altogether.
9:14
And that might indicate that the promise of AI isn’t just overstated.
9:18
It could be an outright lie.
9:20
To reach the singularity, AI needs to be able to solve problems like a human can. Researchers pull
9:25
together data from dozens of sources, compare them against each other, and hold multiple variables in
9:30
mind while building theories and testing them step by step. As new information comes in,
9:35
those theories shift and adapt in real time. Chess grandmasters do something similar. They
9:40
develop hundreds of strategies not just for each formulation of the board, but for
9:44
each opponent they play. A human’s reasoning is never quite complete, evolving with the moment.
9:50
But AI isn’t reasoning. It’s simply matching.
9:53
Those who think AI is merely a shell game point out that we’ve seen it before… and
9:58
we didn’t like it. AI is simply a much more advanced version of the
10:01
autocomplete that tries to write your e-mails for you before you get to type;
10:05
or the automated customer service agent that makes you go through all the steps before you can talk
10:10
to an agent. That’s because AI predicts what the next step is likely to be, and then chooses
10:15
an output based on probability. It draws from enormous datasets across the internet, analyzes
10:20
patterns in how language is used, and generates responses that statistically fit the prompt.
10:25
Most of the time, this will resemble a baseline of an acceptable answer.
10:29
But it doesn’t take much to throw it off.
10:32
One of the best illustrations of the problem with AI right now can be seen
10:35
in a very different kind of AI system. Robotic dogs, used in crowd control,
10:40
can move with incredible precision when everything goes as expected. Every step is calculated,
10:46
every motion tightly controlled. But that precision comes with a weakness. Even
10:50
small disruptions can essentially render them useless until reset. And that’s AI
10:55
right now. It’s incredibly efficient, convincing autocomplete that’s surprisingly easy to confuse.
11:02
And many say that’s not worth it… especially given all it costs.
11:06
Despite these lingering concerns, there is still a massive appetite for investment in AI. Those
11:11
pushing the funding narrative have convinced many that the next update, the next model,
11:16
will fix the current limitations. And that belief comes with a huge cost.
11:20
Each new generation of models doesn’t just require top-tier researchers, it needs massive amounts of
11:25
computing power Even before it rolled out to the public, Google’s Gemini Ultra cost $191 million
11:32
just to train. That’s the cost of a fleet of two modern F-35 fighter jets, just on one model.
11:39
That’s just scratching the surface of the cost.
11:41
It’s not just that this never-ending project costs money, it also requires an enormous
11:46
infrastructure investment. The internet and all its associated services rely on data centers to
11:51
keep them up and running. The onset of AI, and the mass adoption of these services,
11:55
led to a massive surge in demand. In 2024, there was a 690% increase in need
12:02
for AI-driven data services, and it’s expected to continue rising by about a third each year.
12:08
And that’s not just reshaping our internet, it’s reshaping our world.
12:12
Data centers are cropping up around the world, taking up a huge amount of available real estate.
12:17
While the majority of new data centers are abroad, the US holds a total of 43% of the
12:22
world’s facilities. And these centers use a massive amount of energy, juggling millions
12:27
of queries a day. That requires heavy electricity and water use, leading to the communities where
12:32
they’re built seeing heavier emissions, poorer air quality, and even some water shortages.
12:37
Anti-AI activists have been pushing for a moratorium on data centers, citing the
12:42
environmental impact. But the tech sector has pointed out that each AI query has a tiny impact,
12:48
far less of a carbon footprint than eating a hamburger or driving a car. The problem is,
12:52
no one is making just one query. The system has become a constant companion
12:57
for many that it adds up quickly. In fact, the cost of training Grok 4 was estimated
13:01
to be equivalent to the emissions of 17,000 cars over a full year.
13:06
And it’s not the only place where people are feeling the pain.
13:09
Advocates against AI expansion may have an unusual ally… gamers. Despite usually
13:15
favoring advances in technology, video game fans are experiencing the most direct consequences
13:20
of the AI surge. The cost of DDR4 has shot up by over 2000% over the last year, due to the massive
13:28
demand from AI firms. Even if gamers can afford it, they may not be able to buy it. Some of the
13:34
top companies that make RAM chips have gone out of the individual sales business all together.
13:39
There’s a growing perception that Americans are being asked to make major sacrifices on
13:43
a personal and environmental level in order to win the AI arms race.
13:47
But the thing is, we might not even be doing that.
13:51
The top AI firms in the world are all American, including Open AI and Anthropic,
13:56
as well as larger tech conglomerates like Google and X. When it comes to investments,
14:00
there isn’t even a close second place. The United States private sector invested over $470 billion
14:06
in AI research between 2013 and 2024, with the numbers each year skyrocketing after
14:12
that. In 2024 alone, the US invested $109 billion, compared to China’s $9.3 billion.
14:20
And all that might not even land the US in the top twenty.
14:24
In terms of where new AI technology is coming from,
14:27
the United States is still at the front of the pack. Countries like China, Japan,
14:31
and South Korea are competing heavily, but US firms have maintained a clear lead in both
14:36
capability and scale. Other countries are now actively looking to partner, license,
14:40
or invest in American AI companies just to keep up with how fast the technology is advancing.
14:45
But they might wind up being a better investment in the long run, because the US lags well behind
14:50
the pack in terms of acceptance. Only 28.3% of the US population say they use AI regularly in their
14:57
work duties. According to the Stanford AI Index, which tracks AI diffusion based on the share of
15:03
people regularly using generative AI at work, the United States actually ranks around 24th.
15:08
And that puts them well behind the pack.
15:11
Across the world, other countries incorporate AI into their daily routines far faster than
15:16
Americans. Some of the top countries include Ireland, Norway, and France,
15:20
with rates in the 40s. Singapore hovers at 60.9%, while the oil-rich United Arab
15:25
Emirates continues its big tech push with a 64% adoption rate. These countries see
15:31
the promise in AI, and generally view it much more positively than Americans.
15:36
Which raises the question for tech executives - what’s going wrong?
15:40
It’s not a lack of access. All the core AI technology is concentrated in the United
15:44
States. Nor is it lack of affordability, as AI has been offered to the public for
15:49
free ever since the debut of Chat-GPT in 2022. While most now offer premium
15:54
subscriptions for heavy users, especially those who want to generate video and images,
15:59
most functions are available for free at the click of a button.
16:02
But there might be a cultural divide that’s far harder to overcome.
16:06
In Europe, some of the strongest adoption of AI has emerged in countries with a deep tradition
16:10
of strong unions and labor protections. In those places, automation is often framed differently.
16:17
Rather than being seen purely as a threat to jobs, it’s increasingly viewed as a tool
16:21
that could reduce workload, streamline repetitive tasks, and ultimately improve working conditions.
16:27
In tech-oriented cultures like South Korea, the media is primed to portray new technology
16:31
in a positive way. However, in the United States, the picture looks a lot less rosy.
16:36
And people aren’t just worried about it stealing their future.
16:40
The United States has a large population and a comparatively weaker social safety
16:44
net than much of Europe. As a result, many people tend to view AI less as a productivity
16:49
boost and more as a direct threat to their livelihood. That concern has only intensified
16:54
as companies experiment with replacing workers - especially in customer service and support
16:59
roles - with AI chatbots. In several cases, those rollouts have delivered mixed results,
17:04
with firms later scaling back the automation and rehiring human staff to fill the gaps. There’s
17:09
only one thing worse for a new tech innovation than looking like a problem… looking like a loser.
17:16
And to many ordinary Americans right now, AI looks like both.
17:20
Many investors warn that we might be reaching “peak AI”. Heavy investment is likely to continue,
17:25
but the public isn’t being won over. A minority of Americans is increasingly into AI,
17:30
but they haven’t shown the ability to branch out beyond that audience. And as more comes out
17:34
about the technology’s limitations, the fear of the “jagged frontier” may start to chill
17:39
investment. After all, is any major company going to invest their infrastructure in a
17:43
technology that can’t think, only predict according to what it thinks the answer is?
17:49
Maybe not… if they’re actually listening.
17:51
The problem facing the AI market right now is that many of its top proponents are within a bubble.
17:56
They’re spending so much time with their fellow believers, and with the AI technology itself,
18:01
that they can’t see the warning signs. And that means they’ve built a house of cards that
18:05
could collapse at any time. A bad report on the progress of a new AI model, or a disaster caused
18:11
by bad advice from one of the models, could lead to a mass sell-off that would see the AI
18:16
bubble burst. Countless companies lose everything, leading to the worst economic crisis since 2008.
18:22
But that might not be the worst-case scenario.
18:25
What keeps AI skeptics up at night is a world where AI adoption continues full
18:29
speed ahead. Companies and governments heavily incorporate it into essential infrastructure,
18:34
trusting it to make millions of decisions a day. And then something goes wrong. A
18:39
stumbling block turns into a cascading series of events that could potentially crash the economy,
18:44
the internet, or the power grid as the AI tries to unscramble its flawed logic. And
18:49
this is an all-too-realistic situation. After all,
18:53
would you trust your country’s power grid to a technology that still can’t read an analog clock?
18:59
Some sectors aren’t waiting for AI to be “perfect” before deploying it. They’re pushing ahead anyway.
19:04
Governments are integrating AI into military planning and the results are ominous. Find out
