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WTF is Happening with South Korean Economy
WTF is Happening with South Korean Economy
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0:00
Wall Street is panicking. And the problem started 6,000 miles away.
0:04
In the summer of 2026, more than 320,000 South Korean investors saw their accounts liquidated
0:10
in a matter of hours. Why?
0:13
Because the world's most profitable company just did something it hasn't done in 20 years.
0:17
Alphabet, Google’s parent company, burned $5.9 billion in a single quarter. All on AI.
0:24
Big Tech's AI obsession isn't an unlimitless cash machine… it's a value-destroying black hole. When
0:31
Seoul stops trading, New York is next. And you’ll be in the fallout.
0:35
For the last 5 years, the US Nasdaq 100 and the South Korean stock market, the Kospi,
0:41
barely paid attention to each other. The 60 day rolling correlation average,
0:45
how they move over time, stood at 0.16. It was pretty much background noise. By mid-2026,
0:52
it exploded, climbing to between 0.46 and 0.50. In other words, the two markets are moving together.
1:00
And it all comes down to one thing. AI.
1:03
South Korea builds the hardware that keeps the US tech bubble intact.
1:07
Every night, while Wall Street sleeps, companies like Samsung and SK Hynix open for trading. Their
1:12
earnings, their guidance, their panic, all of it lands on trading screens of American investors
1:18
when they finish their coffee. When U.S. traders see major dips in Asia’s hardware supplies,
1:23
it dictates their own behavior. If those companies are in trouble, it sets off a
1:27
chain reaction that impacts companies like Alphabet, who are trying - and failing - to
1:32
reap the benefits of AI infrastructure projects. Korea’s stock exchange is a monitor for the
1:36
health of the entire AI sector. And that monitor just flatlined.
1:41
Behind it all, is Google’s $5.9 billion cash burn. It exposes a circular illusion. A closed loop
1:48
where tech giants manufacture their own demand. Nvidia makes the physical chips
1:52
that power nearly every major AI system on the planet. In 2025, Nvidia invested $100
1:58
billion into OpenAI. OpenAI used a large chunk of that money to buy Nvidia’s chips.
2:04
The cash goes out, and the cash comes right back in.
2:07
Nvidia’s revenue looks strong. OpenAI looks well-capitalized. Valuations go up. But the
2:13
closed loop means they don't even need paying customers to make the balance sheet look good.
2:18
That’s the important part for investors and shareholders.
2:21
They see cash inflow and outflow, and they see confidence in AI systems, making them more likely
2:26
to invest their own money into similar companies. That same problem isn’t solely in investment, but
2:32
in the timing of the returns. Unlike “traditional” investments where people would expect to see
2:37
returns in a year or 2, many AI investors believe that the real payoff is 3 or even 5 years away.
2:44
That “hype” leads to the second part of why AI investment is so rampant.
2:48
FOMO. Investors want to get their foot in
2:51
the door early. If they don’t, someone else ends up reaping the rewards. And that can be millions.
2:56
Nvidia isn’t alone in running this playbook. Microsoft poured billions into OpenAI,
3:01
and that money flowed straight back to Microsoft in the form of cloud computing contracts. It’s
3:06
the same loop under different branding. But Google’s cash burn is what happens when
3:10
a company tries to compete in that environment without relying on circular deals. They try to
3:15
absorb the cost of either building infrastructure or getting the necessary server load. Alphabet
3:20
managed to spend $44.9 billion in just three months. The entire premise was that it was
3:26
banking on its own sustained growth to cover it. It was a record quarter for Alphabet. Revenue
3:31
hit $119.8 billion, up 24%. Operating income climbed 30%. So Google took those profits and
3:39
chose to burn billions in cash. The alternative is losing the AI race to Microsoft, Meta, and Amazon,
3:45
who are all on their own AI spending spree. It’s not just one company making a gamble on AI.
3:51
Every major player in the AI race is using some kind of cover-up to spend
3:55
more but report that it’s spending less. If it pays off, then it’s hailed as a perfect
4:00
strategy. A calculated gamble that came in. But while all that cash was disappearing into data
4:05
centers, a second story was unfolding. And it made things in Korea much worse.
4:10
A report surfaced that a homegrown Chinese company, based in Shanghai,
4:14
was building its own deep ultraviolet lithography machines. The very equipment
4:18
used to manufacture advanced semiconductors. According to the report, the company is planning
4:23
to build 5 of these machines in 2026 and 20 more the following year. Most of the components
4:28
are sourced domestically, with only a handful of parts still coming from Japan. 2 weeks later, the
4:33
company was named: Shanghai Aishengna Electronic Technology Group, also known as Aishengna. And
4:39
it turned out to be majority state-owned. The news sent shockwaves through the markets.
4:44
The existing manufacturing was almost monopolized by ASML, based in the Netherlands. It’s the
4:49
dominant supplier of both deep ultraviolet, or DUV, and extreme ultraviolet, or EUV,
4:55
lithography machines. These are tools that actually etch circuits onto chips at a scale
5:00
measured in nanometers. Out of the two, DUV is considered less technologically impressive,
5:05
but it still delivers results. But ASML has been barred from
5:09
selling its most advanced EUV systems to China. Even if Aishengna makes the less
5:13
advanced version domestically, it means China is suddenly free from the imposed export ban
5:18
that kept its huge industrial potential in check. When the report of the Aishengna's existence went
5:23
out, Samsung Electronics and SK Hynix share prices went down by more than 10% in a single session.
5:29
Tokyo Electron and TSMC dropped alongside them. The fear wasn’t abstract.
5:34
For years, the assumption was that IP restrictions and export controls kept
5:38
China years behind on the equipment needed to make advanced chips. Analysts estimated that
5:43
gap at 7 to 10 years. But the market immediately reacted to the possibility that China might close
5:48
that gap much faster than expected. But there’s another side to this.
5:52
One that has executives worrying. For years, Chinese firms have offered
5:56
lucrative signing bonuses to entice engineers away from Samsung and other Korean chipmakers.
6:01
They have been importing decades of institutional knowledge one hire at a time. Lee Byung-chul,
6:06
a Sejong Institute research fellow, has stated that Samsung and SK Hynix DRAM technology has
6:12
already been leaked to China. He also believes that a separate attempt to leak HBM packaging
6:17
technology was caught before it got out. That’s only half of the problem
6:21
A civil war is erupting in South Korea’s tech industry.
6:24
Samsung is watching its chip engineers - the brains behind the hardware - walk out the front
6:29
door to join SK Hynix. The insane AI profits have been weaponized with eye-watering bonus
6:34
packages of up to $500,000 being offered to poach employees. The company that’s supposed
6:40
to be defending Korea’s semiconductor edge against China is completely helpless as its
6:45
own talent signs up with a domestic rival. It’s not illegal and it isn’t new.
6:50
Corporate espionage, a talent war, and a Chinese domestic lithography program are now threatening
6:55
South Korea’s AI industry. And the global AI market relies on a country with no backup plan.
7:01
If cheap Chinese alternatives start chipping away at this monopoly, the entire financial foundation
7:05
built by American AI giants collapses. But China isn’t stopping there.
7:10
It has another option. It can flood the global market with cheap chips to crush
7:15
South Korea. Or, it can ban chip exports entirely, fund their own AI industry,
7:20
and steal a market of over a billion people. For an industry running on pure speculation,
7:25
that’s a massive problem. Because AI isn’t a finished product.
7:29
Back in 2023, Alphabet’s chairman John Hennessy told Reuters that a single exchange with a
7:34
large language model costs 10 times more than a traditional search. When the numbers were run,
7:39
the added cost of AI searches could run to $6 billion a year. Google says those per-query costs
7:45
have fallen sharply since, as much as 33 times less, as hardware and models get more efficient.
7:51
But this just leads to another problem. Reduced costs mean that the product
7:55
gets used more often. That means burning through
7:58
more resources. It’s an example of Jevons paradox: as technology becomes more efficient,
8:03
we don't save resources, we just use them more. In 2025, Google’s own environmental report
8:09
admitted its emissions had surged by 50% compared to 2019, before the AI trend started.
8:15
Tech giants are burning the world to build a product that has no clear purpose.
8:20
According to a S&P Global Market Intelligence survey, 42% of companies abandoned most of their
8:25
AI initiatives in 2025. That’s more than double the 17% recorded the year before. On average,
8:31
organizations scrapped nearly half of their AI proof-of-concept projects before the projects ever
8:36
reached production. Budgets got approved. Pilots got built. Demos got applause in the boardroom.
8:42
Then… nothing shipped. This pattern has a precedent: IBM.
8:47
IBM spent years and billions of dollars building Watson Health into what was supposed
8:52
to be a revolution in AI-powered medicine. It was supposed to read scans faster than radiologists,
8:57
and cross-reference treatment options. Instead, the project was sold off in pieces in 2022 before
9:03
mainstream AI LLMs grabbed the public’s attention. Watson Health failed because, despite having solid
9:09
results in testing, no company could see how it would make them money. IBM also mandated that
9:14
any prospective buyer abandon its existing tech stack and adopt Watson’s, essentially
9:19
pigeonholing themselves for one solution. The trillion dollar valuations are all resting
9:24
on a lie. The uncontrollable growth is based on investments leading to more investments.
9:28
It’s a corporate carousel. So who actually eats the
9:32
loss when the leverage begins to unravel? Remember those Korean retail investors and
9:36
those 360,000 liquidated accounts? Well, 62% of them belonged to people under 35. These were young
9:43
people who borrowed money because the market had been rising in a way that felt less like investing
9:48
and more like a game everyone was winning. When the leverage snapped back, it snapped back on
9:53
people with the least capacity to absorb it. Some of them are now left owing brokers more money than
9:58
the positions were ever worth. This affects America too.
10:01
A large share of ordinary retirement savings, the kind sitting in index funds and 401(k)s,
10:06
is concentrated in the same AI-heavy companies burning cash at record rates. When you contribute
10:12
to a retirement account without picking individual stocks, a portion of that money
10:16
is flowing into the AI ambitions of a small group of companies, whether you like it or not.
10:20
Passive investing was supposed to be the safe, boring choice after the dot-com crash. The 2008
10:26
housing crash taught a generation not to gamble on individual names. Instead, the index itself
10:32
became the concentrated bet. Then there are pension funds.
10:35
Public-sector retirement systems – the accounts backing teachers, municipal workers,
10:39
and union pensioners – have been increasing their exposure to the same AI-concentrated indices.
10:44
These are some of the most consequential and least visible fault lines sitting inside this entire
10:50
trade. The upside from AI spending, if it ever fully materializes, gets captured by the companies
10:56
making the bets and the executives holding the stock options. If the bet goes wrong instead,
11:01
the losses land on people who never had a say in the decision and can least afford to take the hit.
11:07
That’s the part that gets ignored every time this story gets told in
11:10
terms of stock tickers and index points. A crash isn’t just numbers on a screen.
11:15
It’s a transfer of risk. One passed from the people who chose
11:18
to take it, to the people who didn’t. The worst part? It’s not the first time.
11:23
In August 2024, Japan’s Nikkei and Topix indexes plunged more than 12% in a single session,
11:30
the steepest one-day drop since 1987. The trigger was a surprise rate hike from the
11:35
Bank of Japan that sent the yen surging. It forced a violent unwind of the so-called “carry trade,”
11:41
where investors had been borrowing cheap yen to fund bets on assets like American
11:45
tech stocks. When the yen strengthened, those investors had to sell to cover their positions,
11:50
which pushed the yen higher still, triggering more forced selling. On the same day,
11:54
Korea’s Kospi dropped more than 8% and briefly halted trading.
11:58
Go back further, 25 years to the dot-com bubble, the parallel gets sharper.
12:03
At the peak of that boom, the top 20 companies in the S&P 500 made up 38% of the index’s total
12:09
value. In the 5 years before the crash, the Nasdaq had climbed 572%, pulling in investors who’d never
12:16
bought a stock before. When the crash came, the Nasdaq fell 78% over the space of 2 years,
12:22
wiping out more than $5 trillion in market value. It didn’t reclaim its old high for 15 years.
12:28
Today, the top 20 companies in the S&P 500 account for more than half the index’s total value,
12:34
with roughly 10 of them tied directly to AI and semiconductors. The reasoning investors
12:39
give for holding on sounds almost identical to 2000. There are no meaningful earnings yet,
12:44
but there’s untapped potential in AI. And the companies investing in them are big on FOMO.
12:50
There is one big difference this time. During the dot-com crash, the infrastructure
12:54
that got built, the fiber-optic cable laid by companies that went bankrupt,
12:59
didn’t disappear. Cheap, abandoned fiber ended up carrying the internet traffic of the next 2
13:04
decades. Someone paid pennies on the dollar for that fiber and built real businesses on top of it.
13:09
But AI infrastructure doesn’t leave that kind of gift behind.
13:12
The chips inside these data centers physically degrade within 1 to 3 years, even losing most
13:17
of their economic value the moment the next generation of hardware ships. After 3 years,
13:22
a data center is full of outdated chips and has ruined the local environment by soaking up water,
13:27
emitting noise and pollution, and devastating the local real-estate market. If the AI bet doesn’t
13:32
pay off, that data center is as good as scrap. The AI-era doesn’t leave a legacy. It ends with
13:38
sky high debt, and a pile of useless tech that no one will want in 5 years.
13:43
Put these two stories side by side, and it gets uncomfortable.
13:46
In 2024, we saw how quickly a currency move on the other side of the world can force a same-day,
13:51
double-digit selloff in Seoul. The year 2000 showed what happens when an entire market
13:56
convinces itself that potential is the same thing as profit, right up until the moment it isn’t.
14:01
2026’s crash carries pieces of the leverage and the speed of 2024 while having the concentration
14:07
and the mispriced potential of 2000. And this time it’s unfolding when the two markets most
14:12
linked to it, Korea’s chipmakers and America’s AI giants, are more tightly bound together
14:17
than at any point in the last 5 years. The question that actually matters isn’t
14:22
whether this ends. Bubbles always end.
14:25
The question is who’s still standing when the leverage finally runs out of places to hide,
14:29
and who gets handed the bill they never agreed to pay.
14:32
And China might be ready to pick up the pieces. To learn more, watch “What China Understands About
14:38
AI That the US Doesn’t” to see how it’s ready to shake up the tech sector. Or click on this video.
