0:00.000–0:03.540
We're leading China. We're leading China by a lot. China knows that.
0:03.760–0:07.040
The race between America and China for AI dominance.
0:07.140–0:09.208
There's only going to be one winner here, and
0:09.208–0:11.220
that's probably going to be the US or China.
0:11.380–0:13.380
Who's winning? America or China?
0:13.960–0:17.740
China just beat America in the AI race. And the craziest part?
0:17.900–0:20.891
They're not only beating American AI globally, but
0:20.891–0:22.560
also inside America itself.
0:22.780–0:27.860
Chinese AI models now account for nearly 60% of AI usage by US companies.
0:27.860–0:32.220
Companies like Pinterest, Airbnb and Coinbase are already using them.
0:32.500–0:33.720
And it's easy to see why.
0:34.040–0:35.680
Recently, Kimi K3 dropped.
0:36.040–0:38.640
It's the world's largest open source AI model.
0:38.960–0:43.580
It even beat GPT 5.6 and Fable 5 on some benchmarks.
0:43.780–0:47.340
And demand exploded so fast that new signups had to be paused.
0:47.480–0:51.420
Now, Alibaba is releasing an equally powerful QEN 3.8.
0:51.500–0:53.100
And you can use them all for free.
0:53.240–0:55.120
But that's not even the biggest surprise.
0:55.120–0:56.344
Just a few years ago,
0:56.344–0:57.640
the United States blo
0:57.640–0:58.936
cked China from buyin
0:58.936–1:01.960
g the world's most advanced AI chips and machines.
1:02.060–1:05.680
NVIDIA is trying to unlock those advanced chip sales in China,
1:06.000–1:08.900
but the Chinese government wants to encourage domestic production.
1:09.320–1:12.600
The country with the best AI doesn't just build better chatbots.
1:12.800–1:15.703
It also builds better weapons, better cyber security,
1:15.703–1:17.280
better scientific research,
1:17.580–1:19.560
and eventually a stronger economy.
1:19.840–1:22.720
Which means AI is no longer just a technology race.
1:22.720–1:24.540
It's a race for global power.
1:24.540–1:28.620
And when the world's biggest superpower feels the next best country is catching up,
1:28.880–1:31.880
it doesn't just compete, it changes the rules of the game.
1:32.140–1:37.127
The main priority is to strategically become independent of NVIDIA and
1:37.127–1:39.620
other Western manufactured chips.
1:39.820–1:40.829
In October 2022,
1:40.829–1:41.838
the United State
1:41.838–1:42.776
s blocked China
1:42.776–1:46.380
from buying the world's most advanced AI chips from NVIDIA.
1:46.700–1:50.600
The logic was simple. No advanced chips meant weaker AI models,
1:50.920–1:53.480
and weaker AI models meant China would fall behind.
1:53.480–1:56.920
But this did not stop China or even slow them down.
1:57.160–1:58.835
A few short years later, today,
1:58.835–2:02.120
China is not only building some of the world's best AI models,
2:02.280–2:04.520
it's giving most of them to the public for free.
2:04.760–2:06.260
So how did China manage this?
2:06.520–2:11.000
How did it turn such a setback into one of the biggest AI comeback stories ever?
2:11.300–2:15.200
Before we begin, I put a cheat sheet of all the best Chinese AI tools
2:15.200–2:19.060
and how to use them for free in my WhatsApp community called Staying Ahead.
2:19.360–2:20.960
Grab it from the link in the description.
2:21.340–2:22.580
Now let's dive into the video.
2:23.480–2:30.680
When ChatGPT launched in late 2022, it kicked off a global AI race.
2:30.680–2:33.680
ChatGPT. ChatGPT. ChatGPT.
2:33.680–2:35.480
It's been held as a game changer.
2:35.480–2:39.303
Every country suddenly wanted to build the smartest AI model,
2:39.303–2:40.920
but there was one problem.
2:41.260–2:43.860
Training AI isn't like running an app on your laptop.
2:44.220–2:47.688
An AI model has to process billions of pieces of data until
2:47.688–2:50.000
the model gradually learns patterns.
2:50.000–2:58.380
That requires enormous computing power which comes from thousands of specialized AI chips working together inside massive data centers.
2:58.720–3:01.305
To simply explain chips for those who don't know,
3:01.305–3:03.764
they are parts of a computer that do all the thin
3:03.764–3:05.340
king and calculations for AI.
3:05.740–3:10.380
And there were just three companies that controlled almost everything needed to make those chips.
3:10.580–3:13.180
First was Nvidia which designs these chips.
3:13.700–3:15.860
TSMC in Taiwan manufactured them.
3:15.860–3:17.599
And ASML, a Dutch company,
3:17.599–3:19.418
built the incredibly compl
3:19.418–3:22.580
ex machines needed to manufacture those chips.
3:22.900–3:25.020
And all these were controlled by the US.
3:25.220–3:27.166
At this point, you might be wondering,
3:27.166–3:29.052
if two of these companies aren't even
3:29.052–3:31.120
American, how could the US control them?
3:31.620–3:35.040
The answer lies in something called the Foreign Direct Product Rule.
3:35.320–3:36.938
It was started in 1959 and
3:36.938–3:41.560
essentially says that if a product was made using American technology,
3:41.560–3:45.140
the US government has the power to stop it from being sold.
3:45.140–3:48.140
and this includes products made in a foreign country.
3:48.520–3:51.981
And since TSMC and ASML rely on American technology,
3:51.981–3:54.420
software and intellectual property
3:54.420–3:56.004
to build their products,
3:56.004–4:00.000
the US has the leverage to cut them off from the tools they need to
4:00.000–4:03.324
survive and shut their companies down. So in simple words,
4:03.324–4:05.020
America doesn't just make the
4:05.020–4:08.946
world's best AI chips, it controls who could buy them.
4:08.946–4:12.000
And in 2022, it decided China shouldn't be
4:12.000–4:14.280
allowed to buy them. First, Huawei and
4:14.280–4:17.700
dozens of other Chinese companies were placed on a banned
4:17.700–4:21.958
list, making it extremely difficult for them to buy American technology.
4:21.958–4:23.400
Then the US banned NVIDIA
4:23.400–4:26.664
from exporting its most powerful AI chip then,
4:26.664–4:30.180
the H100, to China. It also stopped companies from
4:30.180–4:34.289
selling China the advanced machines needed to manufacture those chips.
4:34.289–4:35.300
China wasn't just
4:35.300–4:37.879
blocked from buying the world's best AI hardware,
4:37.879–4:40.520
it was also blocked from building it. The situation
4:40.520–4:43.727
was so bad that Nvidia had to create a slightly weaker chip.
4:43.727–4:45.560
And we return now to breaking news
4:45.560–4:51.540
from Nvidia. Nvidia reportedly plans to release a downgraded version of H20 that could legally
4:51.540–4:54.836
be sold to China. So as you see, by every logical measure,
4:54.836–4:57.080
China should have fallen behind in the
4:57.080–5:00.781
AI. Most experts agree that China is still a little behind the US.
5:00.781–5:02.220
If there's anything that
5:02.220–5:03.895
you think people could agree on,
5:03.895–5:07.060
it would be that the most advanced AI technology does not go to
5:07.060–5:10.541
China. It is very plausible that if we get this policy wrong,
5:10.541–5:12.700
China could be overtaking the United
5:12.700–5:16.483
States. But instead, they turned the situation around.
5:16.483–5:19.140
Let me show you how. Problem one, chips.
5:19.460–5:21.290
The first problem was obvious.
5:21.290–5:24.740
China could no longer buy the world's best AI chips from not
5:24.740–5:28.459
only Nvidia, but also microchips for phones from Samsung,
5:28.459–5:31.040
MediaTek, and Qualcomm. And these chips
5:31.040–5:33.824
were being used to bring phones into the 5G era.
5:33.824–5:36.680
Everyone thought Huawei and Chinese companies
5:36.680–5:38.794
would be stuck in 4G era because
5:38.794–5:42.940
the ultra expensive machines were only with US and US allies.
5:43.340–5:46.352
But in 2023, Huawei made the Ascent chips,
5:46.352–5:49.880
put them in its smartphone Mate 60 Pro and shocked
5:49.880–5:52.441
the world by connecting their phones to 5G.
5:52.441–5:55.500
To show you how significant that is, even Apple took
5:55.500–5:58.807
very long to make its own chips to stop paying Qualcomm.
5:58.807–6:01.180
Now, behind that achievement was SMIC,
6:01.180–6:03.551
China's largest semiconductor manufacturer.
6:03.551–6:06.040
Even without access to the world's best machines,
6:06.220–6:10.868
SMIC, found ways to manufacture advanced chips using older equipment.
6:10.868–6:12.340
It wasn't as efficient
6:12.340–6:14.398
or as cheap as TSMC, but it worked.
6:14.398–6:18.220
China wasn't trying to build the world's best chip overnight.
6:18.580–6:22.423
It was trying to make sure it no longer depended on someone else's.
6:22.423–6:23.960
Problem 2, make every chip
6:23.960–6:28.227
work harder. But building domestic chips was only part of the solution.
6:28.227–6:29.720
China's chips were still
6:29.720–6:31.247
slower than Nvidia's and
6:31.247–6:35.320
US export restrictions made those chips very difficult to access.
6:35.320–6:37.723
China was in an unfair position. So first,
6:37.723–6:40.400
they tried to bypass the restrictions through
6:40.400–6:43.561
smuggling from places such as Malaysia, Japan and
6:43.561–6:46.120
Hong Kong. Some even used human couriers
6:46.120–6:47.524
to smuggle in chips.
6:47.524–6:51.820
A student was paid $100 for each of the six chips he carried from
6:51.820–6:54.850
Singapore to China in his luggage. But eventually,
6:54.850–6:57.740
all this barely scratched the surface in helping
6:57.740–6:59.617
China grow its AI industry. So
6:59.617–7:03.520
Chinese engineers asked a different question instead of just
7:03.520–7:06.437
building bigger computers with better chips.
7:06.437–7:08.980
What if we build smarter AI? This is where
7:08.980–7:12.561
companies like DeepSeek completely changed the conversation.
7:12.561–7:14.020
DeepSeek used a technique
7:14.020–7:15.792
called mixture of experts.
7:15.792–7:19.720
Let me explain this using an oversimplified example. Say you
7:19.720–7:22.633
have a company of 200 people and on a particular day when
7:22.633–7:24.660
you want to solve a marketing problem,
7:24.660–7:26.618
you don't call everyone into the meeting.
7:26.618–7:28.520
You only call the marketing team, right?
7:28.860–7:31.487
Previous AI models often worked differently.
7:31.487–7:33.980
Every time you asked a question, the AI would
7:33.980–7:35.735
call the whole team. Basically,
7:35.735–7:39.180
the whole model would calculate the answer. That required huge
7:39.180–7:42.530
amounts of power and made each response more expensive.
7:42.530–7:44.740
To address this, American companies
7:44.740–7:48.019
like OpenAI explored the use of a mixture of experts,
7:48.019–7:50.180
which basically divided the model
7:50.180–7:52.820
into multiple specialized expert clusters.
7:53.220–7:56.820
However, DeepSeq engineers took this concept to the next level.
7:57.160–8:00.000
Instead of dividing the model into dozens of expert clusters,
8:00.000–8:02.857
Instead of dividing the model into dozens of expert clusters,
8:02.857–8:04.120
they sliced it into 256 tiny
8:04.120–8:06.515
hyper-specialized experts. So when
8:06.515–8:10.920
you ask DeepSeq to solve a coding problem, an ultra-efficient route
8:10.920–8:15.326
instantly kicks in and activates just eight of those tiny experts.
8:15.326–8:16.900
The rest stayed asleep.
8:16.900–8:20.418
That means DeepSeek can produce strong answers while
8:20.418–8:22.920
using far cheaper costs. Look at this.
8:22.920–8:26.537
While the best models of GPT and Claude cost $5 and
8:26.537–8:29.360
$10 per million tokens in input costs,
8:29.360–8:33.516
respectively, the best model of DeepSeek costs only $0.
8:33.516–8:36.080
4. And while GPT and Claude cost $30
8:36.080–8:40.704
$50 respectively in output costs, DeepSeek only cost $0.
8:40.704–8:42.920
87. But that wasn't all. The
8:42.920–8:45.485
Chinese didn't stop with their innovation there.
8:45.485–8:47.440
They also invented a technique called
8:47.440–8:49.974
Multi-Head Latent Attention or MLA.
8:49.974–8:53.980
To understand what this does, let's take another example.
8:53.980–8:57.785
Imagine you're talking to an AI about a 100-page document.
8:57.785–8:59.960
Every time you ask a new question,
8:59.960–9:05.020
the AI needs to remember the important details from everything it has already read. So it
9:05.020–9:08.400
It stores all this information in its temporary memory.
9:08.400–9:10.697
Engineers call this the key value cache, but
9:10.697–9:13.180
you can simply think of it as the AI's short-term
9:13.180–9:14.180
memory.
9:14.180–9:17.403
The longer the conversation gets, the more memory the AI needs,
9:17.403–9:18.680
which makes it expensive
9:18.680–9:19.680
to run.
9:19.680–9:23.180
DeepSeq's technique, MLA, compresses that memory.
9:23.180–9:25.593
Instead of storing every detail separately,
9:25.593–9:28.260
it creates a much smaller summary of the important
9:28.260–9:32.200
information and uses that to answer future questions.
9:32.200–9:38.300
It is similar to replacing 100 pages of information with a few pages of well-organized notes.
9:38.300–9:42.821
This reduces the model's short-term memory requirements by over 90%,
9:42.821–9:44.200
allowing it to handle
9:44.200–9:48.260
longer conversations while using far less computing power.
9:48.260–9:51.560
That makes the model cheaper and more efficient to run.
9:51.560–9:53.751
So with all these incredible tricks,
9:53.751–9:56.860
DeepSeek managed to build a world-class AI model for
9:56.860–9:59.561
reportedly just under 6 million dollars while
9:59.561–10:02.540
Anthropic and other large companies were spending
10:02.540–10:05.578
billions of dollars to train their models. And
10:05.578–10:08.460
if you look at the comparison of its V4 Pro Max
10:08.460–10:13.571
model with comparable launches back then like Opus 4.
10:13.571–10:17.660
6 Max, GPT 5.4 and Gemini 3.1 Pro, you'll see
10:17.660–10:20.003
that its performance is comparable. So
10:20.003–10:23.340
that was all about creating efficient software but this
10:23.340–10:25.566
only solves half the problem because
10:25.566–10:28.940
even the best software needs enough hardware from chips
10:28.940–10:30.827
to data centers to train and
10:30.827–10:35.340
run ai models on for those who don't know a data center is a physical
10:35.340–10:38.289
facility that contains the servers and
10:38.289–10:42.220
ai chips that run websites apps cloud services and ai
10:42.220–10:46.140
models along with the storage networking backup power and
10:46.140–10:49.020
cooling systems needed to keep everything
10:49.020–10:53.298
running. And so China was not only short of cutting-edge AI chips,
10:53.298–10:54.620
they also found that
10:54.620–10:58.044
the newly built data centers were actually underused.
10:58.044–10:59.980
So China decided to treat its AI
10:59.980–11:04.117
infrastructure in the same way a country treats roads or electricity,
11:04.117–11:05.660
as public infrastructure
11:05.660–11:06.835
that everyone can use.
11:06.835–11:08.010
The government is buil
11:08.010–11:11.040
ding something called the National Integrated Computing
11:11.040–11:13.928
Power Network by 2030, or even earlier.
11:13.928–11:18.040
To explain it simply, it built many large data centers in
11:18.040–11:19.973
In regions where land was cheaper,
11:19.973–11:23.040
renewable energy was more abundant and there was space
11:23.040–11:24.500
for huge facilities.
11:24.500–11:27.870
China then connected these centers to businesses and
11:27.870–11:30.640
cities through high-speed fiber networks,
11:30.640–11:33.071
allowing businesses, universities and
11:33.071–11:36.360
researchers to rent computing power when they need it.
11:36.360–11:38.380
Here is how it works using an example.
11:38.380–11:43.240
Imagine a small AI startup in Shanghai wants to train a new AI model.
11:43.240–11:44.972
Buying hundreds of GPUs and
11:44.972–11:48.360
building its own data center could cost a lot of money.
11:48.360–11:51.060
Instead, it can rent computing power online.
11:51.060–11:53.479
The startup uploads its code and data and
11:53.479–11:55.400
chooses how much power it needs.
11:55.400–12:00.240
And the platform finds available chips in connected data centers possibly thousands
12:00.240–12:01.600
of kilometers away.
12:01.600–12:04.162
Its engineers can monitor the training remotely and
12:04.162–12:06.200
download the finished model once the work
12:06.200–12:07.200
is complete.
12:07.200–12:11.175
China has not built one fully connected national computing grid yet,
12:11.175–12:12.340
but that is the goal.
12:12.340–12:16.280
computing power easier and cheaper to rent across the country.
12:16.280–12:19.600
This brings us to Problem Number 4 Training the Models
12:19.600–12:25.180
Because even the smartest AI models are useless without enough data to train them. And for
12:25.180–12:28.940
the next generation of AI, text alone is not enough.
12:28.940–12:31.709
Video models need to understand how people move,
12:31.709–12:34.140
how objects interact, and how the physical
12:34.140–12:39.380
world behaves. You cannot teach an AI what running looks like by only describing it in
12:39.380–12:43.147
words. It needs to watch millions of examples of different people,
12:43.147–12:44.560
camera angles, lighting
12:44.560–12:47.184
conditions, environments and movements.
12:47.184–12:49.880
It needs to see how clothes move, how shadows
12:49.880–12:53.350
change and what happens when someone steps into a puddle.
12:53.350–12:54.940
And this is where China has
12:54.940–12:56.111
a major advantage.
12:56.111–12:57.135
China has one of t
12:57.135–13:00.500
he largest digital ecosystems in the world. Platforms
13:00.500–13:02.831
like Douyin, WeChat, TikTok and
13:02.831–13:07.060
other Chinese apps generate huge amounts of text, images,
13:07.060–13:10.451
audio and video every day. For companies like ByteDance,
13:10.451–13:12.500
which owns the popular AI platform
13:12.500–13:17.054
C-Dance, this is not just content, it is valuable training data.
13:17.054–13:19.500
ByteDance owns Dooyan and TikTok,
13:19.500–13:22.314
along with several tools for creating, editing and
13:22.314–13:24.800
recommending videos. This gives the company
13:24.800–13:28.052
access to more than just the video itself. It can also
13:28.052–13:29.900
study how people react to that
13:29.900–13:32.475
video, which clips do people keep watching and
13:32.475–13:34.720
at what moment do viewers lose interest.
13:34.720–13:39.180
Now this does not mean ByteDance uses every private video to train its AI.
13:39.180–13:42.188
We do not know exactly what data each company uses,
13:42.188–13:44.480
but owning some of the world's largest
13:44.480–13:48.760
video platforms still gives Chinese companies a powerful advantage.
13:48.760–13:52.300
Because if chips are the engine of AI, data is the fuel.
13:52.300–13:56.191
And China did not just use that fuel to build competitive AI models,
13:56.191–13:57.720
it also started releasing
13:57.720–13:59.220
many of them for free.
13:59.220–14:02.915
Today, models like DeepSeek, Quen by Alibaba,
14:02.915–14:06.420
Kimi by Moonshot AI, and GLM models from Z.AI
14:06.420–14:09.560
are available completely free to download and run.
14:09.680–14:11.240
And this raises an obvious question.
14:11.560–14:14.040
Running AI companies can cost billions of dollars.
14:14.360–14:19.120
In fact, many major AI companies are still spending far more money than they earn.
14:19.120–14:23.260
So how can Chinese companies offer powerful AI models for free?
14:23.520–14:25.940
If users aren't paying these companies, who is?
14:25.940–14:30.100
The answer is that Chinese AI companies aren't really selling AI models.
14:30.460–14:31.880
The model is only the entry point.
14:32.000–14:33.180
They're selling something much bigger.
14:33.520–14:36.340
1. They're selling the ecosystem, not the model.
14:36.740–14:37.480
Think about Google.
14:37.840–14:39.500
Google gives away Chrome for free.
14:39.820–14:41.620
Not because browsers are cheap to build,
14:41.900–14:44.640
but because Chrome brings people into Google's ecosystem,
14:45.000–14:47.306
where they eventually use advertising and
14:47.306–14:49.100
paid services like Google Cloud.
14:49.460–14:52.320
Chinese AI companies are following the same playbook.
14:52.320–14:54.667
While Western companies like OpenAI and
14:54.667–14:57.015
Anthropic primarily make money by selli
14:57.015–14:59.500
ng access to their intelligence directly.
14:59.840–15:04.040
You pay for a subscription or an API call or how many tokens you consume, right?
15:04.440–15:07.880
Chinese companies often make money later in the user journey.
15:08.240–15:12.380
For example, Alibaba not only wants you to chat with their AI model, Quen,
15:12.780–15:14.391
it wants companies to build and
15:14.391–15:16.560
run their applications on Alibaba Cloud.
15:16.560–15:20.035
Tencent, the company behind the super app WeChat,
15:20.035–15:22.600
not only wants businesses to test its
15:22.600–15:24.120
AI model, Hanuan.
15:24.120–15:27.990
It wants them to use Tencent Cloud, its enterprise software and
15:27.990–15:30.180
the rest of its business ecosystem.
15:30.180–15:34.379
It needs GPU, servers, storage, security, monitoring,
15:34.379–15:36.880
technical support and engineers
15:36.880–15:40.220
who can connect the AI to its existing systems.
15:40.220–15:42.410
That is where the real money can be made,
15:42.410–15:44.800
whether it's cloud hosting, consulting or
15:44.800–15:46.878
even custom AI solutions. So
15:46.878–15:50.860
right now the model may be cheap or even free to download
15:50.860–15:54.888
but everything required to operate it reliably at scale is not.
15:54.888–15:56.560
The model is not always the
15:56.560–16:00.000
final product, it is the customer acquisition strategy.
16:01.820–16:04.520
these models become paid later, because by then
16:04.520–16:06.680
many users may have no other option but
16:06.680–16:07.680
to pay.
16:07.680–16:09.945
Reason 2 Win with distribution Remember,
16:09.945–16:12.080
China couldn't compete with America on
16:12.080–16:14.201
the top models that Anthropic and
16:14.201–16:17.760
OpenAI were constantly innovating, using better chips
16:17.760–16:19.840
and coming out ahead. So China instead
16:19.840–16:22.960
decided to win with distribution and make their products
16:22.960–16:24.176
free and open weight.
16:24.176–16:28.160
This basically means that developers can download the trained model,
16:28.160–16:31.804
run it on their own computers or servers, modify it and
16:31.804–16:34.720
fine tune it for their own use. For example,
16:34.720–16:37.016
a retail company could take the model and
16:37.016–16:39.920
customize it by teaching it its internal policies,
16:39.920–16:41.479
product catalog and more so
16:41.479–16:44.800
it can accurately handle customer support queries for it.
16:44.800–16:47.094
It's a win for companies but it's also
16:47.094–16:50.720
a win for the Chinese companies. If millions of developers
16:50.720–16:53.051
can freely experiment with your model,
16:53.051–16:56.160
they begin building apps on top of it. And every time a
16:56.160–16:59.431
developer chooses your model instead of a competitor's,
16:59.431–17:01.680
they invest time learning how it works,
17:01.680–17:04.976
writing code around it and integrating it into their systems.
17:04.976–17:06.560
That makes them less likely to
17:06.560–17:09.360
switch. It's like if you invested time and
17:09.360–17:12.640
effort on Twitter and built a 100,000 following,
17:12.640–17:15.411
you're less likely to switch to threads. Similarly,
17:15.411–17:17.300
it works for developers using these
17:17.300–17:18.447
models. Secondly,
17:18.447–17:19.522
researchers will
17:19.522–17:22.820
be able to effortlessly study these models and publish
17:22.820–17:26.039
improvements or techniques that others can reuse.
17:26.039–17:28.660
Over time, this creates a network effect.
17:28.660–17:31.487
The more people use the model, the more tools,
17:31.487–17:34.240
tutorials and expertise exist around it. It
17:34.240–17:39.180
That is the same basic reason Android became the world's largest mobile operating system.
17:39.180–17:41.948
Google allowed thousands of manufacturers and
17:41.948–17:44.440
millions of developers to build around it.
17:44.440–17:47.067
And once enough, people are building on your platform,
17:47.067–17:48.780
your ecosystem can grow faster than
17:48.780–17:50.300
any single competitor.
17:50.300–17:53.280
That strategy is already working for Chinese companies.
17:53.280–17:57.080
Look at this chart that's showing the world's top 50 most used AI models.
17:57.080–18:00.880
You'll see that Chinese models are rapidly gaining market share.
18:00.880–18:04.140
And when it comes to the top 20 AI models in the world,
18:04.140–18:05.960
between May and June of 2026,
18:05.960–18:10.760
the monthly token usage of Chinese AI models has surpassed the US models.
18:10.760–18:12.960
But let's look at more specific examples.
18:12.960–18:17.580
Alibaba's QEN models surpassed 1 billion total downloads this year.
18:17.580–18:19.900
Many large US companies also use it.
18:19.900–18:23.900
For example, Airbnb's AI customer service system uses them.
18:23.900–18:27.440
Even Pinterest has been customizing QEN models for their usage.
18:27.440–18:33.480
Pinterest actually says it has reduced costs by as much as 90% in some workloads due to
18:33.480–18:34.960
such open-source models.
18:34.960–18:40.380
And Cursor, one of America's fastest-growing AI coding companies built its coding model,
18:40.380–18:45.940
Composer2, on the base model of Chinese company Moonshot AI's Kimi K2.5.
18:45.940–18:47.040
Listen to that again.
18:47.040–18:52.620
A top US AI company built its proprietary product on top of a Chinese foundation model.
18:52.620–18:56.460
Chinese models are no longer merely cheaper alternatives to American AI.
18:56.460–19:01.860
are becoming part of the infrastructure on which American companies themselves are building.
19:01.860–19:04.260
Reason 3 And this brings us to one of the biggest
19:04.260–19:07.604
reasons that Chinese models are succeeding so well.
19:07.604–19:09.960
It's because China made AI a national
19:09.960–19:11.014
mission. In 2017,
19:11.014–19:12.068
China's State Cou
19:12.068–19:15.580
ncil released the New Generation Artificial Intelligence
19:15.580–19:19.235
Development Plan. Its ambition was enormous. By 2030,
19:19.235–19:21.540
China wanted to become the world's
19:21.540–19:25.860
leading center for AI innovation. Universities expanded AI and
19:25.860–19:27.780
computer science programs.
19:27.780–19:31.236
Governments funded laboratories and research projects.
19:31.236–19:33.140
Cities and provinces created AI
19:33.140–19:36.523
innovation zones where companies could access funding,
19:36.523–19:39.060
infrastructure and local policy support.
19:39.060–19:41.199
The plan even reached across borders.
19:41.199–19:44.340
Chinese researchers who had built their careers in labs
19:44.340–19:47.758
overseas were actively encouraged to come back home,
19:47.758–19:50.340
pulled in with grants, funding and a real
19:50.340–19:52.249
path to do their work in China. But
19:52.249–19:55.180
the best thing China did was to give major companies
19:55.180–19:58.516
specific roles in building national AI platforms.
19:58.516–20:01.620
In 2017, the Ministry of Science and Technology
20:01.620–20:07.600
selected Baidu to develop an open innovation platform for autonomous driving, Alibaba for
20:07.600–20:10.462
smart cities, Tencent for medical imaging, and
20:10.462–20:13.180
iFlytech for intelligent voice technology.
20:13.500–20:16.031
These companies were still private businesses, and
20:16.031–20:18.160
they continued competing fiercely with one
20:18.160–20:23.100
another. Yet they were all working in the same direction for the growth of China. So this brings
20:23.100–20:24.947
us to the last part of the case study.
20:24.947–20:27.780
What can the world learn from China's AI journey? Let's
20:27.780–20:29.426
just clarify one thing first.
20:29.426–20:32.980
China did everything in its power to get ahead in the AI race. Some
20:32.980–20:36.057
American companies claim Chinese labs use distillation.
20:36.057–20:38.380
This means using the answers of a stronger AI
20:38.380–20:40.274
model to help train a smaller one. Then
20:40.274–20:42.820
we know that there was smuggling done of restricted
20:42.820–20:45.357
Nvidia chips through black market routes.
20:45.357–20:48.740
The US charges a co-founder of Supermicro with illegally
20:48.740–20:52.756
diverting billions of dollars of Nvidia powered servers to China.
20:52.756–20:54.620
Some companies may have broken
20:54.620–20:56.630
rules and that should be investigated but
20:56.630–20:59.560
a few smuggled chips cannot build thousands of researchers,
20:59.940–21:02.558
large data centers, new engineering techniques,
21:02.558–21:05.300
cloud platforms and an ecosystem used by developers
21:05.300–21:08.330
around the world. Also China did not succeed because
21:08.330–21:10.740
of one shortcut. It also invested heavily
21:10.740–21:14.500
in research and found ways to make weaker hardware perform better.
21:14.500–21:15.440
The point is that
21:15.440–21:18.017
we should judge both sides by the same standard and
21:18.017–21:19.980
not dismiss all of China's progress as
21:19.980–21:20.980
theft.
21:20.980–21:24.433
Secondly, people think using Chinese AI is a threat to privacy,
21:24.433–21:25.540
they will steal data
21:25.540–21:27.842
and the models are censored by Beijing. But
21:27.842–21:30.080
the West is not far behind. ChatGPT trains
21:30.080–21:31.843
on your conversations by default.
21:31.843–21:34.760
Gemini merges your prompts with your Gmail, your YouTube
21:34.760–21:36.558
and your entire Google history.
21:36.558–21:39.820
Claude quietly changed its privacy policy and opted users
21:39.820–21:41.539
into 5 years of data retention.
21:41.539–21:44.580
The idea that Chinese AI is a threat and Western AI is the
21:44.580–21:46.440
safe choice is completely wrong.
21:46.440–21:47.306
With this in mind,
21:47.306–21:50.480
I have two major lessons that countries need to learn from China's
21:50.480–21:55.111
AI race. First, every country needs some control over its AI future.
21:55.111–21:56.980
Have you heard of sovereign
21:56.980–22:00.873
AI? It does not mean every country must build every chip and
22:00.873–22:02.700
every model by itself. That
22:02.700–22:04.700
would be too expensive and unrealistic for you.
22:06.045–22:10.401
least, a country should have enough of its own computing power,
22:10.401–22:12.045
research talent, data,
22:12.045–22:17.765
models and infrastructure that it is not completely dependent on another country. Today, most
22:17.765–22:22.325
advanced AI technology comes from either the United States or China.
22:22.325–22:27.705
But what happens if the US decides that only American companies can use its best models?
22:27.705–22:30.645
What happens if China makes the same decision?
22:30.645–22:34.945
Countries such as India could suddenly lose access to important technology because of
22:34.945–22:37.825
a political fight between two other countries.
22:37.825–22:40.162
And AI is no longer just a chatbot,
22:40.162–22:44.085
it is becoming part of education, healthcare, defense,
22:44.085–22:48.599
science, coding, manufacturing and almost every modern business.
22:48.599–22:50.025
A company with better
22:50.025–22:54.150
AI can work faster, reduce costs and compete more effectively.
22:54.150–22:55.785
A country without access
22:55.785–22:58.327
to advanced AI may slowly fall behind.
22:58.327–23:01.425
The biggest lesson from China is not that every
23:01.425–23:05.763
country should copy China's political system or its exact strategy.
23:05.763–23:06.885
The lesson is that
23:06.885–23:13.165
a country that depends completely on foreign AI will always be vulnerable to foreign decisions.
23:13.165–23:18.325
India and other countries need to build enough of their own AI ecosystem so they are not
23:18.325–23:21.285
trapped between the US and China.
23:21.285–23:25.107
Because the next major geopolitical blockade may not stop oil,
23:25.107–23:26.765
weapons or physical goods,
23:26.765–23:29.425
it may stop access to intelligence itself.
23:29.425–23:32.968
And second, AI leadership is not about one great chatbot,
23:32.968–23:34.665
it's about creating a whole
23:34.665–23:35.665
ecosystem.
23:35.665–23:40.025
When people talk about the AI race, they usually compare models.
23:40.025–23:41.785
Is ChatGPT better?
23:41.785–23:43.105
Is DeepSeek better?
23:43.105–23:46.009
which model scores higher on a benchmark. But
23:46.009–23:48.225
a model is only the visible part of a
23:48.225–23:52.129
much larger system. To build strong AI, a country also
23:52.129–23:54.905
needs chips to perform calculations,
23:54.905–23:57.161
electricity to power those chips, and
23:57.161–24:00.545
data centers to keep them running. It needs universities
24:00.545–24:04.322
to train engineers, research labs to invent new techniques,
24:04.322–24:06.025
cloud companies to provide
24:06.025–24:11.046
computing power, and businesses that can turn AI into useful products.
24:11.046–24:12.385
China worked on all
24:12.385–24:15.245
these areas at the same time. So now, if Europe,
24:15.245–24:17.885
India and other developing countries want
24:17.885–24:20.920
to secure their futures, they need to step up and
24:20.920–24:22.665
build such ecosystems too.
24:22.665–24:25.632
That was all about why China is winning the AI race and
24:25.632–24:27.385
what we can learn from them. For
24:27.385–24:30.804
more case studies that help you understand where AI is going,
24:30.804–24:32.145
who is gaining power and
24:32.145–24:34.552
where the next major opportunities will emerge,
24:34.552–24:36.725
subscribe to the channel and watch the video
24:36.725–24:38.356
on your screen next because
24:38.356–24:42.185
it explains another major shift happening in AI that most people
24:42.185–24:43.745
are still completely missing.
24:43.745–24:44.385
I'll see you there.
0:00.000–0:03.540
我們領先中國。我們大幅領先中國。中國很清楚這一點。
0:03.760–0:07.040
美國與中國在人工智慧主導權上的競賽。
0:07.140–0:09.208
這裡只會有一個贏家，而且
0:09.208–0:11.220
那很可能是美國或中國。
0:11.380–0:13.380
誰在贏？美國還是中國？
0:13.960–0:17.740
中國剛剛在人工智慧競賽中擊敗了美國。最瘋狂的部分是？
0:17.900–0:20.891
他們不僅在全球範圍內擊敗美國人工智慧，而且
0:20.891–0:22.560
甚至在美國本土也是如此。
0:22.780–0:27.860
中國人工智慧模型現在佔美國公司人工智慧使用量的近60%。
0:27.860–0:32.220
Pinterest、Airbnb 和 Coinbase 等公司已經在使用它們。
0:32.500–0:33.720
很容易看出為什麼。
0:34.040–0:35.680
最近，Kimi K3 發布了。
0:36.040–0:38.640
它是世界上最大的開源人工智慧模型。
0:38.960–0:43.580
它在某些基準測試中甚至擊敗了 GPT 5.6 和 Fable 5。
0:43.780–0:47.340
需求爆炸性地增長，以至於不得不暫停新註冊。
0:47.480–0:51.420
現在，阿里巴巴正在發布同樣強大的 Qwen 3.8。
0:51.500–0:53.100
而且你可以免費使用它們。
0:53.240–0:55.120
但這甚至不是最大的驚喜。
0:55.120–0:56.344
就在幾年前，
0:56.344–0:57.640
美國阻擋中國購買全球最先進的AI晶片與機器
0:57.640–0:58.936
止了中國購買
0:58.936–1:01.960
世界上最先進的人工智慧晶片和機器。
1:02.060–1:05.680
輝達（NVIDIA）正試圖解鎖在中國的先進晶片銷售，
1:06.000–1:08.900
但中國政府希望鼓勵國內生產
1:09.320–1:12.600
擁有最佳人工智慧的國家不僅會打造更好的聊天機器人。
1:12.800–1:15.703
它還會打造更好的武器、更好的網路安全，
1:15.703–1:17.280
更好的科學研究，
1:17.580–1:19.560
以及最終更強大的經濟。
1:19.840–1:22.720
這意味著人工智慧不再只是一場技術競賽。
1:22.720–1:24.540
它是一場全球權力的競賽。
1:24.540–1:28.620
當世界上最大的超級強國感到下一個最佳國家正在追趕時，
1:28.880–1:31.880
它不僅僅是競爭，它改變了遊戲規則。
1:32.140–1:37.127
主要優先事項是戰略性地獨立於輝達（NVIDIA）和
1:37.127–1:39.620
其他西方製造的晶片。
1:39.820–1:40.829
在 2022 年 10 月，
1:40.829–1:41.838
美國
1:41.838–1:42.776
阻止中國
1:42.776–1:46.380
購買來自輝達（NVIDIA）的世界最先進人工智慧晶片。
1:46.700–1:50.600
邏輯很簡單。沒有先進晶片意味著較弱的人工智慧模型，
1:50.920–1:53.480
而較弱的人工智慧模型意味著中國會落後。
1:53.480–1:56.920
但這並未阻止中國，甚至沒有減緩他們的腳步。
1:57.160–1:58.835
短短幾年後的今天，
1:58.835–2:02.120
中國不僅正在打造全球最頂尖的 AI 模型，
2:02.280–2:04.520
還將大多數模型免費提供給公眾使用。
2:04.760–2:06.260
那麼中國是如何做到的呢？
2:06.520–2:11.000
它如何將這樣的挫折轉化為史上最重大的 AI 逆襲故事之一？
2:11.300–2:15.200
在開始之前，我在名為「Stay Ahead」的 WhatsApp 社群中，
2:15.200–2:19.060
整理了一份所有最佳中國 AI 工具及其免費使用方法的速查表。
2:19.360–2:20.960
請從描述中的連結獲取。
2:21.340–2:22.580
現在讓我們深入影片內容。
2:23.480–2:30.680
當 ChatGPT 於 2022 年底推出時，引發了全球 AI 競賽。
2:30.680–2:33.680
[未翻譯]
2:33.680–2:35.480
它被視為改變遊戲規則的存在。
2:35.480–2:39.303
每個國家突然都想打造最聰明的 AI 模型，
2:39.303–2:40.920
但存在一個問題。
2:41.260–2:43.860
訓練 AI 不像在筆記型電腦上運行應用程式那樣簡單。
2:44.220–2:47.688
AI 模型必須處理數十億筆資料，直到
2:47.688–2:50.000
模型逐漸學習到模式。
2:50.000–2:58.380
這需要巨大的運算能力，來自於大型資料中心內數千個專門 AI 晶片協同運作。
2:58.720–3:01.305
為了讓不了解的人簡單說明晶片，
3:01.305–3:03.764
它們是電腦中負責所有思考
3:03.764–3:05.340
和計算的部分，用於 AI。
3:05.740–3:10.380
而幾乎所有製造這些晶片所需的關鍵環節，都由三家公司控制。
3:10.580–3:13.180
首先是設計這些晶片的 Nvidia。
3:13.700–3:15.860
台灣的 TSMC（台積電）負責製造。
3:15.860–3:17.599
而荷蘭公司 ASML，
3:17.599–3:19.418
製造了製造這些晶片所需的極度複雜
3:19.418–3:22.580
製造這些晶片所需的設備。
3:22.900–3:25.020
而這些都受到美國的控制。
3:25.220–3:27.166
到這裡，你可能會想問，
3:27.166–3:29.052
如果這三家公司中有兩家甚至
3:29.052–3:31.120
不是美國公司，美國如何控制它們？
3:31.620–3:35.040
答案在於所謂的「外國直接產品規則」（Foreign Direct Product Rule）。
3:35.320–3:36.938
該規則始於 1959 年，
3:36.938–3:41.560
本質上規定，如果產品是使用美國技術製造的，
3:41.560–3:45.140
美國政府有權禁止其銷售
3:45.140–3:48.140
這包括在外國製造的產品。
3:48.520–3:51.981
由於 TSMC 和 ASML 依賴美國技術、
3:51.981–3:54.420
軟體和智慧財產權
3:54.420–3:56.004
來製造它們的產品，
3:56.004–4:00.000
美國有權切斷他們獲得生存所需工具的途徑，
4:00.000–4:03.324
並讓他們的企業停業。簡單來說，
4:03.324–4:05.020
美國不僅製造
4:05.020–4:08.946
全球最好的 AI 晶片，還控制著誰能購買。
4:08.946–4:12.000
而在 2022 年，美國決定不允許中國
4:12.000–4:14.280
購買這些晶片。首先，華為和
4:14.280–4:17.700
數十家其他中國企業被列入禁令
4:17.700–4:21.958
被列入名單，使得它們購買美國科技變得極為困難
4:21.958–4:23.400
隨後，美國禁止輝達
4:23.400–4:26.664
向中國出口其最強大的 AI 晶片，當時
4:26.664–4:30.180
的型號是 H100。美國還阻止企業
4:30.180–4:34.289
向中國出售製造這些晶片所需的先進設備。
4:34.289–4:35.300
中國不僅
4:35.300–4:37.879
被禁止購買全球最好的 AI 硬體，
4:37.879–4:40.520
它也被禁止自行製造。這種情況
4:40.520–4:43.727
嚴重到輝達不得不創建一款性能稍弱的晶片。
4:43.727–4:45.560
現在讓我們回到輝達的突發新聞。
4:45.560–4:51.540
據報道，輝達計劃推出一款降級版的 H20，可以合法
4:51.540–4:54.836
銷售給中國。所以你看，從任何邏輯標準來看，
4:54.836–4:57.080
中國應該已經在
4:57.080–5:00.781
AI 領域落後。大多數專家同意中國仍略遜於美國。
5:00.781–5:02.220
如果有任何事情
5:02.220–5:03.895
大家能達成共識，
5:03.895–5:07.060
那就是最先進的 AI 技術不會流向
5:07.060–5:10.541
中國。如果我們政策失當，這是非常可能發生的
5:10.541–5:12.700
中國有可能超越美國
5:12.700–5:16.483
，這是非常合理的。但相反，他們逆轉了局勢。
5:16.483–5:19.140
讓我展示一下如何做到的。第一個問題，晶片。
5:19.460–5:21.290
第一個問題很明顯。
5:21.290–5:24.740
中國再也無法從不僅是輝達，
5:24.740–5:28.459
還有三星、聯發科和高通那裡購買全球最好的 AI 晶片，
5:28.459–5:31.040
以及手機用的微晶片。這些晶片
5:31.040–5:33.824
正被用於將手機帶入 5G 時代。
5:33.824–5:36.680
大家都認為華為和中國企業
5:36.680–5:38.794
將被困在 4G 時代，因為
5:38.794–5:42.940
那些超昂貴的設備只掌握在美國及其盟友手中。
5:43.340–5:46.352
但在 2023 年，華為推出了昇騰晶片，
5:46.352–5:49.880
將其放入其智能手機 Mate 60 Pro 中，並通過
5:49.880–5:52.441
將手機連接至 5G 網絡震驚了世界。
5:52.441–5:55.500
為了展示這有多麼重要，甚至蘋果也花了
5:55.500–5:58.807
蘋果花了很長時間才製造出自己的晶片，以停止向高通付費。
5:58.807–6:01.180
而這項成就背後的關鍵是中芯國際（SMIC）。
6:01.180–6:03.551
它是中國最大的半導體製造商。
6:03.551–6:06.040
即使無法取得全球最頂級的設備，
6:06.220–6:10.868
中芯國際仍找到方法，利用較舊的設備製造先進晶片。
6:10.868–6:12.340
它的效率並不高
6:12.340–6:14.398
不如台積電高，成本也不具優勢，但確實可行。
6:14.398–6:18.220
中國並非試圖在一夜之間製造出全球最頂級的晶片。
6:18.580–6:22.423
它旨在確保自己不再依賴他人的晶片。
6:22.423–6:23.960
問題二：讓每顆晶片
6:23.960–6:28.227
發揮更大效能。然而，製造國產晶片只是解決方案的一部分。
6:28.227–6:29.720
中國的晶片仍然
6:29.720–6:31.247
比輝達（Nvidia）的晶片慢，
6:31.247–6:35.320
且美國出口管制使得這些晶片極難取得。
6:35.320–6:37.723
中國處於不利地位。因此，首先，
6:37.723–6:40.400
他們試圖透過
6:40.400–6:43.561
從馬來西亞、日本等地走私
6:43.561–6:46.120
香港等地走私來規避限制。甚至有人使用人肉快遞
6:46.120–6:47.524
來走私晶片。
6:47.524–6:51.820
一名學生從新加坡攜帶六枚晶片到中國，每枚晶片獲得一百美元的報酬
6:51.820–6:54.850
新加坡攜帶六顆晶片到中國，僅需放在行李中，就能獲得100美元報酬。但最終，
6:54.850–6:57.740
這些做法對推動中國人工智慧產業發展而言，僅僅是冰山一角
6:57.740–6:59.617
中國發展人工智慧產業而言，幾乎只是杯水車薪。因此，
6:59.617–7:03.520
中國工程師提出了不同的問題：與其只是
7:03.520–7:06.437
建造擁有更好晶片的更大電腦，
7:06.437–7:08.980
我們能否建構更聰明的AI？正是在這裡，
7:08.980–7:12.561
像深度求索（DeepSeek）這樣的企業徹底改變了對話方向。
7:12.561–7:14.020
深度求索採用了一種
7:14.020–7:15.792
稱為「專家混合」（mixture of experts）的技術。
7:15.792–7:19.720
讓我用一個過度簡化的例子來解釋。假設你
7:19.720–7:22.633
擁有一間200人的公司，而在某一天，當
7:22.633–7:24.660
你想要解決行銷問題時，
7:24.660–7:26.618
你不會召集所有人開會。
7:26.618–7:28.520
你只會召集行銷團隊，對吧？
7:28.860–7:31.487
以往的AI模型運作方式往往不同。
7:31.487–7:33.980
每次你提出問題，AI都會
7:33.980–7:35.735
召集整個團隊。基本上，
7:35.735–7:39.180
整個模型都會運算答案。這需要龐大
7:39.180–7:42.530
的運算能力，並使每次回應的成本更高。
7:42.530–7:44.740
為了解決這個問題，美國公司
7:44.740–7:48.019
像 OpenAI 這樣的公司探索了混合專家（Mixture of Experts）技術的使用，
7:48.019–7:50.180
這基本上將模型進行了分割
7:50.180–7:52.820
分割成多個專門的專家叢集。
7:53.220–7:56.820
然而，DeepSeq 的工程師將這個概念提升到了另一個層次。
7:57.160–8:00.000
他們沒有將模型分割成數十個專家叢集，
8:00.000–8:02.857
他們沒有將模型分割成數十個專家叢集，
8:02.857–8:04.120
而是將其切分成 256 個微小的
8:04.120–8:06.515
超專門化專家。因此，當
8:06.515–8:10.920
你要求 DeepSeq 解決編碼問題時，一條超高效的途徑
8:10.920–8:15.326
會立即啟動，並只激活這八個微小專家。
8:15.326–8:16.900
其餘的則保持休眠。
8:16.900–8:20.418
這意味著 DeepSeek 能夠產生優質的答案，同時
8:20.418–8:22.920
使用更低的成本。看看這個。
8:22.920–8:26.537
雖然 GPT 和 Claude 的最佳模型在輸入成本上分別每百萬個 token 需花費 5 美元和
8:26.537–8:29.360
輸入成本為每百萬個 token 10 美元，
8:29.360–8:33.516
但 DeepSeek 的最佳模型僅需 0.
8:33.516–8:36.080
4 美元。而在輸出成本上，GPT 和 Claude 分別需花費 30
8:36.080–8:40.704
50 美元，DeepSeek 僅需 0.
8:40.704–8:42.920
87 美元。但這還不是全部。
8:42.920–8:45.485
中國人並未在此止步於創新。
8:45.485–8:47.440
他們還發明了一種稱為
8:47.440–8:49.974
多頭潛注意力（Multi-Head Latent Attention，簡稱 MLA）的技術。
8:49.974–8:53.980
要了解這技術的作用，讓我們再看一個例子。
8:53.980–8:57.785
想像你正在與 AI 討論一份 100 頁的文件。
8:57.785–8:59.960
每當你提出一個新問題，
8:59.960–9:05.020
AI 都需要記住它已讀取內容中的重要細節。因此它
9:05.020–9:08.400
它將所有這些資訊儲存在其暫存記憶體中。
9:08.400–9:10.697
工程師稱之為鍵值快取（key value cache），但
9:10.697–9:13.180
你可以簡單地將其視為 AI 的短期
9:13.180–9:14.180
記憶體。
9:14.180–9:17.403
對話越長，AI 所需的記憶體越多，
9:17.403–9:18.680
這使得運行成本
9:18.680–9:19.680
變得昂貴。
9:19.680–9:23.180
DeepSeq 的 MLA 技術壓縮了該記憶體。
9:23.180–9:25.593
它不是分別儲存每個細節，
9:25.593–9:28.260
而是建立一個更小的重要資訊摘要，
9:28.260–9:32.200
並利用該摘要來回答未來的問題。
9:32.200–9:38.300
這類似於用幾頁組織良好的筆記取代 100 頁的資訊。
9:38.300–9:42.821
這將模型的短期記憶體需求降低了超過 90%，
9:42.821–9:44.200
使其能夠處理
9:44.200–9:48.260
更長的對話，同時使用遠少於的運算能力。
9:48.260–9:51.560
這使得模型的運行成本更低且效率更高。
9:51.560–9:53.751
因此，憑藉這些令人驚嘆的技巧，
9:53.751–9:56.860
DeepSeek 成功打造了一個世界級的 AI 模型，
9:56.860–9:59.561
據報僅耗資不到 600 萬美元，而
9:59.561–10:02.540
Anthropic 和其他大型公司則花費
10:02.540–10:05.578
數十億美元來訓練他們的模型。而
10:05.578–10:08.460
如果你將它的 V4 Pro Max
10:08.460–10:13.571
模型與當時相當的發布如 Opus 4.
10:13.571–10:17.660
6 Max、GPT 5.4 和 Gemini 3.1 Pro，你會發現
10:17.660–10:20.003
它的性能相當。所以
10:20.003–10:23.340
這一切都是關於創建高效的軟件，但這
10:23.340–10:25.566
只解決了一半的問題，因為
10:25.566–10:28.940
即使最好的軟件也需要足夠的來自芯片的硬件
10:28.940–10:30.827
到資料中心，用於訓練和
10:30.827–10:35.340
運行 AI 模型。對於那些不知道的人來說，資料中心是一個實體
10:35.340–10:38.289
存放伺服器且
10:38.289–10:42.220
運行網站、應用程式、雲端服務和 AI
10:42.220–10:46.140
模型的 AI 晶片，以及儲存、網路、備用電源和
10:46.140–10:49.020
維持一切運作所需的冷卻系統。因此，中國不僅缺乏尖端 AI 晶片，
10:49.020–10:53.298
運行。因此，中國不僅缺乏尖端 AI 晶片，
10:53.298–10:54.620
他們還發現
10:54.620–10:58.044
新建的資料中心實際上利用率不足。
10:58.044–10:59.980
因此，中國決定將其實體 AI
10:59.980–11:04.117
基礎設施視為國家對道路或電力的管理方式，
11:04.117–11:05.660
作為公共基礎設施
11:05.660–11:06.835
供所有人使用。
11:06.835–11:08.010
政府正在建
11:08.010–11:11.040
建設計算能力稱為國家整合性運算
11:11.040–11:13.928
力網絡」，預計於 2030 年或更早完成。
11:13.928–11:18.040
簡單來說，它在土地較便宜、再生能源較豐富且具備大型設施空間的地區建設了許多大型資料中心
11:18.040–11:19.973
在土地成本較低的地區
11:19.973–11:23.040
再生能源更為豐富，且擁有空間
11:23.040–11:24.500
供龐大設施使用
11:24.500–11:27.870
中國隨後將這些中心透過高速光纖網路與企業和
11:27.870–11:30.640
透過高速光纖網路將這些中心與企業及城市連接起來
11:30.640–11:33.071
讓企業、大學和研究機構能夠在需要時租用運算能力
11:33.071–11:36.360
讓研究人員在需要時租用運算能力
11:36.360–11:38.380
以下透過一個範例來說明其運作方式。
11:38.380–11:43.240
想像上海一家小型 AI 新創公司想要訓練新的 AI 模型
11:43.240–11:44.972
購買數百張 GPU 並
11:44.972–11:48.360
建立自己的資料中心可能會花費大量資金。
11:48.360–11:51.060
相反地，它可以線上租用運算能力。
11:51.060–11:53.479
新創公司上傳其程式碼和資料，並
11:53.479–11:55.400
選擇所需的運算能力規模。
11:55.400–12:00.240
而平台會在可能相距數千公里的連線資料中心尋找可用的晶片
12:00.240–12:01.600
的連線資料中心中尋找可用的晶片。
12:01.600–12:04.162
工程師可以遠端監控訓練過程，並
12:04.162–12:06.200
一旦工作完成，即可下載完成的模型
12:06.200–12:07.200
下載完成的模型。
12:07.200–12:11.175
中國尚未建立完全連線的國家級運算網格，
12:11.175–12:12.340
但這是其目標。
12:12.340–12:16.280
讓運算能力在全國範圍內更容易、更便宜地租用。
12:16.280–12:19.600
這帶我們來到問題四：訓練模型
12:19.600–12:25.180
因為即使是最聰明的 AI 模型，若沒有足夠的資料來訓練它們，也毫無用處。而對於
12:25.180–12:28.940
下一代 AI 而言，僅有文字是不夠的。
12:28.940–12:31.709
影片模型需要理解人們如何移動、
12:31.709–12:34.140
物體如何互動，以及物理
12:34.140–12:39.380
世界的運作方式。你無法僅用文字描述來教導 AI 跑步的樣子。
12:39.380–12:43.147
文字
12:43.147–12:44.560
攝影角度、照明
12:44.560–12:47.184
條件、環境和動作的範例。
12:47.184–12:49.880
它需要看到衣服如何飄動、陰影
12:49.880–12:53.350
如何變化，以及有人踩進水坑時會發生什麼事。
12:53.350–12:54.940
而這正是中國
12:54.940–12:56.111
擁有重大優勢的地方。
12:56.111–12:57.135
中國擁有全球最大
12:57.135–13:00.500
擁有全球最大的數位生態系統之一。平台
13:00.500–13:02.831
如抖音、微信、TikTok 和
13:02.831–13:07.060
其他中國應用程式每天產生海量的文字、圖片、
13:07.060–13:10.451
音訊和影片。對於擁有熱門 AI 平台
13:10.451–13:12.500
即夢（C-Dance）的 ByteDance 等公司來說，這不僅是內容，更是寶貴的訓練資料。
13:12.500–13:17.054
ByteDance 擁有抖音和 TikTok，
13:17.054–13:19.500
以及多個用於創作、編輯和
13:19.500–13:22.314
推薦影片的工具。這讓公司
13:22.314–13:24.800
能接觸到的不僅是影片本身。它還可以
13:24.800–13:28.052
不僅能接觸到影片本身，還能
13:28.052–13:29.900
影片的反應，哪些片段人們會持續觀看，以及
13:29.900–13:32.475
觀眾在什麼時刻失去興趣。
13:32.475–13:34.720
觀眾在什麼時刻會失去興趣
13:34.720–13:39.180
這並不意味著字節跳動會使用每一部私人影片來訓練其 AI。
13:39.180–13:42.188
我們並不清楚每家公司具體使用了哪些數據，
13:42.188–13:44.480
但擁有全球規模最大的
13:44.480–13:48.760
影片平台，仍賦予中國企業強大的優勢。
13:48.760–13:52.300
因為如果晶片是 AI 的引擎，那麼數據就是燃料。
13:52.300–13:56.191
中國不僅利用這些燃料來打造具競爭力的 AI 模型，
13:56.191–13:57.720
它還開始發布
13:57.720–13:59.220
許多模型免費提供。
13:59.220–14:02.915
今天，DeepSeek、阿里巴巴的 Qwen、
14:02.915–14:06.420
月之暗面的 Kimi 以及智譜的 GLM 模型
14:06.420–14:09.560
都可以完全免費下載和運行。
14:09.680–14:11.240
這引發了一個顯而易見的問題。
14:11.560–14:14.040
運營 AI 公司的成本可能高達數十億美元。
14:14.360–14:19.120
事實上，許多大型 AI 公司花費的金額仍遠高於其收入。
14:19.120–14:23.260
那麼，中國企業如何能免費提供強大的 AI 模型？
14:23.520–14:25.940
如果用戶沒有向這些公司付費，誰在付錢？
14:25.940–14:30.100
答案是，中國 AI 公司並非真正地在銷售 AI 模型。
14:30.460–14:31.880
模型僅是一個入口。
14:32.000–14:33.180
它們銷售的是更宏大的東西。
14:33.520–14:36.340
1. 它們銷售的是生態系統，而非模型。
14:36.740–14:37.480
想想 Google。
14:37.840–14:39.500
Google 免費提供 Chrome。
14:39.820–14:41.620
並非因為瀏覽器的開發成本低廉，
14:41.900–14:44.640
而是因為 Chrome 將用戶引入 Google 的生態系統，
14:45.000–14:47.306
用戶最終會在該生態系統中使用廣告和
14:47.306–14:49.100
Google Cloud 等付費服務。
14:49.460–14:52.320
中國 AI 公司正遵循相同的策略。
14:52.320–14:54.667
而像 OpenAI 和
14:54.667–14:57.015
Anthropic 等西方公司主要通過銷售
14:57.015–14:59.500
其智能服務的直接訪問權限來獲利不同。
14:59.840–15:04.040
你需要訂閱、支付 API 調用費用，或根據消耗的 token 數量付費，對吧？
15:04.440–15:07.880
中國公司通常在用戶旅程的後期獲利。
15:08.240–15:12.380
例如，阿里巴巴不僅希望你與它們的 AI 模型 Qwen 進行對話，
15:12.780–15:14.391
還希望企業在阿里雲上開發和
15:14.391–15:16.560
運行它們的應用程序。
15:16.560–15:20.035
超級應用微信背後的騰訊公司，
15:20.035–15:22.600
不僅希望企業測試其
15:22.600–15:24.120
AI 模型 Hunyuan。
15:24.120–15:27.990
還希望它們使用騰訊雲、其企業軟件以及
15:27.990–15:30.180
其餘的商業生態系統。
15:30.180–15:34.379
它需要GPU、伺服器、儲存空間、安全性、監控、
15:34.379–15:36.880
技術支援和工程師
15:36.880–15:40.220
能夠將AI連接到其現有系統。
15:40.220–15:42.410
這才是真正能賺錢的地方，
15:42.410–15:44.800
無論是雲端託管、諮詢還是
15:44.800–15:46.878
甚至是客製化AI解決方案。所以
15:46.878–15:50.860
目前該模型可能便宜甚至免費下載
15:50.860–15:54.888
但可靠且大規模運行它所需的一切並非如此。
15:54.888–15:56.560
模型並不總是
15:56.560–16:00.000
最終產品，它是客戶獲取策略。
16:01.820–16:04.520
這些模型後來會變成付費，因為到那時
16:04.520–16:06.680
許多用戶可能別無選擇，只能
16:06.680–16:07.680
付費。
16:07.680–16:09.945
理由二：贏在分銷 記住，
16:09.945–16:12.080
中國無法在頂尖模型方面與美國競爭，而 Anthropic 和 OpenAI 不斷創新，使用更好的晶片並取得領先。因此，中國反而
16:12.080–16:14.201
Anthropic和
16:14.201–16:17.760
OpenAI不斷創新、使用更好晶片並取得領先的頂級模型上與美國競爭。因此中國改為
16:17.760–16:19.840
決定贏在分銷，並使其產品
16:19.840–16:22.960
決定透過分發來取勝，並讓產品
16:22.960–16:24.176
這基本上意味著開發者可以下載訓練好的模型，
16:24.176–16:28.160
在自己的電腦或伺服器上運行它，修改它並
16:28.160–16:31.804
為其自身用途進行微調。例如，
16:31.804–16:34.720
一家零售公司可以採用該模型並
16:34.720–16:37.016
通過教導其內部政策、
16:37.016–16:39.920
產品目錄等來進行客製化，以便
16:39.920–16:41.479
它能準確處理客戶支援查詢。
16:41.479–16:44.800
這對公司是好事，但對中國公司來說也是
16:44.800–16:47.094
好事。如果數百萬開發者
16:47.094–16:50.720
這對中國公司來說也是一大贏。如果數百萬開發者
16:50.720–16:53.051
他們開始在其之上構建應用程式。而且每次
16:53.051–16:56.160
開發者選擇你的模型而不是競爭對手的模型，
16:56.160–16:59.431
他們就會投入時間學習它是如何運作的，
16:59.431–17:01.680
圍繞它編寫程式碼並將其整合到他們的系統中。
17:01.680–17:04.976
撰寫程式碼並將其整合至他們的系統中
17:04.976–17:06.560
切換。這就像如果你投入時間和
17:06.560–17:09.360
精力在Twitter上並建立了10萬粉絲，
17:09.360–17:12.640
你就不太可能切換到Threads。同樣地，
17:12.640–17:15.411
你比較不會轉向 Threads。同樣地，
17:15.411–17:17.300
模型的開發者來說也是如此。其次，
17:17.300–17:18.447
模型。其次，
17:18.447–17:19.522
研究人員將
17:19.522–17:22.820
能夠毫不費力地研究這些模型，並發表
17:22.820–17:26.039
其他人可以重複使用的改進或技術。
17:26.039–17:28.660
隨著時間推移，這會產生網絡效應。
17:28.660–17:31.487
使用該模型的人越多，
17:31.487–17:34.240
圍繞它的教程和專業知識也隨之存在。它
17:34.240–17:39.180
教程和專業知識就越多。這就是安卓成為全球最大行動作業系統的基本原因。
17:39.180–17:41.948
谷歌允許數千家製造商和
17:41.948–17:44.440
數百萬開發者在其周圍進行開發。
17:44.440–17:47.067
一旦有足夠多的人在你的平台上進行開發，
17:47.067–17:48.780
你的生態系統就能比
17:48.780–17:50.300
任何單一競爭對手成長得更快。
17:50.300–17:53.280
這種策略對中國公司已經奏效。
17:53.280–17:57.080
看看這張顯示全球使用量前50大AI模型的圖表。
17:57.080–18:00.880
你會發現中國模型正在迅速佔據市場份額。
18:00.880–18:04.140
而在全球前20大AI模型中，
18:04.140–18:05.960
2026年5月至6月期間，
18:05.960–18:10.760
中國AI模型的每月token使用量已超過美國模型。
18:10.760–18:12.960
但讓我們看看更具體的例子。
18:12.960–18:17.580
阿里巴巴的QEN模型今年總下載量突破10億次。
18:17.580–18:19.900
許多大型美國公司也在使用它。
18:19.900–18:23.900
例如，Airbnb的AI客服系統就使用了它們。
18:23.900–18:27.440
甚至Pinterest也在為其用途自訂QEN模型。
18:27.440–18:33.480
Pinterest 實際表示，由於採用這些開源模型，其在部分工作負載上的成本已降低高達 90%
18:33.480–18:34.960
這類開源模型，其在某些工作負載上的成本降低了高達90%。
18:34.960–18:40.380
而美國成長最快的AI編碼公司之一Cursor，將其編碼模型
18:40.380–18:45.940
Composer2建立在中國公司Moonshot AI的Kimi K2.5基礎模型之上。
18:45.940–18:47.040
再聽一次這句話
18:47.040–18:52.620
一家頂級美國AI公司在中國基礎模型之上構建了其專屬產品。
18:52.620–18:56.460
中國模型不再僅僅是美國AI的便宜替代品。
18:56.460–19:01.860
它們正成為美國公司自身進行開發的基礎設施的一部分。
19:01.860–19:04.260
原因三，這也帶出了中國模型之所以如此成功的一大關鍵
19:04.260–19:07.604
中國模型如此成功的原因之一。
19:07.604–19:09.960
這是因為中國將AI定為國家
19:09.960–19:11.014
任務。2017年，
19:11.014–19:12.068
中國國務院
19:12.068–19:15.580
國務院發布新一代人工智慧
19:15.580–19:19.235
發展規劃。其抱負巨大。到2030年，
19:19.235–19:21.540
中國希望成為全球
19:21.540–19:25.860
AI創新領先中心。大學擴大了AI和
19:25.860–19:27.780
電腦科學課程。
19:27.780–19:31.236
政府資助實驗室和研究專案。
19:31.236–19:33.140
各城市與省份設立了人工智慧
19:33.140–19:36.523
創新園區，讓企業能夠獲得資金、
19:36.523–19:39.060
基礎設施與地方政策支援。
19:39.060–19:41.199
該計畫甚至跨越了國界。
19:41.199–19:44.340
那些在海外實驗室建立事業的
19:44.340–19:47.758
中國研究人員受到積極鼓勵回國，
19:47.758–19:50.340
透過補助金、資金以及
19:50.340–19:52.249
在中國開展工作的實際途徑吸引他們。但
19:52.249–19:55.180
中國所做的最好的一件事，就是賦予大型企業
19:55.180–19:58.516
在建設國家人工智慧平台方面的特定角色。
19:58.516–20:01.620
2017年，科技部
20:01.620–20:07.600
選定百度開發自主駕駛的開放創新平台、阿里巴巴負責
20:07.600–20:10.462
智慧城市、騰訊負責醫學影像，以及
20:10.462–20:13.180
科大訊飛負責智慧語音技術。
20:13.500–20:16.031
這些公司仍為私營企業，且
20:16.031–20:18.160
它們繼續彼此激烈競爭。然而，它們都朝向同一個方向努力，以促進中國的發展。因此，這帶我們
20:18.160–20:23.100
另一家。然而，它們都朝向中國成長的同一方向努力。因此，這引導我們
20:23.100–20:24.947
世界能從中國的人工智慧歷程中學到什麼？讓我們
20:24.947–20:27.780
世界能從中國的人工智慧發展歷程中學到什麼？讓我們
20:27.780–20:29.426
中國已竭盡所能，在人工智慧競賽中搶先。一些
20:29.426–20:32.980
美國公司聲稱中國實驗室使用知識蒸餾技術。
20:32.980–20:36.057
這意味著利用更強大的人工智慧
20:36.057–20:38.380
模型的解答，來協助訓練較小的模型。然後
20:38.380–20:40.274
我們知道，有透過黑市管道走私受限的
20:40.274–20:42.820
我們知道有進行受限
20:42.820–20:45.357
美國指控超微（Supermicro）的聯合創始人非法
20:45.357–20:48.740
將數十億美元的輝達伺服器轉運至中國。
20:48.740–20:52.756
將數十億美元的英偉達伺服器轉運至中國。
20:52.756–20:54.620
規則，這應該被調查，但
20:54.620–20:56.630
少數走私的晶片無法造就數千名研究人員、
20:56.630–20:59.560
僅靠少量走私晶片，是無法造就數千名研究人員的
20:59.940–21:02.558
雲端平台以及全球開發者使用的生態系統。此外，中國的成功並非因為
21:02.558–21:05.300
雲端平台以及開發者所使用的生態系
21:05.300–21:08.330
研究，並找到讓較弱硬體表現更好的方法。
21:08.330–21:10.740
並非靠捷徑成功，也大量投資
21:10.740–21:14.500
我們應該以相同的標準來評判雙方，並且
21:14.500–21:15.440
重點在於
21:15.440–21:18.017
我們應該以相同的標準來評估雙方，並且
21:18.017–21:19.980
不要將中國的所有進步都視為
21:19.980–21:20.980
竊取。
21:20.980–21:24.433
其次，人們認為使用中國 AI 會威脅隱私，
21:24.433–21:25.540
他們會竊取數據
21:25.540–21:27.842
而且模型受到北京審查。但
21:27.842–21:30.080
西方也不遑多讓。ChatGPT 預設會訓練
21:30.080–21:31.843
預設情況下會根據你的對話進行訓練
21:31.843–21:34.760
Gemini 會將你的提示詞與你的 Gmail、YouTube
21:34.760–21:36.558
以及你所有的 Google 歷史記錄合併。
21:36.558–21:39.820
Claude 悄悄更改了隱私政策，並將用戶
21:39.820–21:41.539
默認為保留數據 5 年。
21:41.539–21:44.580
認為中國 AI 是威脅而西方 AI 是安全選擇的想法
21:44.580–21:46.440
認為中國 AI 是威脅而西方 AI 是安全選擇的觀念完全錯誤。
21:46.440–21:47.306
基於此，
21:47.306–21:50.480
我有兩個主要教訓，各國需要從中國的
21:50.480–21:55.111
AI 競賽中學習。首先，每個國家都需要對其 AI 未來有一定的控制權。
21:55.111–21:56.980
你聽過主權
21:56.980–22:00.873
AI 嗎？這並不意味著每個國家都必須自行製造所有晶片和
22:00.873–22:02.700
每個國家不必自己開發所有模型。那
22:02.700–22:04.700
對你們來說太昂貴且不切實際。
22:06.045–22:10.401
至少，一個國家應該擁有足夠的自有運算能力、
22:10.401–22:12.045
研究人才、數據、
22:12.045–22:17.765
模型和基礎設施，使其不會完全依賴另一個國家。今天，大多數
22:17.765–22:22.325
先進的人工智慧技術來自美國或中國。
22:22.325–22:27.705
但如果美國決定只有美國公司才能使用其最佳模型，會發生什麼事？
22:27.705–22:30.645
如果中國做出同樣的決定，會發生什麼事？
22:30.645–22:34.945
印度等國家可能會突然失去對重要技術的訪問權，因為
22:34.945–22:37.825
另外兩個國家之間的政治鬥爭。
22:37.825–22:40.162
而且 AI 不再只是聊天機器人，
22:40.162–22:44.085
它正成為教育、醫療保健、國防、
22:44.085–22:48.599
科學、編碼、製造業和幾乎所有現代業務的一部分。
22:48.599–22:50.025
擁有更優越
22:50.025–22:54.150
AI 的公司可以工作得更快、降低成本並更具競爭力。
22:54.150–22:55.785
無法取得先進人工智慧的国家
22:55.785–22:58.327
先進 AI 的國家可能會逐漸落後。
22:58.327–23:01.425
中國帶來的最大教訓不是每個
23:01.425–23:05.763
國家都應該複製中國的政治體系或具體策略。
23:05.763–23:06.885
教訓是
23:06.885–23:13.165
一個完全依賴外國 AI 的國家，將始終容易受到外國決策的影響。
23:13.165–23:18.325
印度和其他國家需要建立足夠的自有 AI 生態系統，以免
23:18.325–23:21.285
陷入美中兩強之間的夾縫
23:21.285–23:25.107
因為下一個主要的地緣政治封鎖可能不會阻斷石油
23:25.107–23:26.765
武器或實體商品
23:26.765–23:29.425
它可能會阻斷對情報本身的存取
23:29.425–23:32.968
其次，AI領導力並非在於擁有一個出色的聊天機器人
23:32.968–23:34.665
重點在於打造整個生態系
23:34.665–23:35.665
生態系
23:35.665–23:40.025
當人們討論人工智慧競賽時，通常比較的是模型
23:40.025–23:41.785
ChatGPT 比較厲害嗎？
23:41.785–23:43.105
DeepSeek 比較厲害嗎
23:43.105–23:46.009
哪個模型在基準測試中得分更高。但
23:46.009–23:48.225
模型只是龐大系統中可見的一小部分
23:48.225–23:52.129
更龐大的系統。要打造強大的 AI，一個國家也
23:52.129–23:54.905
需要晶片來執行運算
23:54.905–23:57.161
供電給這些晶片所需的電力，以及
23:57.161–24:00.545
資料中心來維持它們的運作，它需要大學
24:00.545–24:04.322
培訓工程師的大學、研發新技術的研究實驗室
24:04.322–24:06.025
雲端公司來提供
24:06.025–24:11.046
運算能力，以及能將人工智慧轉化為實用產品的企業
24:11.046–24:12.385
中國同時致力於這些領域
24:12.385–24:15.245
這些領域同時進行。因此，如果歐洲、
24:15.245–24:17.885
印度和其他發展中國家想要
24:17.885–24:20.920
為了確保自身未來，他們必須積極行動並
24:20.920–24:22.665
也必須建立這樣的生態系統。
24:22.665–24:25.632
這就是為什麼中國在人工智慧競賽中勝出的原因，以及
24:25.632–24:27.385
我們可以從他們身上學到什麼。為
24:27.385–24:30.804
更多案例研究，幫助你了解 AI 的發展方向
24:30.804–24:32.145
誰正在獲得權力
24:32.145–24:34.552
以及未來重大機會將出現在哪裡
24:34.552–24:36.725
訂閱頻道並觀看影片
24:36.725–24:38.356
接下來螢幕上會出現
24:38.356–24:42.185
它解釋了另一個大多數人尚未察覺的重大 AI 轉變
24:42.185–24:43.745
卻完全沒注意到
24:43.745–24:44.385
我們那裡見
0:00.000–0:03.540
We're leading China. We're leading China by a lot. China knows that.
我們領先中國。我們大幅領先中國。中國很清楚這一點。
0:03.760–0:07.040
The race between America and China for AI dominance.
美國與中國在人工智慧主導權上的競賽。
0:07.140–0:09.208
There's only going to be one winner here, and
這裡只會有一個贏家，而且
0:09.208–0:11.220
that's probably going to be the US or China.
那很可能是美國或中國。
0:11.380–0:13.380
Who's winning? America or China?
誰在贏？美國還是中國？
0:13.960–0:17.740
China just beat America in the AI race. And the craziest part?
中國剛剛在人工智慧競賽中擊敗了美國。最瘋狂的部分是？
0:17.900–0:20.891
They're not only beating American AI globally, but
他們不僅在全球範圍內擊敗美國人工智慧，而且
0:20.891–0:22.560
also inside America itself.
甚至在美國本土也是如此。
0:22.780–0:27.860
Chinese AI models now account for nearly 60% of AI usage by US companies.
中國人工智慧模型現在佔美國公司人工智慧使用量的近60%。
0:27.860–0:32.220
Companies like Pinterest, Airbnb and Coinbase are already using them.
Pinterest、Airbnb 和 Coinbase 等公司已經在使用它們。
0:32.500–0:33.720
And it's easy to see why.
很容易看出為什麼。
0:34.040–0:35.680
Recently, Kimi K3 dropped.
最近，Kimi K3 發布了。
0:36.040–0:38.640
It's the world's largest open source AI model.
它是世界上最大的開源人工智慧模型。
0:38.960–0:43.580
It even beat GPT 5.6 and Fable 5 on some benchmarks.
它在某些基準測試中甚至擊敗了 GPT 5.6 和 Fable 5。
0:43.780–0:47.340
And demand exploded so fast that new signups had to be paused.
需求爆炸性地增長，以至於不得不暫停新註冊。
0:47.480–0:51.420
Now, Alibaba is releasing an equally powerful QEN 3.8.
現在，阿里巴巴正在發布同樣強大的 Qwen 3.8。
0:51.500–0:53.100
And you can use them all for free.
而且你可以免費使用它們。
0:53.240–0:55.120
But that's not even the biggest surprise.
但這甚至不是最大的驚喜。
0:55.120–0:56.344
Just a few years ago,
就在幾年前，
0:56.344–0:57.640
the United States blo
美國阻擋中國購買全球最先進的AI晶片與機器
0:57.640–0:58.936
cked China from buyin
止了中國購買
0:58.936–1:01.960
g the world's most advanced AI chips and machines.
世界上最先進的人工智慧晶片和機器。
1:02.060–1:05.680
NVIDIA is trying to unlock those advanced chip sales in China,
輝達（NVIDIA）正試圖解鎖在中國的先進晶片銷售，
1:06.000–1:08.900
but the Chinese government wants to encourage domestic production.
但中國政府希望鼓勵國內生產
1:09.320–1:12.600
The country with the best AI doesn't just build better chatbots.
擁有最佳人工智慧的國家不僅會打造更好的聊天機器人。
1:12.800–1:15.703
It also builds better weapons, better cyber security,
它還會打造更好的武器、更好的網路安全，
1:15.703–1:17.280
better scientific research,
更好的科學研究，
1:17.580–1:19.560
and eventually a stronger economy.
以及最終更強大的經濟。
1:19.840–1:22.720
Which means AI is no longer just a technology race.
這意味著人工智慧不再只是一場技術競賽。
1:22.720–1:24.540
It's a race for global power.
它是一場全球權力的競賽。
1:24.540–1:28.620
And when the world's biggest superpower feels the next best country is catching up,
當世界上最大的超級強國感到下一個最佳國家正在追趕時，
1:28.880–1:31.880
it doesn't just compete, it changes the rules of the game.
它不僅僅是競爭，它改變了遊戲規則。
1:32.140–1:37.127
The main priority is to strategically become independent of NVIDIA and
主要優先事項是戰略性地獨立於輝達（NVIDIA）和
1:37.127–1:39.620
other Western manufactured chips.
其他西方製造的晶片。
1:39.820–1:40.829
In October 2022,
在 2022 年 10 月，
1:40.829–1:41.838
the United State
美國
1:41.838–1:42.776
s blocked China
阻止中國
1:42.776–1:46.380
from buying the world's most advanced AI chips from NVIDIA.
購買來自輝達（NVIDIA）的世界最先進人工智慧晶片。
1:46.700–1:50.600
The logic was simple. No advanced chips meant weaker AI models,
邏輯很簡單。沒有先進晶片意味著較弱的人工智慧模型，
1:50.920–1:53.480
and weaker AI models meant China would fall behind.
而較弱的人工智慧模型意味著中國會落後。
1:53.480–1:56.920
But this did not stop China or even slow them down.
但這並未阻止中國，甚至沒有減緩他們的腳步。
1:57.160–1:58.835
A few short years later, today,
短短幾年後的今天，
1:58.835–2:02.120
China is not only building some of the world's best AI models,
中國不僅正在打造全球最頂尖的 AI 模型，
2:02.280–2:04.520
it's giving most of them to the public for free.
還將大多數模型免費提供給公眾使用。
2:04.760–2:06.260
So how did China manage this?
那麼中國是如何做到的呢？
2:06.520–2:11.000
How did it turn such a setback into one of the biggest AI comeback stories ever?
它如何將這樣的挫折轉化為史上最重大的 AI 逆襲故事之一？
2:11.300–2:15.200
Before we begin, I put a cheat sheet of all the best Chinese AI tools
在開始之前，我在名為「Stay Ahead」的 WhatsApp 社群中，
2:15.200–2:19.060
and how to use them for free in my WhatsApp community called Staying Ahead.
整理了一份所有最佳中國 AI 工具及其免費使用方法的速查表。
2:19.360–2:20.960
Grab it from the link in the description.
請從描述中的連結獲取。
2:21.340–2:22.580
Now let's dive into the video.
現在讓我們深入影片內容。
2:23.480–2:30.680
When ChatGPT launched in late 2022, it kicked off a global AI race.
當 ChatGPT 於 2022 年底推出時，引發了全球 AI 競賽。
2:30.680–2:33.680
ChatGPT. ChatGPT. ChatGPT.
[未翻譯]
2:33.680–2:35.480
It's been held as a game changer.
它被視為改變遊戲規則的存在。
2:35.480–2:39.303
Every country suddenly wanted to build the smartest AI model,
每個國家突然都想打造最聰明的 AI 模型，
2:39.303–2:40.920
but there was one problem.
但存在一個問題。
2:41.260–2:43.860
Training AI isn't like running an app on your laptop.
訓練 AI 不像在筆記型電腦上運行應用程式那樣簡單。
2:44.220–2:47.688
An AI model has to process billions of pieces of data until
AI 模型必須處理數十億筆資料，直到
2:47.688–2:50.000
the model gradually learns patterns.
模型逐漸學習到模式。
2:50.000–2:58.380
That requires enormous computing power which comes from thousands of specialized AI chips working together inside massive data centers.
這需要巨大的運算能力，來自於大型資料中心內數千個專門 AI 晶片協同運作。
2:58.720–3:01.305
To simply explain chips for those who don't know,
為了讓不了解的人簡單說明晶片，
3:01.305–3:03.764
they are parts of a computer that do all the thin
它們是電腦中負責所有思考
3:03.764–3:05.340
king and calculations for AI.
和計算的部分，用於 AI。
3:05.740–3:10.380
And there were just three companies that controlled almost everything needed to make those chips.
而幾乎所有製造這些晶片所需的關鍵環節，都由三家公司控制。
3:10.580–3:13.180
First was Nvidia which designs these chips.
首先是設計這些晶片的 Nvidia。
3:13.700–3:15.860
TSMC in Taiwan manufactured them.
台灣的 TSMC（台積電）負責製造。
3:15.860–3:17.599
And ASML, a Dutch company,
而荷蘭公司 ASML，
3:17.599–3:19.418
built the incredibly compl
製造了製造這些晶片所需的極度複雜
3:19.418–3:22.580
ex machines needed to manufacture those chips.
製造這些晶片所需的設備。
3:22.900–3:25.020
And all these were controlled by the US.
而這些都受到美國的控制。
3:25.220–3:27.166
At this point, you might be wondering,
到這裡，你可能會想問，
3:27.166–3:29.052
if two of these companies aren't even
如果這三家公司中有兩家甚至
3:29.052–3:31.120
American, how could the US control them?
不是美國公司，美國如何控制它們？
3:31.620–3:35.040
The answer lies in something called the Foreign Direct Product Rule.
答案在於所謂的「外國直接產品規則」（Foreign Direct Product Rule）。
3:35.320–3:36.938
It was started in 1959 and
該規則始於 1959 年，
3:36.938–3:41.560
essentially says that if a product was made using American technology,
本質上規定，如果產品是使用美國技術製造的，
3:41.560–3:45.140
the US government has the power to stop it from being sold.
美國政府有權禁止其銷售
3:45.140–3:48.140
and this includes products made in a foreign country.
這包括在外國製造的產品。
3:48.520–3:51.981
And since TSMC and ASML rely on American technology,
由於 TSMC 和 ASML 依賴美國技術、
3:51.981–3:54.420
software and intellectual property
軟體和智慧財產權
3:54.420–3:56.004
to build their products,
來製造它們的產品，
3:56.004–4:00.000
the US has the leverage to cut them off from the tools they need to
美國有權切斷他們獲得生存所需工具的途徑，
4:00.000–4:03.324
survive and shut their companies down. So in simple words,
並讓他們的企業停業。簡單來說，
4:03.324–4:05.020
America doesn't just make the
美國不僅製造
4:05.020–4:08.946
world's best AI chips, it controls who could buy them.
全球最好的 AI 晶片，還控制著誰能購買。
4:08.946–4:12.000
And in 2022, it decided China shouldn't be
而在 2022 年，美國決定不允許中國
4:12.000–4:14.280
allowed to buy them. First, Huawei and
購買這些晶片。首先，華為和
4:14.280–4:17.700
dozens of other Chinese companies were placed on a banned
數十家其他中國企業被列入禁令
4:17.700–4:21.958
list, making it extremely difficult for them to buy American technology.
被列入名單，使得它們購買美國科技變得極為困難
4:21.958–4:23.400
Then the US banned NVIDIA
隨後，美國禁止輝達
4:23.400–4:26.664
from exporting its most powerful AI chip then,
向中國出口其最強大的 AI 晶片，當時
4:26.664–4:30.180
the H100, to China. It also stopped companies from
的型號是 H100。美國還阻止企業
4:30.180–4:34.289
selling China the advanced machines needed to manufacture those chips.
向中國出售製造這些晶片所需的先進設備。
4:34.289–4:35.300
China wasn't just
中國不僅
4:35.300–4:37.879
blocked from buying the world's best AI hardware,
被禁止購買全球最好的 AI 硬體，
4:37.879–4:40.520
it was also blocked from building it. The situation
它也被禁止自行製造。這種情況
4:40.520–4:43.727
was so bad that Nvidia had to create a slightly weaker chip.
嚴重到輝達不得不創建一款性能稍弱的晶片。
4:43.727–4:45.560
And we return now to breaking news
現在讓我們回到輝達的突發新聞。
4:45.560–4:51.540
from Nvidia. Nvidia reportedly plans to release a downgraded version of H20 that could legally
據報道，輝達計劃推出一款降級版的 H20，可以合法
4:51.540–4:54.836
be sold to China. So as you see, by every logical measure,
銷售給中國。所以你看，從任何邏輯標準來看，
4:54.836–4:57.080
China should have fallen behind in the
中國應該已經在
4:57.080–5:00.781
AI. Most experts agree that China is still a little behind the US.
AI 領域落後。大多數專家同意中國仍略遜於美國。
5:00.781–5:02.220
If there's anything that
如果有任何事情
5:02.220–5:03.895
you think people could agree on,
大家能達成共識，
5:03.895–5:07.060
it would be that the most advanced AI technology does not go to
那就是最先進的 AI 技術不會流向
5:07.060–5:10.541
China. It is very plausible that if we get this policy wrong,
中國。如果我們政策失當，這是非常可能發生的
5:10.541–5:12.700
China could be overtaking the United
中國有可能超越美國
5:12.700–5:16.483
States. But instead, they turned the situation around.
，這是非常合理的。但相反，他們逆轉了局勢。
5:16.483–5:19.140
Let me show you how. Problem one, chips.
讓我展示一下如何做到的。第一個問題，晶片。
5:19.460–5:21.290
The first problem was obvious.
第一個問題很明顯。
5:21.290–5:24.740
China could no longer buy the world's best AI chips from not
中國再也無法從不僅是輝達，
5:24.740–5:28.459
only Nvidia, but also microchips for phones from Samsung,
還有三星、聯發科和高通那裡購買全球最好的 AI 晶片，
5:28.459–5:31.040
MediaTek, and Qualcomm. And these chips
以及手機用的微晶片。這些晶片
5:31.040–5:33.824
were being used to bring phones into the 5G era.
正被用於將手機帶入 5G 時代。
5:33.824–5:36.680
Everyone thought Huawei and Chinese companies
大家都認為華為和中國企業
5:36.680–5:38.794
would be stuck in 4G era because
將被困在 4G 時代，因為
5:38.794–5:42.940
the ultra expensive machines were only with US and US allies.
那些超昂貴的設備只掌握在美國及其盟友手中。
5:43.340–5:46.352
But in 2023, Huawei made the Ascent chips,
但在 2023 年，華為推出了昇騰晶片，
5:46.352–5:49.880
put them in its smartphone Mate 60 Pro and shocked
將其放入其智能手機 Mate 60 Pro 中，並通過
5:49.880–5:52.441
the world by connecting their phones to 5G.
將手機連接至 5G 網絡震驚了世界。
5:52.441–5:55.500
To show you how significant that is, even Apple took
為了展示這有多麼重要，甚至蘋果也花了
5:55.500–5:58.807
very long to make its own chips to stop paying Qualcomm.
蘋果花了很長時間才製造出自己的晶片，以停止向高通付費。
5:58.807–6:01.180
Now, behind that achievement was SMIC,
而這項成就背後的關鍵是中芯國際（SMIC）。
6:01.180–6:03.551
China's largest semiconductor manufacturer.
它是中國最大的半導體製造商。
6:03.551–6:06.040
Even without access to the world's best machines,
即使無法取得全球最頂級的設備，
6:06.220–6:10.868
SMIC, found ways to manufacture advanced chips using older equipment.
中芯國際仍找到方法，利用較舊的設備製造先進晶片。
6:10.868–6:12.340
It wasn't as efficient
它的效率並不高
6:12.340–6:14.398
or as cheap as TSMC, but it worked.
不如台積電高，成本也不具優勢，但確實可行。
6:14.398–6:18.220
China wasn't trying to build the world's best chip overnight.
中國並非試圖在一夜之間製造出全球最頂級的晶片。
6:18.580–6:22.423
It was trying to make sure it no longer depended on someone else's.
它旨在確保自己不再依賴他人的晶片。
6:22.423–6:23.960
Problem 2, make every chip
問題二：讓每顆晶片
6:23.960–6:28.227
work harder. But building domestic chips was only part of the solution.
發揮更大效能。然而，製造國產晶片只是解決方案的一部分。
6:28.227–6:29.720
China's chips were still
中國的晶片仍然
6:29.720–6:31.247
slower than Nvidia's and
比輝達（Nvidia）的晶片慢，
6:31.247–6:35.320
US export restrictions made those chips very difficult to access.
且美國出口管制使得這些晶片極難取得。
6:35.320–6:37.723
China was in an unfair position. So first,
中國處於不利地位。因此，首先，
6:37.723–6:40.400
they tried to bypass the restrictions through
他們試圖透過
6:40.400–6:43.561
smuggling from places such as Malaysia, Japan and
從馬來西亞、日本等地走私
6:43.561–6:46.120
Hong Kong. Some even used human couriers
香港等地走私來規避限制。甚至有人使用人肉快遞
6:46.120–6:47.524
to smuggle in chips.
來走私晶片。
6:47.524–6:51.820
A student was paid $100 for each of the six chips he carried from
一名學生從新加坡攜帶六枚晶片到中國，每枚晶片獲得一百美元的報酬
6:51.820–6:54.850
Singapore to China in his luggage. But eventually,
新加坡攜帶六顆晶片到中國，僅需放在行李中，就能獲得100美元報酬。但最終，
6:54.850–6:57.740
all this barely scratched the surface in helping
這些做法對推動中國人工智慧產業發展而言，僅僅是冰山一角
6:57.740–6:59.617
China grow its AI industry. So
中國發展人工智慧產業而言，幾乎只是杯水車薪。因此，
6:59.617–7:03.520
Chinese engineers asked a different question instead of just
中國工程師提出了不同的問題：與其只是
7:03.520–7:06.437
building bigger computers with better chips.
建造擁有更好晶片的更大電腦，
7:06.437–7:08.980
What if we build smarter AI? This is where
我們能否建構更聰明的AI？正是在這裡，
7:08.980–7:12.561
companies like DeepSeek completely changed the conversation.
像深度求索（DeepSeek）這樣的企業徹底改變了對話方向。
7:12.561–7:14.020
DeepSeek used a technique
深度求索採用了一種
7:14.020–7:15.792
called mixture of experts.
稱為「專家混合」（mixture of experts）的技術。
7:15.792–7:19.720
Let me explain this using an oversimplified example. Say you
讓我用一個過度簡化的例子來解釋。假設你
7:19.720–7:22.633
have a company of 200 people and on a particular day when
擁有一間200人的公司，而在某一天，當
7:22.633–7:24.660
you want to solve a marketing problem,
你想要解決行銷問題時，
7:24.660–7:26.618
you don't call everyone into the meeting.
你不會召集所有人開會。
7:26.618–7:28.520
You only call the marketing team, right?
你只會召集行銷團隊，對吧？
7:28.860–7:31.487
Previous AI models often worked differently.
以往的AI模型運作方式往往不同。
7:31.487–7:33.980
Every time you asked a question, the AI would
每次你提出問題，AI都會
7:33.980–7:35.735
call the whole team. Basically,
召集整個團隊。基本上，
7:35.735–7:39.180
the whole model would calculate the answer. That required huge
整個模型都會運算答案。這需要龐大
7:39.180–7:42.530
amounts of power and made each response more expensive.
的運算能力，並使每次回應的成本更高。
7:42.530–7:44.740
To address this, American companies
為了解決這個問題，美國公司
7:44.740–7:48.019
like OpenAI explored the use of a mixture of experts,
像 OpenAI 這樣的公司探索了混合專家（Mixture of Experts）技術的使用，
7:48.019–7:50.180
which basically divided the model
這基本上將模型進行了分割
7:50.180–7:52.820
into multiple specialized expert clusters.
分割成多個專門的專家叢集。
7:53.220–7:56.820
However, DeepSeq engineers took this concept to the next level.
然而，DeepSeq 的工程師將這個概念提升到了另一個層次。
7:57.160–8:00.000
Instead of dividing the model into dozens of expert clusters,
他們沒有將模型分割成數十個專家叢集，
8:00.000–8:02.857
Instead of dividing the model into dozens of expert clusters,
他們沒有將模型分割成數十個專家叢集，
8:02.857–8:04.120
they sliced it into 256 tiny
而是將其切分成 256 個微小的
8:04.120–8:06.515
hyper-specialized experts. So when
超專門化專家。因此，當
8:06.515–8:10.920
you ask DeepSeq to solve a coding problem, an ultra-efficient route
你要求 DeepSeq 解決編碼問題時，一條超高效的途徑
8:10.920–8:15.326
instantly kicks in and activates just eight of those tiny experts.
會立即啟動，並只激活這八個微小專家。
8:15.326–8:16.900
The rest stayed asleep.
其餘的則保持休眠。
8:16.900–8:20.418
That means DeepSeek can produce strong answers while
這意味著 DeepSeek 能夠產生優質的答案，同時
8:20.418–8:22.920
using far cheaper costs. Look at this.
使用更低的成本。看看這個。
8:22.920–8:26.537
While the best models of GPT and Claude cost $5 and
雖然 GPT 和 Claude 的最佳模型在輸入成本上分別每百萬個 token 需花費 5 美元和
8:26.537–8:29.360
$10 per million tokens in input costs,
輸入成本為每百萬個 token 10 美元，
8:29.360–8:33.516
respectively, the best model of DeepSeek costs only $0.
但 DeepSeek 的最佳模型僅需 0.
8:33.516–8:36.080
4. And while GPT and Claude cost $30
4 美元。而在輸出成本上，GPT 和 Claude 分別需花費 30
8:36.080–8:40.704
$50 respectively in output costs, DeepSeek only cost $0.
50 美元，DeepSeek 僅需 0.
8:40.704–8:42.920
87. But that wasn't all. The
87 美元。但這還不是全部。
8:42.920–8:45.485
Chinese didn't stop with their innovation there.
中國人並未在此止步於創新。
8:45.485–8:47.440
They also invented a technique called
他們還發明了一種稱為
8:47.440–8:49.974
Multi-Head Latent Attention or MLA.
多頭潛注意力（Multi-Head Latent Attention，簡稱 MLA）的技術。
8:49.974–8:53.980
To understand what this does, let's take another example.
要了解這技術的作用，讓我們再看一個例子。
8:53.980–8:57.785
Imagine you're talking to an AI about a 100-page document.
想像你正在與 AI 討論一份 100 頁的文件。
8:57.785–8:59.960
Every time you ask a new question,
每當你提出一個新問題，
8:59.960–9:05.020
the AI needs to remember the important details from everything it has already read. So it
AI 都需要記住它已讀取內容中的重要細節。因此它
9:05.020–9:08.400
It stores all this information in its temporary memory.
它將所有這些資訊儲存在其暫存記憶體中。
9:08.400–9:10.697
Engineers call this the key value cache, but
工程師稱之為鍵值快取（key value cache），但
9:10.697–9:13.180
you can simply think of it as the AI's short-term
你可以簡單地將其視為 AI 的短期
9:13.180–9:14.180
memory.
記憶體。
9:14.180–9:17.403
The longer the conversation gets, the more memory the AI needs,
對話越長，AI 所需的記憶體越多，
9:17.403–9:18.680
which makes it expensive
這使得運行成本
9:18.680–9:19.680
to run.
變得昂貴。
9:19.680–9:23.180
DeepSeq's technique, MLA, compresses that memory.
DeepSeq 的 MLA 技術壓縮了該記憶體。
9:23.180–9:25.593
Instead of storing every detail separately,
它不是分別儲存每個細節，
9:25.593–9:28.260
it creates a much smaller summary of the important
而是建立一個更小的重要資訊摘要，
9:28.260–9:32.200
information and uses that to answer future questions.
並利用該摘要來回答未來的問題。
9:32.200–9:38.300
It is similar to replacing 100 pages of information with a few pages of well-organized notes.
這類似於用幾頁組織良好的筆記取代 100 頁的資訊。
9:38.300–9:42.821
This reduces the model's short-term memory requirements by over 90%,
這將模型的短期記憶體需求降低了超過 90%，
9:42.821–9:44.200
allowing it to handle
使其能夠處理
9:44.200–9:48.260
longer conversations while using far less computing power.
更長的對話，同時使用遠少於的運算能力。
9:48.260–9:51.560
That makes the model cheaper and more efficient to run.
這使得模型的運行成本更低且效率更高。
9:51.560–9:53.751
So with all these incredible tricks,
因此，憑藉這些令人驚嘆的技巧，
9:53.751–9:56.860
DeepSeek managed to build a world-class AI model for
DeepSeek 成功打造了一個世界級的 AI 模型，
9:56.860–9:59.561
reportedly just under 6 million dollars while
據報僅耗資不到 600 萬美元，而
9:59.561–10:02.540
Anthropic and other large companies were spending
Anthropic 和其他大型公司則花費
10:02.540–10:05.578
billions of dollars to train their models. And
數十億美元來訓練他們的模型。而
10:05.578–10:08.460
if you look at the comparison of its V4 Pro Max
如果你將它的 V4 Pro Max
10:08.460–10:13.571
model with comparable launches back then like Opus 4.
模型與當時相當的發布如 Opus 4.
10:13.571–10:17.660
6 Max, GPT 5.4 and Gemini 3.1 Pro, you'll see
6 Max、GPT 5.4 和 Gemini 3.1 Pro，你會發現
10:17.660–10:20.003
that its performance is comparable. So
它的性能相當。所以
10:20.003–10:23.340
that was all about creating efficient software but this
這一切都是關於創建高效的軟件，但這
10:23.340–10:25.566
only solves half the problem because
只解決了一半的問題，因為
10:25.566–10:28.940
even the best software needs enough hardware from chips
即使最好的軟件也需要足夠的來自芯片的硬件
10:28.940–10:30.827
to data centers to train and
到資料中心，用於訓練和
10:30.827–10:35.340
run ai models on for those who don't know a data center is a physical
運行 AI 模型。對於那些不知道的人來說，資料中心是一個實體
10:35.340–10:38.289
facility that contains the servers and
存放伺服器且
10:38.289–10:42.220
ai chips that run websites apps cloud services and ai
運行網站、應用程式、雲端服務和 AI
10:42.220–10:46.140
models along with the storage networking backup power and
模型的 AI 晶片，以及儲存、網路、備用電源和
10:46.140–10:49.020
cooling systems needed to keep everything
維持一切運作所需的冷卻系統。因此，中國不僅缺乏尖端 AI 晶片，
10:49.020–10:53.298
running. And so China was not only short of cutting-edge AI chips,
運行。因此，中國不僅缺乏尖端 AI 晶片，
10:53.298–10:54.620
they also found that
他們還發現
10:54.620–10:58.044
the newly built data centers were actually underused.
新建的資料中心實際上利用率不足。
10:58.044–10:59.980
So China decided to treat its AI
因此，中國決定將其實體 AI
10:59.980–11:04.117
infrastructure in the same way a country treats roads or electricity,
基礎設施視為國家對道路或電力的管理方式，
11:04.117–11:05.660
as public infrastructure
作為公共基礎設施
11:05.660–11:06.835
that everyone can use.
供所有人使用。
11:06.835–11:08.010
The government is buil
政府正在建
11:08.010–11:11.040
ding something called the National Integrated Computing
建設計算能力稱為國家整合性運算
11:11.040–11:13.928
Power Network by 2030, or even earlier.
力網絡」，預計於 2030 年或更早完成。
11:13.928–11:18.040
To explain it simply, it built many large data centers in
簡單來說，它在土地較便宜、再生能源較豐富且具備大型設施空間的地區建設了許多大型資料中心
11:18.040–11:19.973
In regions where land was cheaper,
在土地成本較低的地區
11:19.973–11:23.040
renewable energy was more abundant and there was space
再生能源更為豐富，且擁有空間
11:23.040–11:24.500
for huge facilities.
供龐大設施使用
11:24.500–11:27.870
China then connected these centers to businesses and
中國隨後將這些中心透過高速光纖網路與企業和
11:27.870–11:30.640
cities through high-speed fiber networks,
透過高速光纖網路將這些中心與企業及城市連接起來
11:30.640–11:33.071
allowing businesses, universities and
讓企業、大學和研究機構能夠在需要時租用運算能力
11:33.071–11:36.360
researchers to rent computing power when they need it.
讓研究人員在需要時租用運算能力
11:36.360–11:38.380
Here is how it works using an example.
以下透過一個範例來說明其運作方式。
11:38.380–11:43.240
Imagine a small AI startup in Shanghai wants to train a new AI model.
想像上海一家小型 AI 新創公司想要訓練新的 AI 模型
11:43.240–11:44.972
Buying hundreds of GPUs and
購買數百張 GPU 並
11:44.972–11:48.360
building its own data center could cost a lot of money.
建立自己的資料中心可能會花費大量資金。
11:48.360–11:51.060
Instead, it can rent computing power online.
相反地，它可以線上租用運算能力。
11:51.060–11:53.479
The startup uploads its code and data and
新創公司上傳其程式碼和資料，並
11:53.479–11:55.400
chooses how much power it needs.
選擇所需的運算能力規模。
11:55.400–12:00.240
And the platform finds available chips in connected data centers possibly thousands
而平台會在可能相距數千公里的連線資料中心尋找可用的晶片
12:00.240–12:01.600
of kilometers away.
的連線資料中心中尋找可用的晶片。
12:01.600–12:04.162
Its engineers can monitor the training remotely and
工程師可以遠端監控訓練過程，並
12:04.162–12:06.200
download the finished model once the work
一旦工作完成，即可下載完成的模型
12:06.200–12:07.200
is complete.
下載完成的模型。
12:07.200–12:11.175
China has not built one fully connected national computing grid yet,
中國尚未建立完全連線的國家級運算網格，
12:11.175–12:12.340
but that is the goal.
但這是其目標。
12:12.340–12:16.280
computing power easier and cheaper to rent across the country.
讓運算能力在全國範圍內更容易、更便宜地租用。
12:16.280–12:19.600
This brings us to Problem Number 4 Training the Models
這帶我們來到問題四：訓練模型
12:19.600–12:25.180
Because even the smartest AI models are useless without enough data to train them. And for
因為即使是最聰明的 AI 模型，若沒有足夠的資料來訓練它們，也毫無用處。而對於
12:25.180–12:28.940
the next generation of AI, text alone is not enough.
下一代 AI 而言，僅有文字是不夠的。
12:28.940–12:31.709
Video models need to understand how people move,
影片模型需要理解人們如何移動、
12:31.709–12:34.140
how objects interact, and how the physical
物體如何互動，以及物理
12:34.140–12:39.380
world behaves. You cannot teach an AI what running looks like by only describing it in
世界的運作方式。你無法僅用文字描述來教導 AI 跑步的樣子。
12:39.380–12:43.147
words. It needs to watch millions of examples of different people,
文字
12:43.147–12:44.560
camera angles, lighting
攝影角度、照明
12:44.560–12:47.184
conditions, environments and movements.
條件、環境和動作的範例。
12:47.184–12:49.880
It needs to see how clothes move, how shadows
它需要看到衣服如何飄動、陰影
12:49.880–12:53.350
change and what happens when someone steps into a puddle.
如何變化，以及有人踩進水坑時會發生什麼事。
12:53.350–12:54.940
And this is where China has
而這正是中國
12:54.940–12:56.111
a major advantage.
擁有重大優勢的地方。
12:56.111–12:57.135
China has one of t
中國擁有全球最大
12:57.135–13:00.500
he largest digital ecosystems in the world. Platforms
擁有全球最大的數位生態系統之一。平台
13:00.500–13:02.831
like Douyin, WeChat, TikTok and
如抖音、微信、TikTok 和
13:02.831–13:07.060
other Chinese apps generate huge amounts of text, images,
其他中國應用程式每天產生海量的文字、圖片、
13:07.060–13:10.451
audio and video every day. For companies like ByteDance,
音訊和影片。對於擁有熱門 AI 平台
13:10.451–13:12.500
which owns the popular AI platform
即夢（C-Dance）的 ByteDance 等公司來說，這不僅是內容，更是寶貴的訓練資料。
13:12.500–13:17.054
C-Dance, this is not just content, it is valuable training data.
ByteDance 擁有抖音和 TikTok，
13:17.054–13:19.500
ByteDance owns Dooyan and TikTok,
以及多個用於創作、編輯和
13:19.500–13:22.314
along with several tools for creating, editing and
推薦影片的工具。這讓公司
13:22.314–13:24.800
recommending videos. This gives the company
能接觸到的不僅是影片本身。它還可以
13:24.800–13:28.052
access to more than just the video itself. It can also
不僅能接觸到影片本身，還能
13:28.052–13:29.900
study how people react to that
影片的反應，哪些片段人們會持續觀看，以及
13:29.900–13:32.475
video, which clips do people keep watching and
觀眾在什麼時刻失去興趣。
13:32.475–13:34.720
at what moment do viewers lose interest.
觀眾在什麼時刻會失去興趣
13:34.720–13:39.180
Now this does not mean ByteDance uses every private video to train its AI.
這並不意味著字節跳動會使用每一部私人影片來訓練其 AI。
13:39.180–13:42.188
We do not know exactly what data each company uses,
我們並不清楚每家公司具體使用了哪些數據，
13:42.188–13:44.480
but owning some of the world's largest
但擁有全球規模最大的
13:44.480–13:48.760
video platforms still gives Chinese companies a powerful advantage.
影片平台，仍賦予中國企業強大的優勢。
13:48.760–13:52.300
Because if chips are the engine of AI, data is the fuel.
因為如果晶片是 AI 的引擎，那麼數據就是燃料。
13:52.300–13:56.191
And China did not just use that fuel to build competitive AI models,
中國不僅利用這些燃料來打造具競爭力的 AI 模型，
13:56.191–13:57.720
it also started releasing
它還開始發布
13:57.720–13:59.220
many of them for free.
許多模型免費提供。
13:59.220–14:02.915
Today, models like DeepSeek, Quen by Alibaba,
今天，DeepSeek、阿里巴巴的 Qwen、
14:02.915–14:06.420
Kimi by Moonshot AI, and GLM models from Z.AI
月之暗面的 Kimi 以及智譜的 GLM 模型
14:06.420–14:09.560
are available completely free to download and run.
都可以完全免費下載和運行。
14:09.680–14:11.240
And this raises an obvious question.
這引發了一個顯而易見的問題。
14:11.560–14:14.040
Running AI companies can cost billions of dollars.
運營 AI 公司的成本可能高達數十億美元。
14:14.360–14:19.120
In fact, many major AI companies are still spending far more money than they earn.
事實上，許多大型 AI 公司花費的金額仍遠高於其收入。
14:19.120–14:23.260
So how can Chinese companies offer powerful AI models for free?
那麼，中國企業如何能免費提供強大的 AI 模型？
14:23.520–14:25.940
If users aren't paying these companies, who is?
如果用戶沒有向這些公司付費，誰在付錢？
14:25.940–14:30.100
The answer is that Chinese AI companies aren't really selling AI models.
答案是，中國 AI 公司並非真正地在銷售 AI 模型。
14:30.460–14:31.880
The model is only the entry point.
模型僅是一個入口。
14:32.000–14:33.180
They're selling something much bigger.
它們銷售的是更宏大的東西。
14:33.520–14:36.340
1. They're selling the ecosystem, not the model.
1. 它們銷售的是生態系統，而非模型。
14:36.740–14:37.480
Think about Google.
想想 Google。
14:37.840–14:39.500
Google gives away Chrome for free.
Google 免費提供 Chrome。
14:39.820–14:41.620
Not because browsers are cheap to build,
並非因為瀏覽器的開發成本低廉，
14:41.900–14:44.640
but because Chrome brings people into Google's ecosystem,
而是因為 Chrome 將用戶引入 Google 的生態系統，
14:45.000–14:47.306
where they eventually use advertising and
用戶最終會在該生態系統中使用廣告和
14:47.306–14:49.100
paid services like Google Cloud.
Google Cloud 等付費服務。
14:49.460–14:52.320
Chinese AI companies are following the same playbook.
中國 AI 公司正遵循相同的策略。
14:52.320–14:54.667
While Western companies like OpenAI and
而像 OpenAI 和
14:54.667–14:57.015
Anthropic primarily make money by selli
Anthropic 等西方公司主要通過銷售
14:57.015–14:59.500
ng access to their intelligence directly.
其智能服務的直接訪問權限來獲利不同。
14:59.840–15:04.040
You pay for a subscription or an API call or how many tokens you consume, right?
你需要訂閱、支付 API 調用費用，或根據消耗的 token 數量付費，對吧？
15:04.440–15:07.880
Chinese companies often make money later in the user journey.
中國公司通常在用戶旅程的後期獲利。
15:08.240–15:12.380
For example, Alibaba not only wants you to chat with their AI model, Quen,
例如，阿里巴巴不僅希望你與它們的 AI 模型 Qwen 進行對話，
15:12.780–15:14.391
it wants companies to build and
還希望企業在阿里雲上開發和
15:14.391–15:16.560
run their applications on Alibaba Cloud.
運行它們的應用程序。
15:16.560–15:20.035
Tencent, the company behind the super app WeChat,
超級應用微信背後的騰訊公司，
15:20.035–15:22.600
not only wants businesses to test its
不僅希望企業測試其
15:22.600–15:24.120
AI model, Hanuan.
AI 模型 Hunyuan。
15:24.120–15:27.990
It wants them to use Tencent Cloud, its enterprise software and
還希望它們使用騰訊雲、其企業軟件以及
15:27.990–15:30.180
the rest of its business ecosystem.
其餘的商業生態系統。
15:30.180–15:34.379
It needs GPU, servers, storage, security, monitoring,
它需要GPU、伺服器、儲存空間、安全性、監控、
15:34.379–15:36.880
technical support and engineers
技術支援和工程師
15:36.880–15:40.220
who can connect the AI to its existing systems.
能夠將AI連接到其現有系統。
15:40.220–15:42.410
That is where the real money can be made,
這才是真正能賺錢的地方，
15:42.410–15:44.800
whether it's cloud hosting, consulting or
無論是雲端託管、諮詢還是
15:44.800–15:46.878
even custom AI solutions. So
甚至是客製化AI解決方案。所以
15:46.878–15:50.860
right now the model may be cheap or even free to download
目前該模型可能便宜甚至免費下載
15:50.860–15:54.888
but everything required to operate it reliably at scale is not.
但可靠且大規模運行它所需的一切並非如此。
15:54.888–15:56.560
The model is not always the
模型並不總是
15:56.560–16:00.000
final product, it is the customer acquisition strategy.
最終產品，它是客戶獲取策略。
16:01.820–16:04.520
these models become paid later, because by then
這些模型後來會變成付費，因為到那時
16:04.520–16:06.680
many users may have no other option but
許多用戶可能別無選擇，只能
16:06.680–16:07.680
to pay.
付費。
16:07.680–16:09.945
Reason 2 Win with distribution Remember,
理由二：贏在分銷 記住，
16:09.945–16:12.080
China couldn't compete with America on
中國無法在頂尖模型方面與美國競爭，而 Anthropic 和 OpenAI 不斷創新，使用更好的晶片並取得領先。因此，中國反而
16:12.080–16:14.201
the top models that Anthropic and
Anthropic和
16:14.201–16:17.760
OpenAI were constantly innovating, using better chips
OpenAI不斷創新、使用更好晶片並取得領先的頂級模型上與美國競爭。因此中國改為
16:17.760–16:19.840
and coming out ahead. So China instead
決定贏在分銷，並使其產品
16:19.840–16:22.960
decided to win with distribution and make their products
決定透過分發來取勝，並讓產品
16:22.960–16:24.176
free and open weight.
這基本上意味著開發者可以下載訓練好的模型，
16:24.176–16:28.160
This basically means that developers can download the trained model,
在自己的電腦或伺服器上運行它，修改它並
16:28.160–16:31.804
run it on their own computers or servers, modify it and
為其自身用途進行微調。例如，
16:31.804–16:34.720
fine tune it for their own use. For example,
一家零售公司可以採用該模型並
16:34.720–16:37.016
a retail company could take the model and
通過教導其內部政策、
16:37.016–16:39.920
customize it by teaching it its internal policies,
產品目錄等來進行客製化，以便
16:39.920–16:41.479
product catalog and more so
它能準確處理客戶支援查詢。
16:41.479–16:44.800
it can accurately handle customer support queries for it.
這對公司是好事，但對中國公司來說也是
16:44.800–16:47.094
It's a win for companies but it's also
好事。如果數百萬開發者
16:47.094–16:50.720
a win for the Chinese companies. If millions of developers
這對中國公司來說也是一大贏。如果數百萬開發者
16:50.720–16:53.051
can freely experiment with your model,
他們開始在其之上構建應用程式。而且每次
16:53.051–16:56.160
they begin building apps on top of it. And every time a
開發者選擇你的模型而不是競爭對手的模型，
16:56.160–16:59.431
developer chooses your model instead of a competitor's,
他們就會投入時間學習它是如何運作的，
16:59.431–17:01.680
they invest time learning how it works,
圍繞它編寫程式碼並將其整合到他們的系統中。
17:01.680–17:04.976
writing code around it and integrating it into their systems.
撰寫程式碼並將其整合至他們的系統中
17:04.976–17:06.560
That makes them less likely to
切換。這就像如果你投入時間和
17:06.560–17:09.360
switch. It's like if you invested time and
精力在Twitter上並建立了10萬粉絲，
17:09.360–17:12.640
effort on Twitter and built a 100,000 following,
你就不太可能切換到Threads。同樣地，
17:12.640–17:15.411
you're less likely to switch to threads. Similarly,
你比較不會轉向 Threads。同樣地，
17:15.411–17:17.300
it works for developers using these
模型的開發者來說也是如此。其次，
17:17.300–17:18.447
models. Secondly,
模型。其次，
17:18.447–17:19.522
researchers will
研究人員將
17:19.522–17:22.820
be able to effortlessly study these models and publish
能夠毫不費力地研究這些模型，並發表
17:22.820–17:26.039
improvements or techniques that others can reuse.
其他人可以重複使用的改進或技術。
17:26.039–17:28.660
Over time, this creates a network effect.
隨著時間推移，這會產生網絡效應。
17:28.660–17:31.487
The more people use the model, the more tools,
使用該模型的人越多，
17:31.487–17:34.240
tutorials and expertise exist around it. It
圍繞它的教程和專業知識也隨之存在。它
17:34.240–17:39.180
That is the same basic reason Android became the world's largest mobile operating system.
教程和專業知識就越多。這就是安卓成為全球最大行動作業系統的基本原因。
17:39.180–17:41.948
Google allowed thousands of manufacturers and
谷歌允許數千家製造商和
17:41.948–17:44.440
millions of developers to build around it.
數百萬開發者在其周圍進行開發。
17:44.440–17:47.067
And once enough, people are building on your platform,
一旦有足夠多的人在你的平台上進行開發，
17:47.067–17:48.780
your ecosystem can grow faster than
你的生態系統就能比
17:48.780–17:50.300
any single competitor.
任何單一競爭對手成長得更快。
17:50.300–17:53.280
That strategy is already working for Chinese companies.
這種策略對中國公司已經奏效。
17:53.280–17:57.080
Look at this chart that's showing the world's top 50 most used AI models.
看看這張顯示全球使用量前50大AI模型的圖表。
17:57.080–18:00.880
You'll see that Chinese models are rapidly gaining market share.
你會發現中國模型正在迅速佔據市場份額。
18:00.880–18:04.140
And when it comes to the top 20 AI models in the world,
而在全球前20大AI模型中，
18:04.140–18:05.960
between May and June of 2026,
2026年5月至6月期間，
18:05.960–18:10.760
the monthly token usage of Chinese AI models has surpassed the US models.
中國AI模型的每月token使用量已超過美國模型。
18:10.760–18:12.960
But let's look at more specific examples.
但讓我們看看更具體的例子。
18:12.960–18:17.580
Alibaba's QEN models surpassed 1 billion total downloads this year.
阿里巴巴的QEN模型今年總下載量突破10億次。
18:17.580–18:19.900
Many large US companies also use it.
許多大型美國公司也在使用它。
18:19.900–18:23.900
For example, Airbnb's AI customer service system uses them.
例如，Airbnb的AI客服系統就使用了它們。
18:23.900–18:27.440
Even Pinterest has been customizing QEN models for their usage.
甚至Pinterest也在為其用途自訂QEN模型。
18:27.440–18:33.480
Pinterest actually says it has reduced costs by as much as 90% in some workloads due to
Pinterest 實際表示，由於採用這些開源模型，其在部分工作負載上的成本已降低高達 90%
18:33.480–18:34.960
such open-source models.
這類開源模型，其在某些工作負載上的成本降低了高達90%。
18:34.960–18:40.380
And Cursor, one of America's fastest-growing AI coding companies built its coding model,
而美國成長最快的AI編碼公司之一Cursor，將其編碼模型
18:40.380–18:45.940
Composer2, on the base model of Chinese company Moonshot AI's Kimi K2.5.
Composer2建立在中國公司Moonshot AI的Kimi K2.5基礎模型之上。
18:45.940–18:47.040
Listen to that again.
再聽一次這句話
18:47.040–18:52.620
A top US AI company built its proprietary product on top of a Chinese foundation model.
一家頂級美國AI公司在中國基礎模型之上構建了其專屬產品。
18:52.620–18:56.460
Chinese models are no longer merely cheaper alternatives to American AI.
中國模型不再僅僅是美國AI的便宜替代品。
18:56.460–19:01.860
are becoming part of the infrastructure on which American companies themselves are building.
它們正成為美國公司自身進行開發的基礎設施的一部分。
19:01.860–19:04.260
Reason 3 And this brings us to one of the biggest
原因三，這也帶出了中國模型之所以如此成功的一大關鍵
19:04.260–19:07.604
reasons that Chinese models are succeeding so well.
中國模型如此成功的原因之一。
19:07.604–19:09.960
It's because China made AI a national
這是因為中國將AI定為國家
19:09.960–19:11.014
mission. In 2017,
任務。2017年，
19:11.014–19:12.068
China's State Cou
中國國務院
19:12.068–19:15.580
ncil released the New Generation Artificial Intelligence
國務院發布新一代人工智慧
19:15.580–19:19.235
Development Plan. Its ambition was enormous. By 2030,
發展規劃。其抱負巨大。到2030年，
19:19.235–19:21.540
China wanted to become the world's
中國希望成為全球
19:21.540–19:25.860
leading center for AI innovation. Universities expanded AI and
AI創新領先中心。大學擴大了AI和
19:25.860–19:27.780
computer science programs.
電腦科學課程。
19:27.780–19:31.236
Governments funded laboratories and research projects.
政府資助實驗室和研究專案。
19:31.236–19:33.140
Cities and provinces created AI
各城市與省份設立了人工智慧
19:33.140–19:36.523
innovation zones where companies could access funding,
創新園區，讓企業能夠獲得資金、
19:36.523–19:39.060
infrastructure and local policy support.
基礎設施與地方政策支援。
19:39.060–19:41.199
The plan even reached across borders.
該計畫甚至跨越了國界。
19:41.199–19:44.340
Chinese researchers who had built their careers in labs
那些在海外實驗室建立事業的
19:44.340–19:47.758
overseas were actively encouraged to come back home,
中國研究人員受到積極鼓勵回國，
19:47.758–19:50.340
pulled in with grants, funding and a real
透過補助金、資金以及
19:50.340–19:52.249
path to do their work in China. But
在中國開展工作的實際途徑吸引他們。但
19:52.249–19:55.180
the best thing China did was to give major companies
中國所做的最好的一件事，就是賦予大型企業
19:55.180–19:58.516
specific roles in building national AI platforms.
在建設國家人工智慧平台方面的特定角色。
19:58.516–20:01.620
In 2017, the Ministry of Science and Technology
2017年，科技部
20:01.620–20:07.600
selected Baidu to develop an open innovation platform for autonomous driving, Alibaba for
選定百度開發自主駕駛的開放創新平台、阿里巴巴負責
20:07.600–20:10.462
smart cities, Tencent for medical imaging, and
智慧城市、騰訊負責醫學影像，以及
20:10.462–20:13.180
iFlytech for intelligent voice technology.
科大訊飛負責智慧語音技術。
20:13.500–20:16.031
These companies were still private businesses, and
這些公司仍為私營企業，且
20:16.031–20:18.160
they continued competing fiercely with one
它們繼續彼此激烈競爭。然而，它們都朝向同一個方向努力，以促進中國的發展。因此，這帶我們
20:18.160–20:23.100
another. Yet they were all working in the same direction for the growth of China. So this brings
另一家。然而，它們都朝向中國成長的同一方向努力。因此，這引導我們
20:23.100–20:24.947
us to the last part of the case study.
世界能從中國的人工智慧歷程中學到什麼？讓我們
20:24.947–20:27.780
What can the world learn from China's AI journey? Let's
世界能從中國的人工智慧發展歷程中學到什麼？讓我們
20:27.780–20:29.426
just clarify one thing first.
中國已竭盡所能，在人工智慧競賽中搶先。一些
20:29.426–20:32.980
China did everything in its power to get ahead in the AI race. Some
美國公司聲稱中國實驗室使用知識蒸餾技術。
20:32.980–20:36.057
American companies claim Chinese labs use distillation.
這意味著利用更強大的人工智慧
20:36.057–20:38.380
This means using the answers of a stronger AI
模型的解答，來協助訓練較小的模型。然後
20:38.380–20:40.274
model to help train a smaller one. Then
我們知道，有透過黑市管道走私受限的
20:40.274–20:42.820
we know that there was smuggling done of restricted
我們知道有進行受限
20:42.820–20:45.357
Nvidia chips through black market routes.
美國指控超微（Supermicro）的聯合創始人非法
20:45.357–20:48.740
The US charges a co-founder of Supermicro with illegally
將數十億美元的輝達伺服器轉運至中國。
20:48.740–20:52.756
diverting billions of dollars of Nvidia powered servers to China.
將數十億美元的英偉達伺服器轉運至中國。
20:52.756–20:54.620
Some companies may have broken
規則，這應該被調查，但
20:54.620–20:56.630
rules and that should be investigated but
少數走私的晶片無法造就數千名研究人員、
20:56.630–20:59.560
a few smuggled chips cannot build thousands of researchers,
僅靠少量走私晶片，是無法造就數千名研究人員的
20:59.940–21:02.558
large data centers, new engineering techniques,
雲端平台以及全球開發者使用的生態系統。此外，中國的成功並非因為
21:02.558–21:05.300
cloud platforms and an ecosystem used by developers
雲端平台以及開發者所使用的生態系
21:05.300–21:08.330
around the world. Also China did not succeed because
研究，並找到讓較弱硬體表現更好的方法。
21:08.330–21:10.740
of one shortcut. It also invested heavily
並非靠捷徑成功，也大量投資
21:10.740–21:14.500
in research and found ways to make weaker hardware perform better.
我們應該以相同的標準來評判雙方，並且
21:14.500–21:15.440
The point is that
重點在於
21:15.440–21:18.017
we should judge both sides by the same standard and
我們應該以相同的標準來評估雙方，並且
21:18.017–21:19.980
not dismiss all of China's progress as
不要將中國的所有進步都視為
21:19.980–21:20.980
theft.
竊取。
21:20.980–21:24.433
Secondly, people think using Chinese AI is a threat to privacy,
其次，人們認為使用中國 AI 會威脅隱私，
21:24.433–21:25.540
they will steal data
他們會竊取數據
21:25.540–21:27.842
and the models are censored by Beijing. But
而且模型受到北京審查。但
21:27.842–21:30.080
the West is not far behind. ChatGPT trains
西方也不遑多讓。ChatGPT 預設會訓練
21:30.080–21:31.843
on your conversations by default.
預設情況下會根據你的對話進行訓練
21:31.843–21:34.760
Gemini merges your prompts with your Gmail, your YouTube
Gemini 會將你的提示詞與你的 Gmail、YouTube
21:34.760–21:36.558
and your entire Google history.
以及你所有的 Google 歷史記錄合併。
21:36.558–21:39.820
Claude quietly changed its privacy policy and opted users
Claude 悄悄更改了隱私政策，並將用戶
21:39.820–21:41.539
into 5 years of data retention.
默認為保留數據 5 年。
21:41.539–21:44.580
The idea that Chinese AI is a threat and Western AI is the
認為中國 AI 是威脅而西方 AI 是安全選擇的想法
21:44.580–21:46.440
safe choice is completely wrong.
認為中國 AI 是威脅而西方 AI 是安全選擇的觀念完全錯誤。
21:46.440–21:47.306
With this in mind,
基於此，
21:47.306–21:50.480
I have two major lessons that countries need to learn from China's
我有兩個主要教訓，各國需要從中國的
21:50.480–21:55.111
AI race. First, every country needs some control over its AI future.
AI 競賽中學習。首先，每個國家都需要對其 AI 未來有一定的控制權。
21:55.111–21:56.980
Have you heard of sovereign
你聽過主權
21:56.980–22:00.873
AI? It does not mean every country must build every chip and
AI 嗎？這並不意味著每個國家都必須自行製造所有晶片和
22:00.873–22:02.700
every model by itself. That
每個國家不必自己開發所有模型。那
22:02.700–22:04.700
would be too expensive and unrealistic for you.
對你們來說太昂貴且不切實際。
22:06.045–22:10.401
least, a country should have enough of its own computing power,
至少，一個國家應該擁有足夠的自有運算能力、
22:10.401–22:12.045
research talent, data,
研究人才、數據、
22:12.045–22:17.765
models and infrastructure that it is not completely dependent on another country. Today, most
模型和基礎設施，使其不會完全依賴另一個國家。今天，大多數
22:17.765–22:22.325
advanced AI technology comes from either the United States or China.
先進的人工智慧技術來自美國或中國。
22:22.325–22:27.705
But what happens if the US decides that only American companies can use its best models?
但如果美國決定只有美國公司才能使用其最佳模型，會發生什麼事？
22:27.705–22:30.645
What happens if China makes the same decision?
如果中國做出同樣的決定，會發生什麼事？
22:30.645–22:34.945
Countries such as India could suddenly lose access to important technology because of
印度等國家可能會突然失去對重要技術的訪問權，因為
22:34.945–22:37.825
a political fight between two other countries.
另外兩個國家之間的政治鬥爭。
22:37.825–22:40.162
And AI is no longer just a chatbot,
而且 AI 不再只是聊天機器人，
22:40.162–22:44.085
it is becoming part of education, healthcare, defense,
它正成為教育、醫療保健、國防、
22:44.085–22:48.599
science, coding, manufacturing and almost every modern business.
科學、編碼、製造業和幾乎所有現代業務的一部分。
22:48.599–22:50.025
A company with better
擁有更優越
22:50.025–22:54.150
AI can work faster, reduce costs and compete more effectively.
AI 的公司可以工作得更快、降低成本並更具競爭力。
22:54.150–22:55.785
A country without access
無法取得先進人工智慧的国家
22:55.785–22:58.327
to advanced AI may slowly fall behind.
先進 AI 的國家可能會逐漸落後。
22:58.327–23:01.425
The biggest lesson from China is not that every
中國帶來的最大教訓不是每個
23:01.425–23:05.763
country should copy China's political system or its exact strategy.
國家都應該複製中國的政治體系或具體策略。
23:05.763–23:06.885
The lesson is that
教訓是
23:06.885–23:13.165
a country that depends completely on foreign AI will always be vulnerable to foreign decisions.
一個完全依賴外國 AI 的國家，將始終容易受到外國決策的影響。
23:13.165–23:18.325
India and other countries need to build enough of their own AI ecosystem so they are not
印度和其他國家需要建立足夠的自有 AI 生態系統，以免
23:18.325–23:21.285
trapped between the US and China.
陷入美中兩強之間的夾縫
23:21.285–23:25.107
Because the next major geopolitical blockade may not stop oil,
因為下一個主要的地緣政治封鎖可能不會阻斷石油
23:25.107–23:26.765
weapons or physical goods,
武器或實體商品
23:26.765–23:29.425
it may stop access to intelligence itself.
它可能會阻斷對情報本身的存取
23:29.425–23:32.968
And second, AI leadership is not about one great chatbot,
其次，AI領導力並非在於擁有一個出色的聊天機器人
23:32.968–23:34.665
it's about creating a whole
重點在於打造整個生態系
23:34.665–23:35.665
ecosystem.
生態系
23:35.665–23:40.025
When people talk about the AI race, they usually compare models.
當人們討論人工智慧競賽時，通常比較的是模型
23:40.025–23:41.785
Is ChatGPT better?
ChatGPT 比較厲害嗎？
23:41.785–23:43.105
Is DeepSeek better?
DeepSeek 比較厲害嗎
23:43.105–23:46.009
which model scores higher on a benchmark. But
哪個模型在基準測試中得分更高。但
23:46.009–23:48.225
a model is only the visible part of a
模型只是龐大系統中可見的一小部分
23:48.225–23:52.129
much larger system. To build strong AI, a country also
更龐大的系統。要打造強大的 AI，一個國家也
23:52.129–23:54.905
needs chips to perform calculations,
需要晶片來執行運算
23:54.905–23:57.161
electricity to power those chips, and
供電給這些晶片所需的電力，以及
23:57.161–24:00.545
data centers to keep them running. It needs universities
資料中心來維持它們的運作，它需要大學
24:00.545–24:04.322
to train engineers, research labs to invent new techniques,
培訓工程師的大學、研發新技術的研究實驗室
24:04.322–24:06.025
cloud companies to provide
雲端公司來提供
24:06.025–24:11.046
computing power, and businesses that can turn AI into useful products.
運算能力，以及能將人工智慧轉化為實用產品的企業
24:11.046–24:12.385
China worked on all
中國同時致力於這些領域
24:12.385–24:15.245
these areas at the same time. So now, if Europe,
這些領域同時進行。因此，如果歐洲、
24:15.245–24:17.885
India and other developing countries want
印度和其他發展中國家想要
24:17.885–24:20.920
to secure their futures, they need to step up and
為了確保自身未來，他們必須積極行動並
24:20.920–24:22.665
build such ecosystems too.
也必須建立這樣的生態系統。
24:22.665–24:25.632
That was all about why China is winning the AI race and
這就是為什麼中國在人工智慧競賽中勝出的原因，以及
24:25.632–24:27.385
what we can learn from them. For
我們可以從他們身上學到什麼。為
24:27.385–24:30.804
more case studies that help you understand where AI is going,
更多案例研究，幫助你了解 AI 的發展方向
24:30.804–24:32.145
who is gaining power and
誰正在獲得權力
24:32.145–24:34.552
where the next major opportunities will emerge,
以及未來重大機會將出現在哪裡
24:34.552–24:36.725
subscribe to the channel and watch the video
訂閱頻道並觀看影片
24:36.725–24:38.356
on your screen next because
接下來螢幕上會出現
24:38.356–24:42.185
it explains another major shift happening in AI that most people
它解釋了另一個大多數人尚未察覺的重大 AI 轉變
24:42.185–24:43.745
are still completely missing.
卻完全沒注意到
24:43.745–24:44.385
I'll see you there.
我們那裡見
影片筆記：America Banned Nvidia Chips to Stop China's AI - It Backfired
一句話總結
美國透過出口管制（特別是禁止 NVIDIA 晶片）試圖遏制中國 AI 發展，但中國透過本土晶片製造（華為、SMIC）、軟體架構創新（DeepSeek 的 MoE 與 MLA 技術）以及國家級算力基礎設施建設，成功降低了成本並建立生態系統，反而加速了競爭。
核心重點
- 競賽本質轉變：AI 競賽已從單純的技術競爭轉變為全球權力、經濟實力與基礎設施的競爭。
- 硬體去 NVIDIA 化：儘管美國實施出口管制，中國透過華為昇騰晶片、SMIC 製造及走私途徑，逐步實現供應鏈獨立。
- 軟體架構創新：中國公司（如 DeepSeek）透過 MoE（專家混合）與 MLA（多頭潛注意力）技術，在較弱硬體上實現高效能，大幅降低訓練與推理成本。
- 數據與生態系統優勢：中國擁有龐大的數位生態系統（抖音、微信等）提供海量多模態數據；透過「開放權重」策略，將模型作為獲客入口，最終透過雲端服務與基礎設施獲利。
- 國家戰略佈局：中國將 AI 發展列為國家任務，整合算力網絡，並由國家科學技術部指定企業分工，形成完整的產業生態鏈。
- 全球影響：中國模型已滲透美國公司（如 Airbnb、Pinterest、Cursor）的底層基礎設施；影片呼籲歐洲、印度等國家建立「主權 AI」生態系統以避免依賴。
詳細大綱
A. 中美 AI 競賽現狀與美國管制
- 領先地位爭議：
- 影片主張美國在 AI 領域領先，但中國正在快速追趕。
- 預測最終勝者僅可能是美國或中國。
- 數據顯示，中國 AI 模型佔據美國公司近 60% 的 AI 使用量。
- 提及 Pinterest、Airbnb、Coinbase 等美國公司已使用中國 AI 模型。
- 熱門中國模型：
- Kimi K3：被稱為全球最大的開源 AI 模型，據稱在某些基準測試中擊敗了 GPT 5.6 和 Fable 5。
- QEN 3.8：阿里巴巴發布的強大模型。
- 特點：免費使用、需求爆發導致註冊暫停。
- 美國出口管制 (2022年10月)：
- 禁止中國購買 NVIDIA 最先進的 AI 晶片。
- 邏輯：無先進晶片 -> 較弱 AI 模型 -> 中國落後。
- 管制對象：華為及其他數十家中國公司被列入禁令名單。
- 管制範圍：不僅禁止購買晶片，也禁止出售製造晶片所需的先進機器。
- 美國透過 Foreign Direct Product Rule (外國直接產品規則) 控制非美國公司（因使用美國技術/IP）。
- NVIDIA 的應對：
- 推出降級版晶片 H20 以合法售予中國。
- NVIDIA 試圖解鎖中國市場的高階晶片銷售。
B. 硬體供應鏈與本土製造突破
- 供應鏈依賴結構：
- 過去由三家公司控制：
- NVIDIA：設計晶片。
- TSMC (台灣)：製造晶片。
- ASML (荷蘭)：製造製造晶片所需的複雜機器。
- 華為與 5G 突破：
- 華為推出 Ascent 晶片 並搭載於 Mate 60 Pro 手機。
- 實現 5G 連接，打破預期（預期將停留在 4G）。
- 對比：Apple 耗時較長才實現自製晶片以停止向 Qualcomm 付費。
- SMIC (中芯國際)：
- 中國最大的半導體製造商。
- 在無世界最佳機器的情況下，利用較舊設備製造先進晶片。
- 效率與成本不如 TSMC，但實現了「不再依賴他人」的目標。
- 走私與獲取途徑：
- 透過馬來西亞、日本、香港等地走私。
- 甚至使用人體運送（例如學生從新加坡攜帶晶片回中國）。
C. 軟體效率與架構創新 (DeepSeek 案例)
- 核心問題：
- 本土晶片速度慢於 NVIDIA。
- 出口限制導致晶片獲取困難。
- 目標：不追求更大更快的硬體，而是追求更聰明的 AI。
- DeepSeek 的創新技術：
- Mixture of Experts (MoE, 專家混合)：
- 傳統模型：每次提問調動整個模型（所有計算資源），成本高。
- DeepSeek 做法：將模型切片為 256 個超專業微型專家。
- 運作方式：針對特定問題（如編碼），僅激活極少數專家（如 8 個），其餘休眠。
- 效益：大幅降低計算成本。
- 成本對比：
- GPT/Claude 輸入成本：$5 - $10 / 百萬 token。
- DeepSeek 輸入成本：$0.4 / 百萬 token。
- GPT/Claude 輸出成本：$30 - $50。
- DeepSeek 輸出成本：$0.87。
- Multi-Head Latent Attention (MLA, 多頭潛注意力)：
- 問題：長對話需要大量短期記憶（Key-Value Cache），導致運行昂貴。
- 解決方案：壓縮記憶體，將大量細節轉為少量摘要。
- 效益：減少超過 90% 的短期記憶體需求，支持更長對話，降低運算功率。
- 成本效益對比：
- DeepSeek 訓練世界級模型成本據報低於 600 萬美元。
- 對比 Anthropic 等大公司耗資數十億美元。
- 性能對比：DeepSeek V4 Pro Max 與 Opus 4.6 Max、GPT 5.4、Gemini 3.1 Pro 性能相當。
D. 硬體基礎設施與國家算力網絡
- 數據中心現狀：
- 新建數據中心利用率不足。
- 數據中心定義：包含伺服器、AI 晶片、儲存、網路、備用電源及冷卻系統的實體設施。
- 國家整合算力網絡 (National Integrated Computing Power Network)：
- 目標：預計於 2030 年或更早建成。
- 策略：將 AI 基礎設施視為公共基礎設施（類似道路或電力）。
- 建設地點：土地便宜、可再生能源豐富、空間充足的區域。
- 運作方式：
- 透過高速光纖網絡連接各地數據中心。
- 企業、大學、研究人員可線上租賃運算能力。
- 範例：上海小型 AI 初創公司上傳代碼與數據，平台從遠端數據中心調配晶片進行訓練，工程師遠端監控並下載完成模型。
- 現狀：尚未完全連接成單一國家算力網，但為既定目標。
E. 數據優勢與商業模式差異
- 數據的重要性：
- 智能模型若無足夠數據則無用。
- 下一代 AI 需要理解視頻、物理世界行為（如物體互動、光影變化），僅靠文字訓練不足。
- 中國的數據優勢：
- 擁有全球最大的數位生態系統之一。
- 平台（Douyin, WeChat, TikTok 等）每日產生海量文本、圖像、音頻和視頻數據。
- 企業（如 ByteDance）不僅擁有視頻內容，還擁有用戶反饋數據（觀看時長、流失點等），這是有價值的訓練數據。
- 結論：如果晶片是引擎，數據就是燃料。
- 變現邏輯差異：
- 西方模式：直接銷售智能訪問權（訂閱、API 調用、Token 消耗）。
- 中國模式：模型僅是「入口點（entry point）」，銷售的是更大的生態系統。
- 具體案例：
- Alibaba：不僅希望用戶使用 Quen 模型，更希望企業在 Alibaba Cloud 上運行應用。
- Tencent：不僅希望測試 Hanuan 模型，更希望企業使用 Tencent Cloud、企業軟件及整個商業生態系統。
- 獲利點：雲端託管、諮詢、自定義 AI 解決方案、GPU、服務器、存儲、安全、監控及技術支持。
- 結論：模型是免費的，但可靠運行所需的基礎設施不是。模型是「客戶獲取策略（customer acquisition strategy）」。
F. 分發策略與市場佔有
- 開放權重（Open Weight）策略：
- 中國模型免費且開放權重，開發者可下載、在自有服務器運行、修改及微調。
- 案例：零售公司可將模型微調至內部政策與產品目錄，用於客服查詢。
- 網路效應與轉換成本：
- 開發者投入時間學習、編寫代碼及整合系統後，較少切換競爭對手模型（類似 Twitter 與 Threads 的關係）。
- 研究者可輕鬆研究模型並發布改進技術，形成正向循環。
- 類似 Android 的成功邏輯：讓製造商和開發者圍繞平台構建。
- 市場佔有率數據：
- 2026 年 5 月至 6 月間，中國 AI 模型的月 Token 使用量超過美國模型。
- Alibaba's QEN：今年下載量突破 10 億次。
- 美國公司的依賴：
- Airbnb：使用其 AI 客服系統。
- Pinterest：自定義 QEN 模型，據稱在某些負載下成本降低高達 90%。
- Cursor：其編碼模型 Composer2 基於中國公司 Moonshot AI 的 Kimi K2.5 基礎模型構建。
- 結論：中國模型已成為美國公司構建自有產品的基礎設施。
G. 國家任務與隱私爭議
- 2017 年國家戰略：
- 國務院發布《新一代人工智能發展規劃》，目標是 2030 年成為全球 AI 創新中心。
- 大學擴展 AI 與計算機科學項目。
- 政府資助實驗室與研究項目。
- 城市與省份建立 AI 創新區，提供資金、基礎設施及政策支持。
- 鼓勵海外研究人員回國。
- 企業分工：
- 國家科學技術部指定主要企業負責特定領域：
- Baidu：自動駕駛開放創新平台。
- Alibaba：智慧城市。
- Tencent：醫學影像。
- iFlytech：智能語音技術。
- 企業間仍保持競爭，但方向一致。
- 反駁「偷竊論」：
- 雖然存在蒸餾（distillation）技術爭議及 Nvidia 晶片走私指控（如 Supermicro 聯合創始人被起訴），但這些無法解釋數千名研究人員、大型數據中心及生態系統的建立。
- 中國在研究上投入巨大，並找到讓較弱硬體表現更好的方法。
- 應以相同標準評判雙方，不應將中國進步全部歸結為偷竊。
- 反駁「隱私威脅論」：
- 西方 AI 同樣存在隱私問題：
- ChatGPT：默認訓練用戶對話。
- Gemini：合併用戶提示與 Gmail、YouTube 及 Google 歷史。
- Claude：更改隱私政策，默認用戶選擇加入 5 年數據保留。
- 「中國 AI 是威脅，西方 AI 是安全選擇」的觀點是錯誤的。
H. 給世界的教訓
- 主權 AI（Sovereign AI）：
- 各國需對 AI 未來擁有控制權。
- 並非要求每個國家自建所有晶片和模型，而是擁有足夠的算力、人才、數據、模型及基礎設施，避免完全依賴他國。
- 若美國或中國限制最佳模型的使用權，依賴國（如印度）將面臨技術斷供風險。
- AI 已滲透教育、醫療、國防、科學、編碼、製造等領域。
- 依賴外國 AI 的國家在地緣政治中將始終脆弱。
- 生態系統勝過單一模型：
- 比較模型分數（Benchmark）僅是可見部分。
- 強 AI 需要：晶片、電力、數據中心、大學工程師、研究實驗室、雲端公司及商業應用。
- 中國在這些方面均有佈局。
- 全球呼籲：
- 歐洲、印度及其他開發中國家必須加緊腳步建立類似的生態系統，以確保未來。
工具 / 模型 / 名詞整理
- AI 模型/產品：
- Kimi K3
- GPT 5.6
- Fable 5
- QEN 3.8 / Quen (Alibaba)
- ChatGPT
- DeepSeek (提及 DeepSeq 疑點)
- GPT (提及 GPT 5.4)
- Claude
- Opus 4.6 Max
- Gemini 3.1 Pro
- H100 (NVIDIA 晶片)
- H20 (NVIDIA 降級晶片)
- Ascent 晶片 (華為)
- Mate 60 Pro (華為手機)
- V4 Pro Max (DeepSeek 模型版本疑點)
- C-Dance (ByteDance 擁有的 AI 平台)
- Dooyan (疑點：原文拼寫)
- GLM (Z.AI)
- Hanuan (Tencent)
- Composer2 (Cursor 的編碼模型)
- Kimi K2.5 (Moonshot AI 的基礎模型)
- 公司/機構：
- NVIDIA
- TSMC (台灣積體電路製造)
- ASML
- Huawei (華為)
- SMIC (中芯國際)
- Samsung
- MediaTek
- Qualcomm
- Apple
- OpenAI
- Anthropic
- Alibaba (阿里巴巴)
- Airbnb
- Coinbase
- ByteDance
- Tencent
- Baidu
- iFlytech
- Moonshot AI
- Z.AI
- Cursor
- Supermicro
- 技術/術語：
- Foreign Direct Product Rule (外國直接產品規則)
- Mixture of Experts (MoE, 專家混合)
- Multi-Head Latent Attention (MLA, 多頭潛注意力)
- Key-Value Cache (鍵值緩存)
- National Integrated Computing Power Network (國家整合算力網絡)
- 5G / 4G
- GPU
- Token (輸入/輸出成本單位)
- Open Weight (開放權重)
- Distillation (蒸餾)
- Sovereign AI (主權 AI)
- Benchmark (基準測試)
操作流程整理
- 硬體獲取與製造：
- 中國企業（如華為、SMIC）在受限情況下，利用較舊設備或走私途徑獲取先進晶片技術。
- 華為推出搭載 Ascent 晶片的手機，實現 5G 連接。
- 軟體架構優化：
- 採用 MoE 技術，將模型切片為多個專家，僅激活相關專家以降低計算成本。
- 採用 MLA 技術，壓縮短期記憶（Key-Value Cache），減少記憶體需求。
- 數據收集與訓練：
- 利用抖音、微信等平台的海量多模態數據（文本、圖像、視頻、用戶反饋）進行模型訓練。
- 國家整合算力網絡提供遠端運算能力，企業上傳代碼與數據進行訓練。
- 商業分發與生態構建：
- 提供免費且開放權重的模型，吸引開發者下載、微調及部署。
- 透過模型作為入口，引導企業使用雲端服務、基礎設施及諮詢服務獲利。
- 建立網路效應，增加開發者轉換成本，使中國模型成為美國公司底層基礎設施。
值得注意的限制或風險
- 硬體性能差距：本土晶片（如華為昇騰）速度仍慢於 NVIDIA 先進晶片，需透過軟體創新彌補。
- 供應鏈依賴：儘管有走私和本土製造，但完全獨立於西方技術（如 ASML 機器、TSMC 製造）仍具挑戰。
3.
生字列表
| 生字 | 讀音 | 類型 | 中文 |
|---|---|---|---|
| dominance | /ˈdɒm.ɪ.nəns/ | 主導權；支配地位 | |
| account for | /əˈkaʊnt fɔːr/ | phrasal verb | 佔（比例）；解釋；說明 |
| benchmarks | /ˈbentʃ.mɑːks/ | noun | 基準；標準 |
| exploded | /ɪkˈspləʊdɪd/ | verb | 激增；爆發 |
| open source | /ˈəʊ.pən sɔːs/ | adjective | 開源的 |
| leverage | /ˈlev.ər.ɪdʒ/ | noun | 影響力；優勢；槓桿作用 |
| overtaking | /ˌəʊ.vəˈteɪ.kɪŋ/ | verb (gerund) | 超越；趕上 |
| bypass | /ˈbaɪ.pɑːs/ | verb | 繞過；規避 |
| smuggling | /ˈsmʌɡ.lɪŋ/ | noun | 走私 |
| mixture of experts | /ˈmɪk.stʃər əv ˈek.spɜːts/ | noun phrase | 專家混合模型 |
| infrastructure | /ˈɪn.frə.strʌk.tʃər/ | noun | 基礎設施 |
| distillation | /ˌdɪs.tɪˈleɪ.ʃən/ | noun | 蒸餾；知識蒸餾 |
| sovereign | /ˈsɒv.rɪn/ | adjective | 主權的；獨立的 |
| vulnerable | /ˈvʌl.nər.ə.bəl/ | adjective | 脆弱的；易受攻擊的 |
| ecosystem | /ˈiː.kəʊ.sɪs.təm/ | noun | 生態系統 |
| benchmark | /ˈbentʃ.mɑːk/ | noun | 基準測試 |
生字解說
dominance /ˈdɒm.ɪ.nəns/
意思：主導權；支配地位
解說：指在競爭中佔據絕對優勢或控制地位。在國際競爭語境中，常指技術或市場的控制力。
影片原句
The race between America and China for AI dominance.
美國與中國在人工智慧主導權上的競賽。
The race between America and China for AI dominance.
美國與中國在人工智慧主導權上的競賽。
延伸例句
The company aims for market dominance through aggressive pricing.
該公司旨在通過激進的定價策略來獲得市場主導權。
The company aims for market dominance through aggressive pricing.
該公司旨在通過激進的定價策略來獲得市場主導權。
account for /əˈkaʊnt fɔːr/
意思：佔（比例）；解釋；說明
解說：在此語境下指佔總數的一定比例。也可用於解釋原因。
影片原句
Chinese AI models now account for nearly 60% of AI usage by US companies.
中國人工智慧模型現在佔美國公司人工智慧使用量的近60%。
Chinese AI models now account for nearly 60% of AI usage by US companies.
中國人工智慧模型現在佔美國公司人工智慧使用量的近60%。
延伸例句
Exports account for a significant portion of the country's GDP.
出口佔該國國內生產總值的很大一部分。
Exports account for a significant portion of the country's GDP.
出口佔該國國內生產總值的很大一部分。
benchmarks /ˈbentʃ.mɑːks/
意思：基準；標準
解說：用於評估性能或質量的標準測試或指標。在科技領域指性能測試標準。
影片原句
It even beat GPT 5.6 and Fable 5 on some benchmarks.
它在某些基準測試中甚至擊敗了 GPT 5.6 和 Fable 5。
It even beat GPT 5.6 and Fable 5 on some benchmarks.
它在某些基準測試中甚至擊敗了 GPT 5.6 和 Fable 5。
延伸例句
The new processor sets a new benchmark for speed.
新處理器為速度設定了新的基準。
The new processor sets a new benchmark for speed.
新處理器為速度設定了新的基準。
exploded /ɪkˈspləʊdɪd/
意思：激增；爆發
解說：形容數量或規模突然且迅速地大幅增加。
影片原句
And demand exploded so fast that new signups had to be paused.
需求爆炸性地增長，以至於不得不暫停新註冊。
And demand exploded so fast that new signups had to be paused.
需求爆炸性地增長，以至於不得不暫停新註冊。
延伸例句
Social media usage exploded during the pandemic.
疫情期間社交媒體的使用量激增。
Social media usage exploded during the pandemic.
疫情期間社交媒體的使用量激增。
open source /ˈəʊ.pən sɔːs/
意思：開源的
解說：指軟體原始碼公開，允許任何人查看、修改和分發。
影片原句
It's the world's largest open source AI model.
它是世界上最大的開源人工智慧模型。
It's the world's largest open source AI model.
它是世界上最大的開源人工智慧模型。
延伸例句
Many developers prefer open source software for its flexibility.
許多開發者因其靈活性而偏好開源軟體。
Many developers prefer open source software for its flexibility.
許多開發者因其靈活性而偏好開源軟體。
leverage /ˈlev.ər.ɪdʒ/
意思：影響力；優勢；槓桿作用
解說：指利用某種資源或權力來達到預期結果的能力或優勢。
影片原句
the US has the leverage to cut them off from the tools they need to survive
美國有權切斷他們獲得生存所需工具的途徑
the US has the leverage to cut them off from the tools they need to survive
美國有權切斷他們獲得生存所需工具的途徑
延伸例句
The union has little leverage in negotiations.
工會在談判中幾乎沒有影響力。
The union has little leverage in negotiations.
工會在談判中幾乎沒有影響力。
overtaking /ˌəʊ.vəˈteɪ.kɪŋ/
意思：超越；趕上
解說：指在進度、數量或質量上超過他人。
影片原句
China could be overtaking the United States.
中國有可能超越美國
China could be overtaking the United States.
中國有可能超越美國
延伸例句
Electric vehicles are overtaking gas cars in sales.
電動車在銷量上正在超越燃油車。
Electric vehicles are overtaking gas cars in sales.
電動車在銷量上正在超越燃油車。
bypass /ˈbaɪ.pɑːs/
意思：繞過；規避
解說：指通過非直接的方式避開限制、規則或障礙。
影片原句
they tried to bypass the restrictions through smuggling
他們試圖透過從馬來西亞、日本等地走私來規避限制。
they tried to bypass the restrictions through smuggling
他們試圖透過從馬來西亞、日本等地走私來規避限制。
延伸例句
Users can bypass the firewall using a proxy.
用戶可以通過代理伺服器繞過防火牆。
Users can bypass the firewall using a proxy.
用戶可以通過代理伺服器繞過防火牆。
smuggling /ˈsmʌɡ.lɪŋ/
意思：走私
解說：非法將貨物帶入或帶出某個地區或國家的行為。
影片原句
Some even used human couriers to smuggle in chips.
甚至有人使用人肉快遞來走私晶片。
Some even used human couriers to smuggle in chips.
甚至有人使用人肉快遞來走私晶片。
延伸例句
The smuggling of rare animals is a serious crime.
走私珍稀動物是一項嚴重犯罪。
The smuggling of rare animals is a serious crime.
走私珍稀動物是一項嚴重犯罪。
mixture of experts /ˈmɪk.stʃər əv ˈek.spɜːts/
意思：專家混合模型
解說：一種 AI 架構，將模型分割為多個專門的「專家」子網絡，根據輸入動態選擇激活部分專家。
影片原句
DeepSeek used a technique called mixture of experts.
深度求索採用了一種稱為「專家混合」（mixture of experts）的技術。
DeepSeek used a technique called mixture of experts.
深度求索採用了一種稱為「專家混合」（mixture of experts）的技術。
延伸例句
The mixture of experts architecture improves efficiency in large language models.
專家混合架構提高了大型語言模型的效率。
The mixture of experts architecture improves efficiency in large language models.
專家混合架構提高了大型語言模型的效率。
infrastructure /ˈɪn.frə.strʌk.tʃər/
意思：基礎設施
解說：指國家或組織運作所需的基礎結構，如道路、電力、數據中心等。
影片原句
So China decided to treat its AI infrastructure in the same way a country treats roads or electricity
因此，中國決定將其實體 AI 基礎設施視為國家對道路或電力的管理方式
So China decided to treat its AI infrastructure in the same way a country treats roads or electricity
因此，中國決定將其實體 AI 基礎設施視為國家對道路或電力的管理方式
延伸例句
The country needs to invest in digital infrastructure.
該國需要投資於數位基礎設施。
The country needs to invest in digital infrastructure.
該國需要投資於數位基礎設施。
distillation /ˌdɪs.tɪˈleɪ.ʃən/
意思：蒸餾；知識蒸餾
解說：在 AI 中指利用大型複雜模型的知識來訓練小型高效模型的技术。
影片原句
Some American companies claim Chinese labs use distillation.
一些美國公司聲稱中國實驗室使用知識蒸餾技術。
Some American companies claim Chinese labs use distillation.
一些美國公司聲稱中國實驗室使用知識蒸餾技術。
延伸例句
Knowledge distillation helps reduce the size of neural networks.
知識蒸餾有助於縮小神經網絡的規模。
Knowledge distillation helps reduce the size of neural networks.
知識蒸餾有助於縮小神經網絡的規模。
sovereign /ˈsɒv.rɪn/
意思：主權的；獨立的
解說：指擁有獨立決策權，不依賴外部力量的。在 AI 語境中指自主可控。
影片原句
Have you heard of sovereign AI?
你聽過主權 AI 嗎？
Have you heard of sovereign AI?
你聽過主權 AI 嗎？
延伸例句
The country maintains its sovereign status.
該國維持其主權地位。
The country maintains its sovereign status.
該國維持其主權地位。
vulnerable /ˈvʌl.nər.ə.bəl/
意思：脆弱的；易受攻擊的
解說：指容易受到傷害、批評或影響的狀態。
影片原句
a country that depends completely on foreign AI will always be vulnerable to foreign decisions.
一個完全依賴外國 AI 的國家，將始終容易受到外國決策的影響。
a country that depends completely on foreign AI will always be vulnerable to foreign decisions.
一個完全依賴外國 AI 的國家，將始終容易受到外國決策的影響。
延伸例句
Small businesses are often vulnerable to economic shocks.
小型企業通常容易受到經濟衝擊的影響。
Small businesses are often vulnerable to economic shocks.
小型企業通常容易受到經濟衝擊的影響。
ecosystem /ˈiː.kəʊ.sɪs.təm/
意思：生態系統
解說：指相互關聯的組織、技術、用戶和服務形成的複雜網絡。
影片原句
It's about creating a whole ecosystem.
重點在於打造整個生態系
It's about creating a whole ecosystem.
重點在於打造整個生態系
延伸例句
Apple has built a powerful software ecosystem.
蘋果建立了一個強大的軟體生態系統。
Apple has built a powerful software ecosystem.
蘋果建立了一個強大的軟體生態系統。
benchmark /ˈbentʃ.mɑːk/
意思：基準測試
解說：用於比較不同系統或模型性能的標準測試。
影片原句
which model scores higher on a benchmark.
哪個模型在基準測試中得分更高。
which model scores higher on a benchmark.
哪個模型在基準測試中得分更高。
延伸例句
We need to run a benchmark test on the new server.
我們需要在新服務器上運行基準測試。
We need to run a benchmark test on the new server.
我們需要在新服務器上運行基準測試。
句型解說（含實例）
not only... but (also)...
意思：不僅……而且……
接續：not only + [verb/adjective/noun] + but (also) + [verb/adjective/noun]
解說：用於連接兩個並列的成分，強調後者比前者更重要或更進一步。注意語法一致性，兩側詞性需相同。
影片原句
They're not only beating American AI globally, but also inside America itself.
他們不僅在全球範圍內擊敗美國人工智慧，而且甚至在美國本土也是如此。
They're not only beating American AI globally, but also inside America itself.
他們不僅在全球範圍內擊敗美國人工智慧，而且甚至在美國本土也是如此。
實例
- The software is not only fast but also secure.
這款軟體不僅速度快，而且安全性高。 - She not only passed the exam but also got the highest score.
她不僅通過了考試，還得了最高分。
no longer just... it's...
意思：不再只是……而是……
接續：no longer just + [noun/phrase] + it's + [noun/phrase]
解說：用於澄清或糾正對某事物的舊有認知，強調其現在的本質或範圍已經擴大或改變。
影片原句
Which means AI is no longer just a technology race. It's a race for global power.
這意味著人工智慧不再只是一場技術競賽。它是一場全球權力的競賽。
Which means AI is no longer just a technology race. It's a race for global power.
這意味著人工智慧不再只是一場技術競賽。它是一場全球權力的競賽。
實例
- It is no longer just a hobby; it's a career.
這不再只是一個愛好，而是一份職業。 - The city is no longer just a tourist destination; it's a tech hub.
這座城市不再只是旅遊目的地，而是科技中心。
instead of + [gerund/noun]
意思：而不是……
接續：instead of + [verb-ing/noun]
解說：用於表示選擇前者而放棄後者，或採取替代方案。
影片原句
Chinese engineers asked a different question instead of just building bigger computers with better chips.
中國工程師提出了不同的問題：與其只是建造擁有更好晶片的更大電腦，
Chinese engineers asked a different question instead of just building bigger computers with better chips.
中國工程師提出了不同的問題：與其只是建造擁有更好晶片的更大電腦，
實例
- He chose to walk instead of driving.
他選擇走路而不是開車。 - We should focus on solutions instead of blaming others.
我們應該關注解決方案，而不是指責他人。
not only... but also...
意思：不僅……而且……
接續：not only + [clause] + but also + [clause]
解說：連接兩個完整的子句，強調兩者的並列關係，通常後者程度更深。
影片原句
China wasn't just blocked from buying the world's best AI hardware, it was also blocked from building it.
中國不僅被禁止購買全球最好的 AI 硬體，它也被禁止自行製造。
China wasn't just blocked from buying the world's best AI hardware, it was also blocked from building it.
中國不僅被禁止購買全球最好的 AI 硬體，它也被禁止自行製造。
實例
- The movie was not only funny but also educational.
這部電影不僅有趣，而且具有教育意義。 - He not only apologized but also offered compensation.
他不僅道歉，還提供了補償。
It is [adjective] that...
意思：……是……的
接續：It is + [adjective] + that + [clause]
解說：形式主語句型，用於表達對某件事的判斷、可能性或事實。
影片原句
It is very plausible that if we get this policy wrong, China could be overtaking the United States.
如果我們政策失當，中國有可能超越美國，這是非常可能發生的。
It is very plausible that if we get this policy wrong, China could be overtaking the United States.
如果我們政策失當，中國有可能超越美國，這是非常可能發生的。
實例
- It is likely that the meeting will be postponed.
會議可能會延期，這是很可能的。 - It is obvious that he made a mistake.
他犯了一個錯誤，這是很明顯的。
What if + [clause]?
意思：如果……會怎樣？
接續：What if + [subject] + [verb] ... ?
解說：用於提出假設性問題，探討某種情況發生後的後果或可能性。
影片原句
What if we build smarter AI?
我們能否建構更聰明的AI？
What if we build smarter AI?
我們能否建構更聰明的AI？
實例
- What if we miss the deadline?
如果我們錯過截止日期會怎樣？ - What if the plan fails?
如果計劃失敗了會怎樣？
not because... but because...
意思：不是因為……而是因為……
接續：not because + [clause] + but because + [clause]
解說：用於否定一個原因並提出另一個正確的原因，強調真正的動機。
影片原句
Not because browsers are cheap to build, but because Chrome brings people into Google's ecosystem
並非因為瀏覽器的開發成本低廉，而是因為 Chrome 將用戶引入 Google 的生態系統
Not because browsers are cheap to build, but because Chrome brings people into Google's ecosystem
並非因為瀏覽器的開發成本低廉，而是因為 Chrome 將用戶引入 Google 的生態系統
實例
- I didn't go because I was tired, but because I was sick.
我去不是因為我累了，而是因為我病了。 - It's not because he is lazy, but because he lacks resources.
這不是因為他懶惰，而是因為他缺乏資源。