Well, welcome everyone. What I'd like to do over the next 40 minutes is explain to you why it is that semiconductors today are at the core, not just of advances in technology which we're used to, but also at the center of global

debate in economics and in international politics. And to argue that you can't understand the world around you without putting analysis of semiconductors at the center. Behind all artificial intelligence systems today is computing power. More and more compute is used to train the most successful AI systems, and

this is a trend that has lasted not just for years but for decades. Most of the advances in artificial intelligence have stemmed from using more data to train artificial intelligence systems, which in turn requires more computing power to use in the training of that data. This

chart shows you the most advanced AI systems of their day over the last 70 years, and what you find is that there's been a consistent, extraordinarily rapid rate of increase in the amount of data used for training AI systems. The amount of data used for AI training doubles on

a regular basis, and what's more, over the past 15 years the rate of increase has only accelerated. This is what AI researchers, people at AI labs like OpenAI or Anthropic, refer to as the scaling laws. The intuition is that if you want a better AI system, you

need a bigger AI system, a system trained on larger and larger quantities of data. Which is why the AI systems that today underpin chatbots like ChatGPT are trained on almost all of the text that exists on the internet. And it turns out we're just getting started. We're finding

more and more sources of data to use in training AI systems, and most of the optimism about improvements in AI over the coming decades stem from the belief that if we find yet more data and train yet bigger AI systems, we will have better artificial intelligence as a result.

If you want bigger AI systems trained on more and more data, you need better semiconductors because it's in vast data centers full of the most advanced servers with the most advanced chips inside of them that all AI systems are trained. This is why the world's biggest

tech companies are more focused than ever before on getting access to the cutting-edge AI chips that are used in training these extraordinarily capable systems, and it isn't only tech companies that are focused on computing power as a core metric of success in the world of

artificial intelligence. Governments are too. China, for example, has articulated a goal of increasing the computing power in China by 50% over the coming years. Countries from India to the United Kingdom have set out similar goals because they believe, just like all the world's large tech companies believe,

that better AI systems require more computing power, and therefore access to computing power, access to the cutting-edge chips that enable the training of big AI systems, will be critical not just for technological advances but for their economic and their political future. And if you look today at the distribution of

computing power around the world, what you find is that there are two countries far ahead of the rest. Here's a chart put together by the Blair Institute of the server installation base by country, how many servers every country has installed, and what you find is that there are two

leaders by far first the United States far out in front of the pack second China clearly in second place but also clearly ahead of the rest of the competitors and it's no surprise looking at charts like this that there are also two countries that are setting the terms

of the race to build AI systems these are the countries that have access to the extraordinary volumes of computing power that are needed to train AI systems and these are the countries that are competing right now to gain access to the cutting-edge chips that advanced AI training requires and it's

data centers like this that they're focused above all on building data centers vast warehouses full of thousands and thousands of chips and servers into which data is surged as AI systems are trained data centers like this are rewriting the wiring of the global economy they're reshaping

investment in many different countries they're attracting billions and billions of dollars from some of the world's largest investors who are betting that access to cutting-edge data centers will be economically critical in the future because data centers like this are not only used for training AI systems

they're also used for deploying them that's why AI data centers are being built not only in the United States and in China but in many other regions of the world too in Malaysia the center of the data center boom in Southeast Asia in the Middle East where Saudi Arabia

and the United Arab Emirates are trying to become AI hubs in the developing world many governments are focused on data centers like this as a tool of power in the modern world and it's forcing changes in how big tech companies operate because if you want a cutting-edge data center like this you

need extraordinary volumes of energy to power it, more energy than data centers have ever consumed before. And this is why some of the world's biggest technology companies are now also becoming some of the world's biggest energy investors. Whether it's Microsoft or Meta, Alphabet, or

Amazon, they're spending billions and in some cases tens of billions of dollars building and buying power directly. Because in certain regions, like in the United States, we're seeing the first increase in demand for electricity that we've seen in decades, and it's being driven above all by data centers. It's

artificial intelligence that's demanding more power than ever before, which is forcing tech companies to look harder than they've ever had to look before to guarantee they can get access to the power that they need. That's why, for example, many of the leading AI

entrepreneurs have themselves personally invested in projects to build fusion reactors. It's why Amazon recently announced it was buying a data center located right next to a nuclear power plant, giving you a sense of just how critical power supply is for these cutting-edge data

centers. But of course, the biggest challenge in building an AI data center is not getting the land or constructing the building or even accessing the power, challenging though that can be. The biggest challenge is getting the chips that go inside because at the core of

artificial intelligence systems are many different types of ultra-complex, cutting-edge semiconductors, which make it possible both to train and deploy the types of AI systems that are now being rolled out. And today, it's hard to get access to all of the chips that

You need not just hard for people like you or me, hard for people like Sam Altman, founder of OpenAI, who just a couple months ago was tweeting his apologies that he had to slow down access to certain ChatGPT services because even OpenAI couldn't get access to all of

the advanced GPU chips that it needed. It's these chips, the GPU graphics processing units, and the high bandwidth memory that they're coupled with, which are the currency of the AI era, the most important commodity and also a commodity that until very recently has been in extraordinary shortage. And if you want

to understand the economics of artificial intelligence or the geopolitics of AI, you've got to trace AI all the way back to the chips that AI systems are trained on. And you'll find, as I'll discuss in the remainder of my remarks, that the supply chain of companies that produces

these critical chips is extraordinarily specific and concentrated, which creates great monopolies and extraordinary profits to the companies that play a role in producing the computing power that AI requires. But it also creates extraordinary vulnerability given that many of these critical chips are produced by a single

company, something that in an era of intensified geopolitical competition is in a lot of ways a very dangerous risk. For now, the boom in investment in data centers has been an extraordinary benefit to the number one producer of GPUs, Nvidia, a company that

until recently was best known for producing chips used in video games and computer graphics. But over 15 years ago, Nvidia realized that the exact same chips that can render images beautifully on your computer or in video games can also be used to run the math that

undergirds artificial intelligence and so for a decade and a half, Nvidia and its extraordinary CEO Jensen Huang has been betting on Artificial Intelligence being the real use case of the GPU chips that his company specializes in. They spent this time building out a software

ecosystem to enable programmers and developers to build on top of GPUs in an efficient way. They benefited from academic research which showed that GPUs were far more capable of training AI systems than prior types of chips had been and it's turned Nvidia into a

company valued at trillions of dollars, one of the most valuable companies in the world and also a company whose products are something that the AI world can't live without. It's estimated that today 90% of advanced AI systems, basically every AI system that's not

trained by Google, is trained on Nvidia's chips. It's an extraordinary Market position at the center of the world's biggest technological transition and it's unsurprisingly created concern among other technology companies that their AI Ambitions are dependent on a single company and a single type of

semiconductor. And that's why as AI has become more important, as billions of dollars have been poured into developing AI applications, that many other tech companies have started designing their own GPUs too. It's not just other chip firms like AMD or Intel that are

building AI processors, it's tech companies like Amazon, Meta, Alphabet and Microsoft that are also trying to design their own AI accelerators to compete with Nvidia both because they don't want to have to pay the prices that Nvidia charges, but also because they're worried that their most important technology

might rely on a single provider, a single point of failure, which could threaten their ability to deploy the AI capabilities that they believe their future depends on. Elon Musk has weighed in on this issue too. He said that it's harder to get access to GPUs than it is

to get access to drugs now. Those of you who follow Elon closely might wonder how meaningful that statement actually is, but in fact Elon has spent the last year trying to acquire over 100,000 GPUs to build a massive new data center

in Memphis, Tennessee, where he plans to train a new AI system that's supposed to compete with OpenAI and Anthropic. And if the richest person in the world is struggling to get access to GPUs, you know that supply is indeed extraordinarily tight. So why, why is it

that one company has an almost monopolistic position in the production of these ultra-critical semiconductors? Why can't anyone else design comparable chips that can fit into the exact same supply chains? Well, the reason is that when you start digging into semiconductors like these,

you find that they're among the most complex manufactured goods that humanity has ever made. In fact, nothing really comes close to the complexity of cutting-edge semiconductors, whether the chips in your phone or the chips in your PC or vast data center chips like the one

pictured here. The manufacturing at nanometer scale, that's billionths of a meter, is basically unparalleled in every other segment of manufacturing. And the fact that chips like these can have billions or tens of billions or hundreds of billions of tiny transistors, little electrical switches

that turn circuits on and off mean that there's nothing else that comes close than the complexity of the number of components involved and this supply chain of companies that's necessary to produce chips like this is also the most complex of any that exists in any segment of the

economy in all of human history nothing else really comes close because Nvidia of course doesn't actually manufacture any semiconductors it never has it's made no effort to manufacture it only designs and relies on a series of other companies to do the manufacturing for it

and that's inevitable I think it's inevitable that you need a complex supply chain to manufacture the world's most complex products no company can do it on their own but it means that Supply Chain management is essential for a company like this and anyone who uses

their products and it means that understanding the supply chain understanding the choke points in the supply chain is critical for making sense of how AI systems are actually produced and indeed the supply chain of semiconductors like those that power AI stretch across the world a typical

semiconductor might be designed using software produced in the United States use intellectual property from companies based in the US and Europe rely on chemicals and materials from Japan be produced with manufacturing equipment from the Netherlands United States and Japan be actually manufactured in Taiwan before being sent to China or Malaysia

or somewhere else for assembly into a final product there's scarcely a single chip in the world that's produced entirely in one country almost every semiconductor that exists requires international trade and exchange the use of intellectual property of tools of components of materials from lots of

different countries and it's understandable why we rely on an international supply chain like this because the complexity is just too large, too extraordinary for a single company or a single country to do on its own. Just think about some of the materials that are involved in chip

making, whether it's the Silicon or many of the chemicals that are used in the manufacturing process. They often have to be 99.99999999% pure because misplacing just a handful of atoms in advanced chip making can cause defects in how circuits

work well. There aren't that many companies that know how to produce a material with that many nines of efficiency, and you don't just need one material in chip making, you need dozens. You don't just need one machine in chip making, you need many different types of

machines. You don't just need one type of software to produce an advanced chip, you often rely on multiple types of software, and the same is true for the intellectual property libraries that you draw on. This is why no one today is self-sufficient in semiconductors, not a

single company, not a single country, and it means that the semiconductor supply chain is more than any other segment of the economy reliant on the ability to transfer products and technology across borders, which means that there's also no other segment of the economy that's more

exposed to the intensification of geopolitical conflict between China on the one hand and the United States on the other. So let's pause for a minute and look at the shape of the semiconductor supply chain, who does what in the process of making advanced

Semiconductors: Here's a table that shows you the revenue share of different segments of the semiconductor supply chain by country.  United States, South Korea, Japan, Taiwan, Europe, China, and a handful of other countries on the far left-hand side, and the rows tell you the

supply chain steps that are necessary in producing a chip: First, the EDA, the electronic design automation software, the ultra-specialized tools that are used to design semiconductors. Second, the core intellectual property, the libraries of IP that are used in the design process

as well. Third, the actual manufacturer of silicon wafers, which just a handful of companies can do with the requisite level of purity. Fourth, the production of the fabrication tools, the extraordinarily complex tools – more on them in a moment – that are necessary to

produce advanced semiconductors. Then, the design of semiconductors, the fabrication, and the process of assembly, tests, and package. These are, in broad strokes, the key segments of the semiconductor supply chain, and you'll find, looking at the chart, that no one does it all. The US

doesn't do it all. Taiwan doesn't do it all. Korea can't do it all. Japan can't do it all. You'll find that there's specialization in different countries for every part of the supply chain, and you'll also find, somewhat surprisingly perhaps, that China, the world's

manufacturing superpower, plays a surprisingly small role. In fact, China spent as much money each year over the past decade importing semiconductors as it spent buying oil. Oil! Think about that. China spends more money buying chips than it spends buying oil. In fact, in all of world trade

there's no larger flow of goods than the flow of chips into China: chips from Korea, chips from Japan, chips from Southeast Asia, chips from the US, chips perhaps most importantly from Taiwan. And these chips are used both to assemble devices in China that are then sold to

the rest of the world, like phones or speakers, or used for China's internal consumption. And if you put yourselves in the shoes of China's leadership, this is an extraordinary, a staggering vulnerability: the world's largest manufacturer can't produce all the chips that it needs and yet manufactured

products every year rely on more and more semiconductors. That's why the trend in China has been importing more and more semiconductors from Korea, from Taiwan, every year over the past decade. Without these chips, today China's manufacturing base would freeze. It's

impossible to produce a car, for example, without semiconductors. And most of the chips in the cars made in China are imported. The same is true for computers, the same is true for most of the phones in China, the same is true for medical

devices and construction equipment and agricultural equipment. China imports most of the chips that it relies on, and it doesn't like this status quo. It doesn't like the status quo for economic reasons: chipmaking is a very good business, as Chinese leaders have noticed, as they've

looked around Asia where countries like Japan and Korea and Taiwan have gotten wealthy in no small part because of their mastery of the chip business. But China also doesn't like it for political reasons because China's leaders realize that they import critical semiconductors

largely from countries that are at best competitors and at worst geopolitical adversaries. If you were sitting in Beijing thinking through worst-case scenarios, one of your worst-case scenarios would look like this: What happens to your manufacturing base if

the supply of chips flowing into China gets cut off? Your manufacturing base would freeze up. And so you can understand why over the last 10 years China has tried to become more self-sufficient in semiconductors. In 2014, President Xi

Jinping identified chips as what he calls a core technology. And since then, almost all of China's industrial policy programs like Made in China 2025 have identified semiconductors as a priority, a sector in which China wants to buy less from abroad and produce more at

home. More because this is an industry with advanced technology and high margins, there's simple economic reasons, but also because China thinks, thinks that self-sufficiency would give it political benefits. It'd be less reliant on its neighbors, less reliant on Taiwan, less reliant on Korea, less reliant on

the United States. And so one of the key questions overhanging not just the tech industry, but the entire global economy is to what extent China's self-sufficiency drive will succeed. If it does succeed, the consequences for international trade are profound. The biggest flow in AAL trade

could well be reduced if China's able to produce more of the chips that it needs domestically. And so I think this bears very careful watching for anyone interested in the shape of the global economy. There's actually nothing more important, I think, in all of

international trade than this question because there's no flow of goods that's larger than the problem that China faces. The challenge is that it's hard enough to become self-sufficient in the manufacturing of chips, but there's the rest of the supply chain that matters too. If you can

manufacture all the chips you need, but you can't manufacture all the components that go into all the chips that you need, are you really self-sufficient? And as my table previously showed today, no one is self-sufficient, not the US, not Taiwan, not Japan, not Korea, and so

self-sufficiency is an extraordinarily ambitious goal. If you want to produce all the materials, all the software, all the designs, and all the machine tools yourself, that's a whole lot of R&D you need to master, a whole lot of capital expenditure you need to undertake, and a

whole lot of extraordinarily complex technologies that you must learn how to produce. And it's machines like these that are the biggest challenge that China faces. Pictured here is an extreme ultraviolet lithography machine produced by just one company, ASML of the Netherlands, which has 100%

of world production of these advanced lithography systems. It took ASML around three decades to learn how to produce tools like this, which today are critical to the production of advanced chips at any sort of scale in an economically efficient way. And these tools are without any question the most

complex machine tools that have ever existed. The most cutting-edge tools that ASML produces cost over $300 million a piece to purchase. They involve components like the flattest mirrors humans have ever made, the most powerful laser deployed in a commercial device, and an explosion

happening constantly inside the machine as a tiny ball of tin is pulverized by the laser and explodes into a plasma at a temperature 40 times hotter than the surface of the Sun. If you were looking for a tool to learn how to replicate this, this is not

the one you choose. There's nothing more complex than this, and there's not just one company that produces this tool. ASML assembles it, but it in turn relies on its own supply chain of mind-boggling complexity. There are so many components in these

tools that ASML does not know how many components are inside, but the number is at least in the hundreds of thousands, relying on precision-manufactured parts from Europe, from the United States, and from Japan, which would be extraordinarily difficult to replicate. And indeed, China's

been trying since 2018. It's been illegal to transfer these tools to China, and yet six years later, ASML is still the monopoly player with no one in Japan and the United States or in China having succeeded in replicating what ASML can produce. And this is not the only machine you

need to make advanced chips. This is one of several. There are other machines that can lay down thin films of material just a couple of atoms thick, other machines that can etch tiny canyons into silicon just a couple of atoms wide, yet more machines that can

inspect the semiconductor once it's been manufactured and identify nanometer-scale errors in the manufacturing process. And you need all of these to make chips at the cutting edge at scale. When you start to dig into equipment like this, you can understand why no one is self-sufficient in

Semiconductors: why not a single country in the world has learned to produce all the equipment, all of the materials, all the software that's necessary to make advanced chips. It would be extraordinarily expensive, complex, mind-bogglingly, uh, uh, demanding to

undertake a self-sufficiency drive, and it illustrates the challenges that China's own self-sufficiency drive involves. Because when US government officials look at the future of artificial intelligence and look at the chips that have made advances in

artificial intelligence possible, they want those technologies first to go to friendly countries before adversaries. Which is why since 2018, it's been illegal to transfer these tools to China, and why since 2022 the US has restricted the ability of Nvidia, a US firm, to sell to China

which used to be its second largest market. The bet the US government is making is simple: it's the exact same bet that Google's making, the same bet that Sam Altman of OpenAI is making, the same bet that Anthropic is making, and Mark Zuckerberg of Meta is making. The bets

that if you want better AI systems, you need bigger AI systems. If you want bigger AI systems, you need better GPUs. And if you want better GPUs, you need tools like this to produce them. And that's why the US has injected a new level of geopolitics into the

semiconductor supply chain. If this bet is correct, that better chips are critical for better AI, then tools like this are choke points, choke points in the ability to produce the chips that advanced AI requires. And it's this word 'choke point' that has also spurred China into action, spurred

China into trying to develop its own domestic capabilities spurred China into trying to further substitute out imported products and replace them with Chinese components. In other words, China's strategy and America's strategy are built on the same basic assumptions. China thinks that AI will be very

important, that tools like these will be critical, that the shape of international trade will be determined by whether or not China can be self-sufficient in semiconductors. And so China's poured tens of billions of dollars a year into trying to build, into trying to build

self-sufficiency. The US has the exact same assumptions and for the exact same reasons is trying to cut China off from these tools and the advanced chips that they enable. And the reason that both Beijing and Washington are fixated on AI as critical to the future is not

primarily about ChatGPT. It's not about who will create the next trillion dollar tech company. It's not about whether AI will transform the way we write emails or post on social media. It's because they're both betting that AI will be critical to some of the

core elements of national power. And indeed in the Indo-Pacific region, both governments perceive an arms race underway, an arms race that will be measured in some of the traditional metrics of military power like the number of ships that are built or the number of missiles that are

deployed, but also an arms race that will be shaped by artificial intelligence. And this isn't a projection about the future. This is a statement of reality. It's already the case that next generation military systems that are being fielded right now are built on the assumption

that AI and autonomous action will be at the center of how militaries fight and how spy agencies operate. We see it, for example, in the Russia-Ukraine war where both the Russians and the Ukrainians have poured resources into building out their drone programs. Drones which in

some cases are quite simple but in other cases are increasingly autonomous in their action, and nearly every military that's looked at the Russia-Ukraine war has concluded that future conflicts involving more technologically advanced countries will rely even more heavily on autonomous systems. Well, an autonomous

system is an AI-enabled system and just like if you want your car to drive autonomously you need a whole lot of computing power, so too if you want your drone to fly autonomously it requires the exact same computational inputs. In other words, computing and

drones are two sides of the same coin and it's not just drones, the same is true for intelligence. It's now been nearly a decade at least we know from public sources that the US military has been using AI to make sense of all the

Chip War 2.0: The Global Battle for Semiconductor Supremacy│Chris Miller (The Author of “Chip War")

매경미디어그룹이 주최하는 세계지식포럼은 지식 공유를 바탕으로 세계의 균형 잡힌 성장과 번영을 추구하고자 2000년부터 시작한 아시아 최대 글로벌 비즈니스 포럼입니다. 

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기술과 지정학에서 반도체의 중심적 역할

반도체는 기술 발전과 글로벌 경제 및 정치 논의의 근간을 이루는 핵심 요소입니다.

반도체를 중심으로 한 컴퓨팅 성능은 인공 지능(AI) 시스템에 매우 중요합니다.

AI의 발전은 학습을 위한 데이터 증가에 의존하기 때문에 더 많은 컴퓨팅 성능을 필요로 합니다.

AI 학습에 사용되는 데이터는 지난 15년 동안 정기적으로 두 배로 증가하며 그 속도가 빨라졌습니다.

주요 기술 기업과 정부는 최첨단 AI 칩에 대한 접근성을 우선시합니다.

컴퓨팅 성능의 글로벌 분포와 AI 경쟁

미국과 중국이 서버 설치 대수에서 선두를 달리고 있으며, 이는 두 나라의 AI 개발 우위를 반영합니다.

이들 국가는 AI 학습에 필요한 고급 칩을 확보하기 위해 경쟁하고 있습니다.

수천 개의 칩과 서버로 가득 찬 데이터 센터는 전 세계 투자와 경제를 재편하고 있습니다.

데이터 센터는 전 세계적으로 건설되고 있으며, 많은 정부가 데이터 센터를 권력 도구로 간주하고 있습니다.

첨단 반도체는 AI 시스템을 훈련하고 배포하는 데 매우 중요합니다.

OpenAI조차도 충분한 고급 GPU 칩을 확보하는 데 어려움을 겪고 있습니다.

GPU와 고대역폭 메모리는 AI 시대의 필수품이지만 최근 공급이 부족한 상황입니다.

AI 경제와 지정학을 이해하려면 칩 공급망을 분석해야 합니다.

엔비디아의 우위와 경쟁사의 대응

처음에는 게임용으로 사용되던 Nvidia의 GPU는 이제 AI 교육에 매우 중요한 역할을 하고 있습니다.

엔비디아의 칩은 고급 AI 시스템의 약 90%에 사용됩니다.

다른 기술 기업들은 엔비디아에 대한 의존도를 낮추기 위해 자체 AI 프로세서를 설계하고 있습니다.

엘론 머스크조차도 AI 프로젝트를 위한 GPU를 확보하는 데 어려움을 겪고 있습니다.

반도체 제조 및 공급망의 복잡성

반도체는 나노미터 단위로 제조되는 가장 복잡한 공산품 중 하나입니다.

엔비디아는 칩을 설계하지만 제조는 다른 회사에 의존하기 때문에 공급망이 복잡합니다.

반도체 공급망은 여러 국가가 다양한 단계에 특화되어 있는 글로벌 공급망입니다.

칩 제조에는 매우 순수한 재료와 정교한 장비가 필요하기 때문에 자급자족이 어렵습니다.

반도체 공급망의 지정학적 의미

표는 국가별 반도체 공급망 부문의 매출 점유율을 보여줍니다.

수입 반도체에 대한 중국의 의존도는 심각한 취약점입니다.

반도체 자급자족을 향한 중국의 노력은 경제적, 정치적 요인에 의해 주도되고 있습니다.

중국의 자급자족 성공은 국제 무역에 큰 영향을 미칠 것입니다.

전체 공급망의 복잡성으로 인해 진정한 자급자족은 어려운 일입니다.

ASML의 극자외선 리소그래피 장비는 첨단 칩 생산에 필수적이지만 중국으로의 판매가 제한되어 있습니다.

첨단 칩 제조에는 다양하고 복잡한 기계가 필요하기 때문에 자급자족이 어렵습니다.

미국은 기술적 우위를 유지하기 위해 칩 제조 도구와 칩의 중국 판매를 제한하고 있습니다.

미국과 중국 모두 AI를 국력의 핵심으로 여기고 있으며, 이는 경쟁적인 전략으로 이어지고 있습니다.

AI는 군사 능력과 정보 작전에 영향을 미치는 국가 전력의 핵심 요소로 간주됩니다.

AI로 구현되는 자율 시스템은 전쟁에서 점점 더 중요해지고 있습니다.

인공위성이 수집한 인텔리전스 데이터를 분석하는 데 AI가 사용됩니다.

미국과 중국 모두 군사 및 정보 분야에서 우위를 점하기 위해 우수한 AI 생태계를 개발하는 것을 목표로 합니다.

미국은 중국의 첨단 칩과 컴퓨팅 성능에 대한 접근을 제한하는 것을 목표로 하고 있습니다.

현재는 서구 기업이 AI 개발을 주도하고 있지만, 이는 바뀔 수 있습니다.

지정학적 경쟁이 AI 공급망을 재편하고 있습니다.

중국은 반도체 역량을 향상시키기 위해 노력하고 있지만 도전에 직면해 있습니다.

반도체를 이해하는 것은 AI의 미래와 정치화를 이해하는 데 매우 중요합니다.