Speaker 1: Good morning. The entire Chinese economy should be thought of as open source. New breakthroughs and discoveries are widely shared quickly across their industrial sectors and then are quickly adapted to new products and new technologies. Economists and governments have known for centuries about knowledge spillover. This is what happens when we put large numbers of people and companies in the same geographic area, working in the same or in adjacent industries. In these industrial clusters, innovation happens fast, because when one company does something that is revolutionary, that knowledge is quickly shared. Silicon Valley is one of the best examples in the United States of this, where new technologies and applications are discovered every day, and companies are always suing each other over who really developed what and when. And it's impossible to prevent customers from comparing what different suppliers are doing, or to stop employees from talking to their neighbors and friends, or from quitting one company to start work at another and taking his experience with him. The same dynamics that built Silicon Valley were put into action here in China, but at orders of magnitude higher and everywhere. When China was developing just 30 years ago, their industrial planners built hundreds of clusters across China in every single industry. These clusters share resources and logistics and supply chains, and universities were built to supply engineering and research talent. So China represents by far the biggest and most recent example of industrial-scale clustering, and China's recent history is a boon for researchers and scholars who are attempting now to quantify how knowledge spillovers contribute to innovation and economic growth. Here are a few terrific papers, and they're all recent, and they're not paywalled, and we'll link to them in the video description. Knowledge Spillover Effects from China's Car Manufacturing, which went from zero to the biggest in the world in about 15 years. This one studies spillovers in China's biggest superclusters, clusters of clusters of clusters really, Shanghai, Beijing, and Guangdong, and this is a good one about how it works in science parks. I'll quote from it, and the audience for this one is economists. Knowledge spillovers create benefits for firms besides the companies that make the first discoveries or innovations. That results in market failure because it is a disincentive for firms to do research and development since they cannot enjoy all the revenue streams that result. But the deliberate building of industrial clusters is how policymakers can overcome the reluctance of companies to do research and development. Governments can assume the cost of building the infrastructure, for example, and funding university departments and building supply chains, and clustering dozens or hundreds of fast-moving innovative firms will result in a rising tide, so to speak, that lifts all the boats that are there. Researchers at Company A discover a new process and publish a paper. Engineers at Company B read that paper and apply that process to a product they're building. Managers at Company C want to compete against Company B, so they work with engineers from Company A to build new products for C that are even faster than B's, and so on. A, B, and C are all learning from each other, and everyone is making money while driving costs down, and they're taking markets away from everyone else not in that cluster. All of China is organized in this way. Their entire economy is open source, and if we understand how China's economy works, we should not be surprised when China's open-source economic system so easily gets around the semiconductor bans that are intended to deny Chinese companies the best artificial intelligence. But industry insiders were very surprised by this news, that an unknown Chinese startup company built an AI model that is already better than Facebook's and OpenAI, and the company did it in two months and spent less than $6 million to develop it. The AI model is called DeepSeek, built by a company from Hangzhou, and has 671 billion parameters, and they got there using a lot less computing power than the biggest companies have. This analyst says that DeepSeek had a joke of a budget and needed only 2,048 GPUs and two months. It was previously thought that a comparable model would require about 16,000 GPUs, and DeepSeek required 11 times fewer GPU hours to build. This is an X-link to DeepSeek's report in Analytics. It has 5.5 million views, and the secret, again, is that DeepSeek used open source. CNBC explains how Chinese companies are using open source to build LLMs to innovate faster and spread their use. China's open source models have strong performance and low cost to serve, and it drives innovation faster because it opens up their models to more developers, including developers around the world. One reason these moves are so important is that the AI models will eventually become operating systems across industry lines and applications. What's more, the Chinese AI models have been developed using old chips, older version NVIDIA chips. U.S. companies are running the latest and fastest semiconductors in our AI models, and China's stuck using older chips, but they're getting them to work better. In November, Tencent, a giant tech company here, released their LLM using NVIDIA H20 chips, which are far less powerful than the ones our companies are using. It didn't matter. DeepSeek uses NVIDIA H800 chips, which are cheaper but slower than the H100s because NVIDIA H100s are restricted for export to China. And that didn't matter either. Open source is what matters. China threw open the problem to hundreds of thousands of engineers in dozens of places, and now they're ahead. Eric Schmidt was head of Google and was a key advisor on U.S. AI policy. He told policymakers that the semiconductor export restrictions would hold China back. May 2024 was eight months ago. That's all. And eight months ago, the former CEO of Google was sure that the U.S. lead in AI was two or three years, which is forever, and that the new chip bans would freeze China in place while the U.S. would just race ahead. Six months later, he gave another speech and said, Forget everything he said before. By then, Alibaba's Quen model, which is also open source, was better than anything being done by our companies. Then came Tencent. Alibaba and Tencent are enormous companies, by the way, and if they wanted to develop proprietary AI instead of going open source, they could have, but they went open source and got their models faster and cheaper. And now DeepSeek has done it faster and cheaper still, with less than $6 million. Eric Schmidt has a net worth of $26 billion, and no doubt he understands tech better than anyone he's giving policy advice to in Washington. But to understand what China did and how, it's not important to understand the technology. It's much more important to understand China. And our top people don't understand China. This is Yunnan, the countryside. Be good.