Last week, the Chinese company DeepSeek debuted a new AI model -- and overturned years of conventional wisdom about what it takes to build great AI. The shock unleashed a $1 trillion bloodbath on Wall Street. WSJ’s Stu Woo and WSJ’s Gunjan Banerji unpack DeepSeek's achievement and the market chaos it unleashed. Further Reading: -How China’s DeepSeek Outsmarted America -The Day DeepSeek Turned Tech and Wall Street Upside Down Further Listening: -The Company Behind Chat GPT -The Hidden Workforce That Helped Filter Violence and Abuse Out of ChatGPT Learn more about your ad choices. Visit megaphone.fm/adchoices
Chapter 1: What caused the stock market chaos?
I get to my desk and, you know, around the time the market opens at 9.30, I think everyone is kind of glued to their screens at that point. And they see that this really ugly day for the stock market is beginning. All three major indexes are down a bunch. NVIDIA, of course, is down double digits.
NVIDIA, the AI chip maker. Its stock was tanking.
We could be looking at the biggest drop in market cap on record here for NVIDIA when you take a look at the red across the screen, specifically the NASDAQ 100 futures and the...
So NVIDIA was down more than 10% shortly after the opening bell. It ended the day down 17%. And just to put that into context, that is a market value loss of almost $600 billion. So much money. Right. That is just an insane amount of wealth and amount of value that evaporated within hours. In fact, it is the biggest one-day market value drop on record.
By the end of Monday, about a trillion dollars of value had been wiped from the stock market. But while the drop was historic, Gunjan also had a pretty good idea of why it was happening.
So what traders, people on Wall Street, Silicon Valley was pointing to was this upstart artificial intelligence company, DeepSeek.
DeepSeek. It's an AI company out of China. And over the last few days, its chatbot has been blowing people away. Experts say DeepSeek's AI is just as capable, maybe even more capable, than leading AI chatbots like ChatGPT. But its creators claim it was made for much less money. And that set off a major shakeup in Silicon Valley and on Wall Street.
DeepSeek, this new artificial intelligence competitor, forced everyone involved. to take a look at their portfolios, take a look at their AI products, and really rethink who the winners and losers of this artificial intelligence trade were going to be. All of a sudden, investors were going, hey, are the stocks that we own, is Nvidia, is it worth what we think it's worth?
Welcome to The Journal, our show about money, business, and power. I'm Jessica Mendoza. It's Wednesday, January 29th. Coming up on the show, how DeepSeek sank the stock market.
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Chapter 2: How did DeepSeek challenge AI norms?
So tell us a little bit about the company itself and the AI model. Who made it?
So it's the brainchild of a Chinese guy named Liang Wenfeng. He co-founded this hedge fund in China. It's based in Hangzhou, which is also the same tech hub where the Chinese company Alibaba is based. So DeepSeek grew out of that. So Liang's a pretty smart guy. He studied AI at one of China's top engineering programs.
And what I thought was really interesting about the company was that it had this really unusual hiring practice. Liang wants creative people, but he doesn't really care that much about experience. And he says his hiring principle is hire people with the least amount of experience because his idea is that if you ask someone with work experience to solve a problem,
they're going to say, well, we should solve it like this because this is how I've done it in the past. But if you ask people without experience to solve that same problem, they'll have to sit down, think about the problem, and then they'll figure out the best and freshest and most efficient way to do it.
So that's why a lot of people who work at DeepSeek are either fresh graduates or people with just a year or two of work experience.
And so this sort of takes us to the AI model that that approach kind of created. So before DeepSeek, what was sort of the going assumption about how you make a cutting-edge AI model?
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Chapter 3: What is DeepSeek and how does it work?
Yeah, so the conventional thinking was that if you wanted to make a world-class AI chatbot or AI system, you needed a lot of the world's best AI chips that are super expensive as well.
In the U.S., AI development has been dominated by a handful of big tech companies who've trained their AI models using tons of top-line AI chips. Those chips are largely made by, you guessed it, NVIDIA. The assumption was if you didn't have enough of the right kind of chips, you couldn't build a world-class AI model.
And the other assumption was that a Chinese company could never do that because the U.S. government had set these restrictions on what kind of chips U.S. companies could sell to China. The thinking was that China would never catch up.
So let's take a look at those assumptions. The first of those assumptions is that, like you said, you need a lot of chips to create these high-powered AI models. How did DeepSea sort of undermine that assumption?
Yeah, so DeepSeek released this research paper that explained how it did what it did. And it said that it spent a fraction of the money developing its advanced chatbot. And it did so using less advanced chips. So how can we understand that? So I think a good analogy is that let's look at the first chat GPT that many of us have used. And let's try to understand how that was trained.
So imagine that ChatGPT is like a librarian that's read all the books in the library. And when you ask it a question, it'll give you an answer because it's read that book. But the problem is that to read all those books, that requires a lot of time and a lot of electricity for those computer chips to read those books. So DeepSeek didn't have those resources, so it tried a new approach.
So imagine you're still in the library, and DeepSeek is a librarian, but it hasn't read all those books. What it does instead is that it's focused on being really good at figuring out what book has the answer after you ask it the question. And it turns out that's just as effective as what ChatGPT originally did. It was just as good, but it used a fraction of the resources.
It makes me think a little bit about kind of expert versus journalist in some ways. It's like what we do is we know who to ask and what questions to ask instead of actually like, you know, getting the PhD. We like go to the experts ourselves versus the expert who has to like learn everything about that subject.
Yeah, that's a good example. There's very few of us who can just read all those books and just maintain all that information in their head. And then when we have to figure out, we just kind of like stress out and call everybody we know and try to answer that question within an hour.
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