Chapter 1: What makes NVIDIA the leader in AI chip technology?
In the world of AI computing, one company stands at the top as king.
NVIDIA.
NVIDIA. NVIDIA. NVIDIA. NVIDIA. Can you characterize how big and how dominant NVIDIA is?
Yeah, NVIDIA, most people think that NVIDIA controls 90% or more of the advanced AI chip market.
That's our colleague Robbie Whelan. He covers tech.
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Chapter 2: How has Lisa Su transformed AMD since becoming CEO?
And I can't think of another industry where there's such a market concentration. It's not that Nvidia has chased out the competition or had some sort of nefarious strategy to make it impossible for people to compete with them.
It's that they were very early first movers in this idea that these chips that used to be primarily used for video games were also really, really good for doing AI computing.
As the AI industry is boomed, more and more companies are developing their own AI chips and coming for NVIDIA's crown. But the company that might have the best shot is a small one, one you've probably never heard of, Advanced Micro Devices, or AMD.
So AMD is very much the David to NVIDIA's Goliath. And the CEO of AMD, a woman named Lisa Su, she has a really good track record of slaying giants. She's done it before, but she's never met an opponent like NVIDIA. NVIDIA is truly a Goliath here. And the idea that she's going to take them on, it's very audacious and it's very exciting for a lot of people who watch this industry.
Chapter 3: What significant deal did AMD strike with OpenAI?
When Lisa Su took over AMD a little over a decade ago, its market cap was less than $3 billion. Now, AMD is worth more than $350 billion. And there's no sign of slowing down. A few weeks ago, AMD scored a major coup when it inked a massive chips deal with OpenAI.
Recently, Robby sat down with Su to talk about the deal, the company, and why she believes we're only scratching the surface of the AI revolution.
Where are we in terms of focus right now about going forward and how to get to where you want to be? And where is that that you want to be?
Well, we are in a very special time. So we are probably going faster than we've ever gone before. I mean, I certainly believe that the technology is moving faster than I've ever seen in my career.
Chapter 4: What is the importance of inference computing in AI?
And the role of AMD is to enable all of that with the foundational computing.
Welcome to The Journal, our show about money, business, and power. I'm Ryan Knudson. It's Tuesday, December 9th. Coming up on the show, the CEO taking on NVIDIA and why she's not worried about an AI bubble. Lisa Su rose to the chips industry as an engineer. Early in her career, she worked at IBM and Texas Instruments.
She's an engineer who has a PhD in electrical engineering from MIT. She's deeply interested in what's called device physics, which is sort of the marriage of gadgets and hard science. So she understands how a piece of silicon inscribed with transistors translates into the computing power that appears on your screen when you're a software developer.
Chapter 5: How does AMD's strategy differ from NVIDIA's in the AI market?
She understands it perfectly.
Sue joined AMD in 2012 and took over as CEO two years later. And soon after, she earned a reputation for making bold bets. AMD is an American company that's been a longtime player in the chip space. It was founded in the late 1960s, and it's based in Santa Clara. When Sue joined the company in 2012, it was mainly focused on chips for individual computers and electronics, known as CPUs.
But when she took over as CEO two years later in 2014, she saw some holes in AMD's strategy.
She looked at the product line of what AMD was making and she said, we're not focusing on the right things. We need to be focused on accelerated advanced computing because that's what our customers want.
She would go to these meetings and they would say, it's great that you guys make computer chips that power PCs or mobile devices, but what we really want is a data center server that can help us take our products to the next level.
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Chapter 6: What concerns exist about the potential AI bubble?
So she totally revamped AMD's product line, and AMD started making these chips that are essential to the data center.
Sue started focusing on the chips that were critical for cloud computing, an area that exploded over the last 15 years. And the strategy paid off, and it helped AMD become a major player in a market that had long been dominated by Intel.
She positioned herself in a way that she was in the data center, she ate up Intel's market share in data center computing, and she really leapfrogged Intel.
But the pivot proved consequential for another reason. It gave AMD a foothold in data centers, which had become critical in the rise of AI. After the launch of ChatGPT in 2022, Sue saw an opportunity to pivot the company again.
They were having a board meeting in late 2022. And so she comes to this board meeting and she says, look, I've had this epiphany. I'm going to pivot the entire company.
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Chapter 7: What are the implications of circular financing in tech deals?
She says, you know, artificial intelligence is rising. It's a once-in-a-lifetime opportunity. And we are positioned in a very special way for us to take advantage of that. She said, we're going to revamp our entire product line so that it's now all oriented around artificial intelligence. And this was a major turning point.
This time, Sue wanted to take aim at NVIDIA. Back then, NVIDIA wasn't a household name. But it was clear to Sue that it was on the rise.
ChatGPT was trained on NVIDIA's chips. It took seeing a product like ChatGPT for most people in the world to realize how big NVIDIA was going to get, how big AI was going to get, and I think that that was the real talent that Lisa Su has that shined through in this moment.
Chapter 8: Can AMD compete with NVIDIA in the AI chip market?
She said, this is going to change everything, and we have to be in front of it.
We're going to bet the whole company on it, basically.
Exactly.
We're betting the whole company on it. By the way, NVIDIA's CEO, Jensen Huang, and Su were actually distant cousins. They only met as adults. Fast forward, you know, two to three years. How does that pivot go? Like, what does she actually do?
She goes top to bottom and revamps AMD's entire product line. She rolls out three or four generations of what's called the Instinct Series GPU. So this is the first time AMD in the modern era, in the AI era, is directly competing with NVIDIA. And to be honest, they have a hard time competing with NVIDIA.
I mean, the first few generations of the instinct were generally regarded in the market as not as powerful, not as easy to use as NVIDIA's equivalents. But that's okay for a company like AMD, because what they're doing there is they're trying to get a foothold. They're testing out these products, and they're trying to find customers to sign up and commit to using them.
To take on NVIDIA, Su decided to focus on chips that were one step ahead. Rather than making chips that are good at training AI models, which has dominated the AI market so far, Su wanted to make chips that were good at inferencing. What does that mean? What is inference computing?
So in AI computing, there are two main functions that people need to utilize. When you're developing an AI model or an AI tool, whatever it is, be it a chatbot or a video generation app, you have to first train it, And then you have to make it capable of responding to queries, which is to say you have to run it. And the running it is usually referred to as inference, inference computing.
Inference is when you or I sit down and says, hey, chat GPT, what should I eat for lunch today? And spits out a few recipes for us. That is inference. It's querying the models and it's running them.
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