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Chapter 1: What is the main topic discussed in this episode?
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Chapter 2: What is Meta's multi-billion dollar deal with AMD about?
We know that some of their biggest projects are targeting five gigawatts, right? But they need to get certain regulatory approvals, and they need energy companies to be able to deliver there on the ground. So as for the merging of the energy and the compute here, time will tell as we home in on where those chips will actually go.
It's probably time for a bit of a reality check-in, and I'm hoping that you'll provide it for us. Like, in any given quarter right now, AMD is going to do $10 billion of revenue, all told, every single segment. NVIDIA's data center business is at more than $50 billion a quarter. Yes, that includes networking, but they're now one and the same, right? You can't have chips without the other.
Chapter 3: How is AI impacting market dynamics and investor sentiment?
How do we know if this is evidence that AMD is catching up?
I mean, again, you have to go back to the percentages, the growth percentages. At the moment, both of them would say, you know, this isn't about competition. This is about there's so much demand. A company like Meta, which makes its own chips, is buying from both of us. That would be the answer.
But ultimately, when the market slows down, and you can be the judge of when that happens, then we'll see the market share shift because then it'll really matter.
Chapter 4: What are the implications of AI tools unveiled by Anthropic?
Yeah.
Meta is not a hyperscaler, but it operates its own data centers at hyperscale, if you know what I mean. I'm thinking back to when Mark Zuckerberg was sat next to the president. $600 billion over the next few years. And then he's also talked about this idea that they might misspend $200 billion here or there, here or there. But that's no bad thing. He's front-loading purchases.
Just explain his thesis a bit.
Yeah, his strategy is just that. He's used the language of front-loading capacity. They're going to try to get as much as they can, gobble it up while there's still availability. And this, he says, could be applied not just for AI purposes, but for that core social media business, which still drives more than 98% of their revenue, right?
So they see applications across the board, and they're not worried about getting too much in the interim. Ian, why does AMD have to keep giving its shares away?
Again, we asked Lisa about this, and she said that it, this is Lisa Sue, the CEO, and she said this is different than the open air ideal. The open air ideal is a slightly different arrangement. That is a company, obviously, that is seeking liquidity. Meta clearly does not. need liquidity. This is just a way of showing Meta's commitment to what they're doing.
You've got to remember that, you know, AMD is only a couple of generations into being any type of presence at all in this market. So if a big buyer of this stuff comes along and says, oh, we like your gear. Oh, guess what? We also like you as a company, like your prospects. And that is somehow a bigger validation than just a straight purchase agreement.
And that really was what they were pushing as an idea that, look, this is how closely we are tied together.
Benefiting from the upside, Bloomberg's Ian King and Riley Griffin. Thank you so much for joining us on that roundtable. Meanwhile, coming up, software earnings, they take center stage too. What to expect as AI fears enter the picture. This is Bloomberg Tech.
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Chapter 5: How does Meta's deal with AMD affect its AI ambitions?
It feels into it a bit on the upside, but the company's still down, what, 40% this year. I don't know if it's quite that dramatic, but it's a scary time to be an application software vendor, right? I mean, the most extreme idea that companies are just going to vibe code their own solutions. I don't think anybody really believes that.
But the idea that software vendors lose the kind of pricing leverage they've enjoyed for so long because of that potential disruption, it's really something that markets are struggling to grapple with right now. And it's going to be a really interesting earnings cycle. It's basically pitching itself now as a platform, right?
So historically, Claude is focused on coding and making engineering more efficient. And if you're an Intuit customer, you already use all of Intuit's platforms and data. You just have an agent inside of it. If you're a small business or a consumer, it's the same with DocuSign. We're seeing all these names move. How is it making these companies better, Brody? You cover this beat inside and out.
Well, it's forcing them to really try to drive adoption and use among customers. Like, we all know that a lot of the AI hype was a bit premature in terms of how mature these tools were actually and how ready they were to be used in the enterprise.
And it's really holding toes to the fire that, hey, it's time to make sure your customers are actually using your AI tools or else the market's going to sell your stock and act like you all are going to go poof in a couple of years. Bloomberg's Brodie Ford, always a perfect way with words. We thank you.
Let's get more on the wider AI impact on tech and software with ClearBridge Investment Senior Research Analyst for software, for IT services, Hilary Frisch. Hilary, the perfect person. I go back to, in many ways, what Brodie was just saying. We're questioning just how good the tools are. Have we seen a sudden shift in how brilliant these AI agents are in the last few months?
We have. We're seeing this sell off in software and particularly SaaS because so much is changing so rapidly and these tools are evolving tremendously. That said, there's probably a pretty big disparity between some of the capabilities of the tools and what's happening and likely to happen, probably even intermediate term in the marketplace, particularly the enterprise marketplace.
But investors... shoot first and ask questions at some very distant date. So we're seeing a pretty dramatic compression in terminal multiples of many of the names. At what point are they going to become discerning?
Because at the moment, it's very hard for a CEO, for Arvind Krishna over at IBM, for whether it's ServiceNow, Bill McDermott, to disprove that they're not going to be impacted. That's right. So how do they prove a negative?
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Chapter 6: What challenges does AMD face in the competitive chip market?
Okay, well, let's talk about what's defensible, because there's this drip feed of AI doomerism, shall we say, in these various reports and blog posts coming from the CEO of Anthropic himself to over at Citrini. We've also had Harvard coming out with their piece that we're seeing it being incredibly effective when it comes to financial security. analysis and portfolio decision-making.
The Harvard-led study is talking about how trading, mutual fund trading decisions, 71%, it predicted right. The model was trained over a five-year window, but they're also talking about, look, maybe the outperformance bit is what it misses.
So, from your perspective, where's the outperformance going to come from some of the companies that can withstand this, that can show that they're really effective while using the AI tools?
Sure, sure. I think of security as an example of that. Anthropic introduced a vulnerability management tool, not even management, just a scanner for vulnerabilities in code that's produced on the platform. And that's a build time phenomenon. Most security vendors are runtime.
But they bring a lot to bear that we could talk about from a technological perspective that it's going to be hard for an LLM or an agent to have. They have the physical infrastructure. They cover all the enforcement points. It's partly a physical phenomenon, not just a digital phenomenon per se. But on top of that, when you think about it,
I don't know how entities and especially regulated entities can have the fox guarding the hen house when it comes to security. And kind of the same existed in cloud as well because AI creates so many vulnerabilities, so many new vulnerabilities that we've never had. that it's a major issue when you're looking to what's becoming the greatest attack surface to be the solution to the problem.
Similarly, the data platform vendors are really the platforms for the next generation of AI applications. We're seeing them build on top of the Snowflakes, the MongoDBs, even potentially the Oracles. To some degree, Datadog, that's slightly different, but what... customers are looking for is higher levels of automation to make their lives easier when there's a tsunami of data coming at them.
These infrastructures oftentimes are quite different. The data models are different from LLMs. The infrastructure on which they run are far more efficient than what LLMs do and seem to be able to do. And so I believe the greatest source of automation is going to come from these incumbent vendors. I should have mentioned Databricks, of course, in which ClearBridge has a private investment.
And so solving headaches for customers in the face of a ton of complexity is going to be of paramount importance.
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Chapter 7: How are companies navigating the AI disruption in software?
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