Welcome, everyone, to The Information's TITB. My name is Akash Pasricha. It is Wednesday, May 6th. First up today, AMD shares are rising after their quarterly results yesterday. The company reported a 38% jump in revenue bolstered by its data center segment. We'll get into the results in just a moment. Plus, the information published exclusive reporting with new numbers that highlight the growing link between Anthropic and Google's businesses. We'll bring on the reporters who broke that story. We'll also get our daily check-in from Rocket Drew, who was covering the Elon Musk OpenAI trial. I'll then talk with the CEO of Tessera Labs, which just announced a new multi-million dollar funding round. And we'll close out the show with some insights from the Information Subscriber Survey, highlighting who is winning in the three-way race between Chachupiti, Claude, and Gemini. It's going to be a fun show, so let's get right on into it. AMD posted another big quarter. Revenue jumped 38% to $10.3 billion in Q1, driven by its data center segment. I want to bring on the CEO of Creative Strategies, Ben Beharin, to help us break it all down. Ben, welcome back to the show. It's great to have you here. Yeah, good to be back. Thanks for having me. Okay, so AMD is up. What stood out to you from the results? Yeah, I mean, we weren't terribly shocked. I think, you know, there'd been enough signal around what we'll just say, like data center CPU as a demand trend that I think everybody sort of assumed they would be in a similar spot, as we saw from Intel in terms of just outpacing supply with demand. And part of that's really just being driven, obviously, by changes in infrastructure, agentic inference that's happening, which was not a dynamic we had a year ago. And so there's a number of new things coming to the industry where, obviously, AMD is a huge beneficiary of that. And so really, the spotlight was on CPU growth. They updated their TAM, which sort of aligns with some of our forecasts around the CPU TAM because of agentic. And really, I think overall, right, this is the bulk of the story right now. Not that GPUs aren't, you know, sexy. They and accelerators are still relevant, but big spotlight on CPUs. And that story alone seems to be enough to be driving this part of the market. So when you talk about the total addressable market expanding here, the idea here is what? That agentic inference, I mean, that requires more CPUs in this environment, right? Yeah. And I think the best way to look at it, right, prior to Agentix, if we were just talking about, you know, the framing that we use, which is cloud-native CPUs. So prior to this moment, AMD, even ARM, right, Intel would talk about, you know, we're mostly just selling these to hyperscalers. They're running enterprise software, right? They're running their e-commerce. They're running their email. Like, they're hosting cloud workflows. And that market wasn't growing. It was roughly a $25 billion market a year and staying relatively flat. And CPUs were not super interesting, right? Now people are coming out and saying that number is drastically changing as of even, you know, Lisa Sue said last year, they changed their CPU TAM to going up to 60 billion. So from 25 to 60, which alone at the time was like, that's a pretty big jump, right? And so now saying, well, a JETTIC drives another 40 plus billion, or in their case, more than that, because they're saying 125 billion. We were around 110 at our first shot with this, is again, a drastic increase from what is typically a $25 billion market and not growing. And so that's why I think in the market context, people are seeing that and realizing that the TAM is expanding. It used to be a share-taking environment. When we talked about AMD encroaching on Intel in data centers or even ARM, it's not share-taking anymore. where it's share grabbing in what is essentially, as Lisa said, a 35% year-over-year growth. And just for agentic, we have that growing 65% year-over-year for just agentic. Why is it share grabbing now instead of share taking? And what is the difference there in your mind? Yeah, I mean, again, when it was a $25 billion market, right, and growing, we know how many units that moved every year. It was their ability to grow for AMD or Intel, or even when you thought about like ARMS potential, right, prior to this was purely on take share because it wasn't growing. Now that the market is moving meaningfully to a drastically larger size, both in unit volume, ASP, you know, as a dollar amount, it's really just greenfield growth for CPUs and agentic for new data centers. So in a lot of ways, like we thought about, right, that the GPU expansion story, so NVIDIA while being dominant, right, moving to a trillion dollar market for just AI accelerators. I'm not even including CPUs in this point, was a everybody benefits, right? It's not just dollars there for NVIDIA. There's dollars for AMD. There's dollars for other competitors. There's dollars for, you know, the guys, everybody making their own custom silicon. So in TAM expansion, like we're talking about, it's brand new dollars to go after. It's not I have to take money from my competitor. Right. I guess my question is, so in that story, though, I mean, how confident are you in AMD's prospect to then grow its market share? The market's expanding, I'm with you. But in terms of growing the market share, you know, how sustainable is the current strategy in terms of trying to grab a higher percent of the market? Very. I mean, I think, you know, again, and I'll make this point for everyone in CPUs, right, that's making a claim that they're doing CPUs in the data center. This is a massive expansion that they're going after. From A&B's standpoint, right, what's interesting is they have typically done a lot of customization and collaboration with their hyperscale customers. So, you know, the Googles, the Amazons, the Microsofts of the world. And this will have to extend to Neoclods, to be honest with you. Like this is something I've been arguing with the Neoclan cultists. I know they hate it when I say that, but like the folks in those communities is that most of their infrastructure has been in the training era, right? If you're just installing NVIDIA solutions, right, Blackwells and whatnot, those were training and some inference. Everybody's agreeing that inference infrastructure looks drastically different than training, and that's where CPU racks come in. So there's a ton of data center CPUs that have to get played out. And AMD has always had a strategy of saying we can change our SKUs. We have a one architecture that scales, and we can do agentic tweaks to our CPU, or we can keep them for cloud-native workloads within their pipeline. So, and again, they are deep customers with the hyperscalers. And right now, there's so much CPU demand, like, really more than we ever thought a year ago was going to come up. And that alone, it just means if you're relevant in data center CPU, like, you're going to see market expansion. Right. What did we get from the results around AMD's full rack system and how that competes with NVIDIA? Where is that at and how confident are you that that will actually pose a challenge? Yeah, I mean, not a lot yet, right? They have an event coming up in July called Advancing AI where we're going to learn a whole lot about Helios as well as the successor to Helios which they call MI555 People tried to prowl this on the call right Everybody wants to know what their prior so MI 355 has looked like Again it hasn been gigantic right The role of the market has moved, again, largely to NVIDIA's accelerators as well as custom ASICs, which continue to dominate this conversation. But I think the second half of the year is where this ramp is. And I'll just sort of lumped this as a broad statement, but I think this is relevant for AMD too. We're at such a compute deficit for AI that the reality is almost any viable compute will get adopted. And that's why you saw them do deals with, you know, OpenAI, right, and others. And I expect you'll see them do a whole lot more because you just can't get enough compute for one vendor. You need two, you need three, you may even need four vendors to have enough to meet this demand, right, that everybody's seeing. And so there's a lot of opportunity and growth side. And I think the second half of the year is what, you know, Lisa and management has said, this starts to ramp. And I think once we see what MI455 looks like, so Helios at the advancing AI day and beyond, people will have a little bit more confidence about where that goes and how much they can grow that business, you know, into 27 and 28. Right. Last question for you. So what do you think it would take for investors to fall out of love with AMD? This is obviously, this is boom times for the company, but now we've seen a couple chip companies go through their boom period and then keep booming. But, you know, when does the multiple start to contract, in your opinion? Yeah, I think the debate really is how much capacity, right, can you grow and scale? And if you're just constantly in a supply constrained environment, then most people will model in what they believe, right, your forward projections can be in terms of growth. And so if that's your constraint, then the only upside is can you get more capacity? Like where are you going to get more wafers? Where are you going to get more manufacturing capacity? And that's, to be honest with you, a dynamic that I think sitting in video is that most people just have said, we know how much you can grow or we think we know how much you can grow based on your supply chain. And that's what we're pricing in. And that's really, I think, the dynamic that everybody's working on in their models. So so the downside is right that they've capped. They're capped and everybody knows it and they've modeled that. And can they defeat that narrative? But that would be the stale point if there's anything. The momentum in CPUs remains an upward trajectory. Their business will grow, but how much can they grow really becomes then where the model gets focused on. Right. Great. Well, Ben, I want to thank you for coming on. That is Ben Beharin from Creative Strategies here on TITV. It is no secret that Anthropic and Google's businesses have grown in tandem over the years, But new exclusive reporting from my colleagues, Shreemupiti, Aaron Wu, and Amir Afradi reveals the great extent to which that is now the case. I want to bring on Shree and Aaron to talk more about what they found. Shree, I want to start with you. What did we find out about Anthropik's reliance on Google? We found out that Anthropik committed roughly $200 billion in over five years to Google for computing power in chips. And so that's particularly important just because Anthropic has been scaling its revenue really quickly. So, for example, the company crossed more than $30 billion in annualized revenue last month, up from $9 billion at the end of the year. And so as the company continues to scale and add on more customers, it needs computing power desperately. And so Google is an excellent cloud provider that can serve that need for Anthropic. So that's Anthropic's reliance on Google. The other part of your story that was really interesting is you actually looked at the reliance that both OpenAI and Anthropic have on all the hyperscalers. Tell us a little bit about what you learned there. Totally. So along with Anthropic signing that deal with Google, we also had written about how Google had signed a $100 billion computing deal with Amazon. Amazon also has a deal with OpenAI where OpenAI committed about $100 billion. And so that results in about a 364 revenue backlog for Amazon. And similarly with the Google backlog, which basically means the revenue contracted for future customer revenue, Google has about a 460 billion revenue backlog. And so Anthropic for them represents about 40% of that backlog. And so you just essentially see how intertwined a lot of these cloud providers and AI labs far because as Google and Anthrop, or sorry, Google, Amazon, Microsoft invest money into these labs, the labs then turn back and actually spend money on them as well. And so the other one to mention, of course, is Microsoft, which has a long time relationship with OpenAI and now also has one with Anthropic as well. Right. And there's a great chart in the story that I would encourage people to go look at, but it basically shows, if I'm recalling correctly, you know, of the revenue backlogs that all of these companies have told us that they have, the cloud companies, roughly half of it, give or take, for I think most of these hyperscalers is coming from open AI and Anthropic, right? Do I have that right, Sri? Totally. So much of that revenue that these companies, these cloud providers are projecting will actually come from these AI labs. And so I think that's quite important for investors to know as the AI boom continues on. Okay. So Aaron, let's look at it from the other side now. So how much is Google spending on Anthropic? Yeah. So Google has most recently said that it's investing $10 billion into Anthropic, and there are plans to invest another $30 billion if certain milestones are met. And that's just the most recent amount of investment. Okay. And so Aaron, I mean, taking a step back here, What do you make of the interconnectedness here that Sri pointed out? We've talked about this circular financing in all sorts of different circles. This is one of said circles, and I'm sort of imagining drawing all these overlapping circles on a piece of paper. But when you talk to people, is this inevitable? Is it something to be concerned about? What's your reaction to it? Yeah, I mean, I don't necessarily think it's inevitable. I think this gets back to the big question that everyone's asking, which is, is AI a bubble? Is the bubble going to burst? And so I think you have a week of pretty good earnings for all of these four major hypersales, like Google, for example, did very well. And then you dig into the numbers a little bit and you see that a lot of this growth is coming from Anthropic, a lot of this growth is coming from OpenAI for some of the other providers and even beyond the specific numbers that we have in this that represent the cloud deals that companies have signed with these providers. There's also additional revenue because some of the growth in Google Cloud's revenue that's not about compute comes from the resale of anthropic models. So a lot of this is bound up in these same two companies. And so that's what's stoking fears of a bubble. And the question is obviously, is anyone actually using this AI. Sri, Oracle was in a, Oracle is certainly in this equation, but they have sort of a slightly different case here. What is different about Oracle's relationship with these companies? So Oracle is actually working with OpenAI to build out these data centers on behalf of OpenAI. And so OpenAI has committed about $300 billion towards that relationship. And so I think a lot of investors are also seeing how the data center build out on Oracle will actually play out because it definitely impacts OpenAI in terms of having that compute available And so I think that Oracle is perhaps in a different position from Microsoft Google and Amazon because it's reinventing itself as a major cloud provider to actually take on some of these huge contracts. And so I think that it's quite interesting in that more interconnected relationship that Oracle has with OpenAI because Oracle definitely realized in OpenAI to be able to get on that business. Right. But Erin, correct me if I'm wrong. I mean, Oracle is slightly different in that they're not reselling the models the way that Google and Microsoft are, right? Yeah. So Google and Microsoft and Amazon, they all make additional revenue reselling the models to customers. So like Google, for example, has this thing called the Model Garden. So they offer Google's proprietary models, but they also offer Anthropics models and a lot of different open source models. So there's that additional revenue stream. And Erin, as you look at the reporting questions that you have for this story going forward, what comes to mind? Like I said, I think one of the big questions is how companies are actually using AI. And I mean, like, certainly to some extent, companies are, like we've seen, like the huge revenue growth that Anthropik has that comes from enterprise AI. Another story that I reported yesterday was that Google and private equity firms are in talks to form a new partnership. And so this is Blackstone, KKR, EQT. And this would help distribute AI to the portfolio companies. I mean, like one of the trends that I've written about recently is the difficulties that some companies are having adopting AI. And so this would be beneficial for Google, for example, because all of these portfolio companies would start using it. And as long as you believe that using AI is also beneficial for the portfolio companies, like it's also good for them and private equity firms in that way. Right. Yeah, it certainly seems like a steady drumbeat of news or announcements or stories from you and Sri, mind you, talking about how the private equity firms are really leaning into this to get their portfolios in check. It's one to watch. I want to thank you both for coming on. That is Sri Mupiti and Aaron Wu from our newsroom here at The Information. The Informations Rocket drew is covering the Elon Musk OpenAI trial from the courtroom all week long. Here is his latest update for us on OpenAI President Greg Brockman's second day of testimony in the court on Tuesday. Greg Brockman had a slightly better day in court. After previously facing some really rough questioning from a lawyer for Elon Musk who asked about Brockman's personal finances and his stake in OpenAI, Brockman had the chance to answer some friendly questioning from a lawyer for OpenAI. That gave Brockman the opportunity to explain some of the context around his personal journal. This is the digital diary that Elon Musk's side has presented as though it contains a smoking gun, an admission of guilt from Brockman that he was intending to lie to Elon Musk or deceive him about his plans for OpenAI's future and for its corporate structure. In particular, there was one passage where Brockman contemplated a path forward that he said would be morally bankrupt for him to pursue. Under questioning from OpenAI's lawyer, he explained that that was a path they never ended up taking. That was a path that would have involved firing Elon Musk from OpenAI's board and then creating a for-profit subsidiary without him. In fact, they didn't fire Elon, and when they did create a for-profit subsidiary, the questioning revealed that Brockman and others were looking for ways to include Elon in that plan, were looking for ways to offer Elon equity. Under Brockman's testimony, it was only once Elon was unable to have control over that for-profit entity that Elon gave up on the project and left OpenAI. Elon's side says that he would have had control initially, but that control would have been intended to dissolve or reduce over time as further board members were added and new investors were brought on board. One of the most interesting exchanges during Brockman's testimony today was that Brockman said he doesn't think Elon is very good at AI. He said he knows cars, he knows rockets, sure, but he doesn't know too much about AI. And to illustrate that, he gave an example of when Elon was given a demo of an early iteration of the technology that would later power ChatGPT. And Brockman says while the rest of the team understood the promise of the technology, Elon thought it seemed kind of stupid. He thought kids talking to you on the internet could do a better job. He was not impressed, and the researchers who worked on that technology ended up feeling disheartened. So Brockman said for him, this was an early illustration that he didn't really trust Elon's judgment on matters of AI as far as the technology goes. That was kind of contrasted with an anecdote that Brockman gave earlier in his testimony of a time when Elon asked a bunch of OpenAI researchers to come visit Tesla and improve some of the technology and the processes at Tesla. in Brockman's telling Ilya Sutskaber who is also a co-founder of OpenAI and is regarded as a brilliant AI scientist went to Tesla and gave them some encouraging words of wisdom saying that if you can collect say 10,000 images of roads with poor conditions where Tesla cars currently struggle with self-driving then I guarantee you that AI will be able to handle the problem so that was kind of an illustration of what maybe it does look like to have a natural intuition or a familiarity with AI and a sense of the promises that it has. So that was what we heard from Brockman. Up next in Musk v. Altman, we're expected to hear from Siobhan Zilis, who is a mother of four of Elon Musk's children, but was also on the OpenAI board. The question for her is whether she was sort of observing and honoring her commitments on the board or whether she was also funneling information back to Musk in a personal capacity during that time. So that's what's up next in the Musk-Fyoltman trial. AI platform Tessera Labs announced $60 million in a new funding round led by Andresen Horowitz. I'm joined now by the company's CEO, Kabir Negrecha. Kabir, welcome to the show. It's great to have you here. Great to be here. Thanks so much, Akash. Okay, so I am excited to talk about the company, but I have to ask you the question. You started college at age 13. How does one even do that? It's a long story, long journey. I mean, it wasn't that long. You did the same four years, just you were 13 to 17 years old. That's right. Four years of college, three years of PhD, ended up wrapping it all up around 20. Okay. And what, you just applied at age 12? Ended up skipping high school with some programs for it out in California. So went down that route. Okay. Well, that's a route I would like to hear more about later on. uh let's talk about your news this week so to sarah labs is raising 60 million dollars what does the company do and what do you plan to do with the money yeah so what we do is really ai around it transformation work all of the heavy legacy work around erp migrations activities mergers and acquisitions consolidations revamps of portfolio companies for private equity firms it transformation work all around the world currently done primarily by services activity we instead try to drive via AI. The money, it's really going to help drive our expansion. We been on a good rip so far getting our first few blue chip Fortune 500s and now pushing out to that broader market we really appreciate the support and confidence from Andreessen to help lead our round and drive this activity. So on a good rip so far, how much revenue are you guys generating? I don't know if I can mention specific numbers, but what I can say is that the programs we're going after are multi-billion dollar programs. I think the lowest one I've ever seen was $200 million. These are the heavyweight services engagements of the world, and we're doing what we can to cut down those costs by orders of magnitude. And what was the valuation that you guys raised at? So the valuation we raised at, I'm not sure if I'm able to disclose, but it reflects the substantial value we've been able to bring to those enterprises so far. Great. So I just want to understand this a little bit. So, you know, when you talk about these enterprise, these software transformations going on in enterprises, I mean, are we talking like, you know, the old school digital transformations like, you know, companies trying to get their their IT systems from on prem to the cloud? Like, you know, because there was certainly that transition. Now there's the whole transition of, you know, using AI, which is a whole separate story. But am I correct in understanding that you are actually focused on sort of the more traditional digital transformations that we have been doing for the last 20 years? Very much so. You know, I think we tend to believe that a lot of these digital transformations have already come to fruition, have already ended. What I can say is even the on-prem to cloud transition is very much still alive. We still see it in many, many customers out there that they're running on-prem data centers. Their entire businesses are still being run through mainframes, through systems from the 80s, the 90s. And the reality is we talk about this AI transition and moving to AI usage. Oftentimes, this is gatekept and bottlenecked behind making that first digital transformation. So our view is not just focusing on the AI at the end of the journey, but actually bringing AI into that digital transformation journey to solve the prerequisites for AI. And so how are you using AI then tactically to help with that on-prem to cloud transformation? We're essentially helping to solve out all of those traditionally long-pole items, the ones that would have taken six, eight months, multiple years to drive. Code transformation, data cleanup activities, configuration of new systems, identification of business process gaps. If I'm running a Fortune 500, how do I ensure my supply chain is never disrupted? How do I ensure resiliency of my manufacturing operations? IT is the foundation for these businesses. And if the IT systems aren't there to support that, if your code isn't right, if your data isn't right, nothing else can be done. That's where we're trying to bring the value in. And why did you get so addicted to this particular problem? Of all the problems that a 17-year-old college grad, you're not 17 now, but you're one of the younger founders out there. So why this problem? This is like an ancient problem. I mean, you could have solved anything. Well, my first exposure to the space actually came pretty early. My father used to work in the IT transformation, digital transformation world for many, many years. And I grew up listening to the steering committee meetings and seeing what it's like firsthand, essentially. Later on, once I got into the space, hands on myself, I mean, it is it is addicting in some ways to see the value you can bring to real companies is what i would say when i see a can of coca-cola let's say or a shipping truck out there i like to think hey you know that shipping truck its route was configured by some kind of it application we helped set that coca-cola can float through a manufacturing system we helped optimize those are the ideas i like to have in mind by the way those are all just examples there not any customer names I'm listing. Right. And so, you know, last question for you. The system integrators, you know, the consulting firms that traditionally have helped with this on-prem to cloud transition, digital transformation, now they're getting all the money from companies to help with the AI transition. Why are you not a consulting firm? To me, customers, Fortune 500s, they're not looking for third-party engagement and third-party ownership of their AI strategy anymore. Fortune 500 CIOs, they want the leverage of owning this in-house. They want to own their IT centers. They want to own the AI platform that they can train their folks on to drive these transformation activities, to have that leverage, to own their own AI journey, not to have it managed by a consulting party, third party. Right. But more what I meant is, what is the, you know, you're a tech company, you're, you know, a tech startup. What is the proprietary software or tech that you are building that sort of separates you from just being a tech-enabled consulting firm? Sure. So to me, the big unlock with AI was, hey, here's the opportunity to take expertise, train an AI on it, and effectively productize services, productize expertise. IT consulting is a tremendously expertise-driven world. We've been able to hire those folks with that expertise, but not as consultants, but actually as in-house data resources to train our models. So that's the transition, really, moving away from human capital-driven expertise to AI-first expertise. I'm actually going to be at SAP Sapphire event next week talking about that exact same topic as well. And so you have your own proprietary models in-house that you are using to help with all those tasks that you talked about, you are applying your models to those enterprise software tasks. Our custom models, our proprietary harnesses, our knowledge bases, all of this, we deploy on-premise in our customer environments to drive these transformations. Great. Well, Kabir, I want to thank you for coming on. That is Kabir Nagrecha, the CEO of Tessera Labs, here on TITV. The information's latest subscriber survey points to a tightening three-way race in AI as ChatGPT loses its early lead among our readers. We asked our subscribers which AI chat services they use. 574 people responded. 60% said Claude, 57% said ChatGPT, and 56% used Google Gemini. We first started tracking AI usage back in February 2023, just months after ChatGPT launched. And before competitors like Google and Anthropic rolled out their own tools, Since then, both Claude and Gemini have steadily gained ground. Meanwhile, other chatbots like Grok have lost share compared to our previous survey. We're also seeing readers go deeper with AI using more advanced tools like coding assistants and agents at higher rates than what we saw in September. There are gains for products like Claude Code and OpenAI Codex, while tools like Cursor and GitHub Copilot have declined. For more insights, head to theinformation.com to read the full survey. It is posted on our website. That does it for today's show. A reminder, we are on this stream Monday through Friday at 10 a.m. Pacific, 1 p.m. Eastern. If you can't make it then, episodes are available on theinformation.com, our YouTube channel, or wherever you get your podcasts. Make sure to follow us on social media, on X, Instagram, and TikTok. I'm already excited for our next show tomorrow. Have a great rest of your Wednesday. Bye-bye for now. Thank you.
