Well, what does it say about the expectations that investors have placed on companies like yours that you can release results reaffirming forecasts, surpass estimates in a lot of different ways, and still there to be this sense of disappointment? What does it say about where we are in the hype cycle around AI?
I guess we are kind of like in a very surreal environment. Frankly, I don't think about it. I know it's hard not to, but just focus on fundamentals, create value, and stop thinking about your stock price. Trouble is, it's very hard to do that, but we try to do it well.
I mean, we were just talking with Ali, you know, every month or so, every couple of months there's a new LLM, and it sort of changes the conversation around who's ahead, who's behind. I'm curious about, you know, you have this massive partnership with Google, very important, working with them on their TPUs. I'd love to drill down a little bit with you on that. And in particular, how concerned you might be about Google sort of taking more of the chip development and design process in-house, the term customer-owned tooling. Is that something that keeps you up at night? And are there any provisions that you built into your partnership with them that sort of might preclude that?
Well, you know, that's something we should expect in technology. And we've been in semiconductors for over 20 years. I think that's how long I've been doing this with Broadcom. That's what we do all the time. We have multiple product divisions, each of which we are the best in what we do. And we just keep investing. And essentially, you try to out-engineer any competition. Customer-owned tooling that Google is trying to create, which is trying to design it themselves with the help of perhaps some smaller partners like Marvell, maybe MediaTek, things like that. I call them kind of like angled guidance. But they basically are working for Google to try to achieve customer-owned tooling. So we just compete against my own customer in a different part of it. And the whole idea is to be able to create differentiated product and technology that beats what they have. And all these very nice. It's been helped by the fact that the competitor, the real competitor facing all this is the GPU out of Nvidia. And as long as Nvidia keeps coming generation after generation with superb technology, Google has to create their equivalent technology to match that. And that's where we come in, right?
In the TPU process, it's quite involved. And I'm wondering if there's anything that Google and anything you can do to help Google democratize that process a little bit.
How do you mean democratizing?
Just in the sense of, you know, making TPUs more accessible to mainstream developers and not just the elite AI labs?
Well, that's I think Google is trying to do that by making available the TPU environment through cloud, Google Cloud, GCP. But a big part of that is the software, the operating system, the software, the compilers that go with it. And that's very much in Google's purview. Of course, we would like to help, but I think they got that pretty much under control in terms of trying to push that program.
Yeah. I want to talk a little bit about Anthropic and the ways that these foundation model builders are obviously in great need of capital in order to finance their needs for chips and the compute that are needed to build and develop. Can you talk a little bit about the way that relationship is being structured and financed and the provisions that you are having to take to kind of protect yourself against downside? How concerned are you about the end-user demand materializing and enabling them to meet their commitments?
I am. Frankly, I think in this particular case, we were partners with Google to provide compute capacity through TPUs, which we co-designed with Google for Anthropic. And frankly, there's no shortcut about it. When we first started almost a year ago now, it was basically a leap of faith that Anthropic and generative AI, and Anthropic through its business model of addressing enterprises with code assistance, coding tools, would make a difference. We went in on that leap of faith, and I guess to date, that was a great bet.
They do seem to be in the ascendant, very ascendant at the moment, given the fact that they have leapfrogged ahead of OpenAI on their valuation, filing confidentially for an IPO. So have you not had to worry about, you know, have you not had conversations with investors who might be worried about you getting left holding the bag if things don't materialize the way Anthropic anticipates them?
Well, as I said, we were making a bet on Anthropic. We've been, us and Google, jointly doing that. And I guess we were not just making a bet on Anthropic and its business model. We were also making a bet that generative AI would be something that would take off in a fairly substantial way, particularly among enterprises, the way Anthropic goes. And sure did. And that's pretty good reason for doing this. Started a year ago. I know I'm probably a fraction of what I know today, and what I know today, I probably would be a fraction of what I'll know in six months' time. These things are changing very fast, but the tools we see coming out of Anthropic, interesting. And the same applies to OpenAI and Gemini out of Google. They are just phenomenal in the sense of what they can achieve to help productivity. In Broadcom, we use it. We use those tools for engineering design, code assistance. And it's pretty remarkable what can be achieved in terms of enhancing productivity. I think even making product designs that are probably better than you do it with AI engineers. And these are very good engineers.
Can we talk a little bit more about how you're advising your staff to use these different tools, and have you found yourself having to put limits on that? Where we hear about the era of token maxing, which I hate that term, but the idea of letting people just use without limits tokens. Have you had to put limits on that? And what does that look like?
No, we don't. And frankly, on a bigger picture, we're not on that stage yet. So maybe it's because we're behind, other guys will get to that point and start to put that throttle on what they do. Now, a big part of it is when you use a tool like Opus 4.7 and the engineers start using it, they go through a learning process, even how to use it. You kind of get a tool to be very productive and start, but as you keep using it fairly and you use it long enough, you get very good at it, and then the tools become much more productive. And as you get much more productive, just thinking about it, you can get one very senior engineer to produce an application design in one week, what it would take ten engineers, which we pay $300,000 a year, and take them three months to produce the same thing. The return on investment is still pretty compelling. So to say we're throttling probably is a question of return on investment and what application you're using. But it's also being able to be trained to be able to use it better and better.
