Broadcom Goldman Sachs Communacopia + Technology Conference 2026

Thank you. Thank you for inviting me.

That is a very interesting question, Jim, and I guess I will start off with Broadcom as a company. We, like most large enterprises out there today, have been looking closely, reviewing how we can adopt generative AI through use of those frontier models and possibly even those open weight models that are floating around these days. We have been looking at it, and we have been doing that in earnest now for one year. Part of the benefit we have is we have quite good full access to Mythos Preview when it became available, and we look at how does it help our businesses. Where we have been looking at very closely is obviously we are a technology company in how we create products, how we design products, hardware, as well as software.

We have engaged 15,000 of our 30,000 engineers in the company in looking at both in hardware and software. A lot of insights, a lot of learnings. The biggest issue is finding use case that generates what you call a return on investment that is clearly meaningful and significant. That's not easy to do, and I suspect a lot of companies out there face the same issue. The second thing is two frontier models. Any model created as applications, it's just a tool. They're not meant to literally replace your people, not at least until you realize it's a tool and how would you apply it. We find out that different users, depending on who they are, could use the tool very differently, very effectively, very efficiently, as well as others who are having time really getting anywhere. It's like us. If I use ChatGPT, and frankly, depending on how I ask, how I prompt ChatGPT, I get different sets of answers, and some more useful than others.

Over time, you get better and better at it. There's a limit. What we really find is to find great use cases. My thinking is simply one immediate use case, and that's probably from our perspective, is creating new products. Creating or help us create new technology. What we're trying to ask for is not for it to create it out of thin air as much as create it through our engineers. What we find valuable out of it is they create it with very high quality. The quality is very critical, and that's what those AI tools enable us to do, and on a more accelerated fashion.

For a semiconductor chip, that makes a difference because our engineers can still design the chip, but if they have a bug and have to respin, that's six months and $30 million for leading-edge silicon. OpenAI, Anthropic tools, Gemini tools could help us eliminate the need for that respin. So there's a return on investment, so we find that as purposeful. But in terms of just simply productivity improvements, the return on investment is debatable. So it's really about quality, accelerated time to deployment or creation of leading-edge technology. You don't go back and redo what's in the past. The other application, of course, is cybersecurity through Mythos to scan, to basically secure our infrastructure better. Finally, it's something I haven't used yet.

We haven't used that, but we can imagine you can use something like a Claude Fable 5 in the minds of critical thinkers in specific functional areas to really replicate the software stack that we're using in specific areas. That definitely is big, and that can generate a big return. But that is something that will require a lot more investment, and we haven't reached that part of the thing yet. The sum total of it is still early stage, but we do find it's important, most of all, the users. The good users, and good users are defined as critical thinkers. More in simple terms, the architects. Senior people who are able to understand the context. Understand the environment of where we are, the workflows, and being able to prompt the models to do it.

I can imagine the day when they start using agents, which we are starting to last three months, that becomes even more important. To sum it up, it helps very smart people become smarter.

Yeah. That is why I will not say the converse.

Well, I am a believer in the fact that intelligence, and the more there is of it, is unique. It is like doing semiconductors. When we design a switch, a network switch, that is the best of its generation at a timely basis. The world beats a path to our door. There is no substitute for the best. Second best does not get anything. So I look at this the same way. For AI, as long as there is continual improvement, I would almost say exponential improvement in the LLMs that keep coming out from the frontier model guys, the value is going to accrue over time to the guys who do the best frontier models. Look at it this way. Take the case of a very good frontier model. 1 gigawatt could generate $30 billion ARR, as you know.

The cost to run 1 gigawatt, as you all know, is about $10 billion a year. So you have $20 billion that accrue to the model guys and the application that goes with it, and $10 billion fought over by the power guys, the cloud provider guys, even the chip and memory guys. The value comes, I think, in creating the best models, and through it, the best products to monetize the applications.