The Six Five team discusses AMD Acquires ZT Systems for AI Rack Design Capabilities
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Transcript:
Patrick Moorhead: So here’s the news. AMD is acquiring ZT Systems for $4.9 billion in cash stock. They’re going to do a lot of it out of cash or do a little bit of a debt offering as well. And you might be like, who in the heck is ZT Systems? Well, their biggest customers rumored are AWS and Azure. They design, they integrate, they manufacture and deploy rack level hyperscale AI systems. So think about the entire rack and everything that goes into that and everything that’s connected to it. They have customer support, they have installation, everything. It’s about 2,000 people. They’re located in New Jersey, Secaucus. A company at 10 billion revenue that you never heard of, and they’ve been in business for 30 years. In ’94 they started off with PCs. And in 2024, the company says it ships hundreds and thousands of servers annually. So what does this mean for AMD?
So, competitively, the game has changed. And NVIDIA really drove this with starting at the chip, and then going to the platform, and then doing not only the GPU, but also the CPU and the networking. And hey, let’s just do the whole system. And basically, a DGX rack. And they accelerated that cadence to not once every three years, but once a year. So there’s a lot of innovation, and it’s very hard to keep this wheel of innovation going. So AMD who’s been very competent in hardware, and you don’t get the $4.5 billion backlog without having infrastructure capability, but how do you go from 4.5 billion to attacking an addressable market? Not my numbers, not your numbers, but AMD’s numbers of a $400 billion GPU and accelerator TAM in 2027. You’ve got to find a way, you’ve got to make a big move to parabolically achieve that revenue growth.
And this is exactly what they did here. This adds on to the $1 billion of investment that AMD has made in software with companies like Silo, not that AI and Mipsology. And I believe that this is going to come down to execution. And I think AMD’s Lisa Su, who I got the pleasure of talking with last night is an absolute monster at acquisition. And also, we’re looking at just numbers here, it’s a lot less people and a lot smaller than the last acquisition, which was Xilinx. So I’m not saying this is a layup and not saying there’s zero risk, but it makes total sense. So there’s about 1,000 employees in the design and about 1,000 employees in manufacturing deployment. Lisa is going to spin out and sell the manufacturing arm. It would be totally dilutive. I mean, super micro. Their margins are in the single digits, which would not be good for AMD who operates 40% margins on there. So Dan, I left you a little bit of oxygen, I think a lot of oxygen in the room. I blew through that in about three minutes. What is your take?
Daniel Newman: If I could point everybody out there that’s watching to Pat’s extensive takes, Forbes. I think you posted a LinkedIn article. You definitely use that inside access to give what I would say the most comprehensive rundown of anything I read this morning. I’m not blowing smoke, I’m not blowing smoke. I wrote the second-best analysis because I put some work into that as well. Everybody that’s not watching, that I just winked really loudly. But Pat, look, here’s what’s going on. The number of 400 billion is about twice what our number is in terms of the size of that particular space. But there are reasons to believe that we are grossly underestimating the speed by which AI proliferates. And that has a lot to do with these various bubble perspectives that are out in the marketplace’s perception of a bubble. If it is a slow at first, then all at once sort of implementation at the end.
And then these cloud scale companies, these SaaS and ISP providers are going to be building out AI and delivering it at scale. It’s going to happen a lot faster than most of these CAGR numbers reflect. And that was where Lisa could end up being very accurate with her big ambitions in terms of 400 billion. Having said that, the company’s making the investments here to be able to put the designs together. You didn’t really use the word, Pat. You did in your article, not here, but about being open. But look, there are these two schools of thoughts right now when it comes to design. There’s this all in, end-to-end, DGX Super Cloud, everything built in one place, all NVIDIA, top to bottom. Anything that’s not is all masked as a single SKU, and you just consume it.
And then there’s the other side of the house, which is companies like AMD partnering with companies like Broadcom, building out systems with open architectures, using different standard ethernet network, using advanced GPUs, various different CPU head nodes. And being able to put these things together and then develop upon them. And so that’s also where this investment in software has come into play. You have this big Cuda lock in that everyone talks about. But we’ve seen AMD making big investments in software. We know Intel has referenced a future with one API, which we’ll have to wait until they get to their discreet data center GPU. But having said that, this whole thing is evolving to a software battle. So being able to build the most significant designs, being able to go open verse closed.
And then the other thing that I think is really important to mention is what the hyperscale cloud providers want. And I think there is a push and pull here. Of course, they want what everyone’s going to quickly consume, they want to sell through. And I think NVIDIA has been a big success for them. Having said that, on the other side, I think a lot of them want more choice in openness. They want to be able to bring some competitive price in. They need more capacity. They want to be able to build their own network backends, their own data center. They want to be able to build uniqueness into their data centers with how they handle networking, for instance. We know AWS has gone that route in some capacities, and they want to be able to address that. And so I think AMD should get some credit here for leaving that door open. They’re not going down the manufacturing, they’re not going to go closed end, end-to-end on the box, but they are going to provide the design that enables that open capability, Pat.
So I haven’t spent as much time in this as you have, but it seems like an encouraging way to drive that four and a half billion north, the four and a half billion of committed annual pipeline north. Of course, attaching more Epyc. It’s not just about Instinct, it’s about the whole stack. And then, of course, software. And if it’s true about this expansion into PyTorch and JAX and higher abstraction layers where programming can be done, and of course, they’re adding more capacity, then rockham ROCm can land. And that’s been one of those friction points, is how successful, how quick can the company move with ROCm.
Patrick Moorhead: Yeah, good stuff Dan. And yeah, I failed to bring up that, but I did in my article on the software side. They made three tuck-in acquisitions. And even though ROCm’s has been around forever, it was really focused on HPC, which is not Hyperscaler data center, generative AI. One final thought, Dan, it appears to me that the NVIDIA and AMD combo is putting, with this acquisition, they’re putting even more distance between them and their competitors. I am really, again, interested to see Intel and what they’re doing with next generation accelerators, and in 2025, data center GPUs. Intel has some extensive capabilities, particularly on the hardware side. They get rack scale architecture. In fact, years ago, they actually coined that term, but it’ll be interesting to see how Intel can turn that into gold as well.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
