Q&A with Micron’s VP and GM of Memory

Memory is one of the foundational technologies behind every major compute transition, and right now it is more visible than it has been in years. As AI infrastructure expands across training, inference, and large scale cloud deployments, demand for high performance DRAM and storage has moved sharply up the industry agenda. Whether that means HBM feeding the latest accelerators, LPDDR finding a new role in data center architectures, or fast SSDs supporting ever larger models and data pipelines, memory is no longer a commodity part of the bill of materials.The shift has changed the economics of memory in a very visible way. The industry has spent decades thinking in cycles, with familiar waves of overcapacity and correction, but the current environment feels different. AI has pushed memory further up the value stack, turning it from something many treated as a commodity into one of the key battlegrounds in the buildout of next-generation infrastructure. The biggest companies in the world are now competing not just for compute, but for access to the fastest and densest memory subsystems, and for the storage needed to make those systems useful at scale. As pointed out in this interview, what we are seeing now is not demand appearing from nowhere, but years of expanding use cases across servers, smartphones, automotive, and cloud - finally being supercharged by AI.Thanks for reading More Than Moore! This post is public so feel free to share it.ShareJoining me for this discussion is Praveen Vaidyanathan, VP and GM of Cloud Memory Products in Micron’s Cloud Memory Business Unit. His remit covers the memory technologies now sitting at the heart of modern data center design, from HBM and LPDDR based server solutions through to the storage products increasingly used alongside accelerated compute. In this conversation, we talk about whether this really is just another memory cycle, what changed as AI demand accelerated, how Micron is thinking about HBM4, custom memory, SOCAMM, LPDDR6, and PCIe Gen 6 SSDs, and what it means to plan capacity years in advance in a market where demand can suddenly look very different in the space of a single quarter.The following is a tidied up transcription of the interview.Ian Cutress: So the first question is: is this a cycle? This is the memory industry, We’re so used to ups and downs.

What makes this one different?Praveen Vaidyanathan: I think whether it’s a cycle or not - if you look at history, [then] yes. However, whether something is a cycle or not, we will know three years from now. Do I really know today? Probably not. But I think what is happening with the current scenario is really the underlying fundamentals that are driving this cycle in terms of the demand drivers. Where do people need memory? If I look at going back to the 80s and 90s where people used to think of memory as a PC component, that’s where it all started.It was a KB number; now it’s a very different number these days. That’s where it started, but it then progressed into every single application. You talked about consumer; the advent of the smartphone changed what consumer looks like. So that provided another outlet and then it walked into automotive. Automotive applications today require memory, and then servers came along. So the presence of memory across the entire user space has proliferated so much and I think this has been building over time. And now the super cycle is that history that has built up, and now AI has come upon and that has catapulted and surged the demand for memory and application. So I think it’s fundamental to how systems are architected today and we are looking forward to working in this environment.Ian Cutress: You say it’s built up over time, but if I look at recent history, the last six months, I think Q4 in ‘25 people suddenly went, “Wait, what? It’s not a commodity anymore”. What changed that quarter?Praveen Vaidyanathan: We think there are maybe a couple of things going on. I think as AI came on - let’s say 2022 time frame, when the whiff of generative AI happened with ChatGPT - and people started using that more, there was a lot of investment in core AI infrastructure which was very much accelerated compute. And as people invested in that, I think there were two things that happened: the continuous growth in frontier models and the cost of tokens coming down has increased the usage of AI applications.Ian Cutress: Jevon’s paradox sort of thing - make it cheaper and people will use it.Praveen Vaidyanathan: Exactly.

I think that is happening, and could you predict this? Not when models are evolving on a three-month cycle time. You don’t really predict this. So that has resulted in a surge in demand. Along with accelerator compute, I think there is also a lot of supporting compute around it that people did not invest in long enough and suddenly that investment became necessary. So the two put together kind of came together at the same time. But in this industry, I don’t know why we should be surprised about things happening suddenly. How many times has that happened? And then we all look back and say, “Of course, it’s obvious,”.Ian Cutress: This is why companies like Micron and your competitors are investing every generation in new technology. We track where the tools are being sold for the latest generation process nodes and the topic of the day right now is this transition from HBM3 to HBM4, and we’re seeing a different number of constraints around HBM4. Can you explain how they look at Micron?Praveen Vaidyanathan: So we actually just announced at GTC that we are in volume production with our HBM4. We hit volume production with HBM3E last year and now we are here with the HBM4. HBM4 does two things. One is it allows you to improve the bandwidth of the memory attached to your GPU. The second is it continues to improve the power efficiency curve because you want more bandwidth, you want more performance, but you are limited by the power envelope. So it does that, but every generation of HBM also has more manufacturing complexities. It takes longer cycle times to produce. So you have to innovate not just on the product cycle and product capabilities; you have to innovate on the manufacturing also. As soon as you have this product, everybody wants it and they want to go push the product in the market. So innovating on the manufacturing cycle times and manufacturing efficiency is another thing that we have to keep working on.Ian Cutress: One of the things that always strikes me is that HBM and the JEDEC standards almost seem to be misaligned a little bit, as in JEDEC might be a bit too slow and the demand for HBM might be a little fast. That’s where we’re getting situations where you’ve got a big partner like Nvidia asking for faster than JEDEC.

So what extra has to go in to essentially provide that product line for such a big customer?Praveen Vaidyanathan: I think it’s true for them and a few other customers also. I think what you have to understand is it always starts with “what do the customers want?”. So we talk to them ahead of time to try to figure out what they want. But we do also have to think a little bit ahead and think about what does the industry need. If bandwidth is the key capability of HBM, then that’s what you’ve got to shoot past the puck a little bit to be ready with something.JEDEC is interesting - it doesn’t limit what you can do. It just provides a common platform. It makes it a commodity, but you can still innovate. For example, HBM4 still meets all JEDEC requirements, but it’s faster. Because we all said, “We’re going to design faster”. Our customers said “we want to go there”, so we went there. So, we do think about how we operate within both environments because honestly, having a standardization helps adoption. Everybody can take it and use it, so that helps. But also, the commodity piece of it is not great, which is why custom HBM has come on, where some people want a little bit more than that and they are willing to work with us on that.Ian Cutress: In terms of custom, JEDEC defines a specific size of HBM and how many stacks you can go up to based on bonding and other specifications. Are you seeing customers who want different XY dimensions or different heights, beyond standard stuff like that?Praveen Vaidyanathan: It could go there, who knows. But that’s not where the current trend is. The current trend is you want to keep a few things constant, but within that, what more can you do? We think it’s going to be more functions and features that you can incorporate that are not part of your standard JEDEC offering.This is why we have the logic base dies. That allows you to do more with it once you go to an advanced logic-based die you can do more with it.Ian Cutress: You seem to be doing something a little different on your base die because you’re using an internal 20nm process rather than a TSMC N3 or Samsung Foundry SF4.

What benefits does that bring you?Praveen Vaidyanathan: I think that’s a great question, Ian. I don’t think we’ve said 20, it’s just a general internal logic process for us, but it starts off from the very beginning from a design methodology perspective. When we did our HBM3E, we kind of anticipated that as you scale this technology, innovating on the DRAM stack is going to be most important because that’s what - as you go from 8-Hi to 12-Hi to 16-Hi - you have one base die, but you have lots of DRAM on there. So if you want to think about performance and power optimization, innovate on the DRAM, get to the best DRAM you can, and then bolt on the best logic die that you can get.Our HBM3E was actually 30% more power efficient than any other HBM3E out there. So we had a little bit of an advantage over that. And if you go to HBM4, the same advantage: we were able to get another 20% improvement before HBM3E, so we had less to gain from going to an advanced logic node on the base die and we had all our innovations on the top die. We evaluated everything. We said, “Hey, we want to be paranoid that we’re missing something,”.I think we all grew up as engineers. It’s hard to get away from that. That mindset really helps: have the paranoia, go evaluate everything. We came back to the conclusion that we’ll be able to meet the speeds and performance that the market requires with our own logic. And this also reduces our risk because now we are all in-house. We don’t have to have an external dependence. We have a mature DRAM technology that is the same between HBM3E and HBM4. So less change, less risk, and faster ability to ramp. That was the decision that we made over a very long life cycle in probably three minutes, but those were the key tenets of that.Ian Cutress: How integrated are you with the packaging firms? Because a lot of this HBM is in a CoWoS or a cube type formation. I assume you’ve got to make sure it’s all compatible with them.Praveen Vaidyanathan: Absolutely.