GoKawiilMemory 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.
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Joining 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.
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