AI's next bottleneck? It might not be GPUs. Memory prices have risen 700% this year, while power, cooling and data transfer are all becoming major constraints on the AI infrastructure buildout. At the same time, the semiconductor industry is approaching the physical limits of simply making transistors smaller.
I sat down with Steven Latré, VP of AI at imec and Thema Board Member, and Adam Chambers of Strike Capital, to talk about what comes after the GPU and where the next wave of value in AI infrastructure could be created.
We get into
› Why memory is already one of AI's biggest bottleneck...
What Molly O'Shea said
Written from the verified transcript and checked against it. Every figure links to the moment it was said.
Molly O'Shea hosts a discussion with Stephen from IMEC and Adam from Strike about the future of chips beyond GPUs. Stephen explains that IMEC is a leading semiconductor research facility where major chip makers design next-generation chips, and notes that transistors are now at the depth of one nanometer, nearing physical limits. Adam identifies bottlenecks in inference, including memory, power, cooling, and data transfer. Stephen argues that the software market is immature and that future value will come from closer hardware-software integration. Adam highlights that memory prices rose 700% this year due to a shortage. Stephen predicts a shift away from scaling models, citing that the human brain is more energy-efficient than current chips. Both agree photonics is key to solving bottlenecks. Stephen's hot take is that in 5-10 years, today's AI will be seen as old-fashioned, akin to dial-up internet.
Key takeaways
Stephen says transistors are at one nanometer depth, near the physical limits of Moore's law, requiring more inventive hardware approaches.
Adam reports memory prices surged 700% this year due to a shortage, with Micron, SK Hynix, and Samsung controlling 95% of the market.
Adam predicts cooling will evolve with liquid cooling moving closer to GPUs over the next 5-10 years.
Stephen predicts a shift away from scaling models, with companies abandoning the scaling hypothesis, and expects photonics to be a killer technology.
Adam recommends recruiting or investing in people with European academic backgrounds and American business sense for the best founders.
Okay, amazing. So we're on our final talk for today. Today we have Stephen and we have Adam. Stephen, you come from IMEC. Adam, you're from Strike. So we're going to talk a little bit about what comes after the GPU. So let's start with you. Could you explain what IMEC is?
Sure. I think we're a little bit the most unknown hidden gem in semiconductors. At least unknown for the average Joe. Definitely not unknown for the semiconductor world itself. We're the worldwide leader in chips, but in actually the early stages of building a chip. So hardware is not like software. It's not that you prompt it and suddenly 30 seconds later something comes out of it. No, it's a process of five to 10 years. And we're at a very early stage of that process where we have a unique clean room facility that nobody else in the world has. And so all the biggest chip providers in the world actually first come to us first in Belgium to kind of design that next generation chip that you'll see maybe in seven, eight to 10 years from now, and going through a whole phase of designing that chip. We hand it over then to them to further commercialize it. But in that sense, there's almost no chip in the world today that has not been touched by IMEC in some way. We already exist for 40 years and we've been driving more or less that chip roadmap for the last 40 years.
Well, I think we're definitely going to talk more about that, but indeed that we're actually seeing a huge amount of disruptions going on. I think we're now kind of plateauing in what we call Moore's law. What does that mean? Chips have been becoming bigger and bigger or faster and faster over the last years. We always did that through scaling. So what does that mean? It means that we made a transistor smaller and smaller. And we're at the level that a transistor has about the depth of one nanometer. What is one nanometer? It's about 100,000 times smaller than a human hair. That means that we're at the end of physics of what we can still do. And I think we're going to come into an era where what we do in hardware is going to need to be way more inventive than what we've been doing in the last 10 to 20 years to keep on scaling that roadmap for Moore's law to make sure that chips become faster and faster as they already did the last four decades basically.
Yeah, I think right now GPUs are very good at the training part and we're now looking at the bottleneck that sits in the inference layer. And the bottlenecks that I look at the most and try and look at companies that attack those bottlenecks sit in power and energy, memory, cooling and data transfer. So when we look at the inference side of things, memory, the memory shortage in general and increasing the bandwidth and lowering the latency of getting the useful memory to a GPU as quick as possible is a really interesting and exciting place that needs to be built within. Cooling as well and where that's going. I think bringing the liquid that takes heat away from the chip closer and closer to the GPU itself is how it's going to progress over the next 5 to 10 years. And I think a lot of the value will go to the manufacturing side of that. And yeah, I mean power, everyone knows how much of a constraint and a bottleneck that is and increasing the capacity there is huge.
I recently interviewed Tony Kim of BlackRock at the Raise AI Summit. It's actually in Paris and we're talking about hardware. It's like the hardware era renaissance. Everything's shifting to hardware. Software is getting, it's taking a bit of a beating, but the margins, people don't understand the margins for chips are fantastic and for hardware versus software which is getting eaten by AI and again like the token spend and that kind of thing. So how do you make sense of the shift in value and how like the pyramid kind of flipped?
Well, I think the demand is just so high for the thing that is less fruitful out there, right? So there isn't enough power. There's not enough memory chips. The shortage in memory, I mean, if we look at it, there's only three companies in the world that produce memory, which is 95% of the market: Micron, SK Hynix and Samsung. Two of them are in Korea and a lot of their shift has been filled out by hyperscalers taking over their whole inventory. So you've seen prices rise by 700% this year just for the memory part of the chip. And that will happen across the layer because it's the thing that's in the most demand. The training side and the software side of things is continually getting better, but the physical side is the thing that's harder to get better overnight.
Yeah, to be honest, I don't really think it's necessarily flipped. I think it's more the immaturity of the software market right now. It might seem weird that I say immaturity of the software market, but I think what we've been doing at AI the last four or five years has of course in software been tremendous. But if you look at it from, okay, how complex it is to actually in terms of computations do whatever, I mean large language models can do, it's still very much a brute force approach and as a result the value that you can get out of that is pretty limited. And I think if you're going to get further, we're going to see new types of software evolutions as well which are tied way more closer to hardware. And because of that I think the value will increase way bigger. And I think so we're kind of filling the base of that pyramid already in hardware of creating a lot of value there. I think software will follow in that sense.
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O'Shea, M. (2026, September 30). We’re Reaching the Physical Limits of Chips [Interview transcript]. Sourcery with Molly O'Shea. CEOInterviews.AI. https://ceointerviews.ai/interview/3001165/
MLA
Molly O'Shea. "We’re Reaching the Physical Limits of Chips." Sourcery with Molly O'Shea, 30 Sep. 2026. Transcript, CEOInterviews.AI, https://ceointerviews.ai/interview/3001165/.
BibTeX
@misc{oshea2026_3001165,
author = {Molly O'Shea},
title = {We’re Reaching the Physical Limits of Chips},
howpublished = {Interview transcript, Sourcery with Molly O'Shea. CEOInterviews.AI},
year = {2026},
month = {sep},
url = {https://ceointerviews.ai/interview/3001165/},
note = {Speaker-attributed transcript with timestamps}
}
