IMEC Says Today’s AI Will Look Ancient in 10 Years

Molly O'Shea (0:00)

Welcome to Sourcery. The potential of photonics.

Steven Latré (0:12)

The technology to interconnect these GPUs. Global payroll and HR platform.

Molly O'Shea (0:16)

8 billion operational data points.

Steven Latré (0:18)

Scale-ups over startups. You'd rather have a million of something than one of anything.

Adam Chambers (0:22)

Building the intelligence layer through the fence. There isn't enough power, there's not enough memory chips.

Steven Latré (0:27)

We're at the end of physics of what we can still do.

Molly O'Shea (0:41)

Okay, amazing. So we're on our final talk for today. Today we have Steven and we have Adam. Steven, 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?

Steven Latré (0:57)

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 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 driven more or less that chip road map for the last 40 years.

Molly O'Shea (2:00)

So I should have asked you the secret question because you know all the secrets on chips.

Steven Latré (2:04)

And some of them I'm allowed to answer.

Molly O'Shea (2:06)

What can you answer?

Steven Latré (2:08)

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 in 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 be, needs to be way more inventive than what we've been doing in the last 10 to 20 years, to keep on scaling that road map of more, to make sure that chips become faster and faster, as they already did the last four decades basically.

Molly O'Shea (3:04)

Adam, as you look into the chip industry, how do you make sense of it? How do you map it out?

Adam Chambers (3:10)

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 builded 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 gonna progress over the next five 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 in a bottleneck that is, and increasing the capacity there is huge.

Molly O'Shea (4:16)

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?