Short answer: Hardik Kabaria says VPs of hardware want to ship better products faster. Vinci deploys behind the firewall, recovers from failures on queries that have run seven days, and a 7% better memory could win huge market share.
Watch this answer from Episode 50 (starts at 12:50) · Episode 50 notes and transcript
From Episode 50: Physics Simulation for Chip Design Used to Take Days. This AI Does It in Seconds., starting at 12:50.
Jordan Metzner [12:49]: And let's talk about the downstream impact of that. So, you talk about the scientists who can do, 30 re 30 tests a day, now doing thousands. So that has obviously a downstream impact, and then even more so is bringing that upstream to the, maybe the board design PCB designer or something like that. And then that obviously causes those iterations to move upstream, which means downstream, those iterations are a derivative of those then iterations. Right? And so okay. But all of this adds up to what? What is the final output? Is it more efficient chips? Is it cheaper chips? Is it better design chips? Is it less, failures in fab? Like, what is the now output that these laboratories get once they implement this?
Hardik Kabaria [13:28]: I mean, our question to ourself first is, what is it that the customer want? And our customer, in my mind, is, say, a leader of a hardware engineering company or hardware engineering organization. We often think of them as VP of hardware. And this person's motivation is to ship better product faster. That's it. They want to deploy a better chip. They want a time to market, they want to have a faster data transfer on their memory, a better physical and performance time at a faster time to market. And our goal is to become a tool in their toolkit. So we ensure that we get part of their workflow. We deploy behind the firewall. Their IP stays behind the firewall. They don't have to worry about their data leaking from their firewall and we get integrated into how their engineers work with each other. So you can drag and drop your design file, ask a question. It should be fault tolerant system. There are engineers who have performed queries on our software that took seven days to perform.
Hardik Kabaria [14:35]: So that means whatever system we deploy has to be fault tolerant. Like, of course, we all know compute goes up and down seven days, so that means we should be able to recover and finish the calculation. And of course, now that you are producing gigabytes of data, humans don't make decisions on gigabytes of data, we make decisions on two by two charts. So then you have to synthesize. Okay, can we make the intelligence simple enough for you to make decisions and explore? So all of that has to be part of the product, not just the raw intelligence. And the reason why we care about it because ultimately we keep asking our questions. Our customers would be happy if we help them deploy better product faster.
Jordan Metzner [15:14]: Okay. Yeah. Speed is the net end result, which then
Hardik Kabaria [15:17]: Speed is the net end results with a better performance. Because imagine somebody developed a 7% better performing memory, they would have huge gains in the market share. The same can be said about anything that we see around ourselves. A few percent better performing inference chip or a few percent better performing training chip or a data rate transfer all the way to everything that makes up a data center or an edge device.
Jordan Metzner [15:41]: Yeah. And then, obviously, you get that compute downstream
Hardik Kabaria [15:43]: And You get the compute requirement. And then in the wages AI, it becomes so if I'm inferencing faster, maybe I can infer a larger model and bring the intelligence at the edge. Right? So that we are all hungry for intelligence means we are all gonna be hungry for compute.
