Thomas Ahle notes that scaling AI agents to hardware workloads would cost roughly $10 billion in commercial EDA licensing fees, which he says is part of why AI models remain undertrained for chip design. ✦ AI generated
Thomas Ahle · Machine Learning Street Talk · 2026-06-28 · original ↗
starts at this moment · 6:12
“So you're saying at the moment there are these commercial verifiers and simulators and they cost a ridiculous amount of money. So is it something like $10,000 per seat or something?”
Like say you want to scale this up in a data center with a million agents running. Like you're going to like I mean comput is already expensive but not that expensive. Uh what yeah what is that $10 billion or something right? Just for for those licenses.
verbatim transcript · starts at 6:12
6:12ridiculous amount of money. So is is it something like $10,000 per seat or something? >> Yeah. Per for one CPU kernel. >> Yeah. Like say you want to scale this up in a data center with a million agents running. Like you're going to like I mean comput is already expensive but not that expensive. Uh what yeah what is that $10 billion or something right? Just for for those licenses. And I think
6:34actually that's one of the reasons also why AI still is not as popular in the um in the hardware space because um they haven't been able to train like the the models are not as trained to this kind of workloads because they just um it's not feasible like you don't have all of the open source code out there to start the training but you also don't have the
6:57tools that they need to learn to use. you don't have the um yeah all of the like the task like you you can't just all of the reinforcement learning on top of it. I mean I mean they they clearly are doing some of that um like we can also see from model generation to model generation that they are getting better but it's just it's kind of day and night
7:16between like Python or JavaScript. we've been sort of developing these EDA tools in house uh using um using AI um having you know I I think I I probably have the world records of the longest running agents having like some 20 GBT agents running for around 6 months now still making making progress so as as I understand correctly so similarly to you know anthropic khini had this blog post out and you know they
7:44had a functional specification of a C compiler and they had about 40,000 agents and they reproduce a C compiler. You did a similar thing for any by the way, right? >> Well, I mean that's an interesting thing as well, you know, because this is what I want to get to, right? That it's tantalizing that that there there are there are some domains that are so well evolved that we have um you know, that
8:04might be very complex, but but we have a functional specification and and it's reasonably coherent. So, so the idea is we we get a shitload of agents and we reproduce the function of this software based on these tests and we do it recursively, agentically and and so on. Now, my contention with this is I think that it's not about where you end up. It's not about the functions and the