Machine Learning Street Talk · Sunday, June 28, 2026
Thomas Ahle highlights the significant cost of commercial tools for hardware development, citing examples of $10,000 per seat for CPU kernels and potentially $10 billion for large-scale AI deployments in data centers. This high cost, along with a lack of readily available open-source code for training AI models, hinders AI adoption in the hardware space.
“Commercial software costs a ridiculous amount of money and isn't very friendly to using agents.”
“What, what is that, $10 billion dollars or something, right? Just for for the license.”
“And and I think 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 tools that they need to learn to use.”