The AI war is moving from models to machines and I don’t think enough people are talking about it

okay so I’ve been thinking about this for a while and finally wrote it out properly everyone’s still arguing about benchmarks and which model is smarter but like… that’s starting to feel like the wrong fight? the more interesting question is where the model actually runs. on your device, in a cloud DC, on some edge hardware, inside enterprise infrastructure. that placement question is quietly becoming more important than the model quality question a few things that got me thinking about this recently: microsoft’s project solara is not a laptop. it’s basically a concept for hardware built around agents from the ground up, and they’re reportedly doing it on android not windows which says a lot about what they think “agent-native” actually needs to look like nvidia pushing local inference via RTX spark is interesting because it basically challenges the assumption that anything serious has to live in the cloud. latency, privacy, enterprise control requirements, there are real reasons to want compute closer to the user bytedance apparently building custom CPUs is the one that really made me stop. because agentic workloads aren’t just GPU jobs. agents call tools, manage state, orchestrate steps, interact with software systems. that’s a different workload profile entirely and big companies are starting to customize silicon around it anyway I wrote the whole thing up for towards AI if anyone wants to read it. not trying to just drop a link, genuinely curious if people here think the infrastructure angle is getting underplayed or if I’m reading too much into it \[link in comments\]