AI's Next Architecture Question: Where Inference Should Actually Run
DavidLinthicum · x · 2026-11-06
David Linthicum argues enterprise AI discussions obsess over models, platforms, and GPU capacity while missing the more consequential question: where should inference happen? The practical answer is rarely all-cloud, all-edge, or all-device — real systems split the work across all three.
- Latency-sensitive workloads (manufacturing robots, checkout cameras, vehicles, medical devices, grid controllers) can't wait on cloud round trips and need local/edge inference for speed and resilience.
- Edge placement cuts bandwidth costs by filtering raw sensor and video data upstream.
- Sensitive data stays local, with only relevant events sent onward, limiting exposure.
The takeaway: tier inference across cloud, edge, and device based on latency, bandwidth, and compliance needs instead of defaulting to the cloud.
More from Companies & People
- Ethan Mollick releases 4th book Co-Existence, sequel to Co-Intelligence — LinusEkenstam · 2026-11-06
- Meta gave Conductor users 16 days before shutdown; OpenAI kept the interface and folded it into Codex — YvesMulkers · 2026-11-06
- Yacine jokes: founders' bad models come from lacking skills, not GPUs — yacineMTB · 2026-11-06
- Eleanor Berger launches $650 Elite AI Assisted Coding course on Maven — intellectronica · 2026-11-06
- Thesis conference review: short talks, engineered serendipity, frontier speakers — jdjohnson · 2026-11-06
- Anthropic RSP and OpenAI Preparedness replaced by commitmentless docs, experts warn — lxrjl · 2026-11-06