alt Hacker NewsI think a lot of businesses don’t actually need cloud AI at all. Once workloads stabilize, cloud is mostly a convenience tax. Most business use cases (docs, forecasting, monitoring, support, control systems) don’t need frontier models or hyperscale elasticity. Efficient models running locally are already “good enough”. Continuous inference + data gravity + latency/privacy constraints make owned edge hardware economically and operationally sensible again.
I've been prototyping using LLMs for some borderline use cases, and the cost isn't really the concern, it's the reliability. Using less than the most frontier model seems irresponsible if it could mean the difference between 99.95% reliability and 99% reliability, and that's the threshold where you should've hired a human to do it because you lost more money on that 0.95% error rate than you saved on salaries. (I don't actually have any use cases where this kind of calculation makes sense, but in principle I think it applies to most uses of LLMs, even if you can't quantify the harm.)