There’s a growing narrative that as open models and local devices get better and on-device AI capabilities improves, we’ll shift away from cloud compute and hyperscaler datacenter spending will slow down (bearish AI buildout). It’s early but ppl like Gavin Baker (who’s opinion I regard highly) has mentioned COULD be a bearish case. To be fair, edge AI definitely has its place, privacy, offline use, etc. But as a threat to overall cloud capex? I just don’t buy it. -Local hardware faces strict VRAM, thermal, and other hardware limits. Top-tier compute will require massive datacenter clusters for quite a long time to come. -Buying pricey rigs that sit idle 90% of the day is terrible capital efficiency. Cloud data centers aggregate demand all day. -Cloud API prices keep plummeting to pennies per million tokens. (the new GLM 5.3 flash is 10% $ of Gemini 3.7 flash !! and comparable too) . Paying for local hardware to run big models makes no economic sense. Not to mention hyperscalers build non-consumer chips (like TPUs) to run specialized models. -Even if P2P/distributed AI computing takes off, consumer 2 consumer networking can’t touch datacenter interconnect speeds.
- Lastly, we’ve seen this movie before. On prem servers lost to the cloud years ago because managing local hardware is expensive, hard to scale, and quickly gets outdated. I would like some pushback on my view. I think edge AI will handle basic local tasks or stuff that make sense to run in the background constantly (like video survaillance etc), but that will only ramp up overall AI usage and push complex queries back to the cloud. Not to mention tons more unlocks that’s coming down the pike (2 hr high quality feature length films ain’t gonna be made on a Mac). What am I missing? (btw I am software dev that heavily relies on AI and have played with local models, so I have decent experience in both areas). submitted by /u/yangastas_paradise
Originally posted by u/yangastas_paradise on r/ArtificialInteligence