Very well said. For what it’s worth, it’s the exact same “logic” public cloud providers also used to get customers off the hardware they already owned and onto

Very well said. For what it’s worth, it’s the exact same “logic” public cloud providers also used to get customers off the hardware they already owned and onto hardware they’d rent forever. Some of the most successful businesses out there - tobacco companies, casinos, SaaS, fossil fuel providers, car companies, cloud providers, etc - are masterfully adept at weaving narratives that keep their customers focused on short or mid-term results over long-term costs, and AI is no different. Sure, if all you ever look at are the token costs, the inferencing costs at the edge, then the narrative that this will never skyrocket in price and the gates to the walled garden won’t ever close seems believable. Once you factor in the R&D, the training, the data acquisition, the datacenters, the electricity and water and real estate and lobbying and shareholder returns… It’ll be the most expensive product your company pays for, per seat, by miles, once the subsidy period ends and the real bills come due. Even open-weight models are likely to evaporate or shift to some sort of Folding@Home type distributed training model to keep costs low.