training models requires expensive hardware

Right now. In ten to twenty years this won't be the case. Also consider the diminishing returns from adding more hardware to the problem of training AI: Despite monopolizing the entire world's supply of DRAM, AI models are only gaining marginal improvements.

The curve of hardware expense against model capabilities is moving to intersect unless something drastic changes. Big AI needs a huge breakthrough in order to stay ahead of that curve. I don't see that happening because all the big breakthroughs that are happening now are in regards to efficiency which makes things worse for them and makes training cheaper, faster.

Also LLM models are built on diminishing returns and constant hype cycles. This is part of the business model. Even in the open source LLM scene folks are constantly looking for the new release, and i often hear them get tired of an llm repeating the same patterns over and over again. And then there is the public that start to recognize the speech patters of a certain model or when something is made with an llm. It is already affecting the credibility of some magazines and individual journalists.

Open source image generation depends on buckets of loras and refinement. tons of training for the vending machine. So this idea of having your favorite ol' isolated model on your pc for years of personal use sounds incompatible with the logic of this technology.

Most people have PCs in their homes of which 99% of their computational power goes unused. Is there a reason training models couldn't be done p2p?