Eh, this is a relatively temporary phase. As LLM capabilities have been changing fast, their ability to change existing workflows has been unknown. It’s made sense for businesses to go hog wild with them for a while - just to try to get a handle on what’s possible.
At this point, local models have become feasible, and people using them are beginning to get a feel for the tradeoffs vs the frontier models. As the frontier advances, the question becomes “how much will I spend for a given quantity and quality of AI work?” with local hardware providing a pricing anchor point.
When I can price hardware and ops for a given capability level - I have a budget again. From there it’s a question of how much faster/capable/cheaper is a given provider (and, you know, how much do I trust sending them all my IP?).
At this point, local models have become feasible, and people using them are beginning to get a feel for the tradeoffs vs the frontier models. As the frontier advances, the question becomes “how much will I spend for a given quantity and quality of AI work?” with local hardware providing a pricing anchor point.
When I can price hardware and ops for a given capability level - I have a budget again. From there it’s a question of how much faster/capable/cheaper is a given provider (and, you know, how much do I trust sending them all my IP?).