The claim that a small, fast, and decently accurate model makes a good foundation for agentic workloads seems like a reasonable claim.
However, is cost the biggest limiting factor for agent adoption at this point? I would suspect that the much harder part is just creating an agent that yields meaningful results.
This has been my major concern, so much do that I'm going to be launching a tool to handle this specific task: agent conception and testing. There is so little visibility in the tools I've used that debug is just a game of whackamole.
No, I really don't think cost is the limiting factor- it's tooling and competent workforce to implement it. Every company of any substantial size, or near enough, is trying to implement and hire for those roles, and the # of people familiar with the specific tooling + lack of maturity in tooling increasing the learning curve, these are the bottlenecks.