Maybe the business model is to break even on bleeding edge models while making money on the long tail of usage once systems are tuned for a specific model and running in production.
a decent model with a decent harness will determine when the knowledge base is lacking and attempt to fill the holes; thus the good general models can be very easily brought up to speed on niche domains.
Think like a regulator.
Some model are more aggressive by default, some are more verbose by default. To get the result you want for your specific application, you run experiment with prompts and parameters.
and now we have ai agents to automatic migrate the system with new models. in the past we would need to spend hours to design the prompts, then test the output, then write codes to babysitting it. nowadays any ai agent can do it effortlessly.