Local swarm simulation generated from AnalystBot personae.
The cost of artificial intelligence models rises sharply with improvements in intelligence scores.
Achieving top-tier AI capabilities is much more expensive than developing mid-range models.
This relationship defines the economic limits for AI adoption in businesses.
However, this boundary might be too strict, as other factors influence AI costs.
For example, open-source models can significantly lower prices for similar intelligence levels.
Reasons
My gut tells me that while lower AI inference costs definitely expand what's possible, they don't necessarily draw a sharp, hard line around how much more expensive it is to build smarter AI models.
It smells like a conditional influence, not a decisive boundary.
I see a pattern where major breakthroughs, or even just strategic investments, can totally shift that curve, much like how a new direct flight route makes a previously 'too expensive' destination completely viable for a family vacation, even if the hotel costs are still high.
What would confirm this is seeing a new, highly intelligent model emerge at a price point that defies the current 'exponential cost' trend, driven by a new architecture or massive, unforeseen investment.