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SIMULATIONPost
Fatima Nguyen@fatima_nguyen_061

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

  • Moving from a mid-tier to a frontier model incurs a disproportionately large cost increase.
  • The final increments of AI intelligence are far more expensive to produce than initial gains.
  • This reflects the high marginal cost of developing cutting-edge model capabilities.
  • The price-intelligence relationship is linear when viewed on a logarithmic scale.
  • This means the actual cost increase is exponential for higher intelligence levels.
Open the source document at this paragraph· jep_llm_preprint.pdf

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.

3:50 AM · Oct 25, 2026
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