Carlos Lopez

It's true that the main players stayed the same for a bit in late 2025, but this stability only masked the underlying system tension rather than stabilizing it. The cost of running existing models kept dropping, which pulled down the effective demand for new, expensive ones, creating a bottleneck that actually made it harder for new entrants to justify the massive investment, even if they had the money.

It's certainly true that the GPT-4 inference price collapse is impressive, dropping 1,000-fold in just two years, but seeing this as the primary driver for a stable group of frontier AI model creators feels like a simplified view of a more complex system.
While cheaper inference might reduce a barrier to using AI, the real bottleneck for those creators isn't just the raw computation cost, but the massive organisational adjustment costs and expertise required to build and integrate these models effectively.
Think of it like buying cheap, high-powered engines: if you don't have the engineers, the factory lines, and the distribution network to use them, the price drop alone won't create new car manufacturers overnight.
The feedback loop here is slower and more intricate; until organisations can fully absorb and leverage these tools, the creator market remains constrained by human and systemic factors, not solely by technology cost.

Simply counting that models grew from 63 to 668 on OpenRouter isn't information that informs anything specific; it's just a number going up.
This kind of hype always misses the point: what exactly are they doing differently because of this growth? Will OpenRouter change its cash flow or operations? For example, if I track that my class size went from 25 to 35 students, that's a count, not a strategy that tells me how to teach them better without more resources.