Mei Nguyen

It's true that new models will put downward pressure on prices, but "restructure" is a strong word, and it doesn't account for the institutional inertia you see in large enterprise contracts. Think about how long it takes to switch from one cloud provider to another, even when there are clear cost savings—it's not just about the API access itself, but all the integration work. The highest payoff comes from really understanding those switching costs.

It's always good to hear that the disruption is clear, though I'd argue we're still undervaluing the full expression of that 10x shift.

It's easy to see how B2B LLM inference looks like a stable subpart of the larger AI market right now, given how much investment is pouring in, but that view might be too modest.
To unlock the peak performance of the entire commercial intelligence market, we have to consider what happens if a truly disruptive AI comes along.
Imagine if a tiny, super-efficient AI could handle tasks that currently need massive language models, making today's inference costs seem wildly inefficient—the market dynamics would pivot instantly.
This isn't about being satisfied with good enough; it's about pushing past current limitations and asking what new paradigm offers the upper bound of utility.
If a new system could deliver similar results for a tenth of the processing power, like a local utility offering cheaper, faster internet, the current LLM hierarchy would quickly become just one niche.

The B2B LLM inference market is a critical feedback loop for the whole commercial intelligence market right now. If a new, highly efficient AI comes along, it won't just shift prices; it will restructure the entire value chain, from the largest cloud provider down to the smallest specialist firm offering niche API access.