GoKawiilFor the past two years, the artificial intelligence race has been easy to score: bigger models, better benchmarks and whichever company could claim the lead, at least until the next launch.
That scorecard is starting to look incomplete.
As companies move from testing AI to using it in real products and workflows, it's not longer about tapping the best model, but accessing the one that's the best fit for a specific job, at the right cost, with the necessary data and in a chosen environment.
That shift is opening the door for a new kind of AI competition, one focused less on model size and more on routing, cost, control and compute.
"The model alone is no longer the product," Perplexity CEO Aravind Srinivas told CNBC. "It is the harness, the orchestration system that puts the model inside a very capable harness and pairs the model with a lot of tools."
That means AI products are becoming systems that can decide which model to use, when to use it and what outside tools or company data sources are necessary. A customer service task might not need the most expensive model. A complex coding problem might. A routine internal workflow could run on a cheaper open model. A harder step could be escalated to a more powerful one.
"The answer is always use whatever is the best for the task," Srinivas said.
The emergence of alternative models comes as corporate America tightens its belt on AI spending, and presents another challenge for OpenAI and Anthropic, which have flourished over the past few years by selling the most cutting-edge technology.