AI companies are shifting competition focus from model size to cost-effective routing systems as enterprises move from testing to production deployment. Perplexity CEO Aravind Srinivas told CNBC that the model alone is no longer the product, emphasizing orchestration systems that pair models with tools for specific tasks. The shift reflects corporate America tightening AI spending, with companies seeking task-appropriate models rather than always using the most expensive options.
Perplexity Previews System Using Chinese Open Model
Perplexity this week previewed a new system for its computer-use product built around GLM 5.2, an open model from China's Z.ai. The system is designed to let a cheaper model handle more of the work while calling in a stronger model only when needed.
"The model alone is no longer the product," 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."
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, while a complex coding problem might. A routine internal workflow could run on a cheaper open model, with harder steps escalated to more powerful ones.
Benchmark Partner Predicts Open-Weight Token Dominance
Open-weight models, which can be downloaded, tuned and run by companies themselves, are becoming more capable and cheaper to run than premium proprietary models from the biggest AI labs.
Benchmark general partner Peter Fenton told CNBC his belief that 90-plus percent of the tokens created will come out of open-weight models over the next 18 to 24 months, possibly even by the end of the year. Tokens are the units of data AI models process and generate.
"The inference margins generated by the frontier model companies, I think, are going to come under pressure when you can run those without the markup that they're providing, when you have good enough models from open weights," Fenton said.
Fenton said the move to open models is not only about saving money. In some cases, smaller models that are tuned for a specific task can be faster and perform better than larger general-purpose models.
Ollama Reports Fortune 500 Adoption Rate
Benchmark invested in Ollama, a company that makes it easier for developers and enterprises to download, run and manage open models.
Ollama CEO Jeff Morgan said the company has been adopted by more than 85% of the Fortune 500, including companies in regulated industries such as aviation, insurance and health care.
"One thing is where the model's from and where it was created and trained," Morgan said. "But the more important thing to these businesses we speak to is where it runs and how it runs."
Morgan said many companies start with smaller models running close to their own data, then expand to larger open models as they get more comfortable.
Chinese Labs Create Strategic Competition in Open Models
The rise of open models creates a strategic challenge for the U.S. Many of the most competitive open-weight models are coming from Chinese labs, including Z.ai and DeepSeek. That has made open-source AI a business issue, a policy issue and a national competitiveness issue.
Srinivas said the U.S. should support open models because they make AI more affordable and accessible.
"If you want the benefits of AI to be widely distributed to small businesses in America and American allied countries, then you really need AI to be a lot more affordable," Srinivas said. "And open source is the only way to do that."
The shift could also affect the massive data center buildout underway across the tech industry. The current AI boom assumes demand will keep flowing to large cloud data centers filled with high-end chips. Srinivas says some AI work may eventually run locally instead, on devices owned by consumers or businesses.
That wouldn't eliminate the need for data centers, but it could create a more hybrid AI system, with routine tasks run locally and the most difficult work getting sent to a more powerful model in the cloud.
FAQ
What did Perplexity preview this week regarding AI models?
Perplexity this week previewed a new system for its computer-use product built around GLM 5.2, an open model from China's Z.ai. The system is designed to let a cheaper model handle more of the work while calling in a stronger model only when needed.
Why are companies shifting from using the biggest AI models to routing systems?
As companies move from testing AI to using it in real products and workflows, they need to access models that are the best fit for specific jobs at the right cost, rather than always using the most expensive models. Corporate America is also tightening its belt on AI spending, making cost efficiency a priority.
How many Fortune 500 companies have adopted Ollama?
Ollama CEO Jeff Morgan said the company has been adopted by more than 85% of the Fortune 500, including companies in regulated industries such as aviation, insurance and health care.
