Palo Alto Networks CEO Says Token Costs Slow Enterprise AI Adoption

For enterprises to adopt artificial intelligence on a large scale, token costs need to drop, Palo Alto Networks CEO Nikesh Arora said Thursday (July 9).

Speaking on CNBC’s “Squawk on the Street,” Arora said the cost needs to drop 20% over the next 12 months and 90% by the following year, CNBC reported Thursday.

Asked about OpenAI CEO Sam Altman’s comments to CNBC that OpenAI’s latest model is 54% more efficient for coding, Arora said, “I think 54% is a good start. I think we probably need another turn at it.”

It was reported in June that after seeing the costs of AI rise, companies are looking to better manage their use of the technology.

Companies that encouraged their employees to use AI tools when the costs were lower are now using a variety of methods to cut back, such as introducing usage caps, encouraging employees to use the right tool for each task, sharing cost-saving ideas such as switching to models that are older and cheaper, and adopting open-source models.

The report also found that there is an opening for Chinese AI labs that are able to charge less than the U.S. companies due to their more efficient models and China’s lower energy costs.

It was reported in May that “token shock” had hit some of Silicon Valley’s biggest spenders.

For example, Uber exhausted its full-year 2026 AI budget by April, leading Chief Technology Officer Praveen Neppalli Naga to say the company was “back to the drawing board” and Chief Operating Officer Andrew Macdonald to say Uber would weigh token costs directly against the cost of hiring engineers.

PYMNTS reported at the time that agentic coding tools compound the cost exposure relative to standard chatbot interactions because while a single-turn conversation generates one inference call, an agentic session generates many more.

The PYMNTS Intelligence report “The Enterprise AI Benchmark Report: Financial Services Pulls Ahead in the Enterprise AI Race” found that companies across financial services and insurance, healthcare, and media and advertising are putting more money behind AI.

As they do so, these enterprises are beginning to decide which projects deserve real capital and which still need proof, the report said.