By PYMNTS | October 19, 2026
Israeli startup DoubleAI is reportedly set to introduce what it describes as a new category of artificial intelligence (AI).
The company, whose backers include Nvidia and Dell, calls this new category “code execution” models (CEMs) which place the AI “between code and silicon,” the Wall Street Journal (WSJ) reported Monday (Oct. 19).
The models can help run software and determine how to make code function better, helping companies lower their computing costs, the report said.
Co-Founder and CEO Amnon Shashua told the WSJ the main challenge in making CEMs work was learning how to verify the code. DoubleAI’s breakthrough was finding a method of formally and mathematically performing that verification, he said.
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DoubleAI’s first CEM, called “Maxwell,” is designed to address the problem of surging compute costs for running graphics processing units (GPUs) and central processing units (CPUs) by enhancing their performance, the WSJ said.
“More hardware is being built, but the need for compute is rising faster than the ability to build GPUs and CPUs,” Shashua said. “If you can utilize the hardware better, then you can achieve something incredible in terms of savings.”
The WSJ notes that compute prices have jumped as demand has risen and supplies have shrunk. The cost-per-hour of renting computing power from Nvidia H100 chips on a one-year contract has climbed 60% in the last year, the report added, citing data from SemiAnalysis.
Against this backdrop, finance teams are looking for AI agents that can manage their companies spending on AI itself, PYMNTS wrote in July.
“Dynamic budget reallocation using real-time cost data is the highest-ranked use case for agentic AI among CFOs surveyed by PYMNTS Intelligence,” that report said.
The research showed that 43% of CFOs expect that agents that can continuously scan spending patterns, spot overruns and move funds toward higher-priority areas to have a major impact. This figure illustrates the shift taking place: finance teams don’t just want visibility into AI spending after the fact, but a system that actively manages it in real time.
“Companies are betting that the next phase of enterprise AI adoption depends less on pushing employees toward heavier AI usage and more on building the financial infrastructure to prove that usage is worth what it costs,” the report said.