Palantir Technologies Chief Executive Alex Karp has delivered one of his strongest critiques yet of the commercial strategies adopted by leading artificial intelligence developers OpenAI and Anthropic, arguing that their token-based pricing models have become disconnected from what enterprise customers actually need as AI deployment costs continue to surge.
Speaking to CNBC’s Squawk Box on Wednesday, Karp said businesses are moving beyond measuring AI usage by token consumption and are instead demanding clear financial returns from their investments, a shift that could reshape competition across the rapidly evolving AI industry.
“I’m not throwing shade at them, but something has gone completely wrong,” Karp said. “The basic view among enterprises in this country is I’m going to chillax and waste my time with tokens.”
Karp’s remarks come as businesses grapple with the rapidly increasing cost of deploying advanced AI systems.
Successive generations of frontier models have become substantially more expensive to operate because they require greater computing power, larger graphics processing unit (GPU) clusters, and more sophisticated infrastructure.
As a result, many companies are moving away from optimizing token consumption, an approach often referred to as “tokenmaxxing,” and instead evaluating AI projects based on measurable business outcomes and return on investment.
The shift is prompting enterprises to reconsider whether relying on proprietary models from companies such as OpenAI and Anthropic offers sufficient value relative to their costs. Instead, organizations are now exploring open-weight AI models, which provide access to model parameters and can often be customized for enterprise use while operating at significantly lower costs.
Chinese AI Adds Competitive Pressure
The growing adoption of cheaper AI alternatives comes as Chinese developers continue narrowing the technological gap with leading U.S. AI companies. Open-source and open-weight Chinese models have become increasingly capable while remaining considerably less expensive to deploy, raising concerns within the U.S. technology sector that China’s AI ecosystem could erode the commercial advantage long enjoyed by American frontier laboratories.
Karp warned that the industry should not underestimate the pace of Chinese innovation.
“The industry should not underestimate the speed at which China is making progress in building AI models,” he told CNBC.
His comments echo broader concerns in Washington, where policymakers have intensified scrutiny of AI competition as Chinese firms rapidly improve their models despite U.S. restrictions on advanced semiconductor exports.
Many enterprises are now investing in proprietary AI systems tailored to their own operations rather than relying exclusively on third-party frontier models. Custom-built models allow companies to optimize performance for specific business tasks while maintaining tighter control over costs, intellectual property, and sensitive data.
That strategy aligns closely with Palantir’s own business model.
Earlier this week, the company expanded its partnership with Nvidia, combining Nvidia’s AI infrastructure and software with Palantir’s data integration platform to develop customized AI systems for U.S. government agencies.
The collaboration reflects growing demand for AI platforms that organizations can own, manage and adapt internally instead of depending entirely on externally hosted models.
Karp framed the debate as one of technological independence rather than simply pricing.
On Tuesday, Palantir published a nine-point “AI sovereignty” manifesto on social media platform X, noting that governments and enterprises should maintain ownership of their data, computing infrastructure and AI models.
The document criticized “tokenmaxxing” as a flawed commercial approach while encouraging organizations to retain control over the full AI technology stack.
According to Karp, enterprise customers want ownership rather than dependence.
“What aligns me with Nvidia, and I think is what the technical customers want, which is control over their compute, their models, their data stack and their alpha,” he said.
“They want to know they own the means of production. It’s not being transferred to someone else.”
The concept of AI sovereignty has gained traction as organizations seek to reduce reliance on external providers amid concerns over data privacy, cybersecurity, regulatory compliance and long-term operating costs.
Investors appeared to welcome Palantir’s positioning within the evolving AI industry. The company’s shares rose 8% on Wednesday, extending gains as investors increasingly see Palantir as a beneficiary of enterprise demand for customized AI deployments rather than generalized consumer-facing models.
While OpenAI and Anthropic continue to dominate frontier model development, enterprise customers are becoming more selective about how they deploy artificial intelligence. They’re now placing greater emphasis on measurable productivity gains, ownership of critical AI infrastructure, and long-term cost efficiency instead of simply accessing the most advanced models available.
