TLDR: On July 1, CEO Alex Karp went on CNBC to discuss Palantir’s expanded Nvidia partnership and delivered a nineteen-minute broadside against OpenAI and Anthropic: enterprises are “livid” because they are paying for “tokens that create no value” while the labs harvest their data and alpha. Karp is right that enterprises are frustrated with exploding token costs and frontier labs capturing too much value/control. But he is wrong that Palantir’s platform (Ontology + Nemotron) is the necessary cure that makes agents “safe, useful, and precise”. Those properties come from the specification and verification core, which Palantir has a strong version of, but does not own. Open weights are not the frontier. The labs are already moving inside customer perimeters. Therefore, Palantir is half right on the diagnosis but overclaims on the prescription.
Something remarkable happened on CNBC’s Squawk Box last week (29 June – 5 July 2026). Palantir CEO Alex Karp was ostensibly there to discuss Palantir’s expanded deal with Nvidia in “Sovereign Environments,” but instead spent most of his airtime attacking the companies whose products his own platform resells.
Karp delivered what we can call a televised monologue against the frontier labs. Enterprises, he said, are “livid” because they are paying for “tokens that create no value” while the labs cache their data, absorb their alpha, and levy what he called a “wealth tax” on American business. The arrangement extended to national security is “effing insane.“ When Squawk Box co-host Becky Quick observed that he sounded pretty angry, Karp answered that he was merely channeling the voice of American business and dared them to privately call CEOs and tell them, “Madman Karp is on TV saying we’re livid,” to see if they would admit they are.
Despite the theatrics, Karp’s CNBC broadside against OpenAI and Anthropic resonated because it tapped into a genuine anxiety spreading through enterprise AI. Companies are watching their token bills explode, questioning whether foundation model providers are capturing all the value and their data along the way, and wondering who will ultimately control the enterprise AI stack.
It also somewhat overshadowed Palantir’s news. The Nvidia deal deserves attention because it addresses a structural peculiarity. Palantir was one of the earliest players to make a bid to seize the orchestration layer, the most valuable terrain in Orchestration Economics. And yet, it was attempting to do so without owning the model layer. [More on this later this week, when I revisit the evaluation of Palantir against my Three Laws of Agentic Value, which I published earlier this year in the Manifesto.]
Palantir expanded its partnership with Nvidia to deploy Nvidia’s open-weight Nemotron AI models on sovereign, air-gapped Blackwell infrastructure, enabling government agencies and critical infrastructure operators to run AI entirely within secure, self-controlled environments. The deal gives Palantir its first integrated model offering, allowing it to move beyond simply brokering third-party models. It strengthens the company’s pitch that customers can deploy a complete AI stack without relying solely on frontier model providers.
While Karp’s diagnosis of soaring token costs and growing distrust of frontier labs is largely correct, his proposed solution points to the wrong destination.
He argues that enterprises need companies like Palantir to make large language models “safe, useful, and precise.” In fact, the application layer itself is rapidly being unbundled. What remains defensible is the specification-and-verification core within it: the machine-readable definition of how an organization works, what its objectives are, which constraints matter, and how an AI agent knows when it has completed a task correctly. That layer is becoming the scarce asset as agents commoditize much of traditional enterprise software.
Palantir possesses one of the strongest examples of it in its Ontology, but it does not own the category. Frontier labs, data platforms, and enterprises themselves are all converging on the same ground while simultaneously redrawing the boundary between models and applications.
So, Palantir’s enthusiastic evangelism for open weights, sovereignty, and the demonization of the Big Model labs is both opportunistic and a strategic necessity. Despite otherwise strong earnings, the company’s stock is down about a quarter this year and, at its late-June trough, was down almost half from its November 2025 peak. And yet, Palantir’s market capitalization prices it at about 40 times forward sales, about double the multiple at which Anthropic recently raised its Series H at a $965bn valuation, suggesting there’s a strong case that it remains overpriced.
Given Palantir's obvious self-interest in making this case, I want to evaluate Karp’s diagnosis as well as his broader prescription. But I also want to zoom back to understand the implications for Palantir, which remains one of the more intriguing case studies for decoding the way value is migrating in the Agentic Era.