Palantir CEO: Enterprises are dissatisfied with "cutting-edge labs" like OpenAI and Anthropic, which only pursue token maximization.
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David Sacks strongly supports Palantir CEO's criticism of AI labs: True enterprise AI security lies in controlling one's own data, models, and computing power.
According to Beating, David Sacks, co-chair of the U.S. President's Council of Advisors on Science and Technology, published an article supporting Palantir CEO Alex Karp's sharp criticism of cutting-edge AI labs, stating that the mainstream media's portrayal of his interview as a "disastrous outburst" precisely demonstrates that Karp hit the nail on the head. Sacks points out that true "AI security" in a corporate environment is not abstract "alignment research" or government-led certification systems, but rather the ability to control one's own data, model weights, and computing power—preventing cutting-edge labs from "absorbing" a company's proprietary knowledge and turning it into the next product. He quotes Karp as saying, "They want to own their means of production, not hand them over to others." Sacks cites the conflict between Figma and Anthropic as a prime example: three days before the release of Claude Design, Anthropic's Chief Product Officer was still a member of Figma's board of directors, and Figma's founder stated that Anthropic "hadn't always been honest with them"; subsequently, Figma's stock price plummeted while Anthropic's valuation soared. He further listed products such as Claude Science, Claude Security, Claude Legal, and Claude Code, pointing out that Anthropic consistently targets vertical sectors originally served by companies that relied on its models, following a consistent pattern: "Observe where value is created first, then jump in." Sacks believes that the perception of open-source models as "dangerous" is not true for companies—retaining choice at the model layer and deciding who can use their core strengths is the real bottom line for corporate security. Previously, Palantir partnered with NVIDIA to deploy Nemotron's open AI models in sovereign environments, serving the US government and critical infrastructure customers, helping organizations train and deploy AI locally while maintaining complete control over data and intellectual property. Palanitir CEO Alex Karp recently gave a scathing interview on CNBC's "Squawk Box," criticizing leading AI model companies as "completely wrong" in their approach to selling AI. Karp emphasized that companies are currently dissatisfied with "cutting-edge labs" like OpenAI and Anthropic, believing they only pursue token maximization, wasting companies' time and money while handing over proprietary value and intellectual property. Karp stated that companies are "angry" and will strive to own their own AI production resources rather than relying on third parties. On June 29th, Palantir partnered with Nvidia to deploy Nvidia Nemotron open AI models in a sovereign environment, primarily serving the US government and critical infrastructure.
Serenity: Funds in China's primary market are flowing into physical AI and world models, with funding for cutting-edge models concentrating on leading companies.
According to Mars Finance, on July 3rd, Serenity published an article stating that, based on AI investment in China's primary market, institutional funds are flowing towards embodied intelligence, physical AI, and world models. The article cites approximately $23.56 billion in funding for large-scale models/LLMs, $15.74 billion for AI infrastructure and technology layers, $13.36 billion for embodied intelligence/physical AI, $8.79 billion for AIGC applications, and $3.82 billion for autonomous driving and other Top-20 clusters. However, this figure is not entirely comparable to the aforementioned categories. Early-stage, purely basic model funding has essentially closed, with more funds flowing to established leading companies and world model companies. The article believes a similar trend may emerge in the US market, with funds concentrating on leading companies like Anthropic and OpenAI, and that "world models have become the biggest consensus in early-stage investment." Several months ago, Serenity believed that 4D AI/world modeling would be the most noteworthy area to watch, mentioning that AEVA might offer exposure to this sector. However, there are currently no clear pure-play targets in the market, and we may need to wait for the next batch of IPOs in this field. Serenity stated that AIGC applications are the most mature area of AI technology commercialization, but there are currently no clear winners. Funds continue to flow into AI infrastructure and the semiconductor supply chain, while a large amount of capital is rotating into physical AI, embodied intelligence, humanoid robots, and world modeling, with cutting-edge modeling continuing to concentrate on leading companies.
Cosmos Labs Co-CEO: dYdX's shift to RWA is a rational choice with limited impact on ATOM.
According to Foresight News , Cosmos Labs co-CEO Barry Plunkett tweeted his comments on dYdX's partnership with Robinhood to launch Arcus. dYdX has proven it can run seriously on-chain and drive industry development, but in recent years has faced pressure from new-generation perpetual contract competitors like Hyperliquid and Lighter, the overall decline of DeFi, and Web 2.5 products like Kalshi. He sees dYdX's shift to RWA through its partnership with Robinhood, which has strong distribution capabilities, as a "rational choice." He believes the impact on ATOM is very limited. The dYdX Chain remains a sovereign chain, and its fees, security, and value accumulation contribute very little to ATOM. The ATOM community also did not pay for the migration of dYdX to Cosmos. This further confirms the Cosmos team's assessment: for teams with distribution capabilities and leading products, having an underlying platform is crucial. Cosmos is currently focused on building tokenized deposit solutions for banks.
Arthur Hayes takes CEO role at Flop Labs ahead of Q4 airdrop
Hayes revealed his new role as Flop Labs CEO and teased a “massive airdrop” from the AI inference protocol in the fourth quarter of 2026.
Report: Fixed computing power masks the true capabilities of AI; the evolutionary speed of cutting-edge intelligent agents is underestimated by 60%.
According to Beating's monitoring, the UK AI Security Institute points out that current mainstream AI agent testing has significant blind spots. Evaluation methods with fixed computing power limits severely underestimate the true capabilities and iteration speed of models. The research team tested the performance of several cutting-edge large-scale models in benchmarks such as cybersecurity, software engineering, and mathematics. The test results show that the performance of an agent is not a fixed score, but rather a curve that continuously increases with test-time compute. In network attack and defense tests, when the computing power budget increased from 2.5 million tokens to 50 million tokens, the upper limit of the complexity of tasks that the most advanced agents could overcome (equivalent to human time) skyrocketed from 2 hours to 14 hours. Many attempts that failed with low computing power could eventually complete the task if given sufficient computing power to allow the agent to explore and correct errors. The new model's utilization efficiency of test-time compute power is significantly higher than that of the old model. Under a sufficient budget, the measured trend of cutting-edge capability evolution (the slope of the fitted curve) is approximately 60% steeper than in low-computing-power tests, demonstrating that traditional assessments severely underestimate the true iteration speed of AI. However, this computing power advantage has its limits; in fields such as healthcare where immediate feedback is lacking, increasing computing power does not necessarily improve the performance of intelligent agents. As inference costs decrease, low-budget assessments may lead decision-makers to underestimate the risks of AI agents in practical applications.
Palantir CEO Alex Karp vehemently criticizes the large-scale token fee model.
On July 1st, Palantir CEO Alex Karp stated during an appearance on CNBC's "Squawk Box" that the current general attitude towards AI in the business world is one of resistance—buying a bunch of tokens without getting any real value, and instead handing over their intellectual property and data. He criticized the business model of large model vendors charging based on tokens: since tokens are so valuable, why don't they share the profits, instead of charging based on usage?