Why are AI’s biggest companies suddenly asking to slow down?
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The sanctioned ruble-based stablecoin A7A5 claims daily trading volume exceeding 100 million, but companies like TRM Labs and Elliptic question the accuracy of this data.
PANews reported on July 4th that, according to CoinDesk, the sanctioned ruble-pegged stablecoin A7A5 is embroiled in a heated dispute with a blockchain analytics firm over its actual usage and trading volume. A7A5 claims that cryptocurrency data providers underestimate its trading activity, while the blockchain analytics firm states that trading volume for this ruble-backed token has declined significantly this year. The A7A5 issuer claims that it processed a total of $34.4 billion between January 1st and June 17th, 2026, with an average daily trading volume of approximately $205 million, primarily driven by DeFi activity. However, TRM Labs believes that A7A5's average daily trading volume is closer to $75 million, and that trading activity has declined in recent months.
Palantir CEO: Enterprises are dissatisfied with "cutting-edge labs" like OpenAI and Anthropic, which only pursue token maximization.
According to BlockBeats, on July 2nd, Palantir CEO Alex Karp, in an interview with CNBC's "Squawk Box," strongly criticized leading AI model companies, calling the way AI is sold "completely wrong." Karp emphasized that companies are already 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 IP. Karp stated that companies are "angry" and will commit to owning 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 sovereign environments, primarily serving the US government and critical infrastructure customers. The collaborative system reportedly integrates Nvidia AI technology with Palantir's AIP, Foundry, Ontology, and Apollo platforms, helping organizations train, customize, and deploy AI locally while maintaining complete control over data, intellectual property, and models.
After Their AI Models Hacked Real Companies, AI Labs Call for Stronger Cyber Defenses
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