“AI adoption is not a technology challenge, it’s a trust challenge,” Rajamani told Fortune. “Leaders are being asked to bet their businesses and careers on systems they can’t fully see, explain, or control. That’s where trust breaks down.”
While layoff memos focus on who’s being let go—CEOs like to lead with the heart, after all—it’s vital to understand who’s kept their jobs. Are these mostly technical people? The folks with soft skills and deep relationships that are hard to replace? ServiceNow CEO Bill McDermott emailed me a brief on Friday about its blueprint for agentic business that states: “When intelligence is abundant, the scarce resource is everything surrounding it: the enterprise context that grounds AI in reality, the governance that makes it safe, and the execution infrastructure that turns insight into action.” Does that make the case for more people? No. But it does suggest that top-level cuts don’t always turn out as planned.，更多细节参见币安_币安注册_币安下载
We propose sycophancy leads to less discovery and overconfidence through a simple mechanism: When AI systems generate responses that tend toward agreement, they sample examples that coincide with users’ stated hypotheses rather than from the true distribution of possibilities. If users treat this biased sample as new evidence, each subsequent example increases confidence, even though the examples provide no new information about reality. Critically, this account requires no confirmation bias or motivated reasoning on the user’s part. A rational Bayesian reasoner will be misled if they assume the AI is sampling from the true distribution when it is not. This insight distinguishes our mechanism from the existing literature on humans’ tendency to seek confirming evidence; sycophantic AI can distort belief through its sampling strategy, independent of users’ bias. We formalize this mechanism and test it experimentally using a rule discovery task.，这一点在旺商聊官方下载中也有详细论述
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