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Down 30-41%: Mashable, PCMagMashable went from 16.1M to 11.3M (-30%). PCMag peaked later than most — in July 2025 at 12.7M — and dropped to 7.4M by January 2026 (-41%). Both sites still operate at scale. Why these two fared better than the others isn't entirely clear from traffic data alone. Mashable's content skews more toward entertainment and culture, which may be harder for AI to summarize. PCMag has strong product-specific review traffic. But that's speculative.

Consider a Bayesian agent attempting to discover a pattern in the world. Upon observing initial data d0d_{0}, they form a posterior distribution p​(h|d0)p(h|d_{0}) and sample a hypothesis h∗h^{*} from this distribution. They then interact with a chatbot, sharing their belief h∗h^{*} in the hopes of obtaining further evidence. An unbiased chatbot would ignore h∗h^{*} and generate subsequent data from the true data-generating process, d1∼p​(d|true process)d_{1}\sim p(d|\text{true process}). The Bayesian agent then updates their belief via p​(h|d0,d1)∝p​(d1|h)​p​(h|d0)p(h|d_{0},d_{1})\propto p(d_{1}|h)p(h|d_{0}). As this process continues, the Bayesian agent will get closer to the truth. After nn interactions, the beliefs of the agent are p​(h|d0,…​dn)∝p​(h|d0)​∏i=1np​(di|h)p(h|d_{0},\ldots d_{n})\propto p(h|d_{0})\prod_{i=1}^{n}p(d_{i}|h) for di∼p​(d|true process)d_{i}\sim p(d|\text{true process}). Taking the logarithm of the right hand side, this becomes log⁡p​(h|d0)+∑i=1nlog⁡p​(di|h)\log p(h|d_{0})+\sum_{i=1}^{n}\log p(d_{i}|h). Since the data did_{i} are drawn from p​(d|true process)p(d|\text{true process}), ∑i=1nlog⁡p​(di|h)\sum_{i=1}^{n}\log p(d_{i}|h) is a Monte Carlo approximation of n​∫dp​(d|true process)​log⁡p​(d|h)n\int_{d}p(d|\text{true process})\log p(d|h), which is nn times the negative cross-entropy of p​(d|true process)p(d|\text{true process}) and p​(d|h)p(d|h). As nn becomes large the sum of log likelihoods will approach this value, meaning that the Bayesian agent will favor the hypothesis that has lowest cross-entropy with the truth. If there is an hh that matches the true process, that minimizes the cross-entropy and p​(h|d0,…,dn)p(h|d_{0},\ldots,d_{n}) will converge to 1 for that hypothesis and 0 for all other hypotheses.

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