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A number everyone is quoting, and mostly misreading Over the past year, one statistic has become shorthand for enterprise AI skepticism: 95% of generative AI pilots fail to deliver measurable P&L impact. It comes from MIT's Project NANDA, in a study titled The GenAI Divide: State

A magazine starts selling ads to robots. The harder problem is what happens after they read them. Advertising has always required an audience willing, however reluctantly, to pay attention. Time, a magazine venerable enough to have covered both world wars, has found a new one: machines. Since July it has

A Republican and a Democrat were run through the same four-turn AI probe. The distortions weren't partisan. They were structural. The finding in one line We measured how AI models represent political figures across a full voter conversation, not a single query. The same failure patterns showed

A post making the rounds this week put it well: in AI search, you don't occupy a results page, you occupy a clause. "For running shoes, most people recommend ___." That position isn't bought at auction. It accumulates upstream, in reviews, citations, and cultural presence

A sanctioned Wagner front group just showed why "generative engine optimization" was always measuring the wrong thing. A new report from CASM, the Centre for the Analysis of Social Media at the UK think tank Demos, tested five leading AI chatbots against fifty propaganda claims sourced from a

Every finding we've published so far assumes a person is still there. Someone opens a chat, asks a few questions, reads a recommendation, and either acts on it or doesn't. Even when a brand gets displaced at the third or fourth turn of that conversation, a

A summer thought experiment for executives who think they've already priced in AI Every framework in this Journal so far assumes something convenient. It assumes the agent doing the recommending has no interests of its own. It ranks. It weighs context. It survives or doesn't survive