Out of Tokens
Cognitive Offloading: What We Lose When AI Thinks For Us with Michael Gerlich
di Shae Wang
We've always offloaded memory -- to books, to phone numbers in our pockets, to the calculator. But what happens when we start offloading not what we know, but the act of thinking itself? Prof. Dr. Michael Gerlich has spent his career on that question. A sociologist and behavioral scientist, he heads the Center for Strategic Corporate Foresight and Sustainability at SBS Swiss Business School, has published nearly thirty papers in five years, and advised presidents and cabinets across Central Asia and the Caucasus. His new book, The Convenience Trap, was featured in Time. In this episode, he draws the line most of the AI conversation skips: offloading data is old news, but generative AI offers to offload the whole thinking process; it does so by predicting what you want to hear, not by giving you the truth. The result is a slow handover of agency that happens while you still believe you're the one deciding. We get into: Why AI is different in kind, not just degree, from every tool before it -- and why comparing it to the calculator misses the point entirely The counterintuitive case that AI can make you a sharper thinker, but only if you use it in a way almost nobody does What the MIT essay study revealed about learning, memory, and cognitive offloading Why "AI will free up your time for the things that matter" may be one of the most misleading promises of the moment The reframe every organization skips: AI adoption isn't an IT problem, it's a change-management problem and why the real risk on the job isn't someone with AI replacing you What a candle can teach us about the limits of machine intelligence
Everyone agrees AI is about to get smarter. Jo Guldi thinks intelligence was never the bottleneck. The real constraint is something quieter and far harder to fake: the infrastructure that tells us what actually happened. Guldi is a historian who grew up coding on the Texas "Silicon Prairie," then became, in 2008, the first person ever to hold a faculty post in "digital history." She's now Professor of Quantitative Methods at Emory, a historian of capitalism and infrastructure, and the author of The Dangerous Art of Text Mining. She's spent her career on the seam between data science and the archive, which makes her uniquely clear-eyed about what large language models can and can't do. Her argument cuts against the moment. As AI becomes the way most people get their information, the scarcest resource won't be answers -- it'll be provenance: the ability to trace where a document came from and whether it's real. And the institutions that guard that have been quietly starved of investment for decades, right as we've gained the power to manufacture convincing fakes at scale. We get into: Why a document's "biography" may become the most valuable thing in the AI era, and how a genocide once hidden in an unmarked archive proves the stakes The "right to a verifiable past," and how losing it resembles life under an authoritarian regime Why the world's archives -- measured in shelf kilometers, much of it too sensitive to digitize -- won't be swallowed by the labs any time soon What her lab found mapping historical disagreement across 300+ languages of Wikipedia, and why "when did your country begin?" is a more loaded question than it sounds "White-box" history: using LLMs inside a pipeline you can actually inspect, instead of trusting a black box's version of the past Why, if AI can write the essay and the code, a liberal-arts education might be the job training nobody saw coming