Whatever the output gains promised by LLMs, their initial productivity surge is erased over time, and replaced by heavier workloads—and that leads to workers experiencing “cognitive fatigue, burnout, and weakened decision-making.”

All this from research out of the notoriously pro-worker rag [checks notes] Harvard Business Review: https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies-it

"I hate these New Year’s that fall like fixed maturities, which turn life and human spirit into a commercial concern with its neat final balance, its outstanding amounts, its budget for the new management. They make us lose the continuity of life…

I want every morning to be a new year’s for me. Every day I want to reckon with myself, and every day I want to renew myself. No day set aside for rest. I choose my pauses myself"

—Antonio Gramsci
https://viewpointmag.com/2015/01/01/i-hate-new-years-day/
https://www.patreon.com/posts/56-special-on-76608973

There are definitely many real-world issues and problems where we could benefit from significantly more quantitative (and mathematical) analysis and thinking than we do currently. But, at the same time, there are certain dimensions of our modern society where we overcorrected for this, and allowed quantitative reasoning to dominate at the expense of other important modes of thought, or to be deployed in a highly imbalanced fashion.

As anyone who has encountered a word problem in their high school math classes knows, the first step in quantitative reasoning is to assign numerically precise metrics as proxies for one's goals, parameters, and variables. Most of our wants and needs are quite qualitative in nature: happiness, comfort, security, companionship, and the like. But these are too fuzzy to be optimized and analyzed by the mathematics of quantitative reasoning. Which, to oversimplify things, leaves us with basically two options: either use more qualitative modes of thinking, such as "gut feelings", emotional responses, or drawing on past experiences of similar situations, accepting any cognitive biases that result from doing so; or to create quantitative proxies for these goals, and then optimize those proxies in a more dispassionate (and hopefully more objective) fashion. (1/5)

Alfred Tarski has shown that Euclidean geometry, unlike arithmetic (see Gödel's theorem), is decidable. Any statement can be proven to be either true or false.

Unfortunately the algorithm for finding such a proof has doubly exponential complexity. Math is programming.