Turing Award winners and field pioneers are converging on the same diagnosis: scaling large language models will not produce general intelligence. Part 1 of a series on the cracks in the AI scaling narrative.

Last week, I admitted I’m part of the problem. Despite advocating restraint, I’ve felt the pull to post more, feed the algorithm, and add to the noise. We’re all drowning in a flood we helped create.

Even while preaching generative restraint, I felt the pull to post more. The attention economy is a systemic trap made of individual choices, and recognising your own part in it is where the solution begins.

If external constraints can no longer stabilize the linguistic interface, then the stabilizing function must move inward: from the environment to the mind, from architecture to practice, from imposed limits to cultivated awareness. The word for this is wisdom.

Our linguistic infrastructure is experiencing serviceability failure, fracturing shared reality, overwhelming attention, creating authenticity paradoxes, and widening the gap between knowledge and wisdom, with LLMs accelerating the crisis by eliminating the last friction in language production.