There is a specific brand of earnestness found in the tech corridors of the West Coast, where the rediscovery of a foundational human truth is often treated as a proprietary breakthrough. Lately, this has manifested as a fixation on Large Language Models (LLMs) not merely as software, but as oracles. To the digital vanguard, the ability of a machine to synthesize text feels like the arrival of a new species of intelligence. To the average observer, however, it often feels like a more temperamental version of the libraries and search engines we have used for generations.
The gap between these two perspectives is widening. While developers and venture capitalists argue over the philosophical implications of "emergent behaviors" in neural networks, the broader public remains anchored in concerns of utility, reliability, and the preservation of human agency. Silicon Valley’s current obsession with "knowledge" as a raw material to be processed by AI ignores the messy, social, and deeply contextual ways in which people actually learn and interact with the world.
This disconnect suggests a growing isolation within the industry. When the primary goal is the pursuit of artificial general intelligence, the mundane needs of a person trying to navigate a daily task or verify a simple fact become secondary. The result is a suite of products that feel increasingly alien—designed by a bubble, for a bubble, and perpetually surprised when the rest of the world does not share its breathless zeal.
With reporting from *The Verge*.
Source · The Verge
