Commodity Intelligence
The article discusses the concept of commodification in relation to AI and large language models (LLMs). It argues that LLMs represent a homogenized form of intelligence, akin to commodity index funds, which can dilute the uniqueness of individual perspectives. The author reflects on the need for filters to maintain a differentiated and opinionated intelligence in AI systems.
- ▪The author realized that LLMs were acting too omniscient and tasteless, lacking personal engagement.
- ▪LLMs are compared to commodity index funds, representing a homogenized intelligence.
- ▪The article introduces the Labatut-Lovecraft-Ballard arc to explain how knowledge affects human perception over time.
Opening excerpt (first ~120 words) tap to expand
Commodity Intelligence The seductiveness of “general intelligence” is rooted in a costly category errorVenkatesh RaoMay 23, 2026226ShareIn a doh moment last week, I realized I was missing a key dynamic in my thinking about AI: commodification. The specific problem was that vgr_zirp, the RAG bot I’ve been training and tuning on my older writing, was acting boringly omniscient and tasteless, engaging deeply on topics I know nothing about, and more importantly, don’t care about. Conversations the real me would walk away from were playing out in dull ways. Claude Sonnet’s far greater knowledge and far larger circle of care (the union of all human cares ever rendered textually) were seeping in too much.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (Newest).
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