Why the AI Renaissance Keeps Not Arriving (and why it won't under the current post-training regime)
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Why the AI Renaissance Keeps Not Arriving (and why it won't under the current post-training regime)<br>The subtle and frustrating nature of manifold collapse
James Baker<br>Jun 12, 2026
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Everyone remembers the first time AI really works. You ask the model for an essay, a strategy, a name for the thing you’re building, and it comes back in seconds, polished and coherent and better than what most people you know could write in hours. It feels like the start of a renaissance that took only a small kick to get going.<br>Then you use it every day for a year, and you slowly realize that the model has only a few ways of doing everything. Every brainstorm is the same brainstorm-shaped object and every essay has the same skeleton. Put simply, the outputs are locally excellent and globally identical.<br>Thanks for reading James’s Newsletter! Subscribe for free to receive new posts and support my work.
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I call this manifold collapse. The model never explores the whole landscape of possible ideas. It circles a small, well-worn region of it, and inside that region it travels what I think of as latent grooves, a few deep and reliable paths it falls into no matter how you phrase the request. While words change every time, the “groove” doesn’t, and this pattern is well replicated across the field. People working with AI produce better individual work while the pool of everyone’s work grows measurably more alike. Everyone gets a better paragraph but the world gets more paragraphs that feel (and functionally are!) the same.<br>What’s scarier is when you scale it up and see the societal level effects of such an monotinicy. Every knowledge profession is adopting these tools, because for each individual they genuinely help. Consultants, lawyers, marketers, founders, and researchers all draft through the same handful of models, which means they all draw on the same ten thousand or so moves, because that is what the shared region contains. The floor rises but the tail (where some of the truly helpful work comes from) completely vanishes. Things like breakthrough legal theories, category-defining companies, and new art movements that were never medians done slightly better. What you’re left with is a society that keeps producing its best average work ever while the frontier stalls.<br>What’s worse is that mistakes synchronize and compound. When everyone reasons through the same system, everyone misses the same argument, crowds the same trade, and chases the same direction at the same time, like a monocrop farm sitting one blight from disaster. In a lawsuit it turns almost comic. If both sides draft with the same model, your opponent’s AI anticipates your argument because it would have written your argument. The loop even closes across generations. People learn to write and think from model outputs, models train on text shaped by models, and each pass compresses the culture a little further.<br>This is why the AI renaissance keeps not arriving. A true renaissance was never about producing more high-quality artifacts, its when frontier itself expands, when new mediums and new scenes and weird people push against consensus until the consensus moves. A system trained to satisfy consensus hands you the median of everything humanity has tried, instantly. That feels like genius exactly once. Smarter models actually make this worse. In turn, when a billion people draw from the same grooves, we end up in a weird monoculture.<br>The evidence
A 2025 meta-analysis of 28 studies and 8,214 participants quantified the trade. Working with AI improves a person’s creative performance relative to working alone (Hedges’ g = 0.27) and imposes a large negative effect on idea diversity across participants (g = -0.86). Doshi and Hauser established causality in Science Advances. Writers given AI story ideas produced stories rated more creative and more enjoyable, with the effect concentrated among less creative writers, yet the AI-assisted stories were significantly more similar to one another. The authors frame it as a social dilemma, in which adoption is individually rational and collectively narrowing. Anderson, Shah, and Kreminski found the same pattern in ideation. ChatGPT users generated more ideas in more detail, but the ideas were less semantically distinct across users.<br>Wenger and Kenett closed the obvious escape hatch, the hope that the sameness belongs to one particular model, by testing a broad set of LLMs against humans on standardized divergent-thinking tasks (Alternative Uses, Divergent Association, Forward Flow). Their 2026 PNAS Nexus paper carries the result in its title, “Large language models are homogeneously creative.” Model responses resemble other models’ responses far more than human responses resemble other humans, even after controlling for response structure. Homogenization belongs to the model...