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OpenAI cut GPT-5.6 Luna's price 10x on OpenRouter. Usage exploded 13x. This is the Jevons paradox playing out in live data, and it's the most bullish possible signal for the entire inference economy.
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At 19:31 · chapter starts 19:12
The Jevons paradox — the counterintuitive idea that cheaper resources drive more consumption — has been theorised about extensively in AI circles but rarely demonstrated with clean data. Alex delivers that data: OpenAI cut GPT-5.6 Luna's price by 10x on OpenRouter, and within two weeks usage grew 13x. [1] — Alex Atallah "OpenAI cut GPT-5.6 Luna's price 10x on OpenRouter. Usage exploded 13x. This is the Jevons paradox playing out in live data, and it's the mo…" 19:31 The growth then stabilised at that 13x multiple and continued growing at the same underlying rate as before. Alex notes that Luna is now in the top 3–5 models by token volume on OpenRouter — the first time an OpenAI model has cracked that ranking in a very long time. He also acknowledges the methodological challenge: OpenRouter captures roughly 1.5–2% of total token volume and has a selection bias toward companies that believe in the multimodel thesis. That said, as the platform scales, its data becomes increasingly representative of broader market behaviour.
Token prices have fallen approximately 90% over the last 18 months, raising questions about whether lower prices help or hurt OpenRouter's revenue model.
After OpenAI cut GPT-5.6 Luna's price 10x on OpenRouter, usage grew 13x — a near-perfect real-world Jevons paradox demonstration.
GPT-5.6 Luna is now in the top 3–5 models by token volume on OpenRouter, the first time an OpenAI model has reached that ranking in a very long time.
In July alone, OpenRouter added 70 models — one every 10 hours. And that's before agent labs like Cognition, Cursor, and Jeff Dean's new venture have even started releasing their own models in earnest.
OpenRouter launched 70 new models in July 2025, roughly one model every 10 hours, reflecting the explosive pace of AI model development.
By 2006, AI algorithms were stuck because they were being trained on almost no data. Fei-Fei Li's insight: human children see tens of thousands of object categories by age 6 — so machines needed massive data too. ImageNet's 15 million images, combined with GPU power and better algorithms, triggered the modern AI revolution in 2012.
The ImageNet challenge pitted machines against humans on recognizing 1,000 object categories. Humans clocked a ~4% error rate. In 2012, a neural network smashed previous AI performance — and by 2016, machines had surpassed humans entirely. That single benchmark created the modern AI era.
When video was added to AI training data in 2023, something clicked: machines could generate plausible motion without knowing muscle anatomy — just from watching millions of cat videos. Sora's January 2024 release proved AI had crossed into temporal, physical understanding.
AI is trained on the internet — the largest archive of human behavior ever assembled. But the most profound human thoughts, Picasso's creative flash, a private childhood memory tied to a gray cup, have never been uploaded anywhere. That's the gap AI cannot close.
AlphaGo's Move 37 against Lee Sedol shocked Go masters — no human had ever conceived it. But Fei-Fei Li urges caution: Go has fixed mathematical rules, and AI's bigger compute simply found a configuration human memory couldn't retain. That's creativity in a constrained space, not the open-ended kind.
Surface-level intuition — the kind you can describe in words — is just context, and AI already handles that. But the deeper kind: the feeling shaped by what you ate, your hormones, and a mood you can't name? That has no sensory apparatus feeding it to any machine. It's inaccessible, and will remain so until brainwave-level sensors exist.
Self-driving cars already exist. But the real robot revolution — robots assisting the elderly, fighting wildfires, supporting overworked nurses — is a 20-30 year arc, not a 2-year one. Hardware plus AI moves slower than software alone, but the impact will be civilizational.
Language AI is powerful, but humans evolved in a spatial, physical world. WorldLabs is building foundational models for spatial and 3D intelligence — letting people generate entire environments from a sentence or sketch. The applications span filmmaking, robotics training, architecture, and healthcare.
Modern AI didn't emerge from one breakthrough. It took three things converging at once: mature neural network algorithms, the ImageNet large-scale dataset, and fast GPU computing. When all three lined up around 2012, the revolution was inevitable.
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