Snapshot · The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
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.
Where this was said
At 21:05 · 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.
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.
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.
There are more than 90,000 Flock surveillance cameras currently in use around the United States.
A 2023 report estimated that 10 million Americans own Ring cameras, roughly 1 in 5 households having a video-enabled doorbell.
The ImageNet dataset collected 15 million images to drive machine learning, becoming a cornerstone of the modern AI revolution.
A Stanford graduate student benchmarked human performance on the ImageNet 1,000-category challenge at roughly 4% error rate, a figure AI surpassed by 2016.
From the 2012 ImageNet breakthrough, it took only about 3–4 more years for AI algorithms to surpass human performance in naming 1,000 object categories.
Flock's surveillance network scans more than 20 billion license plates per month across the United States.
OpenAI's Sora, released in January 2024, demonstrated AI's ability to generate realistic video from text prompts, marking a key milestone in video generation.
AlphaGo's Move 37 against Lee Sedol was a move that human Go masters had never considered, illustrating a unique form of AI creativity within constrained mathematical rules.
The internet is not a random data source — it is the largest-ever multimodal archive of human behavior including text, images, video, and audio accumulated over decades.
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