Podbit · God Mode Podcast
Investor Gavin Baker laid out a stark supply-demand mismatch for AI tokens: demand is growing 10x next year while supply grows only 3x. Frontier labs won't cut pre-training to free up capacity, so consumer prices are heading up.
Where this was said
At 17:40 · chapter starts 15:02
Rik pulls up the token-cost chart that had been circulating on his timeline: Anthropic, OpenAI, Moonshot, Alibaba Qwen all show visible bars. DeepSeek V4 Flash is barely a sliver. The gap between proprietary subscriptions and DeepSeek's open-source API is so small that the subscriptions actually win on pure token value. But then the news drops mid-recording: DeepSeek has announced major API price increases. Ben unpacks the nuance — DeepSeek is open-source, so users can theoretically pull the model and run it elsewhere, meaning the official API hike is really about DeepSeek trying to make money rather than a fundamental model cost increase. Still, the direction of travel is clear: even the cheapest corner of the AI market is now moving toward monetisation, and the era of austerity pricing may be giving way to something more expensive across the board.
DeepSeek, previously so cheap its pricing barely registered on token-cost charts, announced significant API price increases mid-episode.
Gavin Baker argued on Invest Like the Best that AI token demand is growing 10x next year while supply is only growing 3x, implying prices must rise.
The fastest path to Twitter growth isn't volume — it's forming sharp opinions about how the platform works and sharing them immediately. People cluster around those who understand the rules and say so out loud.
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.
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