Podbit · God Mode Podcast
DeepSeek's API was so cheap it literally didn't register on a token-cost comparison chart. Then, mid-episode, news broke of significant DeepSeek price hikes. Even the cheapest open-source alternative is now joining the move toward monetisation.
Where this was said
At 15:00 · chapter starts 13:16
The conversation sharpens into token efficiency. The viral report measures token value per dollar of subscription, but Rik flips the frame: what matters for anyone not on a capped plan is how many tokens each model burns per task. Opus 5 can consume close to 100 million tokens where Terra completes the same work in around 1.5 million — a difference approaching 65 to 100 times. Ben contextualises how enterprises are justifying this by comparing it to headcount savings; a $20,000-per-employee AI bill can beat a 20% staffing increase. But both hosts agree: they would never want to pay Anthropic's API rates for Opus at scale. The subsidy is the only reason the current arrangement works for small builders.
Opus 5 can spend nearly 100 million tokens on a task where a cheaper model like Terra uses only 1.5 million, a ~65x difference in token consumption.
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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