Podbit · The Diary Of A CEO with Steven Bartlett
There are very few jobs left where Godin can't find an AI to do the work faster and cheaper. The future belongs to people whose work can't be written down as rules — those who build trust, connect humans, and exercise taste. AI is electricity; the question is what movie theater you're going to build.
Where this was said
At 1:36:20 · chapter starts 1:35:40
Godin's AI credentials are unusual for a humanist thinker: he took Doug Lenat's foundational AI PhD course at Stanford in 1983 and has been writing about the technology for decades. His two core claims are stark. First, this is the biggest change in the world since electricity — and companies that waited for electricity to go away in 1890 did not survive. [1] — Seth Godin "There are very few jobs left where Godin can't find an AI to do the work faster and cheaper. The future belongs to people whose work can't …" 1:36:20 Second, the choice facing every worker is binary: either AI becomes your boss, using you as a gig worker for the tasks it can't yet do, or you use AI as a tool to amplify what machines cannot replicate. What machines cannot replicate, Godin argues, is any job whose rules cannot be written down — the curator's taste, the connector's trust network, the doctor who spends more time with patients because AI handles the paperwork. Bartlett's fiancée's breathwork retreat business is the perfect example: what women get from it is mostly each other. AI will never replace that.
Godin frames AI as a civilisation-level shift equivalent to electricity, warning that workers who wait for it to go away will fare as badly as blacksmiths who ignored electrification.
Godin argues the fundamental AI choice every worker faces is whether AI becomes their boss (reducing them to a gig worker for tasks AI can't yet do) or their tool (amplifying uniquely human skills like trust and network-building).
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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