This episode of The Super Cycle isn’t about token prices, charts, or short-term narratives. It’s about something much bigger: AI becoming the cheapest labor force in history, and crypto becoming the incentive engine that coordinates it.
Here’s the full video on YouTube:
Why Everyone Keeps Calling Bittensor “Bitcoin for AI”
The conversation starts where most serious crypto-AI discussions now start: Bittensor ($TAO).
Bittensor is basically a decentralized AI infrastructure network designed to reward people for producing useful intelligence like models, compute, data, and evaluation.
But what makes it powerful is its structure.
Bittensor separates the ecosystem into roles:
- Miners (ML engineers producing outputs)
- Validators (evaluating the quality of outputs)
- Subnet owners (designing the “game” and incentive structure)
If you can define a task clearly and measure it properly, the network can reward whoever performs best.
And that’s the point: Bittensor isn’t trying to build one AI model. It’s trying to build a marketplace where intelligence competes.
That’s why the “Bitcoin for AI” comparison keeps coming up. As Bitcoin monetizes energy and computation, Bittensor is trying to monetize intelligence itself.
Subnets: The Secret Sauce Most People Still Don’t Understand
One of the best explanations in the episode comes from Max, founder of Score.
He breaks down what a subnet actually is:
A subnet is basically a slot inside Bittensor where a team can bring a specific AI problem and turn it into a competitive marketplace.
But there’s a catch. The hardest part is building the incentive system so miners can’t cheat. Because if miners can game the rewards, the entire network becomes useless.
So the real innovation isn’t “AI on-chain”, it’s incentive design.
And that’s why Bittensor attracts a certain kind of person: people who think like engineers, economists, and game theorists at the same time.
Score’s Vision: “Vision AI Needs Its GPT Moment”
Max describes Score as a vision AI research company building what is basically AutoML for computer vision.
The idea is simple:
Instead of hiring expensive CV engineers, companies should be able to drop a video into a system and get back:
- the right model
- the right output
- even deployable code
In other words:
“Drop video → get intelligence.”
And if you think about where the world is going—cameras everywhere, sensors everywhere—this is not a niche problem. It’s a foundational one.
Max gives a strong example: football scouting.
No scout can realistically watch 100,000 hours of footage across multiple leagues to find hidden talent.
But a vision agent can.
And once that exists, the competitive edge in scouting shifts from “who has the best human scouts” to who has the best intelligence pipeline.
The Bigger Vision: Cameras Stop Recording, and Start Working
One line from Max hits hard:
“Everywhere there’s a camera, you can make it intelligent.”
That’s the real shift. Cameras today are passive. They record.
But in the AI supercycle, cameras become active agents:
- detecting problems
- predicting events
- alerting decision makers
- preventing losses
They mention an example of a petroleum company using vision agents to monitor gas stations. Because when something breaks, and nobody notices, the business bleeds money silently.
So vision AI becomes less about “cool technology” and more about: automation that directly saves or generates revenue.
The Real Thesis: AI Is About to Collapse the Cost of Human Labor
The episode ends with the real punchline: The global GDP is around $100 trillion per year.
And roughly 50% of that is labor.
Meaning the market opportunity for automation is about $50 trillion.
This is the biggest economic incentive humanity has ever seen. And if AI keeps improving while cost keeps collapsing, we’re heading into a world where human work becomes expensive by default.
Not because humans are useless, but because machines are cheaper.
And if AI can perform most tasks for a cent, the only thing that matters is who owns the systems, the data, and the coordination layer.
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