Who Should Control AI? Jordan Miller on Truth, Prediction & the Future (Ep. 332)

About this episode

Artificial intelligence is advancing faster than ever—but the biggest question may not be whether AI can predict the future.

It may be who controls it.

Tonight, Joe sits down with Jordan Miller, founder of the Satori Network, for a thoughtful conversation about artificial intelligence, decentralized technology, blockchain, prediction systems, and the future of human decision-making.

Jordan explains why he believes prediction—not language—is the true measure of intelligence, how decentralized AI differs from centralized models, and why giving a handful of corporations control over artificial intelligence may be one of the defining challenges of our generation.

Together they'll explore:

• What Satori is and how it works
• Can AI actually predict future events?
• Why decentralization matters
• The relationship between AI, blockchain, and human intelligence
• The ethical questions surrounding predictive technology
• Who should control artificial intelligence?
• How emerging technologies may shape truth, communication, and society

Whether you're excited about AI, skeptical of the hype, or simply curious about where technology is heading, this conversation offers a balanced look at one of the most important developments of our time.

Guest: Jordan Miller
Founder, Satori Network
Learn more:
https://satorinet.io

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Executive Producer John Seeley

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(00:01:14) Joe introduces the AI and future prediction debate

(00:05:20) Jordan Miller joins to discuss Satori Network

(00:06:29) From philosophy to intelligence and brain research

(00:11:16) How crypto and distributed consensus changed his path

(00:14:24) Why prediction is central to real intelligence

(00:17:20) Modeling AI after the brain without copying it

(00:22:13) What people misunderstand about AI awareness

(00:29:34) AI as a kind of collective subconscious

(00:34:26) Building Satori as a decentralized future oracle

(00:40:24) Unique redundancy and the wisdom of the crowd

(00:46:55) Can humanity handle knowing more about the future

(00:50:22) Where the model could fail and how it filters bad data

(01:00:16) Final reflections, resources, and Joe's takeaway

- Joe "Rooz" Russiello

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