EP02: Here’s Why Bittensor’s Incentives Crush Big Tech’s AI Monopolies for Good

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Jul 22, 2025

This discussion dives into the revolutionary potential of Bittensor in reshaping AI through decentralized incentives. They brainstorm an AI diagnostic model for emergency medicine, showing real-world applications. The conversation highlights how Bittensor can commoditize AI and disrupt traditional tech monopolies like Google, fostering competitiveness through unique mining processes. They also touch on the implications of off-chain computations for scalability and how Bittensor could democratize access to AI technologies, redefining the future of innovation.

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ANECDOTE

ER AI Diagnostic Model Example

James Altucher pitches an ER AI diagnostic model using labeled patient videos for quick, accurate diagnoses.
Joseph Jacks explains how BitTensor supports training and inference through subnets with open data control.

INSIGHT

BitTensor Surpasses Open Source

BitTensor combines open source with economic incentives to commoditize technology beyond previous open source impact.
Incentives drive massive community involvement, surpassing traditional open source collaborative effects.

ANECDOTE

Reimagining Google with BitTensor

James Altucher describes building a Google-like search engine on BitTensor using incentivized web spider subnets.
Validators reward miners for performance, enabling efficient, community-driven search operations.

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Hosted by James Altucher and Joseph Jacks.

In this episode, James and Joe brainstorm real-world AI use cases on Bittensor, like building an ER diagnostic model. They explore Bittensor as an upgrade to open source through incentives, distributed training (e.g., Templar subnet), off-chain computation parallels to Bitcoin, repricing AI/commodities, and its potential to disrupt centralized tech via "incentivism" and continuous learning.

Key Timestamps & Topics:

00:00:00 - Intro: Bittensor's disruption to AI incentives, governance, and improvement; early internet parallels.
00:01:00 - Real-World Use Case: Brainstorming an ER AI diagnostic model using Bittensor subnets (storage, training, inference).
00:07:00 - Commoditization: Bittensor surpasses open source by aligning intrinsic/extrinsic incentives.
00:17:00 - Search Engine Example: Reimagining Google via Bittensor's competitive subnets for spiders and categorization.
00:22:00 - Off-Chain Computation: Bittensor's Bitcoin-inspired design for infinite scalability.
00:33:00 - Consensus & Corruption: Probabilistic validation, subjective outputs, and real-world parallels.
00:40:00 - Templar Subnet: Distributed training for trillion-parameter models; Jensen Huang's views on decentralization.
00:46:00 - Repricing Assets: Bittensor democratizes AI superpowers, protects against arbitrary valuations.
00:50:00 - Inflation & Productivity: Fiat vs. Bitcoin/Bittensor; human error in monetary policy.
01:02:00 - Bittensor's Future: As "incentivism"—redefining capitalism without regulation.
01:09:00 - User Interfaces & Opportunity: Bittensor's "1991 internet" stage; need for better front ends.
01:15:00 - Open Source Limits: Missing economic models; Bittensor as successor with liquidity.
01:21:00 - Templar Economics: Speculation on scalable training; subnet competition.
01:26:00 - Distributed Challenges: Heterogeneous hardware vs. centralized homogeneity.
01:35:00 - Age of Experience: Continuous learning AI; Bittensor's evolving incentives.
01:36:00 - Jensen's Pushback: Slowing open source/decentralization to protect monopolies.
01:39:00 - Energy Subnets Idea: Incentivizing renewables/SMRs for AI power needs.
01:41:00 - Wrap-Up: Bittensor as carbon credits alternative; teaser for next episode.

Key Takeaways:

Bittensor upgrades open source by adding extrinsic economic incentives, enabling commoditization beyond centralized labs.
Off-chain computation allows infinite scalability for distributed training, potentially surpassing giants like Google in heterogeneous environments.
As "incentivism," Bittensor reprices AI and protects against arbitrary valuations/inflation, democratizing tech participation.
Subnets like Templar could achieve trillion-parameter models permissionlessly, addressing energy/compute bottlenecks via incentives.

Resources & Links:

Bittensor Official: bittensor.com
Taostats (Explorer/TAO App): taostats.io
Subnet 56 (Gradients): taostats.io/subnets/56
Subnet 3 (Templar): taostats.io/subnets/3
Subnet 64 (Chutes): taostats.io/subnets/64
Subnet 4 (Targon): taostats.io/subnets/4
Subnet 13 (Dataverse): macrocosmos.ai/sn13
xAI: x.ai
Follow Hosts: @jaltucher & @josephjacks_ on X

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