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

The TAO Pod - A podcast by James Altucher, Joseph Jacks

Categories:

Technology

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:

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


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