# The Decentralized Future of Private AI with Illia Polosukhin - #749

Page: https://stenobird.com/podcast/twiml-ai-podcast/the-decentralized-future-of-private-ai-with-illia-polosukhin-749
Text version: https://stenobird.com/podcast/twiml-ai-podcast/the-decentralized-future-of-private-ai-with-illia-polosukhin-749.md
Podcast: [The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)](https://stenobird.com/podcast/twiml-ai-podcast)
Published: 2025-09-30T16:22:00+00:00
Episode link: https://twimlai.com/podcast/twimlai/the-decentralized-future-of-private-ai/
Audio file: https://pscrb.fm/rss/p/traffic.megaphone.fm/MLN2189764781.mp3?updated=1762292711
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JSON: https://stenobird.com/v1/public/podcasts/twiml-ai-podcast/episodes/the-decentralized-future-of-private-ai-with-illia-polosukhin-749
Duration seconds: 3903

## Resource

Illia Polosukhin, co-author of the Transformer paper, explains how decentralized infrastructure can solve the growing privacy and liability risks of centralized AI. He details a vision for a decentralized cloud using secure enclaves to enable private model training and verifiable inference.

## Highlights
- Main idea: Centralized data collection is transitioning from a gold mine to a legal liability due to global regulations like GDPR
- Practical takeaway: Developers can use decentralized clouds and secure enclaves to run software on user data without ever actually seeing or possessing that data
- Failure mode: The lack of transparency in 'open weights' models leads to wasted research resources as scientists struggle to reproduce results without knowing the full training process
- Main idea: A tokenized incentive model can reward website owners for contributing data to training sets while maintaining strict privacy through encryption
- Practical takeaway: Trust in AI agents requires formal verification at the invocation layer to ensure composable guarantees as different AI systems call one another

## Topics

Transformer Architecture, Decentralized AI, Confidential Computing, Data Privacy, Blockchain, Near AI, Machine Learning, Formal Verification, AI Agents

## Chapters
- 1:00 — The Transformer Legacy and Near AI: Illia discusses his transition from developing the Transformer architecture at Google to founding Near AI to tackle the challenges of scalable, private intelligence.
- 6:00 — The Shift from Data Assets to Liabilities: An exploration of how increasing global privacy regulations make holding user data a significant risk for developers, necessitating a shift toward pushing software to the user.
- 10:40 — Confidential Computing and Secure Enclaves: How new hardware generations and NVIDIA-enabled secure modes allow for running Docker containers in environments that protect both user data and model weights.
- 20:30 — The Decentralized Cloud Architecture: A look at the middle layer where users contribute data for processing and model providers provide intelligence, all while maintaining a zero-knowledge relationship with the cloud provider.
- 25:35 — Tokenized Incentives for Data and Training: How a token-based system can facilitate the crawling of the web and reward content owners for contributing data to model training without compromising privacy.
- 35:15 — The Problem with Open Weights vs. Open Process: Critiquing the current state of open-weight models and proposing a shift toward open training processes that allow for monetization and reproducibility.
- 58:50 — Formal Verification and AI Agent Composability: The necessity of formal verification to ensure that as AI agents begin calling other AI services, they adhere to predictable, verifiable, and secure properties.

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## Transcript

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