“With great power comes great responsibility, especially when it comes to shaping the minds of artificial intelligences.” — Ted Chiang
Overview
While blockchain transforms value transfer and AI reshapes how we work and interact, skepticism persists. The excessive hype has attracted numerous opportunists, following a familiar pattern seen in crypto. However, blockchain projects are now focusing on meaningful intersections between AI and blockchain technologies.
Is the Hype Justified?
Recent discussions question whether AI has peaked, with some speculating that training data quality affects model performance. Industry speakers expressed optimism about the trajectory until artificial general intelligence (AGI) emerges.
Michael Heinrich (0g Labs Co-Founder) emphasized that AI represents a fundamental computing shift, where AI agents handle tasks on our behalf rather than direct human interaction. He anticipates broader availability of AI-powered robots this decade.
Evgeny Vakhteev (Guru Network CEO) noted that increased device availability creates opportunities for personalized dApps with customized workflows.
SQD CEO Dmitry predicted that data access — rather than proprietary AI — will provide competitive advantage, suggesting decentralized networks can offer better-structured, accessible data.
Why Combine AI & Blockchain?
Data Ownership & Consent Issues
Currently, large technology companies train AI models on creator data without consent. This raises ethical concerns around transparency and bias enforcement.
Michael Heinrich stated that if using AI societally, transparency into algorithmic workings and alignment with human interests becomes essential. He highlighted concerns about corporate data sourcing practices.
Evgeny Vakhteev explained that data ownership secured through cryptography could resolve compliance concerns preventing feature releases, like analytics tools restricted from Bloomberg Terminal deployment.
Content Authenticity & Feed Transparency
As AI-generated content proliferates, identifying creator identity and content origins matters. Dmitry emphasized that timeline ownership transparency regarding content creators becomes crucial amid misinformation concerns.
Open social networks like Lens and Farcaster enable customized feeds where users filter for verified human creators, possible only with publicly accessible data.
Use Cases: AI x Blockchain
On-Chain AI Agent Capabilities
Michael envisions AI agents executing any on-chain human activity — DeFi trading, portfolio monitoring, NFT minting — and enabling “intent-based transactions” where users specify desired outcomes across chains without managing technical complexity.
User Experience Focus
Guru Network began with B2B products, expanding to retail applications. AI components include AI-generated NFT collections for service access and automation layers facilitating cross-chain trading decisions.
Evgeny emphasized that demonstrating practical AI applications within blockchain contexts proves key to mainstream adoption.
Privacy-First Approaches
Subsquid’s client-side indexing research enables local AI model operation, enhancing privacy and reducing third-party dependency. This approach allows users to maintain data independence while leveraging AI capabilities.
Conclusion
AI represents a persistent technological shift. The meaningful intersection with blockchain lies in addressing centralized AI’s transparency, consent, and data ownership limitations — creating more equitable, transparent systems aligned with user interests.
For the full discussion recording, visit X/Twitter.