Decentralised networks offer a new path for agentic AI

The integration of philosophy with artificial intelligence development represents a critical need as we advance into an era of increasingly complex AI systems. The call for deeper understanding of underlying principles in AI resonates with historical scientific inquiry, like Ben Franklin’s experimental work on electricity. Abhishek Singh’s recent presentation on agentic AI offers philosophical frameworks for addressing fundamental challenges in AI development, particularly through the lens of decentralization and networked intelligence systems.

The big picture: Singh’s presentation on “Chaos, Coordination and the Future of Agentic AI” highlights the philosophical tension between centralized and decentralized approaches to artificial intelligence development.

• He proposes a “trilemma” framework balancing continuity, heterogeneity, and scalability that must be addressed for effective AI systems.
• The researcher connects decentralized networks and algorithms to what he terms an “emergent phenomenon” of AI agency, suggesting a fundamental shift in how we conceptualize intelligence.

Two competing models: Singh contrasts the dominant centralized AI paradigm with an alternative decentralized approach that could reshape how intelligence functions in complex systems.

• “One way to think about how these two mental models fit together is: the way we are doing solving intelligence right now is by this idea of one big, large (system) sitting at one large, big tech company and being capable of doing all the tasks at the same time.”
• The decentralized perspective envisions “lots of small brains interacting with each other. None of the single small brains is powerful enough, but then together, (they are.)”

Key challenges: Implementing decentralized AI systems presents several distinct obstacles that researchers must overcome.

• Privacy, verification, and orchestration concerns represent significant hurdles in developing decentralized systems.
• User experience design for crowd-based systems and network engineering introduce additional complexity to the development process.
• Singh notes that complex models and large-scale collaboration contain inherent problems requiring innovative solutions.

Development initiatives: MIT researchers are advancing a framework called NANDA (Networked Agents and Decentralized AI) to address the core challenges of decentralized artificial intelligence.

• The initiative involves Singh collaborating with Ramesh Raskar at MIT to develop practical implementations of decentralized AI systems.
• NANDA appears positioned as a potential vehicle for bringing theoretical concepts of networked intelligence into practical applications.