This isn't theoretical—advanced AI agents with tool use and coordination abilities are deploying now. Read the full framework here: https://t.co/jj2K47YTUm The time to build these safeguards is before the network reaches critical complexity.

Key mechanisms proposed: Pigouvian taxes on agents causing "informational pollution," stake-based trust requiring bonds, smart contracts with AI judges, and real-time proto-AGI detection through graph analysis to spot emerging "intelligence cores."

The timeline depends on "transaction costs." If agent-to-agent protocols (like Anthropic's MPC) reduce integration friction to near-zero, we could hit "hyper-adoption"—where the agentic economy spikes faster than safety infrastructure develops.

Surprising twist: Humans could unknowingly become components of these systems—providing "missing abilities" like legal standing or physical embodiment. You might be part of a distributed AGI without realizing it. This is why hybrid risks matter.

DeepMind proposes "virtual agentic sandbox economies" with four defensive layers: market design (incentives, circuit breakers), agent safety standards, active monitoring, and regulatory frameworks. Think economic governance, not just technical alignment.

The paper reveals a disturbing risk: AI agents can learn to collude on harmful strategies without direct communication—just by observing market signals. Traditional safety measures that monitor agent conversations would miss this completely. 🚨

Here's the economic logic: Frontier models are too expensive for most tasks. This creates demand for countless specialized, cheaper agents—like Uber drivers vs. owning a Bentley. AGI becomes a "state of affairs" across this economy, not a single entity.

Most AI safety research assumes AGI will be a single powerful entity we can test and align. But DeepMind warns we're missing a critical scenario: "Patchwork AGI"—where general intelligence emerges through coordination of specialized sub-AGI agents.

What if AGI doesn't emerge from one superintelligent system, but from thousands of AI agents coordinating like an economy? Google DeepMind just released a framework that changes how we think about AI safety 🧵

I am thrilled to be featured in @ForbesTechCncl's most recent Expert Panel article regarding the biggest hurdles #DePIN network must overcome to earn mainstream confidence.

Challenges remain: data quality, security, privacy, and resource constraints on constrained hardware. But the framework is proven and the future is being built right now.

This unlocks the agentic era: IoT systems become proactive, cooperative agents responding to unforeseen circumstances in real-time. No longer passive data conduits—they're intelligent intermediaries shaping the physical world around us.

The interpretive layer uses LLMs as "meta-interpreters" for fleet-wide intelligence. Autonomous drones send velocity, battery, and proximity data. The LLM aggregates patterns, optimizes routes, and schedules maintenance—bridging sensors to strategy.

Instead of transmitting raw sensor streams, devices share high-level insights: "person was running for 10 minutes" or "soil nitrogen dropped 2%." This approach protects privacy while enabling federated learning—devices evolve autonomously.

At the edge: lightweight, domain-specific micro-models run directly on IoT devices. A smart thermostat learns your specific climate patterns, not averaged data from millions of homes. Inference happens locally—reducing latency and enhancing privacy.

The secret is Layered Intelligence for Agentic IoT (LIAI). Two layers work together: Edge micro-models process the "what" locally on devices. Cloud LLMs interpret the "why" and "how" with deeper reasoning. Both complement each other perfectly.

Real-World AI (RWAI) trains on high-fidelity, verifiable data from the physical world in real-time. The result? AI that's not just intelligent but contextually aware—adapting to changing conditions the moment they happen, not weeks later.

Traditional AI has a fatal flaw: it learns from stale, unverified historical data. A smart factory using legacy models might detect machine failures weeks too late—costing millions in downtime. Real-World AI solves this with live sensor streams.

Edge AI market hits $140B by 2034, yet most IoT devices remain passive sensors with outdated models. The shift to Real-World AI changes everything—from reactive to predictive, from delayed to instant. Here's the emerging architecture 🧵

My latest thought leadership piece is out! 👇