Decentralized Physical Infrastructure Networks (DePIN) represent a paradigm shift in how physical infrastructure is built and maintained. Instead of relying on centralized corporations, DePIN protocols use token incentives to motivate individuals to deploy and operate infrastructure — from GPU servers and storage drives to wireless hotspots and sensors.

The DePIN sector has grown from a niche concept to a multi-billion dollar category within crypto, with projects spanning compute, storage, wireless, mapping, and energy infrastructure.

The AI revolution is fundamentally constrained by three resources: compute, data, and bandwidth. All three are currently dominated by centralized providers (AWS, Google Cloud, Azure) with limited capacity and premium pricing.

• The GPU crisis — Training and running large AI models requires enormous GPU capacity. Centralized providers have multi-month waitlists and prices are rising.
• Data silos — The best training data is locked in corporate silos. Decentralized data exchanges can unlock this value.
• Bandwidth costs — Serving AI models at scale requires significant bandwidth. Distributed networks can reduce costs.

DePIN protocols directly address each of these constraints by creating open markets for compute, data, and connectivity.

Render Network (RNDR) — The leading decentralized GPU rendering network, now expanding into AI/ML compute. Render connects GPU owners with artists and AI researchers, offering competitive pricing against cloud providers.

Akash Network (AKT) — An open-source, decentralized cloud computing marketplace. Akash offers GPU instances for AI workloads at 70-80% lower cost than centralized alternatives.

Filecoin (FIL) — The largest decentralized storage network, critical for storing AI training datasets, model checkpoints, and inference outputs at scale.

Arweave (AR) — Permanent, immutable data storage. Essential for preserving training data provenance and model versioning.

Helium (HNT) — Decentralized wireless infrastructure that can provide connectivity for edge AI devices and IoT sensors.

The AI + DePIN convergence creates a layered market structure:

• Compute Layer (RNDR, AKT, IO) — GPU compute for AI training and inference
• Storage Layer (FIL, AR) — Data storage for training sets and models
• Network Layer (HNT, WIFI) — Connectivity for distributed AI infrastructure
• Orchestration Layer (FET, TAO) — Coordination and optimization across infrastructure

Each layer has different risk/reward profiles and growth trajectories. The compute layer is currently the highest-demand sector due to the GPU shortage.

DePIN projects typically use a burn-and-mint equilibrium (BME) or stake-for-access model:

In BME models, users burn tokens to access infrastructure services, while providers earn newly minted tokens for providing resources. This creates a demand-driven token economy where usage directly impacts token dynamics.

Evaluating DePIN tokenomics requires understanding: emissions schedule, real service revenue vs. speculative demand, provider economics (is it profitable to run a node?), and network utilization rates.