Market Landscape
The artificial intelligence infrastructure market is currently defined by three major forces: centralized cloud providers, emerging mobile AI solutions, and decentralized physical infrastructure networks (DePINs). Each has strengths but also leaves gaps that EdgeMob is uniquely positioned to fill.
Centralized Cloud AI
Leaders: AWS, Azure, Google Cloud, OpenAI, Anthropic.
Strengths: High performance, enterprise adoption, scalable GPU clusters.
Limitations: Expensive, centralized control, data privacy risks, inaccessible to smaller teams or regions with limited connectivity.
Mobile AI Startups
Examples: Efforts around on-device inference (Apple Core ML, Google Tensor, Qualcomm NPUs).
Strengths: Efficient on-device execution, privacy benefits, leveraging local NPUs.
Limitations: Proprietary ecosystems, lack of developer-first open tooling, no incentive layer for scaling compute across users.
DePIN (Decentralized Physical Infrastructure Networks)
Examples: Filecoin (storage), Helium (wireless), Render (GPU rendering).
Strengths: Proven model for decentralized infrastructure using tokenized incentives.
Limitations: Focused on servers, storage, or bandwidth—not mobile compute. Limited direct application to AI inference.
EdgeMob Positioning
Bridging the Gap: Combines mobile-first execution with decentralized coordination, something not covered by clouds, mobile AI startups, or current DePIN networks.
Unique Value: Unlocks billions of smartphones as the most abundant and underutilized compute layer.
Developer Focus: Provides SDKs, registries, and APIs designed for rapid adoption by both Web2 and Web3 builders.
By sitting at the intersection of these three market categories, EdgeMob creates a new class of AI infrastructure—mobile-native compute at decentralized scale—filling critical gaps left by centralized and existing decentralized solutions.
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