AI data is expensive — Perceptron built a decentralized network that cuts costs 92%
Most AI training data flows through centralized providers like Scale AI and Appen — expensive, slow, and controlled by a handful of platforms. DECENTRALIZED DATA INFRASTRUCTURE is what Perceptron is building, and it's already serving 700,000+ nodes and generating real revenue.nPeter Anthony, Co-Founder of Perceptron Network, breaks down how a mesh of user-run nodes delivers AI training data at 92% lower cost than legacy providers, why the Data Questing Platform is the next frontier for distributed micro-tasking, and how the $10M AI Data Fund is accelerating teams with free data access. Perceptron has 700K+ nodes, 300K daily active users, and live client contracts — and they're about to launch the infrastructure that turns the community into the AI economy's labeling backbone. The decentralized data mesh works because you cut out the middleman; the Data Questing Platform does distributed precision labeling with incentive alignment that Scale AI and Mechanical Turk can't; and the $10M AI Data Fund matters because teams get access with no pre-funding, reshaping how AI training data is sourced.
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Timestamps:
0:00 Peter Anthony — Perceptron Overview
1:16 How Much Data We Generate Daily
3:04 Nodes, Bandwidth, and Earning Points
5:22 Passive vs. Active Data Contribution
7:03 How Earning Rewards Are Calculated
10:46 Cost Advantage Over Centralized Providers
17:00 The $10M AI Data Fund
20:54 Long-Term Vision and TGE Timeline
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