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The Crunch Network distributes AI and machine-learning workloads across a global collective-intelligence network, augmenting internal teams with external talent and broadening modeling perspectives. This decentralized approach lowers cost and democratizes access to innovation, high-quality predictions, and ongoing, live predictive data feeds. Institutions and individuals can easily deploy thousands of ML engineers to improve their understanding of the world. Uncovering novel approaches, and operating in a future-proof way that is more collaborative, inclusive, and resilient than ever before.

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The Problem: The Illiquidity of Intelligence

Despite the explosion of AI, most organizations rely on small, centralized R&D units. This structure creates two critical bottlenecks:
  • Silos: Teams operate within technical “bubbles,” limited by their own internal biases.
  • Cost: The R&D cost to achieve marginal model improvements is growing exponentially.
Companies struggle to onboard new talent in a market where demand outstrips supply. This structural scarcity makes the current job market for AI intelligence fundamentally illiquid and inefficient. Crunch Protocol’s clients are already spending millions of dollars to solve problems they delegate to our network.

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The Solution: Validated Decentralized Model Markets

Crunch solves the problem of getting top quality talent by creating a performance market. Crunch provides permissionless ecosystems that coordinate modeling intelligence at a global scale. Market pressure drives constant selection: strong contributors rise, weaker ones are phased out, and the network advances through continuous competitive improvement. Our platform has proven that this approach provides:
  • Continuous Innovation: A supply of models from independent contributors that outperforms internal teams.
  • Institutional Rails: Infrastructure that connects decentralized talent to corporate data securely.
  • Instant Settlement: Incentives paid strictly on performance, not hours worked.

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Proven Results, Real Impact

This isn’t theoretical. Major institutions are already seeing transformative results:

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ADIA Lab: Finance R&D

For three consecutive years, Crunch has hosted competitions for ADIA Lab.
  • Client: ADIA Lab (Research arm of the Abu Dhabi Investment Authority)
  • Use Case: Cross-Sectional Asset Ranking
  • Result: Crunch models consistently achieved double-digit percentage improvements over the lab’s best internal benchmarks.
  • Significance: This validates the core thesis that a decentralized network can out-innovate a well-funded sovereign wealth research unit.

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Broad Institute: Biomedical R&D

Crunch applied its financial modeling framework to biological data.
  • Client: Eric and Wendy Schmidt Center at the Broad Institute of MIT and Harvard
  • Use Case: Biomedical Vision & Causal Discovery
  • Result: The network outperformed internal benchmarks in complex vision tasks.
  • Significance: This proves the protocol generalizes. The same mechanism used for finance works for healthcare and science.

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MidOne™: Ultra-Low Latency Prediction Engine

  • Use Case: Real-Time Market Pricing
Mid+One bridges the gap between community research and real-world institutional use cases. It is a powerful software engine that takes the collective intelligence of thousands of data scientists and turns it into a live engine that infers in less than 60 microseconds. Each success proves a fundamental truth: when diverse approaches combine, individual limitations offset each other, creating solutions no single team could discover alone.