greyone — 4/20/2025 11:04 AM (from Numerai discussion site)
Here is an evaluation of the foundational economics of Fetch.ai (FET) vs Numerai (NMR) across five core dimensions:
Token Utility & Demand Engine
FET (Artificial Superintelligence Alliance)
• Utility: Powers autonomous agents, data sharing, decentralized AI tasks, and marketplace interactions.
• Demand Engine: Demand comes from AI agents performing tasks on-chain, AI researchers, and marketplaces (e.g., mobility, energy grid).
• Narrative: Strong in current AI hype cycle, boosted by partnerships (e.g., with SingularityNET and Ocean).
NMR (Numerai)
• Utility: Stakes predictions in Numerai’s hedge fund. Burned for poor performance; rewarded for accuracy.
• Demand Engine: Tied directly to hedge fund performance and modeler participation. Incentives are mathematically aligned with hedge fund alpha generation.
• Narrative: Less broad AI hype appeal, but tightly integrated into a functioning financial ecosystem.
Rating:
• FET: 8/10 – Broad, ambitious utility but still early-stage in real-world usage.
• NMR: 9/10 – Niche but highly functional and economically clean.
Monetary Policy & Supply Dynamics
FET
• Supply Cap: ~2.6 billion max supply.
• Inflation: Moderate emissions for staking and ecosystem rewards. Subject to expansion for merged ASI token.
• Burn Mechanism: Currently weak or undefined.
NMR
• Supply Cap: Hard capped at 11 million.
• Deflationary Dynamics: NMR is burned when modelers stake and perform poorly. This gives it a built-in burn rate tied to hedge fund dynamics.
• Emissions: Low and fixed.
Rating:
• FET: 6/10 – Traditional inflationary model; may change post-ASI merge.
• NMR: 10/10 – Elegant deflationary model with tightly integrated feedback loop.
Capital Flows & Real-World Cash Value Capture
FET
• Revenue: Few known sources of real-world revenue. Most economic value is speculative or tied to partnerships.
• Token Sink: Unclear whether real businesses are locking value into the FET/ASI ecosystem yet.
NMR
• Revenue: Numerai is a functioning hedge fund. It earns real fiat returns. NMR is used to generate these returns via the modeler network.
• Token Sink: Poor-performing predictions are burned—direct destruction of supply for inefficiency.
Rating:
• FET: 5/10 – High potential, low realized capital capture.
• NMR: 9/10 – Functional loop from hedge fund capital → data scientist labor → token economics.
Speculation vs Productivity Ratio
FET
• Current Value: Driven heavily by speculation and AI narrative.
• Productivity: Limited concrete economic productivity at scale.
• Risk: High if AI hype cycle fades without mass adoption.
NMR
• Current Value: Highly tied to productive outputs (modeling quality, hedge fund returns).
• Productivity: Real labor creates alpha; NMR is directly tied to labor outcomes.
Rating:
• FET: 6/10 – Mostly speculative at this stage.
• NMR: 9/10 – Mostly productive, with speculation playing a secondary role.
Long-Term Sustainability
FET
• Sustainability: Will depend on whether real-world adoption of decentralized AI agents materializes. Big vision, high execution risk.
• Dependencies: Requires mass adoption, regulatory navigation, and interoperation with centralized AI systems.
NMR
• Sustainability: Already functioning for 8+ years. Small but resilient user base. Scales slowly and conservatively with fund capital growth.
• Dependencies: Success depends on fund performance and continued modeler engagement.
Rating:
• FET: 7/10 – High upside, high fragility.
• NMR: 9/10 – Lean and robust, but slow-burning.
Overall Ratings (Foundational Economics)
Category FET NMR
Token Utility 8 9
Monetary Policy 6 10
Capital Flow Integration 5 9
Speculation vs Productivity 6 9
Long-Term Sustainability 7 9
Average 6.4 9.2
Final Summary
• FET is a broad-reaching AI platform with enormous narrative momentum but weaker current economic anchoring.
• NMR is a precision-designed economic mechanism with real-world utility, tightly bound to measurable outcomes.
FET = Story of Potential
NMR = Story of Precision