A Self‑Evolving Decentralised AI Substrate for the Post‑Cloud Internet
White‑paper v0.2 — May 2025
1 Executive Summary
The global AI boom lives on hyperscale clouds owned by a handful of corporations. That model delivers scale but concentrates fragility: a single region outage, sanction or cable cut can silence critical AI services. NeuroNet proposes a complementary layer—a planet‑scale, trust‑minimised, edge‑AI mesh that behaves like a distributed nervous system. It learns locally, reasons collectively and heals itself when the backbone fails. Think “AI as the substrate beneath the next Internet” rather than an app that runs on top of it.
2 Problem Statement
- Single‑point fragility — central clouds are chokepoints for both compute and policy.
- Data‑sovereignty drag — privacy law blocks cross‑border training, starving central models of diversity.
- Energy cliff — cloud inference power draw is on track to exceed 10 % of global electricity by 2030.
- Capture risk — five firms control >80 % of AI compute; their priorities may conflict with public resilience.
- Adversarial surface — poisoned data or model exploits can ripple instantly across centrally hosted AI.
3 Vision & Core Design
3.1 Principles
- Edge‑first, cloud‑optional
- Cryptographic trust, not institutional trust
- Modular & hot‑swappable: every layer can be upgraded or rolled back independently.
- Self‑evolving: on‑device liquid neural nets (LNN) adapt continuously; global consensus only for policy or catastrophic fixes.
- Energy‑aware routing shifts workloads to green or idle nodes.
3.2 Layer Stack (L0 → L5)
| Layer | Function | Primary Tech |
|---|---|---|
| L0 Physical Mesh | 5G / Wi‑Fi / LPWAN / Starlink | SD‑WAN + content‑centric networking |
| L1 Identity & Crypto | PQC signatures & ZK‑SNARK attestations | CRYSTALS‑Dilithium, Halo 2 |
| L2 Data & Model Exchange | Gossip‑style server‑less FL with Byzantine‑resilient aggregation & differential‑privacy outlier slicing | DP‑SGD, Trim‑medians |
| L3 Runtime Sandbox | WASM containers & eBPF filters; hot‑swap in seconds | WASI‑NN, OCI images |
| L4 Model Primitives | Liquid NN, Mamba, LoRA, MoE compiled through TVM → ONNX | PyTorch 2.x |
| L5 API Layer | Vision / NLP / sensor fusion endpoints | OpenAPI, GraphQL |
Footnote: Every container follows a unit‑test → static‑analysis → 3‑of‑5 quorum sign‑off → staged rollout pipeline; proofs are logged on‑chain and can be rolled back within minutes.
4 Technical Building Blocks (TRL Snapshot 2025)
- Liquid Neural Networks (TRL 5) — 10× lower power than CNNs, continuous adaptation.
- Edge NPUs (TRL 7) — Horizon Journey 5, Google Edge TPU, Apple M4, Blaize Pathfinder (< 5 W).
- Byzantine‑resilient Federated Mesh Learning (TRL 4) — server‑less FL with poisoning defence (KRUM–Trim mix).
- Post‑Quantum Crypto (TRL 6) — NIST Round‑3 algorithms fit in constrained devices.
- Zero‑Knowledge Attestation (TRL 5) — 15 ms proofs on Cortex‑A.
- RL‑guided Packet Routing (TRL 3) — Stanford MANET experiments show 23 % latency cut under jamming.
5 Governance, Security & Ethics
- DAO Workflow: proposal → 5‑day public debate → zk‑rollup vote with 67 % quorum (stake + reputation).
- Transparency Logs: Merkle‑tree ledger records every patch, vote & model hash (IPLD).
- Red‑Team + Watchdog AI: external red‑team rotates every 90 days; a micro “watchdog‑AI” audits telemetry and fires on‑chain alerts when anomaly score > 3σ.
- Abuse‑Response: malicious modules quarantined automatically; emergency kill‑switch can force global rollback through DAO majority.
- Incentives: node operators earn compute credits; bug‑hunters paid from a 2 % inflation pool; all emissions taper after Year 5.
6 Deployment Roadmap & Economic Plumbing
| Year | Milestone | CapEx | Key Metrics |
| 2025 Q4 | Publish ref‑spec & SDK | $2 M | 3 chip vendors prototyped |
| 2026 Q2 | Pacific Disaster MVP (500 nodes) | $15 M | 50 % faster SAR mapping |
| 2027 | Series‑A style fund‑raise; launch incentive layer | $200 M | 5 k validators, 1 M tx/day |
| 2028 | Minimum Viable Network 1.0 (10 k nodes/5 regions) | $500 M | energy‑aware scheduler live |
| 2030 | Break‑even | — | tx‑fee revenue ≥ op‑ex |
| 2035 | Global Mesh (150 k nodes/40 countries) | — | <150 ms cross‑continent TTL |
7 Pilot Use‑Cases (Early ROI Signals)
| Sector | Pain‑Point | NeuroNet Edge |
| Disaster response | Cable outage kills cloud AI | Local LNN maps + drone swarm; 50 % faster victim geo‑tag |
| Rural tele‑medicine | No broadband for imaging | On‑device ultrasound; 90 % data‑plan saving |
| Grid cyber‑security | Isolated SCADA | TrustZone NPU anomaly detection; 95 % attack catch w/o Internet |
| Battlefield swarms | GPS‑denied / jammed | LPI/LPD mesh; <100 ms swarm latency |
8 Glossary (Jargon Buster)
LNN – Liquid Neural Network.
FL – Federated Learning.
ZKP – Zero‑Knowledge Proof.
CCN – Content‑Centric Networking.
KRUM – Byzantine‑resilient aggregation rule.
9 References (selected)
- Hasani R. et al., “Liquid Neural Networks,” Nature Machine Intelligence, 2024.
- EU Horizon H‑FL Consortium, “Serverless Federated Mesh Learning,” White‑paper v0.8, 2025.
- Feng Y. et al., “AI‑Optimised Routing in MANETs,” IEEE INFOCOM, 2025.
- NIST PQC Project, Round‑3 Finalist Algorithms, 2024.
- Horizon Robotics, “Journey 5 Edge NPU Datasheet,” 2025.
10 Disclaimer
This white paper is an AI-generated visionary document intended for exploratory and conceptual purposes only. It outlines a possible future architecture and roadmap but does not represent an active project, funded initiative, or commercial offering. All forward-looking statements are speculative and subject to significant technological, regulatory, and capital risks. This document does not constitute legal, financial, or investment advice. The authors disclaim any liability for decisions made based on this content. If any content herein coincides with an existing real-world project or initiative, such overlap is purely coincidental and unintentional.
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