Why Existing Decentralized AI (DeAI) Approaches Are Inadequate
Decentralizing AI is not a new idea. Multiple projects and research efforts have attempted to marry AI with blockchain or peer-to-peer networks. However, existing decentralized AI (DeAI) approaches have consistently failed to meet the requirements of trust, security, and scalability. Many have merely replicated the shortcomings of centralized systems, or introduced new ones.
Below, we examine the key limitations of past efforts, and how Nesa’s approach is fundamentally different.
❌ Limited Trust and Verification Mechanisms
Most DeAI platforms still rely heavily on off-chain computation (source), with no robust way to verify results:
Inference is performed on nodes that users must trust to be honest.
Platforms like SingularityNET or Fetch.ai may use blockchain for coordination, but the actual computation happens off-chain.
Users have no cryptographic assurance that the output is correct or that the model even ran as claimed.
Some networks attempt mitigation via reputation systems, redundant execution, or escrow, but these mechanisms introduce friction and still do not guarantee correctness.
This is the core of the “verification dilemma”:
It’s hard to confirm that an AI output is correct without re-running the entire computation—defeating the point of delegation.
Nesa explicitly addresses this through zkDPS (Zero-Knowledge Decentralized Proof System) and consensus-based verification. Either a proof of correct execution is provided, or multiple nodes must independently agree on the output.
🔐 Privacy Not Fully Preserved
Many DeAI platforms claim to be privacy-preserving, but fall short in practice:
Some rely on federated learning or secure multi-party computation (MPC) to protect training data, but not inference inputs.
In many cases, users must send raw input data to remote nodes for inference—often operated by third parties.
Approaches based on homomorphic encryption remain largely impractical for large, real-time models due to extreme computational overhead.
Others explore secure enclaves, but these introduce hardware trust assumptions and carry performance or deployment constraints.
Nesa avoids these pitfalls with a different strategy:
It builds on Equivariant Encryption (EE) and HSS-EE, which allow model inference to be performed on encrypted embeddings using additive secret sharing, without leaking raw inputs to any server.
The user locally embeds their input and splits it into shares that are individually meaningless to each compute node.
These shares are processed securely and efficiently, even for deep models, using GPU-native primitives and protocols optimized for transformer inference.
No node ever sees the full input, intermediate activations, or final output—ensuring strict confidentiality throughout the computation pipeline.
This approach offers end-to-end inference privacy in a decentralized setting, without requiring trusted hardware or incurring FHE-level latency.
🧱 Performance and Scalability Bottlenecks
Early decentralized AI systems have faced major issues scaling up:
Blockchain-based computation is too slow and expensive for real-world inference, so nearly all DeAI projects execute AI off-chain.
Performing inference redundantly across nodes to gain consensus multiplies cost and latency.
Networks like Bittensor attempt to crowdsource language model training across many participants—but performance still lags far behind centralized infrastructure.
Other limitations include:
Hardware fragmentation: Many DeAI platforms require participants to run specialized GPU nodes, limiting network size.
Developer friction: Packaging and deploying models often demands low-level infrastructure knowledge.
The result: most DeAI platforms can only support toy workloads or narrow use cases.
Nesa approaches this differently:
It uses sharded execution (BSNS) to split models into sequential blocks, each small enough to run on modest machines.
A uniform execution environment ensures determinism and compatibility.
Additional optimizations (e.g., caching, two-phase commit, VRF-based node selection) reduce latency and coordination overhead.
Nesa is one of the first to make large-scale, performant decentralized inference practical, not just possible.
🧨 Summary: Why Most DeAI Platforms Fall Short
In theory, decentralization solves many AI problems. In practice, most existing projects:
Fail to provide cryptographic trust guarantees
Compromise on privacy in real-world usage
Cannot handle large-scale, latency-sensitive workloads
Rely on market dynamics without formal checks and balances
A 2023 survey found that most “AI token” projects operate off-chain and simply add payments to conventional APIs, delivering no real architectural innovation.
✅ How Nesa Differs
Nesa is a private, decentralized AI framework designed to address:
Trust — via zk-proofed or consensus-verified inference
Privacy — via SVE + split learning
Scalability — via model sharding and deterministic execution
Nesa does not simply decentralize AI coordination. It rebuilds AI execution to be secure, verifiable, and scalable—without trusting any single node.
This is why Nesa is often called:
“AI sharding” or “the AI equivalent of a blockchain.”
By solving the open problems that plagued previous attempts, Nesa moves DeAI from prototype to infrastructure.
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