Ninad Naik, Co-founder of Mira Network, on Decentralized Verification Layers, The Current State of AI, and Autonomous AI Systems | Ep. 456

In an exclusive interview with Cryptonews.com, Ninad Naik, Co-founder and COO of Mira Network, discussed how decentralized verification layers can address one of AI’s biggest problems — hallucinations and bias — and why Web3 infrastructure is essential to solving them.

With more than a decade in leadership roles at Uber and Amazon across AI, marketplaces, and hardware, and prior experience in investment banking and strategy consulting, Naik brings both deep technical and operational expertise to the challenge of building trustworthy AI.

“AI Is a Probability Machine — It’s Not Always Right”

Naik began by explaining a fundamental limitation of current large language models (LLMs):

“LLMs are a subclass of neural networks, which are probability machines. They never know the answer with certainty — they just guess the best answer.”

Even the most advanced AI models carry a risk of giving wrong or fabricated responses, commonly called “hallucinations.” This becomes a serious issue when AI is used for autonomous decision-making or in high-stakes environments like law, medicine, or finance.

“We believe that if you don’t fix this, AI essentially becomes nothing more than slightly better SaaS… Our goal is autonomous intelligence — AI that can work on your behalf while you touch grass.”

Why AI Hallucinates — and the Risks

Hallucinations occur because models are trained on vast datasets that include conflicting or outdated information. Naik illustrated this with a simple example:

“If in the training set, some percentage of text says Philadelphia was the capital of the US, that probability exists in the model. So it can never be 100% certain DC is the answer.”

The problem isn’t just academic. In legal tech, AI-generated briefs have cited entirely fabricated cases — in over 200 recorded instances globally — resulting in fines and professional sanctions.

“Even for frontier models, we’re seeing hallucination rates in the 25% to 30% range. That means three out of ten outputs are wrong. You wouldn’t let that go solo.”

Applying Blockchain Consensus to AI Truth

The inspiration for Mira’s approach came from crypto:

“How do you know a Bitcoin transaction is valid? If an honest majority of nodes agrees. We asked — why not apply that to AI?”

Mira’s system queries multiple LLMs, compares their outputs, and uses consensus rules (e.g., 3 of 3 agreement) to verify facts. This requires engineering that can normalize different AI responses into a comparable format before checking for agreement.

Every verified fact is written to a blockchain, producing a verifiable certificate showing which models agreed and which answer they gave.

“We are now economically securing the truth… In a world of AI-generated content, you need to know what’s real and what’s not.”

Why Verification Must Be Built on Web3

Naik stressed that decentralization is essential — not just to reduce hallucinations, but to fight bias:

“Hallucination bites you now. Bias bites you ten years from now… You cannot eliminate bias in a central fashion.”

If the verification network were controlled by a single operator, their choice of models could skew results over time. A decentralized architecture allows the community to run diverse models, with transparency on which ones are used.

Incentives, Tokens, and Honest Nodes

To ensure honesty in verification, Mira built a hybrid proof-of-stake and proof-of-work blockchain:

• Node operators stake tokens to participate.
  
• They perform AI inference as proof-of-work and are rewarded.
  
• Malicious or dishonest behavior results in slashing.
  

This creates an economy around truth verification, while securing the “knowledge chain” — a ledger of verified facts that others can use as a public resource.

“Anyone could build a bias-free, real-time Wikipedia from our chain.”

Decentralized Identifiers and the “Gig Economy” of Truth

Mira is also working on decentralized identifiers (DIDs) and an SDK to let developers build applications that integrate generation, verification, authentication, and payments.

“Over time, anyone with excess compute should be able to participate… bringing their resources to the network.”

This opens up possibilities for a “truth gig economy,” where contributors earn micropayments in Mira tokens for running verification tasks.

From API to Consumer Tools

The core product is an API that returns a binary verdict — fact or not fact — with a verification trail. This enables multiple consumer and enterprise use cases:

• Grammarly-style fact checks for any webpage.
  
• Community Notes-like tools with higher accuracy (70% → 95% using consensus).
  
• Integration into legal, medical, or financial tech to reduce liability.
  

“Any business where the cost of being wrong is significant will end up using the Mira API.”

The Road Ahead

Mira has recently integrated with partners in the edtech space and is preparing to open its closed beta to the public. The team is expanding across ecosystems including EVM and Solana, with a focus on onboarding developers and enterprises that need verifiable AI output.

Naik’s vision is clear: in a future where AI generates most content, trust will come from systems that can economically secure the truth. By combining consensus mechanisms, blockchain incentives, and decentralized governance, Mira aims to be that truth layer for the AI era.