The Problem with Public AI

Every day, millions of employees open ChatGPT, Claude, or Gemini and paste in company data such as sales forecasts, customer lists, legal memos, and source code. It feels harmless, but every word travels to a server owned by someone else, stored on their infrastructure, and potentially used to train their next model.

The numbers are staggering. According to LayerX Security, 77% of enterprise employees have pasted company data into ChatGPT. Metomic found that 34.8% of those inputs contain sensitive information, up from 11% in 2023. IBM’s 2025 Cost of a Data Breach report reveals that 20% of breaches now involve “shadow AI,” costing organizations an average of $670,000 more than conventional incidents.

OpenAI CEO Sam Altman has acknowledged the challenge: “We haven’t figured out [legal confidentiality] yet for ChatGPT.” Samsung banned ChatGPT after engineers leaked proprietary code. Apple restricted internal use. A federal court has ordered OpenAI to retain all consumer chat logs, including deleted messages, creating a permanent subpoena risk. If your employees use public AI, your most sensitive data may sit on someone else’s servers, subject to their policies and legal obligations.

Which Industries Are Feeling the Most Pressure?

Sarson Funds researched which industries are investing the most in private AI solutions and feeling the greatest regulatory pressure. The global private AI market is projected at $11.1 billion in 2025, growing to $113.7 billion by 2034. These five industries lead that spending.

Table 1: Top 5 Industries Moving Toward Private AI

Rankings are based on Dimension Market Research’s Private AI Market Report (2025), cross-referenced with Menlo Ventures’ State of Generative AI in the Enterprise (2025) and Fortune Business Insights’ U.S. AI Market analysis. Positioning reflects both current spending and regulatory intensity.

How Blockchain Makes AI Private

When most people hear “blockchain,” they think of Bitcoin. But blockchain is simply a shared digital ledger that no single company controls. It functions as a system of checks and balances built directly into the technology. Here is how it solves the AI privacy problem.

Your Data Never Leaves Your Control

In traditional AI systems, your data travels to a company’s server. In blockchain-based AI, the model comes to your data rather than the other way around. Think of it like hiring a consultant who works in your office under your supervision, instead of sending confidential files to their headquarters. Encrypted protocols ensure that even network nodes processing your request cannot read your actual inputs.

Proving Without Revealing: Zero-Knowledge Proofs

Zero-knowledge proofs allow one party to prove something is true without revealing the underlying data. Imagine showing a bouncer your ID to prove you are over 21, while the bouncer only sees a green checkmark and never your name, address, or birthdate. In AI systems, this means the network can verify and process your data without ever accessing the raw information. Advances in zk-SNARKs have reduced proof generation costs by more than 80%.

Encrypted Computation and Immutable Audit Trails

Secure multiparty computation divides data into fragments across multiple nodes, similar to tearing a secret document into ten pieces and giving each to a different person in a different country. No single node can reconstruct the original document. Projects like Venice AI implement zero data storage with end-to-end encryption, while NEAR Protocol uses Confidential Intents to enable private cross-chain AI execution. Every interaction is logged on the blockchain, recording who ran what model and when. The underlying content remains encrypted and private, creating compliance-ready records for regulations such as SOX, HIPAA, and GDPR without exposing the underlying data.

Subpoena Resistance and Decentralized Governance

With centralized AI, a single subpoena can expose every conversation. Blockchain distributes data across a global network so that no single entity possesses complete records. Token holders govern model updates and safety parameters through voting, while smart contracts enforce rules automatically. Privacy protection comes from cryptography and distributed architecture rather than a terms-of-service agreement that can change at any time.

The Best of Both Worlds: On-Prem Security Meets Cloud Flexibility

Enterprises face an uncomfortable choice. On-premises AI provides maximum control but at enormous cost. Enterprise GPUs cost $25,000 to $40,000 each, clusters run into the millions, procurement takes three to six months, and dedicated staff can cost $38,000 or more per month. Hardware often becomes obsolete within two to three years. Cloud AI platforms such as AWS, Azure, and Google Cloud offer flexibility but surrender control. Data travels to third-party servers, the CLOUD Act introduces sovereignty risks, vendor lock-in can be difficult to unwind, and costs may spike unpredictably.

Blockchain compute networks solve both problems simultaneously. Aethir operates a decentralized GPU cloud spanning more than 440,000 enterprise-grade containers across 94 countries, with costs up to 86% lower than AWS, Azure, or Google Cloud. There is no hardware to purchase and no DevOps team required. 0G / Zero Gravity offers AI-optimized storage at $8 to $9 per terabyte per month, which is roughly 70% to 80% cheaper than hyperscale providers.

Organizations can scale from one GPU to thousands on demand with no procurement delays. Venice AI is fully compatible with the OpenAI API, allowing teams to switch providers without rewriting code. Workloads are distributed across a global network, so if one node fails, others automatically pick up the load. Open-source protocols and standard formats such as Docker and Kubernetes help prevent vendor lock-in.

Top 10 Blockchain-Based Private AI Companies

Sarson Funds has identified ten companies building the infrastructure for private, decentralized AI. These projects span the full technology stack, from raw GPU compute to privacy-first inference, AI-native execution layers, and autonomous agent frameworks.

Table 2: Top 10 Blockchain-Based Private AI Companies