What is Privasea AI (PRAI): How Privacy Computing is Reshaping the Web3 Ecosystem

Core Positioning and Technical Architecture of Privasea AI

Privasea AI (PRAI) is a privacy computing protocol based on Fully Homomorphic Encryption (FHE) technology, aiming to address data privacy leakage and computing power bottlenecks in AI and machine learning. The project is led by the former DePIN protocol NuLink team. In March 2024, it completed a $5 million seed round (led by Binance Labs) and raised a $180 million valuation in a Series A round by January 2025. Its technical architecture consists of three layers:

• HESea Encryption Engine: Integrates TFHE, CKKS, and other algorithms, supports arithmetic and logic operations on encrypted data, and improves computing efficiency by a thousand times compared to traditional solutions.

• Privanetix Computing Network: Distributed nodes execute FHE computation tasks, with a PoW+PoS hybrid mechanism to incentivize participants.

• Developer Toolchain: Offers APIs and open-source libraries to seamlessly integrate with mainstream AI frameworks such as TensorFlow and PyTorch.

Application scenarios include medical data privacy analysis (e.g., encrypted medical record processing), financial anti-fraud model training, and Web3 identity verification (generating encrypted facial vector NFTs to replace traditional KYC).

This Token insight article discusses the technical architecture of Privasea AI, its tokenomics, and the impact of the Binance TGE event.

Tokenomics and Allocation Mechanism

The total supply of Privasea AI tokens is 1 billion, with the following allocation: early contributors (9.04%), investors (13.45%), treasury (10.05%), team (8%), and ecosystem incentives (45%). Token functions include payment for privacy computing services, governance voting rights, and staking rewards (expected annual yield of 12%-18%). The ecosystem incentive portion is used for staking in the computing network and node rewards, aimed at driving the growth of distributed computing resources.

Under the token unlocking plan, the investor share (134.5 million PRAI) presents potential market selling pressure risks due to phased unlocking. For instance, the first unlocking in Q3 2025 may lead to short-term price volatility.

Binance TGE Event and Market Dynamics

On May 14, 2025, from 18:00 to 20:00 (UTC+8), Binance Wallet’s 16th TGE event will launch the PRAI token. Subscription requires Alpha Points through PancakeSwap, and 40 million PRAI tokens are reserved for follow-up activities. In terms of market response, exchanges like KuCoin and XT have announced the listing of PRAI/USDT trading pairs during the same period. Market maker Jump Trading has pledged $80 million in liquidity support. If computing demand reaches 50% of expectations after the mainnet launch, PRAI’s market cap may exceed $500 million (a 177% increase from the TGE initial valuation).

Investors can track PRAI price movements and on-chain data in real time via the JuCoin market page.

Potential Risks and Future Outlook

Despite the promising outlook of Privasea’s technology, the following challenges remain:

• Technology Maturity: Large-scale commercialization of FHE requires further algorithmic optimization, while competitors like Inco and Fhenix are catching up rapidly.

• Regulatory Pressure: The U.S. SEC’s scrutiny of the PoS mechanism’s compliance could impact the staking ecosystem.

• Market Volatility: Token unlocking by early investors may trigger sell-offs, requiring close attention to changes in token circulation.

Privasea AI future plans include launching the DeepSea mainnet in Q3 2025, supporting Cosmos IBC protocol for cross-chain interoperability, and building an AI model marketplace (where developers can upload encrypted models for incentives). In addition, GDPR and HIPAA compliance templates will be built in to meet the needs of healthcare and finance scenarios.

Industry analysis suggests the privacy computing market could reach $200 billion by 2030. Privasea ai’s “FHE + DePIN” model may become a crucial bridge between Web3 and the real-world economy. However, technology reliability, user education, and regulatory alignment remain key bottlenecks for commercial adoption.