From "Holy Grail" to Cornerstone: How Does FHE Reshape the Web3 Privacy Computing Ecosystem?

I have said in many articles before that AI Agent will be the "redemption" of many old narratives in the Crypto industry. In the last wave of narrative evolution around AI autonomy, TEE was once at the forefront. However, there is another technical concept that is even more "unpopular" than TEE and even ZKP, FHE - Fully Homomorphic Encryption, which will also be "reborn" due to the promotion of the AI track. Below, I will sort out the logic for you through examples:

FHE is a cryptographic technology that allows direct computation on encrypted data and is regarded as the "Holy Grail". Compared with popular technology narratives such as ZKP and TEE, it is in a relatively unpopular position, and its core is mainly constrained by overhead and application scenarios.

Mind Network focuses on FHE infrastructure and has launched MindChain, an FHE Chain focused on AI Agent. Although it has raised over 10 million US dollars and has gone through several years of technological development, it is still underestimated by the market due to the limitations of FHE itself.

However, Mind Network has recently released a lot of good news about AI application scenarios. For example, the FHE Rust SDK it developed was integrated into the open source large model DeepSeek, becoming a key link in AI training scenarios and providing a secure foundation for the implementation of trusted AI. Why can FHE be used in AI privacy computing? Can it achieve a curve overtaking or redemption with the help of AI Agent narrative?

Simply put: FHE fully homomorphic encryption is a cryptographic technology that can be directly applied to the current public chain architecture, allowing arbitrary calculations such as addition and multiplication to be performed directly on encrypted data without having to decrypt the data first.

In other words, the application of FHE technology can encrypt data from input to output. Even the nodes that maintain the public chain consensus for verification cannot access the plaintext information. In this way, FHE can provide technical underlying guarantees for the training of some AI LLM in vertical segmented scenarios such as medical care and finance.

FHE can become a "preferred" solution for traditional AI large model training to enrich and expand vertical scenarios and combine with blockchain distributed architecture. Whether it is cross-institutional collaboration of medical data or privacy reasoning in financial transaction scenarios, FHE can become a supplementary choice with its uniqueness.

This is not abstract, and it can be understood with a simple example: for example, AI Agent, as a C-side application, usually has access to AI models provided by different vendors, including DeepSeek, Claude, OpenAI, etc., but how to ensure that the execution process of AI Agent will not be affected by the big model background that suddenly tampers with the rules in some highly sensitive financial application scenarios? This will inevitably require the input prompt to be encrypted. When the LLMs service provider directly calculates and processes the ciphertext, there will be no forced interference and modification that affects fairness.

So what about the concept of "trusted AI"? Trusted AI is a FHE decentralized AI vision that Mind Network is trying to build, including allowing multiple parties to achieve efficient model training and reasoning through distributed computing power GPUs without relying on central servers, providing FHE-based consensus verification for AI Agents, etc. This design eliminates the limitations of the original centralized AI and provides privacy + autonomy dual protection for the operation of web3 AI Agents under a distributed architecture.

This is more in line with the narrative direction of Mind Network's own distributed public chain architecture. For example, in special on-chain transactions, FHE can protect the privacy reasoning and execution process of Oracle data of all parties, allowing AI Agent to make autonomous trading decisions without exposing positions or strategies, etc.

So, why is it said that FHE will have a similar industry penetration path as TEE, and will bring direct opportunities due to the explosion of AI application scenarios?

Previously, TEE was able to seize the opportunity of AI Agent because the TEE hardware environment can realize data hosting in a private state, which in turn allows AI Agent to independently host private keys, allowing AI Agent to achieve a new narrative of autonomous asset management. However, TEE's custody of private keys actually has a flaw: trust must rely on third-party hardware providers (such as Intel). In order for TEE to work, a distributed chain architecture is needed to add an additional set of open and transparent "consensus" constraints to the TEEs environment. In contrast, PHE can exist entirely based on a decentralized chain architecture without relying on a third party.

FHE and TEE have similar ecological niches. Although TEE is not widely used in the web3 ecosystem, it has long been a very mature technology in the web2 field. In comparison, FHE will gradually find its value in both web2 and web3 with the outbreak of this round of AI trends.

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In summary, it can be seen that FHE, an encryption technology that is the holy grail of encryption, is bound to become one of the cornerstones of security and is likely to be further widely adopted under the premise that AI becomes the future.

Of course, despite this, we cannot avoid the cost issue of FHE in algorithm implementation. If it can be applied in the web2 AI scenario and then linked to the web3 AI scenario, it will surely release the "scale effect" unexpectedly and dilute the overall cost, allowing it to be more widely used.