I previously mentioned in multiple articles that AI Agents would be the "redemption" of many Crypto industry old narratives. In the previous narrative evolution around AI autonomy, TEE was once hyped, but there is another technology concept more "niche" than TEE and even ZKP - FHE (Fully Homomorphic Encryption), which will also be "reborn" driven by the AI track. Below, I will clarify the logic through examples:
FHE is a cryptographic technology that allows direct computation on encrypted data, viewed as a "Holy Grail", and is relatively niche compared to popular technologies like ZKP and TEE, mainly constrained by overhead and application scenarios.
Mind Network is precisely focused on FHE infrastructure and has launched MindChain, an FHE Chain dedicated to AI Agents. Despite securing over ten million dollars in funding and years of technical cultivation, market attention remains underestimated due to FHE's limitations.
However, Mind Network has recently released several positive announcements around AI application scenarios. For instance, their developed FHE Rust SDK has been integrated by the open-source large model DeepSeek, becoming a critical link in AI training scenarios and providing a security foundation for trustworthy AI. Why can FHE perform in AI privacy computing, and can it achieve a sudden breakthrough or redemption through the AI Agent narrative?
Simply put: FHE is a cryptographic technology that can directly act on the current public chain architecture, allowing direct computation like addition and multiplication on encrypted data without prior decryption.
In other words, FHE technology's application can ensure end-to-end encryption from input to output, where even nodes maintaining public chain consensus cannot access plaintext information. This makes FHE capable of providing technical underlying guarantees for AI LLM training in vertical scenarios like healthcare and finance.
FHE can become a "preferred" solution for expanding traditional AI large model training into vertical scenarios and combining with blockchain distributed architecture. Whether in cross-institutional medical data collaboration or privacy inference in financial transaction scenarios, FHE can serve as a complementary choice.
This is not abstract; a simple example makes it clear: For instance, an AI Agent as a C-end application typically connects to different AI large models from providers like DeepSeek, Claude, and OpenAI. But how to ensure the AI Agent's execution process won't be arbitrarily modified by the backend models in highly sensitive financial application scenarios? This necessarily requires encrypting the input Prompt, so when LLMs service providers compute directly on ciphertext, they cannot forcibly interfere or modify, thus maintaining fairness.
What about the "Trustworthy AI" concept? Trustworthy AI is a decentralized AI vision Mind Network attempts to construct, including allowing multiple parties to achieve efficient model training and inference through distributed computing power GPU without relying on central servers, providing FHE-based consensus verification for AI Agents. This design eliminates the limitations of centralized AI, offering privacy and autonomy guarantees for web3 AI Agents in a distributed architecture.
This aligns more closely with Mind Network's distributed public chain architecture narrative. For example, in special on-chain transaction processes, FHE can protect privacy inference and execution of Oracle data for all parties, enabling AI Agents to make autonomous trading decisions without exposing positions or strategies.
So why say FHE will have a similar industry penetration path as TEE and bring direct opportunities through AI application scenarios?
Previously, TEE could seize AI Agent opportunities because its hardware environment could host data in a privacy state, thereby allowing AI Agents to autonomously host private keys and achieve a new narrative of autonomous asset management. However, TEE's private key custody has a fundamental flaw: trust depends on third-party hardware providers (like Intel). To make TEE effective, a distributed chain architecture is needed to add an additional transparent "consensus" constraint to the TEE environment. In comparison, FHE can completely exist based on a decentralized chain architecture without relying on third parties.
FHE and TEE have similar ecosystem positioning. Although TEE is not widely used in the web3 ecosystem, it's already a mature technology in the web2 domain. Similarly, FHE will gradually find its value in both web2 and web3 during this AI trend's explosion.
In conclusion.
As seen above, FHE, this encryption Holy Grail technology, will inevitably become one of the security cornerstones under the premise of AI becoming the future, with a high possibility of being further widely adopted.
Of course, we cannot avoid the computational overhead cost of FHE algorithm implementation. If it can be applied in web2 AI scenarios and then link with web3 AI scenarios, it will likely release an unexpected "scale effect" to dilute overall costs, enabling more widespread application.
