I have previously mentioned in several articles that AI Agents will be the "redemption" of many old narratives in the crypto industry. In the last wave of narrative evolution surrounding AI autonomy, TEE was once elevated to the forefront, but there is another even more "niche" technical concept than TEE and even ZKP—FHE (Fully Homomorphic Encryption), which will also gain "rebirth" due to the AI track. Below, I will outline the logic through examples:

FHE is a cryptographic technology that allows computations to be performed directly on encrypted data and is regarded as the "Holy Grail." Compared to popular technical narratives like ZKP and TEE, it occupies a relatively niche position, primarily constrained by costs and application scenarios.

Mind Network focuses on the infrastructure of FHE and has launched the FHE Chain—MindChain, which is dedicated to AI Agents. Despite raising over ten million dollars and undergoing several years of technical development, market attention remains underestimated due to the limitations of FHE itself.

However, recently, Mind Network has released several positive news items surrounding AI application scenarios. For example, its developed FHE Rust SDK has been integrated into the open-source large model DeepSeek, becoming a key component in AI training scenarios and providing a secure foundation for the realization of trustworthy AI. Why can FHE perform well in AI privacy computing, and can it leverage the narrative of AI Agents to achieve a leapfrog or redemption?

In simple terms: FHE (Fully Homomorphic Encryption) is a cryptographic technology that can directly operate on the current public chain architecture, allowing arbitrary computations such as addition and multiplication on encrypted data without the need to decrypt the data first.

In other words, the application of FHE technology enables data to be fully encrypted from input to output, meaning that even the nodes verifying consensus on the public chain cannot access plaintext information. This allows FHE to provide a technical underpinning for training some AI LLMs in vertical segments such as healthcare and finance.

FHE can become a "preferred" solution for enriching and expanding vertical scenarios in traditional AI large model training while integrating with blockchain distributed architecture. Whether it is cross-institutional collaboration of medical data or privacy inference in financial transaction scenarios, FHE can serve as a supplementary option due to its unique characteristics.

This is not abstract; a simple example clarifies it: For instance, an AI Agent aimed at C-end applications typically connects to various AI large models provided by different vendors, including DeepSeek, Claude, and OpenAI. But how can we ensure that in some highly sensitive financial application scenarios, the execution process of the AI Agent is not suddenly influenced by a large model backend that changes the rules? This inevitably requires encrypting the input prompts, so when LLM service providers directly process the ciphertext, there will be no forced interference that affects fairness.

So what about the other concept of "trustworthy AI"? Trustworthy AI is a decentralized AI vision that Mind Network aims to build with FHE, allowing multiple parties to achieve efficient model training and inference through distributed computing power (GPU) without relying on a central server, providing consensus verification based on FHE for AI Agents. This design eliminates the limitations of centralized AI, providing dual guarantees of privacy and autonomy for web3 AI Agents operating under a distributed architecture.

This aligns more closely with the narrative direction of Mind Network's own distributed public chain architecture. For example, during special on-chain transaction processes, FHE can protect the privacy inference and execution processes of various Oracle data, enabling AI Agents to make autonomous decisions in trading without exposing positions or strategies.

So, why do we say 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 Agents because the TEE hardware environment can manage data in a privacy-preserving state, allowing AI Agents to autonomously manage private keys and achieve a new narrative of autonomous asset management. However, there is a fundamental flaw in TEE's management of private keys: trust relies on third-party hardware providers (e.g., Intel). To make TEE effective, a distributed chain architecture is needed to add an extra layer of public and transparent "consensus" constraints to the TEE environment. In contrast, PHE can exist entirely based on a decentralized chain architecture without relying on third parties.

FHE and TEE share a similar ecological niche; although TEE is not widely applied in the web3 ecosystem, it is already a very mature technology in the web2 field. In contrast, FHE will gradually find its value in both web2 and web3 amid the current explosion of AI trends.

In summary, it can be seen that FHE, as a cryptographic "Holy Grail" level technology, will inevitably become one of the cornerstones of security under the premise of AI becoming the future, with a high likelihood of being further widely adopted.

Of course, despite this, we must also address the cost issues associated with the implementation of FHE algorithms. If it can be applied in web2 AI scenarios and then linked to web3 AI scenarios, it is likely to unexpectedly release a "scaling effect" that dilutes overall costs, allowing for more widespread application.

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