I have previously mentioned in multiple articles that AI Agents will be the 'redemption' of many old narratives in the Crypto industry. In the previous wave of narrative evolution around AI autonomy, TEE was once elevated to the forefront. However, there is a less popular technical concept compared to TEE, and even ZKP—FHE (Fully Homomorphic Encryption)—which will also gain 'rebirth' due to the drive of the AI track. Below, I will sort out the logic through examples.

FHE is a cryptographic technology that allows direct computation 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.

And Mind Network is focused on the infrastructure of FHE, launching the FHE Chain MindChain, which is dedicated to AI Agents. Despite raising over ten million dollars and undergoing several years of technical cultivation, the market attention remains underestimated due to the limitations of FHE itself.

However, recently, Mind Network has launched several favorable news around AI application scenarios. For instance, its developed FHE Rust SDK has been integrated into the open-source large model DeepSeek, becoming a key part of AI training scenarios and providing a secure foundation for trusted AI. Why can FHE perform in AI privacy computing? 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 act on the current public chain architecture, allowing arbitrary calculations such as addition and multiplication on encrypted data without needing to decrypt it first.

In other words, the application of FHE technology allows for end-to-end encryption of data from input to output. Even nodes that maintain public chain consensus for verification cannot access plaintext information. This enables FHE to provide a technical foundation for training some AI LLMs in vertical scenarios such as healthcare and finance.

This allows FHE to become a 'preferred' solution for enriching and extending vertical scenarios of traditional AI large model training combined with blockchain distributed architecture. Whether it is cross-institutional collaboration of medical data or privacy inference in financial transaction scenarios, FHE can become a complementary choice due to its uniqueness.

This is not abstract at all; it can be understood with a simple example: for instance, an AI Agent as an application aimed at the C-end usually connects to different suppliers providing AI large models, including DeepSeek, Claude, OpenAI, etc. But how can we ensure that in some highly sensitive financial application scenarios, the execution process of the AI Agent will not be suddenly altered by the large model backend? This necessitates encrypting the input prompts so that when LLMs service providers directly process the ciphertext, there will be no forced interference that affects fairness.

So what about the other concept of 'trusted AI'? Trusted AI is a decentralized AI vision that Mind Network attempts to construct based on FHE, allowing multiple parties to achieve efficient model training and inference through distributed computing power GPUs, without relying on a central server, providing consensus verification based on FHE for AI Agents, etc. This design eliminates the limitations of centralized AI, providing dual guarantees of privacy and autonomy for web3 AI Agents operating in a distributed architecture.

This aligns even more with the narrative direction of Mind Network's own distributed public chain architecture. For example, during special on-chain transactions, FHE can protect the privacy inference and execution process of all parties' Oracle data, allowing AI Agents to make autonomous trading decisions without revealing positions or strategies, etc.

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

Previously, TEE's ability to seize opportunities for AI Agents was due to the TEE hardware environment, which allows data to be managed in a privacy state, enabling AI Agents to autonomously manage private keys and achieve a new narrative of autonomous asset management. However, there is a significant flaw in TEE's key management: trust relies on third-party hardware providers (e.g., Intel). To make TEE effective, a distributed chain architecture is needed to add a set of extra public and transparent 'consensus' constraints to the TEE environment. In contrast, PHE can completely exist based on a decentralized chain architecture without relying on a third party.

FHE and TEE have similar ecological positions. 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 under this wave of AI trends.

That's all.

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

Of course, despite this, the cost issue of FHE during algorithm implementation cannot be avoided. If it can be applied in web2 AI scenarios and then linked to web3 AI scenarios, it is expected to unexpectedly release a 'scalability effect' that dilutes overall costs, allowing for more widespread application.