I have previously stated in multiple articles that AI agents will be the "redemption" for many old narratives in the crypto industry. In the previous wave of narrative evolution around AI autonomy, TEE was once hailed as a hot topic, but there is also a more "niche" technology concept FHE - fully homomorphic encryption, which will also be "reborn" due to the driving force of the AI race. Below, we will summarize the logic through case studies: FHE is a cryptographic technique that allows direct computation on encrypted data and is considered the "Holy Grail". Compared to popular technologies such as ZKP and TEE, FHE is relatively unpopular in terms of narrative, with its core mainly constrained by costs and application scenarios. And @ mindnetwork_xyz is dedicated to the infrastructure of FHE and has launched the FHE Chain - MindChain, which focuses on AI agents. Despite raising over ten million dollars and years of technological cultivation, the market attention is still underestimated due to the limitations of FHE itself. However, recently Mind Network has released many positive news around AI application scenarios. For example, its developed FHE Rust SDK has been integrated with the open-source big model DeepSeek, becoming a key part of AI training scenarios and providing a secure foundation for the implementation of trusted AI. Why can FHE perform well in AI privacy computing, and can it use the narrative of AI agents to achieve a corner overtaking or redemption? Simply put, FHE fully homomorphic encryption is a cryptographic technique that can be directly applied to the current public chain architecture, allowing for arbitrary calculations such as addition and multiplication on encrypted data without the need for prior decryption. In other words, the application of FHE technology can achieve full encryption of data from input to output, and even nodes that maintain public chain consensus for verification cannot access plaintext information. This enables FHE to provide technical support for the training of some AI LLMs in vertical segmentation scenarios such as healthcare and finance. Let FHE become a "preferred" solution for traditional AI large model training with rich and extended vertical scenarios, 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 unique features. This is actually not abstract, it can be understood with a simple example: for example, As a C-end application, AI agents usually integrate AI models provided by different vendors such as DeepSeek, Claude, OpenAI, etc. into their backend. However, how can we ensure that the execution process of AI agents in some highly sensitive financial application scenarios will not be affected by the backend of large models that suddenly tamper with rules? This inevitably requires encrypting the input Prompt. When LLMs service providers directly calculate and process the ciphertext, there will be no forced interference or alteration that affects fairness. So what about the other concept of 'trusted AI'? Trusted AI is an FHE decentralized AI vision that Mind Network attempts to build, which includes allowing multiple parties to achieve efficient model training and inference through distributed computing power GPUs without relying on central servers, and providing consensus verification based on FHE for AI agents. This design eliminates the limitations of centralized AI and provides a dual guarantee of privacy and autonomy for the operation of web3 AI agents in a distributed architecture. This is more in line with the narrative direction of Mind Network's distributed public chain architecture. For example, in special on chain transactions, FHE can protect the privacy of Oracle data inference and execution processes for all parties, enabling AI agents to make autonomous decisions on transactions without exposing positions or strategies, and so on. 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 agents thanks to its hardware environment that enables data to be hosted in a private state, allowing AI agents to autonomously host private keys and achieve a new narrative of autonomous asset management. But there is actually a flaw in TEE's custody of private keys: trust relies on third-party hardware providers (such as Intel). To make TEE work, a distributed chain architecture is needed to attach an additional set of transparent "consensus" constraints to the TEE environment. In contrast, PHE can exist based on a decentralized chain architecture without relying on third parties. FHE and TEE have similar ecological niche situations. Although TEE is not widely used in the web3 ecosystem, it is already a very mature technology in the web2 field. In comparison, FHE will gradually find value in both web2 and web3 under the explosion of this AI trend. above. In summary, it can be seen that FHE, a holy grail level encryption technology, will inevitably become one of the cornerstones of security with AI as the future, and has the possibility of further widespread adoption. Of course, despite this, the cost issue of FHE in algorithm implementation cannot be avoided. If it can be applied in the web2 AI scenario and then linked to the web3 AI scenario, it is bound to unexpectedly release the "scale effect" and dilute the overall cost, making it more widely applicable. Note： If you find the article useful, please kindly support it with "one click three links". Friends who appreciate my continuous input of valuable content can follow me or visit my Twitter homepage and click on the Substack column to subscribe (currently free). More in-depth and professional investment research and analysis content, especially content that is not suitable for public sharing on Twitter, will be available there.