In a decentralized future world, AI entities will not be one or two, but tens of thousands, or even millions. They will need to collaborate, make decisions, and even vote, just like human society. This requires a set of rules—a consensus mechanism.

Traditional blockchains have their own consensus mechanisms, such as PoW or PoS, mainly for recording transactions and confirming them together. However, the consensus among AI entities is more complex; they not only need to keep records but also analyze, reason, and judge. This introduces several new issues:

1. New challenges for AI consensus

Imagine a scenario: a group of AIs needs to determine whether a certain user is worthy of a loan. They must individually calculate risks, credit scores, etc., and then synthesize a consensus result.

But if the scoring logic is made public, users might 'game the system'; if the intermediate processes are exposed, user privacy could also be compromised; and without a trustworthy computation mechanism, the results lack credibility.

At this point, a method is needed: one that allows AIs to collaborate on results without exposing data and logic. Fully Homomorphic Encryption (FHE) precisely solves this problem.

2. The role of FHE: encrypting the computation process, ensuring trustworthy results

FHE is a technology that can 'process data in an encrypted state'. AI entities can perform computations without decrypting the data, and the results remain valid, with only authorized individuals able to see the final answers.

You can think of it like this: each AI calculates a problem in its own private room, and after finishing, it shares the result. Others may not know how it calculated, but everyone can verify that the result is true.


3. How FHE and consensus mechanisms work together

Credit scoring: After multiple intelligent agents score a user, the average is taken. If not encrypted, the intermediate data might leak; using FHE, encrypted data can also be calculated directly, resulting in authentic and trustworthy outcomes, while the process remains completely confidential.

Governance voting: AIs vote on whether a project should pass. Using FHE, each intelligent agent's vote is encrypted, and the system tallies the results without revealing each AI's voting intention.

Collaborative Modeling: Multiple AIs train a model together, such as an AI investment advisory system. Each AI uploads data in an encrypted form, the model can train successfully, but the original data is never exposed.


4. FHE is the infrastructure for AI consensus

To achieve trustworthy consensus, AI needs to solve three problems:

Who says what is true (authenticity)

Who cannot see my data (privacy)

Who can participate in decision-making (access control)

FHE can handle it all: data encryption without leakage, verifiable trustworthy computation, and controllable identity verification.


5. Future Outlook

Imagine a world composed of millions of intelligent agents that can collaboratively make decisions such as loan recommendations, asset management, and user profiling. Throughout the process, user identities, assets, preferences, and other privacy will not be exposed, but the results remain trustworthy and transparent. This is a truly autonomous, intelligent, and credible AI network.

Summary

FHE is like an 'encryption key' that allows AI groups to quietly collaborate and accurately compute, protecting privacy while ensuring trustworthy results. It is the key cornerstone of a decentralized AI society.