In decentralized networks, TEE hardware creates a trusted computing environment known as Decentralized Confidential Computing, spawning numerous interesting and market-focused use cases in the crypto space, including Agents, DePIN, and social media account management.

TEE isolates sensitive data in a protected CPU enclave during processing. The contents of the enclave (including the data being processed and the processing methods) are visible only to authorized program code, and no one else (including cloud service providers) can access or know about it.

The evolution of TEE revolves around three main concepts: developer experience, performance, and security.

• Developer Experience: Intel SGX revolutionized secure hardware by providing strong isolation capabilities close to native performance, but it posed challenges for developers. With the introduction of frameworks like Gramine, the developer experience has significantly improved.

• Performance Improvement: As TEE evolves into Confidential VMs and GPUs (such as Intel TDX and H200), developers can directly deploy unmodified applications.

• Security: The security of TEE relies on the trust root provided by hardware manufacturers, but this also brings dependence on proprietary hardware and closed-source implementations.

As TEE continues to evolve, trade-offs become more complex:

• Enhancing the developer experience may increase the Trusted Computing Base (TCB).

• Open-source instruction sets may reduce performance but improve verifiability.

• Improving the TCB through projects like Yocto may complicate deployment.

From creating interesting social experiences to establishing a fair global financial system, training large models with sensitive data, and achieving global-scale private identity management, we need to reach the fifth level of TEE —— the holy grail of encrypted computing. The future prospects are exciting.

• MEV-Related Use Cases: Flashbots began exploring the role of TEE in the transaction lifecycle as early as December 2022, with the primary goal of achieving trustless collaboration in the transaction supply chain.

• AI-Related Use Cases: TEE's applications in the AI field include data collection & labeling, model training, model inference, and autonomous agents.

TEE's applications in AI training and inference can ensure the isolation and encryption of training data and computing, while generating cryptographic proofs and model fingerprints for public verification. In addition, TEE can also empower agents with the ability to operate independently and autonomously.

For Crypto, TEE is a highly usable and rapidly developing trusted computing tool, with its evolution focusing on developer experience, performance, and security, while being constrained by hardware. As underlying services/SDKs become more mature, the barriers to using TEE technology for various projects are also decreasing. We look forward to seeing more explosive applications based on TEE.

In decentralized networks, TEE hardware creates a trusted computing environment known as Decentralized Confidential Computing, spawning numerous interesting and market-focused use cases in the crypto space, including Agents, DePIN, and social media account management.

TEE isolates sensitive data in a protected CPU enclave during processing. The contents of the enclave (including the data being processed and the processing methods) are visible only to authorized program code, and no one else (including cloud service providers) can access or know about it.

The evolution of TEE revolves around three main concepts: developer experience, performance, and security.

• Developer Experience: Intel SGX revolutionized secure hardware by providing strong isolation capabilities close to native performance, but it posed challenges for developers. With the introduction of frameworks like Gramine, the developer experience has significantly improved.

• Performance Improvement: As TEE evolves into Confidential VMs and GPUs (such as Intel TDX and H200), developers can directly deploy unmodified applications.

• Security: The security of TEE relies on the trust root provided by hardware manufacturers, but this also brings dependence on proprietary hardware and closed-source implementations.

As TEE continues to evolve, trade-offs become more complex:

• Enhancing the developer experience may increase the Trusted Computing Base (TCB).

• Open-source instruction sets may reduce performance but improve verifiability.

• Improving the TCB through projects like Yocto may complicate deployment.

From creating interesting social experiences to establishing a fair global financial system, training large models with sensitive data, and achieving global-scale private identity management, we need to reach the fifth level of TEE —— the holy grail of encrypted computing. The future prospects are exciting.

• MEV-Related Use Cases: Flashbots began exploring the role of TEE in the transaction lifecycle as early as December 2022, with the primary goal of achieving trustless collaboration in the transaction supply chain.

• AI-Related Use Cases: TEE's applications in the AI field include data collection & labeling, model training, model inference, and autonomous agents.

TEE's applications in AI training and inference can ensure the isolation and encryption of training data and computing, while generating cryptographic proofs and model fingerprints for public verification. In addition, TEE can also empower agents with the ability to operate independently and autonomously.

For Crypto, TEE is a highly usable and rapidly developing trusted computing tool, with its evolution focusing on developer experience, performance, and security, while being constrained by hardware. As underlying services/SDKs become more mature, the barriers to using TEE technology for various projects are also decreasing. We look forward to seeing more explosive applications based on TEE.