Model substitution in LLM APIs is a documented problem. Research: "Are You Getting What You Pay For? Auditing Model Substitution in LLM APIs" Finding: Providers have financial incentives to silently swap expensive models for cheaper ones. Users have no way to verify what's actually running. Brave just solved this with cryptographically verifiable AI. The implementation: @brave Leo now uses @near_ai @nvidia Trusted Execution Environments for provable privacy and model transparency. This is hardware-enforced cryptographic guarantees. THE ARCHITECTURE: TEE-enabled Nvidia GPUs create hardware-isolated secure enclaves with full encryption of data and code during inference. Cryptographic attestation reports contain model hashes and execution code hashes. Remote attestation verifies genuine Nvidia TEE running unmodified open-source code. THE GUARANTEES: - Confidentiality: Even a fully compromised OS cannot access TEE memory (hardware isolation) - Integrity: Cryptographic proof of exact model and code executing - Verifiability: Open-source chain from code to hardware attestation THE VERIFICATION CHAIN: User selects model → @brave validates @near_ai cryptographic attestation → confirms @nvidia TEE hardware → proves DeepSeek V3.1 running unmodified → green ✅ badge displayed This eliminates three critical problems: (1) Privacy-washing: Math over marketing. Cryptographic proofs replace privacy policies. (2) Model substitution: Hardware-enforced proof you're getting the model you selected/paid for. (3) Trust requirements: Hardware guarantees replace legal agreements. COMPARISON TO APPLE PRIVATE CLOUD COMPUTE: Similar TEE approach, different philosophy: - Apple: Closed ecosystem, proprietary verification, limited auditability -Brave: Open-source code, user-verifiable attestations, full transparency TECHNICAL IMPLICATIONS: This shifts the security model from: - Trust-based (policies, agreements, promises) -> Verification-based (cryptography, hardware, math) From software controls that can be bypassed to hardware enforcements that cannot. The Nvidia Hopper architecture enables this with minimal performance overhead (benchmarks show near-zero in many cases). Combining CPU TEEs (@intel TDX) with GPU TEEs creates end-to-end confidential computing for LLM inference. PRIVACY RESEARCH PERSPECTIVE: This is the privacy-by-design architecture we should demand: - Cryptographically verifiable (not just auditable) - Hardware-enforced (not policy-enforced) - Independently verifiable (not trust-us verification) - Addresses real economic incentives (model substitution, data monetization)