Why can I not run my private data through a model I do not own?

Every query you send to a remote AI model reaches the provider in the clear, and you have no cryptographic guarantee the input was not logged, retained, or used in future training. Fully homomorphic encryption eliminates that exposure but runs LLM inference at roughly 0.2 tokens per second, four orders of magnitude below any production requirement. Trusted execution environments keep latency near-normal but substitute hardware attestation for mathematical proof, meaning you are trusting a chip vendor rather than the cryptography. Multi-party computation distributes the trust but requires bandwidth and coordination rounds that do not scale to billion-parameter models. Medical notes, legal drafts, and financial records remain excluded from the most capable frontier models because no solution simultaneously clears the privacy bar and the throughput bar.