Why can I not run my private data through a model I do not own?
機会
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.
重要な理由
The entire class of sensitive-data AI applications is locked out until compute-side privacy reaches the same standard as wire-side privacy.
機会をどう評価するか
Opportunity Scoreは測定値ではなく、私自身の見解です。どれほど痛みを伴うか、どれほど頻繁に影響を与えるか、そして今日時点で解決策がいかに少ないか。スコアが高いほど、構築する価値が高いと私は考えています。
それが現れたときにどれほどの痛みをもたらすか。
実際にどれほど頻繁に人々がそれに直面するか。
今日時点で、それに対する優れたツールがいかに少ないか。