Originally published at vinpatel.com
Homomorphic encryption for AI was supposed to be a lab curiosity, too slow to leave the whiteboard. Google just built production tooling around it instead.
Three events plot the curve. In 2009, Craig Gentry's dissertation delivered the first fully homomorphic encryption scheme, letting you compute on encrypted data without ever decrypting it, at a computational cost that made it useless outside a lab. In 2021, Google open-sourced a fully homomorphic encryption transpiler, tooling that let ordinary engineers compile existing code into FHE circuits instead of hand-writing the cryptography themselves. In 2026, Google published a blog post explaining how it is making private AI practical with homomorphic encryption, laying out the engineering required to run AI inference on data that is never decrypted, not in memory, not on disk, not even inside the model doing the math.
The through-line is that each step removed a reason to say no. Gentry's scheme proved FHE was mathematically possible but practically absurd. The 2021 transpiler proved ordinary engineers could target FHE without a cryptography PhD. This latest push targets a narrower, harder problem: making FHE fast enough that a model can run inference on encrypted inputs and still return an answer before the user gives up. That is a different bar than proving a mechanism works in a paper. It is the bar that decides whether a hospital, a bank, or a government agency can send sensitive data to a cloud model at all.
That is why this lands differently now than it would have five years ago. Every enterprise AI rollout runs into the same wall: the data that would make the model useful is exactly the data compliance will not let leave the building unencrypted. Homomorphic encryption is the only widely discussed technique that removes that tradeoff entirely, because the server doing the computation never sees the plaintext. If Google can make that fast enough for real inference workloads, FHE stops being a research talking point and becomes a line item on enterprise procurement forms. For another example of a security guarantee that trades raw performance for trust, look at what happened when DNSSEC broke .de domains — cryptographic correctness and speed have always pulled against each other, and FHE is that tension pushed to its extreme.
Here is the falsifiable part. If this is real engineering progress and not a showcase, expect at least one other major cloud provider, AWS, Microsoft, or a frontier lab, to publish its own homomorphic-encryption-based confidential inference feature by the end of 2027. If nobody ships anything comparable by then, FHE for AI stays exactly where it has lived since 2009: proof that something is possible, not a thing anyone actually runs.
Get calls like this before the takes settle. Subscribe at vinpatel.com/subscribe/ for one AI signal a day, sent straight to your inbox.
Top comments (0)