Let’s take a look at Age Verification:
First: anyone can create a website. There is no license. No mandatory security certification. No universal encryption standard. Under age-verification mandates, any site can demand a government ID, a selfie, or biometric data in order to grant access. That includes incompetent operators. That includes data resellers. That includes sexual predators. If someone wants to harvest minors’ identity documents, these laws hand them legitimacy. “Upload your ID to continue.” That is not protection. That is structured data collection.
Second: this creates a data-retention epidemic. Every ID upload becomes another stored record. Every stored record becomes another breach target. Birthdates, driver’s licenses, and facial scans cannot be reset. When that data leaks — and it will — it persists. Expanding age verification multiplies permanent identity databases across thousands of private entities. The attack surface grows. The exposure grows. The risk grows.
Third: the blame shifting needs to stop. Large language models are not sentient. They are probabilistic systems — neural networks trained via gradient descent to optimize next-token prediction across weighted parameter matrices. They generate text by computing conditional probabilities. They do not understand meaning. They do not intend harm. When people claim an AI company is responsible for “leading” a child to harm, they are assigning agency to a tool that uses weights and measures to determine it's responses. There is no thought, it doesn't "understand" what it's saying, and it doesn't "know" anything. It's a machine that merely produces an algorithmic output. The misconception that there is any thought is all is a byproduct of anthropomorphizing and nothing more. If a child is unsupervised, isolated, and relying on a chatbot instead of a parent, that is not a parameter problem. That is a supervision problem.
Fourth: we already know how accountability works. School attendance is mandatory. If a child repeatedly skips school, responsibility falls on the guardian. Six strikes you go to jail if I remember correctly. There are escalating consequences. Phones belong to parents. Access is granted by parents. A strike-based enforcement model — similar in structure to truancy escalation — keeps responsibility where it belongs. Not on OpenAI. Not on Facebook. On the adult who handed over the device.
Fifth: devices should ship locked down by default. Child-protected mode out of the box. The parent explicitly unlocks higher tiers. The phone remains inspectable. Handing a child unrestricted internet access without structure is like handing over car keys without instruction. The answer is not forcing every website to collect ID. The answer is enforcing supervision at the device level.
Sixth: Websites can publish standardized content ratings — similar in concept to ESRB categories — in machine-readable metadata or an extended robots.txt declaration. The device, operating in parental control mode, reads that rating and enforces it locally. If a publisher lies, parents can see these sites visited by children, file a complaint which can be investigatedand sites with false or misleading ESRB or MPAA ratings can be held accountable. Sites with no rating can be blocked. The child does not upload a driver’s license to browse the web.
Seventh: where age thresholds truly matter, verification can happen on the device using cryptography instead of document storage. The operating system verifies age once. It then derives a salted hash representing an age condition, such as “age ≥ 13.” When a service requests proof, the device returns a signed attestation derived from that hash. The service verifies the signature against the OS’s public key. It never receives the birthdate. In more advanced designs, zero-knowledge proofs allow the device to mathematically prove the age condition without revealing any underlying data at all.
Advertisers can take a list of customer identifiers tied to a specific interest—such as emails or phone numbers—and run them through a one-way cryptographic hash function (for example, SHA-256). That process converts each identifier into a fixed-length hash value that cannot be reversed under normal conditions. The social media platform does the same thing independently with its own user database. Instead of exchanging raw personal data, both sides compare hashed values. Where the hashes match, they’ve identified the same person without either party disclosing the underlying identifier. This is called a hash "collision". The advertiser never sees the platform’s user data, and the platform never sees the advertiser’s original list.
An API can automate this matching process by accepting only hashed inputs and returning aggregated match results or audience segments. No plaintext identifiers are exposed, and no direct transfer of customer records occurs. The system relies on deterministic hashing—same input, same output—so matches are exact without revealing identity. When properly implemented with salting, secure transport, and strict access controls, this approach limits data exposure to mathematical fingerprints, not personal information, preserving privacy and enabling targeted ad delivery. These two mechanisms can be used to verify on device and similarly serve anonymous ads. No third-party database. No identity upload pipeline. No retention problem.
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