Cryptobeaver

I’m often asked about the possibility of using frontier models in an open source environment. I believe that one way or another, frontier companies will eventually adopt the same approach as @near_ai has to privacy, secure enclaves, and end-to-end encryption. It’s possible to encrypt even a closed model and upload it to a decentralized cloud like NEAR AI Cloud. The weights are guaranteed to be hidden from everyone, but inference can be monetized, while users are guaranteed that their data won’t be visible to the model owner. Right now, the frontier model companies are training on user data, but that window is probably closing and most training will be done on synthetic data. So they won't be as incentivized to keep or utilize users' data. Not to mention, over time user data will become even more of a liability than it is already. Enterprises are already paying the big AI companies not to train on their data, albeit with verbal trust rather than mathematical guarantees, but it’s clearly possible. So NEAR isn't working against the frontier model builders. They can use our infra or our approach. Everyone can – and should – have a more User-Owned AI paradigm where frontier models are verifiable and it will be better for everyone.

Your AI conversations aren't privileged. Yesterday, Judge Jed Rakoff ruled that 31 documents a defendant generated using an AI tool and later shared with his defense attorneys are not protected by attorney-client privilege or work product doctrine. The logic is simple: an AI tool is not an attorney. It has no law license, owes no duty of loyalty, and its terms of service explicitly disclaim any attorney-client relationship. Sharing case details with an AI platform is legally no different from talking through your legal situation with a friend (which is not privileged). You can't fix it after the fact, either. Sending unprivileged documents to your lawyer doesn't retroactively make them privileged. That's been settled law for years. It just hadn't been tested with AI until now. And here's what really hurt the defendant: the AI provider's privacy policy (Claude), in effect when he used the tool, expressly permits disclosure of user prompts and outputs to governmental authorities. There was no reasonable expectation of confidentiality. The core problem is the gap between how people experience AI and what's actually happening. The conversational interface feels private. It feels like talking to an advisor. But unless you negotiate for an enterprise agreement that says otherwise, you're inputting information into a third-party commercial platform that retains your data and reserves broad rights to disclose it. Judge Rakoff also flagged an interesting wrinkle: the defendant reportedly fed information from his attorneys into the AI tool. If prosecutors try to use these documents at trial, defense counsel could become a fact witness, potentially forcing a mistrial. Winning on privilege doesn't make the evidentiary picture simple. For anyone advising clients or managing legal risk, this is a wake-up call. AI tools are not a safe space for clients to process their counsel's advice and to regurgitate their legal strategy. Every prompt is a potential disclosure. Every output is a potentially discoverable document. So what do we do about it? First, attorneys need to be proactive. Advise clients explicitly that anything they put into an AI tool may be discoverable and is almost certainly not privileged. Put it in your engagement letters. Make it part of onboarding. Don't assume clients understand this, because most don't. Second, if clients want to use AI to help process legal issues (and they clearly will, increasingly), then let's give them a way to do it inside the privilege. Collaborative AI workspaces shared between attorney and client, where the AI interaction happens under counsel's direction and within the attorney-client relationship, can change the analysis entirely. I'm excited to be planning this kind of approach, and I think it's where the industry needs to head. storage.courtlistener.com/recap/gov.usco…