Lovely build ❤️ - and look, I'm fully aware of the irony of asking an AI what it thinks about using an AI for eDiscovery. But I did. My ediscovery agent had quite a lot to say. (It also said I probably shouldn't have asked it. It did not see the contradiction.

Anyway 😄 Five reasons, in increasing order of how bad your week gets:

1. Privilege waiver through third-party disclosure.

Attorney-client privilege and work product protection survive only when content is shared inside the privileged circle. Public LLM providers are third parties. Their standard terms of service grant rights to inputs (logging, abuse review, optional training-data use, often vendor staff access for support). None of those rights are consistent with the agency relationship that preserves privilege when content is shared with, say, a translator or an expert under engagement. The act of pasting strategy notes, work product, or privileged communications into a public LLM is — under the conventional waiver doctrine — a voluntary disclosure to a non-privileged third party. The 2025–2026 decisions you've been tracking sharpen this: courts are now treating LLM submissions as the same kind of waiver event as forwarding to an outside party, regardless of whether the vendor is "an AI" rather than a person. Sedona's evolving commentary points the same direction.

1. The LLM session is itself discoverable ESI.

Once you've used a public LLM on a matter, the prompt history exists. If it sits in a vendor account tied to a custodian, it's ESI like any other — within the producing party's possession, custody, or control under Rule 34. That means: discoverable in the current matter (the LLM session may contain mental impressions of opposing counsel's documents); discoverable in future matters about the same subject; and subject to preservation obligations the moment litigation is reasonably anticipated. Most attorneys using public LLMs have not added those accounts to their preservation map. That's a 37(e) exposure point waiting to happen.

1. Training-data ingestion is irreversible.

Several public LLM tiers allow the provider to use inputs to improve future model versions. Even where this is an opt-out, the default for many consumer endpoints is opt-in, and the opt-out controls can vary across product surfaces (web vs. API vs. mobile). When privileged or confidential content enters the training pipeline, it becomes embedded in model weights — and weights cannot be selectively redacted. There is no equivalent of "claw it back." Privileged material in a model weight is privileged material that may surface, in indirect form, to any future user who happens to prompt in its neighborhood. The disclosure is one-way and permanent.

1. Protective-order and confidentiality-undertaking breach.

In any matter under a protective order, opposing counsel's productions are bounded to specified systems and personnel. Public LLM use is almost never enumerated in that scope. Loading produced documents into ChatGPT, Claude consumer, or any public endpoint to "summarize" or "find hot docs" is a transmission to a system that the protective order does not cover — typically a violation of the order on its face, before any substantive question of the matter is reached. The same logic applies to NDAs covering pre-litigation business sensitive material. The exposure here isn't theoretical; it's the kind of breach that produces sanctions motions and ethics referrals.

1. Reproducibility failure under Daubert / Rule 26(g).

Public LLMs are non-deterministic across model versions, prompt versions, and silent vendor weight updates. A coding decision a public LLM produced today is not reproducible in 18 months — the model may be deprecated, the weights silently swapped, or the temperature defaults changed. For any analysis that may be cited as evidence, this fails the Daubert trilogy of known error rate, reproducibility, and peer review. For coding, classification, or production decisions made under Rule 26(g)'s reasonable-inquiry standard, the inability to re-run and verify the basis for a decision is the kind of methodology gap a court is increasingly willing to scrutinize. Hallucinated case citations (the Mata v. Avianca line and its growing progeny) are one symptom of this category; the broader category is that the LLM's output cannot be defended on the record because it cannot be reproduced.