Signal // 26 May 2026
A theme running through this edition: AI that fills in what it doesn't know — with something that looks right.
Meta forces 7,000 staff into AI roles — managers axed, no opt-out
The latest Big Tech restructure follows a template now: surviving employees reassigned to AI build-out, middle management cut, and the whole thing dressed up so it doesn't look like redundancy. MedComms agency heads should expect holding-group owners to cite this as a structural model. The pressure to show AI is replacing roles — not just assisting them — is building whether or not anyone is ready for what happens to the work that still needs doing.
Congress moves to kill Medicare AI prior authorisation pilot after GAO rebuke
The WISeR programme — using AI to approve or deny Medicare care without explicit Congressional sign-off — is now a live political target. The GAO found the CMS bypassed statutory requirements. If the pilot falls, expect it to harden the regulatory narrative around AI in any clinical decision-support context for years. For health comms teams working on payer-facing content or supporting clients in utilisation management, this is the backstory to know before your next brief.
AI peer review outscores top human reviewers — and misses 74% of what they find
A controlled study pitting GPT-5.2 against Nature-family paper reviewers found the AI outperformed the best human on correctness and significance scores. It also clustered on the same issues as every other AI reviewer, showed 16 documented weaknesses, and missed nearly three-quarters of the unique findings real reviewers caught. The headline capability is real. So is the structural limitation. For medical writing teams evaluating AI-assisted manuscript review, this is the most honest evidence to date about what you're actually buying.
AI builds structured tables — then cites sources it invented
Research on LLMs generating comparative tables found models routinely attach plausible-looking citations to rows drawn from parametric memory rather than actual documents. The fix — an auditor layer — only partially works. For health comms and medical affairs teams using any AI tool to build evidence summaries, dossiers, or SLR tables, this is a structural failure mode, not an edge case. Every citation in an AI-generated table needs independent verification. Every one.
Using LLMs to simulate patients or HCPs? You're running an observational study
Researchers showed that LLMs simulating patients or healthcare professionals as synthetic research participants don't behave like randomised subjects — the model's training data contaminates comparisons in ways that mirror observational study bias. If your organisation is using AI-generated personas to pre-test messaging, simulate advisory boards, or model intervention effects, the outputs need to be treated as observational, not experimental. The bias may not be visible without checks the field isn't routinely applying yet.
That's it for this edition. Back next week.
— Ned