Research Lunch Club matches go out tomorrow, Friday 31 July. Join today and you'll be matched in time for next month's meetups in August — no cold DMs, no scheduling back-and-forth, just lunch with three or four other researchers who get what you do.

🔄 AI, Margins, and the New Division of Labour: Three Reversals Reshaping How Research Operates by Kate Towsey for The ResearchOps Review | Published July 23, 2026, Kate Towsey argues that AI and the post-pandemic shift from growth to profit models are reversing three assumptions about how research runs. Teams move from reactive service desks to proactive insight pipelines, operations stops being back-office support and becomes the mechanism by which the craft thrives, and the human-AI question shifts from who gets replaced to what the two can do together that neither could alone. She grounds each reversal in concrete practice, from AI agents that assemble weekly PESTLE briefings to DoorDash's guided research skill and an interview coach that gives researchers feedback on their own transcripts. The takeaway: decide explicitly where human judgment applies and where machines excel, then design the handoffs so quality survives scale.

⚖️ Delphi perspectives: AI and organisational emotion – efficiency or anxiety? by Christina Tarbotton and Shane Hanson for Research Live | Published July 24, 2026 as part of the MRS Delphi series on how AI is reshaping value, expertise and accountability in research, this piece argues the real emotional challenge of AI adoption is not fear of the technology but the anxiety of ambiguity. AI capability moves at software speed while organisational culture moves at human speed, so tools land before anyone explains what they mean for people's roles. When skills like statistical modelling become accessible to non-specialists through templates, the professional identity researchers built on technical mastery destabilises, and the do-more-with-less efficiency narrative carries an implicit threat employees hear clearly. The authors' prescription is cultural rather than technical: explicit, role-level conversations about what AI handles and what stays human, in place of efficiency rhetoric.

🧬 The New User Research Participant is AI generated user: Understanding Synthetic Users and Digital Twins by Reetika Gupta | Published July 23, 2026, this article untangles four terms researchers increasingly hear as if they were interchangeable: AI personas, synthetic users, survey-based AI predictions, and interview-based digital twins. The author frames them as a spectrum defined by how much real human data underlies the output, from a researcher-imagined "Priya" persona at one end to a digital twin built from one real person's in-depth interviews at the other. It walks through when each type is useful, mainly early-stage hypothesis generation and pressure-testing before real recruiting, and where each breaks down, illustrating with an example where synthetic users cluster around a bland middle while real users show a genuine 20/40/40 split of strong opinions. The piece concludes that an AI-generated user is only ever as good as the human data and assumptions behind it, and should supplement rather than replace real participants for high-stakes decisions.

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