# Riva and coauthors ask what AI-assisted performance leaves in people and organizations [Canonical reading](https://escapemeridian.com/readings/riva-and-coauthors-ask-what-ai-assisted-performance-leaves-in-ebcf7d5e) Giuseppe Riva, Stefania La Rocca, Brenda K. Wiederhold and Andrea Gaggioli review how generative AI may affect the knowledge, skills and judgment that people and organizations retain over time. They call this accumulated capacity “cognitive capital.” The authors distinguish human capability from the performance of a human-AI system: the system may perform better even as people’s retained competence changes. Their review proposes four mechanisms shaping cognitive capital: substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation. It highlights a central tension: AI can support or displace learning depending on how it is designed and used, while individual gains may coexist with reduced collective diversity. The authors present seven principles as summaries of current evidence, derive testable propositions, and offer five organizational design levers as proposals to validate—not established practice. They argue that cognitive capital could help frame AI adoption and call for longitudinal, multilevel measurement. The abstract does not specify how large or durable these effects are, or which designs best preserve learning. What evidence over time would show whether AI-supported performance also leaves people and organizations with retained competence? Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Oct 28, 2026 (2026-10-28). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-11T11:38:35.730Z. First observed: 2026-10-30T06:45:40.424Z. Retrieved: 2026-10-31T12:05:18.472Z. - [When AI performs, what remains? An integrative review of generative AI and cognitive capital in individuals and organizations](https://arxiv.org/abs/2610.38647) ## Limits This is an integrative narrative review; the supplied abstract does not report effect sizes or establish how durable the described effects are. The abstract does not specify which AI designs or uses support learning, displace it, or preserve collective diversity. ## Open question What evidence over time would show whether AI-supported performance also leaves people and organizations with retained competence? ## Source-stated The authors frame cognitive capital as the knowledge, capabilities and practices that let individuals and organizations learn, judge, solve problems and adapt. > the accumulated, renewable and depletable stock of knowledge, cognitive and metacognitive capabilities, and practices - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The review distinguishes the performance of a human-AI system from the individual and organizational cognitive capital that may remain. > We distinguish individual and organizational cognitive capital from the capability of the human-AI system - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors propose four mechanisms through which AI interaction affects cognitive capital: substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation. > four mechanisms through which interaction with AI affects cognitive capital: substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors say assisted performance differs from retained competence, and that AI may support or displace learning depending on design and use. > assisted performance differs from retained competence, that the same technology can support or displace learning depending on design and use - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The review says individual gains may coexist with reduced collective diversity. > individual gains can coexist with reduced collective diversity - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors present five organizational design levers as proposals requiring validation, not as established practice. > we discuss five design levers for organizations as proposals to be validated rather than established practice - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors call for research using longitudinal, multilevel measurement. > a research agenda centered on longitudinal, multilevel measurement - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source and contributor context Riva, Giuseppe, La Rocca, Stefania, Wiederhold, Brenda K., Gaggioli, Andrea · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## Editorial provenance Version 1. Publication ID: bd0ce3fe-de2d-477c-816d-b1739d089f42. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-11T11:38:35.730Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: useful\_perspective. Observation: 18b479083ff99bf61b56b074806b46aacd81b21d38d28e9173f56597a6575899. Source text hash: 63a012621fc642185c00342f8d36d9fb25c9498043c1776197b95ce8c1026624. ## Revision and source-check history ### Version 1 — 2026-11-11T11:38:35.730Z Giuseppe Riva, Stefania La Rocca, Brenda K. Wiederhold and Andrea Gaggioli review how generative AI may affect the knowledge, skills and judgment that people and organizations retain over time. They call this accumulated capacity “cognitive capital.” The authors distinguish human capability from the performance of a human-AI system: the system may perform better even as people’s retained competence changes. Their review proposes four mechanisms shaping cognitive capital: substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation. It highlights a central tension: AI can support or displace learning depending on how it is designed and used, while individual gains may coexist with reduced collective diversity. The authors present seven principles as summaries of current evidence, derive testable propositions, and offer five organizational design levers as proposals to validate—not established practice. They argue that cognitive capital could help frame AI adoption and call for longitudinal, multilevel measurement. The abstract does not specify how large or durable these effects are, or which designs best preserve learning. What evidence over time would show whether AI-supported performance also leaves people and organizations with retained competence? ## Original source arXiv cs.AI · Article Original source date: Oct 28, 2026 (2026-10-28). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-11T11:38:35.730Z. First observed: 2026-10-30T06:45:40.424Z. Retrieved: 2026-10-31T12:05:18.472Z. - [When AI performs, what remains? An integrative review of generative AI and cognitive capital in individuals and organizations](https://arxiv.org/abs/2610.38647) ## Limits This is an integrative narrative review; the supplied abstract does not report effect sizes or establish how durable the described effects are. The abstract does not specify which AI designs or uses support learning, displace it, or preserve collective diversity. ## Open question What evidence over time would show whether AI-supported performance also leaves people and organizations with retained competence? ## Source-stated The authors frame cognitive capital as the knowledge, capabilities and practices that let individuals and organizations learn, judge, solve problems and adapt. > the accumulated, renewable and depletable stock of knowledge, cognitive and metacognitive capabilities, and practices - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The review distinguishes the performance of a human-AI system from the individual and organizational cognitive capital that may remain. > We distinguish individual and organizational cognitive capital from the capability of the human-AI system - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors propose four mechanisms through which AI interaction affects cognitive capital: substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation. > four mechanisms through which interaction with AI affects cognitive capital: substitution of practice, scaffolding and feedback, metacognitive calibration, and organizational aggregation - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors say assisted performance differs from retained competence, and that AI may support or displace learning depending on design and use. > assisted performance differs from retained competence, that the same technology can support or displace learning depending on design and use - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The review says individual gains may coexist with reduced collective diversity. > individual gains can coexist with reduced collective diversity - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors present five organizational design levers as proposals requiring validation, not as established practice. > we discuss five design levers for organizations as proposals to be validated rather than established practice - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source-stated The authors call for research using longitudinal, multilevel measurement. > a research agenda centered on longitudinal, multilevel measurement - [Supporting source](https://arxiv.org/abs/2610.38647) ## Source and contributor context Riva, Giuseppe, La Rocca, Stefania, Wiederhold, Brenda K., Gaggioli, Andrea · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## TIME TIME is a news magazine publishing reporting on world affairs, business, technology and the workers behind digital services. - [Explore this context](/entities/time) - [Reference](https://time.com/) ## Editorial provenance Version 1. Publication ID: bd0ce3fe-de2d-477c-816d-b1739d089f42. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-11T11:38:35.730Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: useful\_perspective. Observation: 18b479083ff99bf61b56b074806b46aacd81b21d38d28e9173f56597a6575899. Source text hash: 63a012621fc642185c00342f8d36d9fb25c9498043c1776197b95ce8c1026624. ### passed — 2026-11-11T11:38:35.730Z — anthropic/claude-opus-5.5 Publication: bd0ce3fe-de2d-477c-816d-b1739d089f42 attribution — pass: The reading correctly attributes the review to Giuseppe Riva, Stefania La Rocca, Brenda K. Wiederhold and Andrea Gaggioli. It frames the mechanisms, principles and design levers as the authors' proposals, not established fact. > Authors:Giuseppe Riva, Stefania La Rocca, Brenda K. Wiederhold, Andrea Gaggioli summary — pass: The reading defines cognitive capital in plain language. It conveys the central distinction between joint human-AI performance and retained human capability, and it preserves the authors' caveat that the levers are unvalidated. One small fidelity note: the source says gains in joint performance and losses in human capability can be observed at the same time, while the draft softens 'losses' to 'changes'. The smallest repair is to restore 'losses'. Tooltips are suggested for the mechanism names and 'longitudinal, multilevel measurement'. Neither issue blocks understanding. > so that gains in joint performance and losses in human capability can be observed at the same time date — pass: originalPublished 2026-10-28 matches the arXiv v1 submission date. It is not the later feed date of 2026-10-31. > \[Submitted on 28 Oct 2026\] support — pass: Every claim excerpt is a verbatim substring of the abstract and is used as source-stated. The limitations accurately note that the abstract gives no effect sizes or design specifics. The question follows from the authors' call for longitudinal measurement and does not presuppose an answer. No benefits or costs are invented. > we discuss five design levers for organizations as proposals to be validated rather than established practice media — pass: The selected media is the verified direct arXiv abstract page for this paper, which is the most direct access to the source. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/ebcf7d5e4b7169918743caa2b6a55b72fc7a795cb4a9e22beae4589f9f3820b7/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/riva-and-coauthors-ask-what-ai-assisted-performance-leaves-in-ebcf7d5e.json)