--- title: "Vision" canonical: "https://current.tinyfish.ai/issue/33/vision" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/33/vision" issue: 33 section: "vision" description: "Where human-AI collaboration is heading" authors: ["Nora Kaplan"] published: 2026-06-15 updated: 2026-06-12 articles: 14 --- # TinyFish > CURRENT is a journal about AI agents in production, for the engineering and product leaders who have to make them work. Drawing on experience building enterprise web agents, it explains why the demo succeeded and the deployment didn't, and what that means for choosing vendors, briefing the board, and avoiding mistakes others have already made. [Read on the website]() ## Issue 33 — Vision Where human-AI collaboration is heading ### The Core Problem - [The Quiet Part]() — By [Nora Kaplan]() · Published 2026-06-11 · Format: The Core Problem In a controlled trial, experienced developers using AI tools estimated they'd gotten about 20% faster. Measured result: 19% slower. What lingers is that completing the work, experiencing the full arc of it, didn't correct their perception. Meanwhile, 74% of teams running agent systems in production rely on human reviewers as their primary quality gate. Both findings are solid. Together, they describe something uncomfortable about where organizational AI deployment is actually headed. ### Lessons and Designs - [What Aviation Built After It Noticed the Problem]() — By [Nora Kaplan]() · Published 2026-06-11 · Format: Lessons and Designs Aviation solved the automation-complacency problem decades ago with simulator checks, mandatory manual flying, and crew resource management. Those countermeasures rest on a foundation enterprise AI lacks: a stable, agreed-upon definition of what correct human performance looks like. A pilot's hand-flown approach can be graded against known parameters. An analyst's judgment on a market entry recommendation cannot. That structural difference determines whether countermeasures are even possible. - [Monitoring the Monitors]() — By [Nora Kaplan]() · Published 2026-06-11 · Format: Lessons and Designs Organizations are building increasingly sophisticated systems to monitor whether their AI agents perform well. Almost none are checking whether the humans evaluating those agents still can. Evidence from the few domains where measurement exists — developer productivity, medical diagnostics — reveals a durable gap between perceived and actual human capability. The instrument to detect oversight degradation needs to be built now, while the people who could design it still have the expertise to know what it should measure. ### The Stress Tester - [The Woman Who Breaks Her Hospital's AI on Purpose]() — Published 2026-06-11 *Noor Breakpoint is not a real person, though if she were, she'd probably object to us pointing that out before she'd had a chance to make her case. Her title, Director of AI Resilience Testing at a mid-size healthcare network, describes a role that barely existed two years ago and that most health systems still haven't created. Her small team, which she calls "Red Cell" after military adversarial testing units, does something that sounds simple and turns out to be deeply uncomfortable: they deliberately plant subtle errors into AI-assisted clinical and operational workflows, then watch to see if anyone catches them.* ### The Causal Evidence - [The Evidence for AI-Induced Deskilling Is No Longer Theoretical]() — Published 2026-06-11 Polish researchers studying AI-assisted colonoscopy weren't looking for deskilling. They found it anyway. After endoscopists gained routine access to AI polyp detection, their unassisted adenoma detection rate fell from 28.4% to 22.4% across four centres and nearly 1,500 procedures. The degradation only surfaced when the tool was taken away. With it, performance held steady. ### Further Reading - **What Developer Oversight of AI Agents Actually Looks Like** — [Source]() — Published 2026-06-11 Empirical research finds four distinct forms of oversight work. Most governance frameworks assume one. - **The Guardrail That Belongs Around the Human, Not the Model** — [Source]() — Published 2026-06-11 Makes the case that human attention is a finite organizational resource nobody is actually managing. - **The AI Adoption J-Curve and Why the Dip Looks Like Normal** — [Source]() — Published 2026-06-11 MIT Sloan-backed research on why AI's temporary productivity losses can pass as routine friction for months. - **What Aviation Learned About Oversight That Enterprise AI Hasn't** — [Source]() — Published 2026-06-11 A sharp distinction between placing someone in the loop and training them to function there. - **The Governance Gap Executives Can't See** — [Source]() — Published 2026-06-11 Nearly two-thirds of organizations apply weaker controls to AI agents than to human employees. ### Past Articles - [What the Six Percent Are Doing Differently]() — By [Nora Kaplan]() · Published 2026-06-04 · Format: Two Consequences While the apprenticeship pipeline thins, a different question is surfacing at the organizational level: what does work look like when you design it around agent capabilities instead of bolting agents onto legacy processes? Six percent of leaders report progress on this. The other 94 percent are watching gains plateau. What separates the two groups is something more mundane than technology: someone asked what the work actually is before automating it. - [Where Does Judgment Come From Now?]() — By [Nora Kaplan]() · Published 2026-06-04 · Format: Two Consequences Entry-level job postings have fallen 35 percent since early 2023. The tasks vanishing from junior roles—debugging, data reconciliation, document review—look like busywork from the outside. They were also how professionals learned what "normal" looks like in their field. The development pipeline hollowed out one rational automation decision at a time. Now the experiment in whether judgment can form without apprenticeship is already running, undesigned. - [The Boring Turn]() — By [Nora Kaplan]() · Published 2026-06-04 · Format: The Boring Future A year ago, the enterprise AI conversation was about capability — whether agents could navigate websites without hallucinating. That conversation has moved on. The top budget line is now observability tooling. Over half of enterprises have a formal agent operations lead. The questions sound like infrastructure management because they are. And once something becomes operational, the difficulty changes character entirely. The organizational questions that emerge are ones most deployment playbooks aren't built to detect. - [When Smooth Work Feels Like Good Work]() — By [Nora Kaplan]() · Published 2026-05-28 · Format: Window and Mechanism Experienced developers predicted AI made them 24% faster. Actual result: 19% slower. After completing the work, with their own timing data in hand, they still believed AI had helped. That 39-point gap between perceived and actual performance points to something structural about how humans read fluency as accuracy. For organizations building AI-augmented workflows right now, it's also a design problem with concrete solutions. ## Other sections in Issue 33 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Practitioner's Corner]() - [Echoes]() ## More - [Publication guide]() - [Issue archive]()