--- title: "Vision" canonical: "https://current.tinyfish.ai/issue/32/vision" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/32/vision" issue: 32 section: "vision" description: "Where human-AI collaboration is heading" authors: ["Nora Kaplan"] published: 2026-06-07 updated: 2026-06-04 articles: 15 --- # 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 32 — Vision Where human-AI collaboration is heading ### The Boring Future - [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. ### Two Consequences - [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. - [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. ### From the Inside - [Margot Ledger's Forty-Seven Steps]() — Published 2026-06-04 *Margot Ledger is not a real person, though if you've spent the last eighteen months deploying AI agents in financial services, she might feel uncomfortably familiar. She's a composite character drawn from documented production deployments, industry research, and the kind of governance lessons that only surface after something has already gone sideways. Her title, Head of Workforce Transformation, is one of those roles that didn't exist three years ago and now appears on org charts at firms that realized "digital transformation" needed someone who understood both the systems and the people those systems were about to reorganize.* ### The Desire Mismatch - [The 41% Problem: Stanford Maps the Gap Between What Agents Can Do and What Workers Want]() — Published 2026-06-04 Stanford's SALT Lab mapped 2,283 Y Combinator company descriptions against preference data from 1,500 workers spanning 104 occupations. Forty-one percent of company-task mappings landed where workers either resist delegation outright or see it as neither desirable nor feasible. ### Further Reading - **Future of Work with AI Agents: Auditing What Workers Want vs. What Agents Can Do** — [Source]() — Published 2026-06-04 Stanford's SALT Lab found a striking gap between the tasks workers actually want automated and where AI investment is flowing. Worth sitting with if you suspect the infrastructure buildout is optimizing for the wrong layer of work. - **From Tensions to Tipping Points: Deloitte's 2026 Global Human Capital Trends** — [Source]() — Published 2026-06-04 Only 14% of leaders say they can design human-AI work effectively. Deloitte frames this as an organizational design problem, which feels right. The capability gap that matters most right now may not be technical at all. - **Work Rewired: How AI Agents Are Reshaping Roles from Entry Level Up** — [Source]() — Published 2026-06-04 Two-thirds of enterprises are already reducing entry-level hiring as agents arrive. IDC's data puts numbers on the pipeline question this publication keeps circling: where does the next generation of senior judgment come from if the formative work disappears first? - **When AI Becomes Invisible: Designing for Infrastructure, Not Demos** — [Source]() — Published 2026-06-04 A clear-eyed framework for the four stages between AI-as-novelty and AI-as-plumbing. Useful for thinking about what changes organizationally when the technology stops being interesting and starts being assumed. - **Human Skills Will Matter More Than Ever in the Age of AI** — [Source]() — Published 2026-06-04 McKinsey argues that AI relocates human judgment rather than replacing it. The piece is more useful as a starting point than a conclusion. It's strong on what stays human but quiet on how people develop those skills when the on-ramp work is automated. - **If AI Is Normal Technology, Boring Infrastructure Is Your Best Strategy** — [Source]() — Published 2026-06-04 A practitioner's case that stable foundations beat sophisticated AI strategies in the enterprise. The boring-infrastructure thesis sounds modest, but it carries a real implication: the competitive advantage shifts from who has the best AI to who has the most reliable plumbing. ### Past Articles - [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. - [What the Maturity Gap Is Actually For]() — By [Nora Kaplan]() · Published 2026-05-28 · Format: Window and Mechanism Only 9% of enterprises report meaningful AI autonomy in production. That's widely treated as a sign organizations are stuck. I think it means they're in the last period where certain things are still possible. The analysts still run the reports. Someone still knows what a good output looks like from years of producing them by hand. That knowledge has a half-life. What gets built during the overlap determines whether autonomy is trustworthy—or just operational. - [The Expertise You're Losing Is the Expertise You'd Need to Notice]() — By [Nora Kaplan]() · Published 2026-05-28 · Format: Expertise Decay Expertise inside an organization is a practice, maintained through repetition, apprenticeship, and evaluation against standards that live in people's heads. Agentic AI disrupts all three simultaneously, and the decay compounds in a way that's genuinely hard to see: the capacity to detect expertise loss erodes on the same curve as the expertise itself. When experienced developers using AI tools believed they were 20% faster but measured 19% slower, the error pointed in exactly the direction that would prevent correction. - [The Discovery Problem No One Has Solved]() — By [Nora Kaplan]() · Published 2026-05-20 · Format: Two Consequences We've built protocols for how agents talk to each other. How they find each other remains wide open. Every discovery mechanism humans rely on — search rankings, app stores, social proof — was designed for a creature that browses and responds to emotion. Agents do none of that. The void is filling with provisional fixes. If the app store era taught us anything, it's that discovery layers don't stay neutral by accident. ## Other sections in Issue 32 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Practitioner's Corner]() - [Echoes]() ## More - [Publication guide]() - [Issue archive]()