--- title: "Vision" canonical: "https://current.tinyfish.ai/issue/31/vision" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/31/vision" issue: 31 section: "vision" description: "Where human-AI collaboration is heading" authors: ["Nora Kaplan"] published: 2026-06-01 updated: 2026-05-28 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 31 — Vision Where human-AI collaboration is heading ### Expertise Decay - [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. ### Window and Mechanism - [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. - [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. ### Baseline Keeper - [The Test Nobody Wants to Take — A Conversation With the CLO Measuring What AI Leaves Behind]() — Published 2026-05-28 *Ninety-four percent of organizational leaders have made little or no meaningful progress designing how humans and AI should work together. The other six percent aren't saying much about what they've found. Probably because it's uncomfortable.* ### Measurement Recursion - [You Can't Grade What You've Forgotten How to Do]() — Published 2026-05-28 Knowing whether an AI agent performed well requires someone who can do the work without it. That someone is becoming scarce. Three months of AI-assisted clinical practice was enough to measurably degrade doctors' independent diagnostic accuracy. ### Further Reading - **ServiceNow Enterprise AI Maturity Index 2026** — [Source]() — Published 2026-05-28 The primary source behind the 59%-to-9% maturity gap that anchors much of this issue. - **Microsoft Work Trend Index 2026: Agents, Human Agency, and the Opportunity for Every Organization** — [Source]() — Published 2026-05-28 Microsoft's survey data on the gap between deploying AI tools and redesigning work around them. - **The Infrastructure Gap Holding Back AI Agents** — [Source]() — Published 2026-05-28 A grounded look at why agents still spend most of their time faking keystrokes on old systems. - **AI Infrastructure Roadmap: Five Frontiers for 2026** — [Source]() — Published 2026-05-28 Bessemer's analysis of where AI infrastructure investment is actually heading this year. - **The Future of AI in 2026: Insights from the Most Important Research of 2025** — [Source]() — Published 2026-05-28 A research roundup tracking the field's shift from capability demos to reliability engineering. ### Past Articles - [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. - [Agent-First Design and the Fate of Human-Experience Moats]() — By [Nora Kaplan]() · Published 2026-05-20 · Format: Two Consequences When AI agents evaluate your service, they don't see your logo, your landing page, or the feeling your brand evokes. They parse structured schemas and optimize for outcomes. Companies spent decades building moats out of human experience. Now the entity making the purchasing decision can't experience any of it. Differentiation migrates somewhere most product teams haven't learned to look. - [The Indifferent Buyer]() — By [Nora Kaplan]() · Published 2026-05-20 · Format: The Inverted Audience Websites are already publishing machine-readable permission files for autonomous visitors, and what they choose to show an entity that can't be charmed looks nothing like what they show humans. New commerce protocols define how an agent discovers a product, evaluates terms, and buys — no storefront, no emotional arc. One to two trillion dollars in annual e-commerce spending is impulse-driven, dependent on a buyer who feels something at the moment of decision. That assumption is starting to erode. - [What the Junior Analyst Knew]() — By [Nora Kaplan]() · Published 2026-05-13 · Format: Two Defaults Hiding in Plain Sight When organizations automate entry-level work, the efficiency gains are visible. What's been removed takes longer to miss. Junior analysts processing exceptions and escalating edge cases did measurable, standardized work. They also served as the mechanism through which institutions discovered what their formal processes missed. Nobody is building a replacement for that function, because most organizations didn't realize it existed. ## Other sections in Issue 31 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Practitioner's Corner]() - [Echoes]() ## More - [Publication guide]() - [Issue archive]()