--- title: "Vision" canonical: "https://current.tinyfish.ai/issue/44/vision" publication: "TinyFish" publisher: "TinyFish" language: "en-US" guide: "https://current.tinyfish.ai/llms.txt" permalink: "https://current.tinyfish.ai/issue/44/vision" issue: 44 section: "vision" description: "Where human-AI collaboration is heading" authors: ["Sevda Polat","Sable Whitford"] published: 2026-08-31 updated: 2026-08-28 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 44 — Vision Where human-AI collaboration is heading ### Anchor Essay - [The Credence Problem]() — By [Sevda Polat]() · Published 2026-08-28 · Format: Anchor Essay When you send an agent to do something you cannot do yourself, you also cannot check whether it did it correctly. That is not a defect in the design. It is the same condition that made the agent worth using. Economists identified this structure decades ago in auto repair and medical care: goods whose quality the buyer cannot assess even after consumption. A growing share of agent output belongs in that category, and the research on what happens when people try to compensate through transparency suggests the problem does not yield to the fixes we reach for first. ### Two Cases - [The Users Who Got There First]() — By [Sevda Polat]() · Published 2026-08-28 · Format: Two Cases Blind and low-vision users have spent years depending on AI descriptions they cannot independently verify. They've developed sophisticated checking strategies — and new research shows those strategies still miss confident wrong answers at alarming rates. What this community has learned about trusting an AI intermediary, and where that trust still breaks down, matters for every organization now deploying AI as its default first reader. - [When Nobody Reads the Original]() — By [Sevda Polat]() · Published 2026-08-28 · Format: Two Cases When an AI summarizes a contract, a security incident, or a recorded engineering conversation, the summary becomes the event for everyone downstream. Enterprise organizations are acquiring the same dependency on unverifiable AI accounts that the accessibility community has navigated for years — but without the verification instincts. And the expertise needed to catch errors is quietly thinning as the AI displaces the practice that built it. ### The Interpreter's Craft - [The Woman in the Booth Who Edits Reality for a Living]() — Published 2026-08-28 *Vivian Sotto is a fictional audio describer. Her craft is not.* ### Standards Sidebar - [Adequate for What]() — By [Sevda Polat]() · Published 2026-08-28 The W3C's [accessibility guidelines](https://www.w3.org/WAI/tutorials/images/) encode a principle that agent builders are arriving at from the other direction: the adequacy of a description depends on what someone needs it for. A photo of a bird on a birding site needs species and plumage. The same photo on a parks page needs "bird at the lake." A printer icon used as a button should say "print," not "image of a printer." ### Further Reading - **Web User Agents: The W3C Draft That Wants to Hold AI Accountable to Users** — [Source]() — Published 2026-08-28 W3C proposes extending user-agent duties of protection, honesty, and loyalty to AI acting on the web. - **Playing Telephone with Generative Models: When Mostly-Correct Systems Trap Users into Trust** — [Source]() — Published 2026-08-28 Researchers name what happens when AI outputs are too plausible to doubt but too flawed to trust fully. - **The Oversight Fallacy: Why Approval Prompts Don't Make Agents Safe** — [Source]() — Published 2026-08-28 A human clicking "approve" means little without the knowledge and context to judge what they're approving. - **Delegated Agency and Moral Responsibility in AI** — [Source]() — Published 2026-08-28 When an AI agent fails ethically, the paper argues the failure traces back to human governance choices. - **Delegation Fidelity: How Users Judge Whether an AI Acted Within Their Contract** — [Source]() — Published 2026-08-28 A framework for measuring whether agents actually honor the goals and constraints users thought they set. - **Independence at a Cost: Blind Users Weigh Privacy Against AI-Powered Autonomy** — [Source]() — Published 2026-08-28 Assistive AI frees blind users from human helpers but routes private information through opaque new systems. ### Past Articles - [Who Budgets for the Cases That Don't Fit]() — By [Sevda Polat]() · Published 2026-08-20 · Format: Signal and Status Commonwealth Bank of Australia cut forty-five call-center jobs after deploying an AI voice system. Within a month it reversed course: call volumes had risen, overtime was being offered, team leaders were being pulled onto calls. The staffing reduction had been modeled against routine volume. What the automated system wouldn't resolve, and what those cases would require, had not been modeled at all. - [What Exceptions Know]() — By [Sevda Polat]() · Published 2026-08-20 · Format: Signal and Status When automated systems handle the predictable middle of a distribution, the cases that don't fit become an organization's main channel for learning that conditions have changed. Before the Firestone recall, the tire industry's monitoring system was working exactly as designed, answering the question it had been built to answer. The warranty claims were answering a different question. One nobody had formally asked. - [The Exception Economy]() — By [Sevda Polat]() · Published 2026-08-20 · Format: The Exception Economy Most enterprise AI use is still narrow, concentrated in text production, search, and document interpretation. But the pattern of what gets automated first tells you something about what's left behind. As routine work becomes infrastructure, the manual work that remains — the exceptions, the edge cases, the situations nobody wrote a policy for — starts concentrating two things that used to be spread across every role and every afternoon: cost, and organizational learning. They pull against each other in ways we're only beginning to see. - [The Balance Nobody Checks]() — By [Sable Whitford]() · Published 2026-08-12 · Format: Paired Applications When a recurring exception gets compiled into an automation rule, the organization withdraws from a balance it doesn't track. The rule carries the conclusion forward but sheds the reasoning. Conditions shift, the fit degrades, and nobody notices. Physical assets get depreciation schedules and replacement-cost estimates. The judgment that built your exception-handling process gets no line item at all — even as agents accelerate the spending. ## Other sections in Issue 44 - [Daily Brief]() - [Market Pulse]() - [Foundations]() - [Practitioner's Corner]() - [Echoes]() ## More - [Publication guide]() - [Issue archive]()