JHDD UI UX Report — 2026.10.11

Larry Tesler, a foundational figure in UX, famously drove a car whose license plate read NO MODES.

The prevailing narratives around artificial intelligence often suggest a simplification of user experience, yet the reality reveals a redistribution of cognitive load and systemic complexity. This shift places new, often unseen, demands on users and design systems, requiring them to manage implicit contexts and unseen processes. The promise of an effortless AI interaction frequently masks an underlying structure where complexity has merely relocated, not vanished.

The New Siri’s greatest strength is identified as relevant personal context, yet the interaction patterns provided for users to manage or even understand this context remain largely implicit. This creates an unacknowledged cognitive burden, as evidenced by Claude power users who struggle to manage and locate their context libraries effectively. The interface often fails to provide clear affordances for editing, finding, or even knowing the extent of the contextual data influencing the AI’s responses. Mainstream industry opinion frequently frames AI as an inherent reducer of friction, simplifying user journeys. However, this view overlooks the new “meta-work” users must perform: constructing mental models of an AI’s understanding, debugging its misinterpretations, and implicitly curating its operational environment without a clear design system for doing so. This shifted burden on the user’s mental model represents a significant, often unmeasured, usability challenge.

Larry Tesler’s insight, that complexity never truly leaves the system but merely changes hands, provides a critical framework for understanding AI’s current impact on UX. Tesler, who dedicated his career at Xerox PARC and Apple to deleting confusing states, understood the user’s need for clear interaction models. AI-assisted interfaces often remove explicit controls and feedback, creating an illusion of simplicity while relocating the underlying complexity to the AI’s opaque algorithms or the user’s unassisted interpretation. This contradicts the prevailing narrative that AI inherently simplifies user interactions. It instead introduces new accessibility challenges, as users with varying cognitive styles or technical proficiencies must now infer system states and manage complex, invisible data structures. Within two years, product teams will be compelled to integrate explicit design system components for AI explainability and context management, offering users greater transparency and control over their personalized AI environments.

The resistance to this explicit complexity management stems from multiple directions. One significant force is the commercial pressure to demonstrate immediate efficiency gains through AI, often leading to rapid deployments that prioritize perceived simplicity over true user understanding. This mindset is reflected in the debate surrounding synthetic users in research. While some defend synthetic users as a pragmatic tool for efficiency, their use can inadvertently detach design decisions from the genuine complexities faced by diverse human users, thus perpetuating interfaces that abstract rather than clarify. This efficiency-driven approach often sidelines comprehensive user research into the actual cognitive load imposed by implicit AI interactions.

UI UX professionals must adapt their user research methodologies to uncover the hidden complexities that AI introduces. This involves moving beyond surface-level task completion metrics to specifically observe and document the mental effort users expend when an AI’s behavior is opaque or inconsistent. This week, a professional should conduct a qualitative study focused on how users attempt to “correct” or “guide” an AI-driven feature, specifically asking what information they wish they had to achieve better outcomes, and how their current mental model of the AI forms without explicit guidance.

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Curated References