Notes.

Synthesizes how designer careers will transform in the AI era from 37 peer-reviewed papers. Identifies five structural changes: (1) roles shift from execution to direction and curation, (2) GenAI is a double-edged sword that both expands creativity and increases fixation, (3) de-skilling risk is particularly pronounced for juniors, (4) design judgment and handling uncertainty emerge as core competencies, (5) displacement effects are empirically confirmed in the freelance market.

Organizes industry efforts to close the gap between AI's actual capabilities and user expectations through UX, drawing on 33 sources across three streams: vendor guidelines (Google PAIR / Microsoft HAX / Apple HIG / IBM / OpenAI / Anthropic / Meta), public surveys (Pew / Stanford HAI) and consulting surveys (McKinsey / BCG / Deloitte / Gartner), and practitioner patterns.

A review of 32 academic sources on techniques for resolving through UX the divergence between what AI can actually do and what users expect (the expectation-capability gap). Organized into five areas: trust calibration, mental models, explainability, cognitive forcing functions, and expectation violation theory.

An industry overview of the relationship between design and craft, collected across seven domains: cultural policy, the luxury industry, the Maker Movement, design firms and technology companies, Japanese monozukuri, software craftsmanship, and craft in the age of AI. Includes source tracking and position assessment.

A cross-sectional comparison of the intersection between AX (Agentic Experience) and design, drawing on 109 sources: 36 academic and 73 industry. Three points of convergence between industry and academia are identified (agency distribution as the central problem, transparency alone being insufficient, and context setting the ceiling for accuracy), along with three points that industry omits but academia addresses (junior designers' learning pathways, loss of exploratory opportunities through efficiency optimization, and structural limitations of oversight UIs). While the academic standing of the term 'AX' itself remains unsettled, the underlying problem space is advancing rapidly under the rubric of human-agent interaction research.

Drawing on 73 industry sources, this note examines how the concept of AX has been received, defined, and implemented across industry from 2025 to 2026. Four structural patterns emerge. (1) Every vendor proclaims the shift 'from UX to AX,' yet definitions diverge (Maeda = decision surface, Nielsen = Intent Layer, Salesforce = bidirectional design, Apple/Google = declarative primitives). (2) Design principles converge on 'transparency, control, and intent-first,' but the granularity of intervention differs. (3) The gap between hype and reality is quantitatively documented (Gartner: 40% cancellation forecast; Forrester: true autonomy remains rare; McKinsey: only 6% are high performers). (4) Developer testimony reveals structural constraints (93% approval fatigue, multi-agent 15x cost, context quality as the decisive factor).

A review probing the academic underpinnings of 'From UX to AX' (Maeda 2026). As of 2026, peer-reviewed papers using the term AX itself barely exist, but adjacent research on human-agent interaction / AI delegation / agentic AI UX is surging, centered on CHI, CSCW, and DIS. Three structures emerge from 36 sources: (1) the design of agency distribution is the central problem (who controls what, and when); (2) the trade-off between trust and oversight (intermediate checking is optimal but does not guarantee full accuracy); (3) the gap between industry promotion and actual UX (demonstrated by an SR of 102 commercial agents plus testing with 31 users). Includes 7 review papers and 12 empirical studies from CHI/CSCW/DIS.

MCP (Model Context Protocol) has rapidly gained adoption as a common connectivity layer across design tools from 2025 to 2026. With the Figma MCP server (beta June 2025), Figma Config 2026 Connectors, and Claude Design's design system import capability, the agentic loop between design and code is closing. Two structural findings: (1) design system maturity determines the upper bound of agent output accuracy (officially recognized by Figma); (2) the best agents operate with the fewest tools (Vercel experiment: reducing from 15 to 1 tool raised success rate from 80% to 100% and improved speed by 3.5x). MCP is simultaneously a protocol for inter-tool communication and a mechanism that transforms design systems into an 'agent context layer.'

A single phrase — 'vibe coding' — coined by Karpathy in February 2025 became an industry term within a year. UI-generation agents such as v0, Bolt, Lovable, and Replit Agent form its infrastructure, yet the distinctions drawn by Willison ('vibe engineering') and Osmani ('AI-assisted engineering') mark the fault line of production quality. While prototyping has been compressed to hours, design judgment, testing, and review remain as irreducibly human processes, shifting the bottleneck from 'making' to 'deciding.' In design production, the ability to evaluate the quality of generated UI is a prerequisite for effective agent use.

In the first half of 2026, Figma, Adobe, Canva, and Anthropic simultaneously introduced agentic capabilities for design. All four companies espouse the principle that 'agents execute while humans judge,' yet they differ in the granularity of judgment and the timing of human intervention. Figma enables divergence and selection on the canvas; Adobe automates workflows across 60+ tools followed by review; Canva delivers a finished artifact from a single prompt for fine-tuning; Claude Design provides bidirectional code-design synchronization. The shared structural constraint is that the maturity of a team's design system sets the upper bound on agent output quality.