Shuichiro Ogawa
日本語

Notes

Research, experiments, and reflections on AI and design, published along with the process of making them.

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June 2026

  • The Genealogy of Generative AI Engineering and Its Assessment from a Design Perspective

    2026-06-30

    Organizes from primary sources the four-stage genealogy of engineering practices leveraging generative AI — from prompt engineering (2022) through context engineering (Lutke 2025-06) and harness engineering (Anthropic 2025-11 / OpenAI 2026-02) to loop engineering (Osmani 2026-06-07) — and assesses it along three axes where design should engage (context architecture, evaluation as design, and tensions at professional boundaries). Eval engineering is included as a cross-cutting practice. As the bottleneck shifts from 'writing' to 'judging' and 'looping,' the design profession is moving its center of gravity toward quality judgment, structuring of information environments, and definition of evaluation criteria.

  • The Economics of AI and Design — Reading the Structural Transformation of Design Labor Through Six Economic Theories

    2026-06-29

    A map note that cross-analyzes AI's impact on the design labor market through six theories: the task-based model (Acemoglu & Restrepo), job polarization (Autor), the economics of information goods (Shapiro & Varian), the economics of the creative economy (Caves / Throsby), platform economics (Rochet & Tirole), and Baumol's cost disease. It identifies the structural changes in design that each theory illuminates and contrasts them with empirical data (BLS statistics, DID analyses of the freelance market).

  • 'Design Is Not Craft' — Industry x Academia Roundtable

    2026-06-28

    A simultaneous roundtable on 'design ≠ craft' with three industry personas (firm/consulting/independent office) and three academic critics (theory/education/industry-career). Renders visible the industry side's triple deadlock of unbillable taste, human-hour model collapse, and practitioner reproduction breakdown alongside the academic side's arguments on the indispensability of embodied repetition and the historical contingency vs. conceptual necessity distinction, all as a divergence table. Records all rounds R1-R3.

  • The Generative Art Ecosystem: Industry Intelligence

    2026-06-28

    An industry research note organizing generative art's copyright landscape, market data, tools, and practitioner testimony across three source tiers

  • Structural Factors behind Designer Pay Stagnation — Academic Review (2026)

    2026-06-28

    Organizes the structural reasons why designer compensation stagnates relative to engineers, PMs, and data scientists, drawing on 34 peer-reviewed papers. Six structural factors: (A) incomplete professionalization (Abbott's jurisdiction theory / Larson's market closure); (B) the wage penalty on cultural creativity (Hwang 2014: technical CCI > cultural CCI); (C) lower earnings toward Throsby's 'inner circle' (Been et al. 2023: full Dutch administrative data); (D) normalized precarity (second-job rate 2x / 'wageless life' / flexploitation); (E) the commodification of design thinking eroding jurisdiction (Johansson-Sköldberg 2013); (F) AI deskilling diminishing bargaining power (Shukla CHI 2025 / Jiang CHI 2026: 54% report reduced influence).

  • Structural Factors behind Designer Pay Stagnation — Industry Trends (2026)

    2026-06-28

    Examines the structural reasons why designer compensation stagnates relative to engineers, PMs, and data scientists, drawing on BLS public statistics (T1: 18 items), industry surveys (T2: 18 items), and practitioner testimony (T3: 19 items) totaling 55 sources. Graphic designer median $61,300 is 46% of SWE's $133,080. Ten-year growth: GD +2%, SWE +15%, DS +34%. Structural factors: oversupply (bootcamp graduates 6,500-6,900/year), disproportionate layoffs (1.7x expected), measurement difficulty, C-suite absence, platform race to the bottom. McKinsey Design Index shows correlation with enterprise value but makes zero mention of designer compensation.

  • Designer Careers That Will Generate Value Over the Next Five Years: Industry Trends (2026)

    2026-06-28

    An analysis of designer career paths likely to generate value in the AI era, drawing on 47 sources across job market and compensation data (BLS/Glassdoor/LinkedIn), consulting surveys (McKinsey/BCG/WEF), vendor trends (Figma/Vercel), and design leader perspectives (Maeda/Zhuo/Nielsen/Spool). Growth areas: AI Experience Designer, Design Engineer, Content Designer. Contracting areas: Graphic Designer, UX Researcher (down 89% from peak hiring).

  • Designer Careers That Will Generate Value over the Next Five Years — Academic Review (2026)

    2026-06-28

    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.

  • The AI Expectation-Capability Gap and UX — Industry Trends (2026)

    2026-06-28

    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.

  • The AI Expectation-Capability Gap and UX — Academic Review (2026)

    2026-06-28

    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.

  • The Relationship Between Design and Craft: Industry Intelligence

    2026-06-28

    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.

  • AX and Design: A Cross-Comparison of Academic and Industry Perspectives

    2026-06-27

    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.

  • Industry Discourse on Agentic Experience (AX): What Vendors, Consultants, and Practitioners Are Saying

    2026-06-27

    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).

  • The Academic Foundations of Agentic Experience (AX): Where Human-Agent Interaction Research Stands

    2026-06-27

    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 and Design Systems: The Infrastructure Layer for Agent Integration

    2026-06-27

    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.'

  • Vibe Coding and UI Agents: Natural-Language Production and Its Boundaries

    2026-06-27

    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.

  • Design Agent Tools in 2026: The Current State of Autonomous Production

    2026-06-27

    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.

  • Ten Generative AI Industry Topics Ranked by Impact on Design Professions and Careers (June 2026)

    2026-06-19

    An analytical note ranking ten generative AI industry topics as of June 2026 by their degree of structural transformation of design professions and careers (roles, pricing, employment, organizational structure). The four evaluation axes are: (1) displacement of core tasks, (2) pricing power, billing, and name recognition, (3) employment, junior pipeline, and organizational change, and (4) structural certainty of impact. Top-ranked topics include generative UI / labor commoditization / vibe coding / agentic workflows / junior pipeline erosion; lower-ranked include image and video generation / regulation / tool market / enterprise adoption. Rankings reflect structural career change rather than tool novelty. Pricing and name-recognition dynamics connect to democratization-recommodification-paradox / design-pricing-vs-ai-commoditization / the gaps in livelihood viability and initial entry; role discussions connect to designer-role-ai-roundtable / ai-design-near-term-flashpoints. Most industry figures originate from vendor or listicle sources and require primary verification; unconfirmed claims are explicitly marked [unverified].

  • AI Flashpoints Certain to Arrive in the Next 1-3 Years — Three Design Leads' Near-Term Flashpoints (2026-2029)

    2026-06-09

    A debate log (hub) in which the leads of a design firm, a strategy consultancy, and an independent studio (business-only) independently enumerated the AI flashpoints 'almost certain to ignite within 1-3 years' (R1), cross-rebutted one another (R2), conceded the danger of deliverable-based pricing under a client counterargument and retracted the R2 claims (R3), and developed survival prescriptions for offices carrying large numbers of good-enough designers (R4). Cross-cutting findings are extracted into three atomic notes. Figures carry caveats as position talk and correlation-not-causation.

  • Does AI Make Smart People Smarter and Everyone Else Shallower? — A Two-Lineage Debate Between Industry and Academia

    2026-06-09

    Debates the Zenn article (pdfractal) 'Does AI make smart people smarter and the less smart shallower?' across two lineages: the academic lineage (peer-reviewed research) and the industry lineage (the trend reports practitioners actually read, from public institutions, research firms, and consultancies). The opposition is not 'academia vs. industry' but 'the cognitive science camp (deterioration, widening) vs. the industry report camp (= relaying labor economics = compression, leveling-up)'. The article stands on the minority cognitive-science side. The key to reconciliation is the article's §6 'inside/outside the boundary' = metacognition, which shakes hands with the jagged frontier and with the warning signals industry reports themselves emit (trough of disillusionment, literacy deficits).

  • The Democratization-Recommodification Paradox: How AI Turns 'Anyone Can Do It' into 'No One Can Charge for It'

    2026-06-09

    When AI democratizes exploration and production (expanding the base of participants), the relative scarcity that mid-tier practitioners relied upon as their basis for billing evaporates. Scarcity migrates to 'the handful at the top whom AI cannot replicate,' reinforcing rents (enclosure and symbolic capital) exclusively at the upper tier. Because democratization lowers barriers only at the bottom without disturbing the closure mechanisms at the top, inequality widens asymmetrically. This is the paradox in which democratization destroys the livelihoods of its own beneficiaries.

  • AI Commoditization Resilience of Design Billing Models — Output-Based Billing Is Most Vulnerable, and Retainer Stickiness Does Not Mean High Margins

    2026-06-09

    AI resilience of billing models ranks 'outcome-based > time/human-hour > output (deliverable)' (F and C diverge on relative vulnerability of human-hour vs. output billing). The moment a deliverable is defined as a thing, its price slides toward AI's marginal cost. Retainer arrangements are hard to replace, but that means 'hard to fire,' not 'billable at a premium' --- switching cost ≠ gross margin. Profitability requires vertical specialization with named referrals plus offloading operations to cheaper headcount/AI (i.e., headcount reduction as the funding source). The 'gatekeeper business' splits into upper tier (policy design = high-value engagements) and lower tier (inspection = floor pricing) and collapses.

  • The Role of Designers in the AI Era — Industry x Academia Roundtable (Business-Only Revision)

    2026-06-07

    A joint discussion record in which three industry personas — design firm / strategy consulting design lead / independent design office — argue exclusively from three perspectives: profitability, billability, and AI efficiency gains (zero academic references). Contrasts the industry's cost-benefit calculus with the academic's theoretical examination.

  • AI Adoption in the Design Industry: Perspectives from Policy, Data, and Product Developers (2026)

    2026-06-07

    An integrated summary of AI adoption in the design and creative industries, compiled from three source tiers: official bodies and primary data (labor statistics, adoption rates, copyright and standards), the factual portions of think tank and consulting reports (with positional advocacy stripped), and first-person testimony from product developers (Figma, Adobe, Canva, Vercel).

  • AI in the Design Industry: An Academic Review (2026)

    2026-06-07

    An integrative summary of 37 academic publications on AI/generative AI/LLM adoption in design (UX/UI, product, graphic, education), collected through a lightweight scoping review and organized into seven thematic clusters: co-creation, UI generation, evaluation automation, creativity effects, role transformation, and education.

  • Recent AI Applications in the Design Context (2026)

    2026-06-07

    A survey report on recent AI applications in the design domain as of 2026 (generative UI, agentic AI, Figma, synthetic users, accessibility)