# Kevin M. Storer, Ph.D. > Dr. Kevin M. Storer helps organizations navigate the structural shock of AI-driven work. Explore research, speaking, and advisory services. Public Ghost content for AI and LLM tooling. This file includes a bounded export of public pages first, then recent public posts. Append `.md` to any post or page URL to get the content in Markdown (for example, `/example-post.md`). ## Pages ### Home URL: https://kevinstorer.com/home/ Last updated: 2026-08-10T13:35:25.000Z # AI modernization isn't a technical problem. It's a human one. AI modernization initiatives are being fundamentally misdiagnosed as a software update rather than a structural shock to the meaning of work. By managing this transition as a purely economic or technical issue, leadership overlooks critical human infrastructure required to sustain it: worker well-being, a definitive predictor of organizational productivity. But ignoring the human impacts of AI-driven work isn't a future policy debate—it is the immediate operational threat that will dictate the success or failure of AI investments. [Book Kevin to Speak](https://kevinstorer.com/speaking) [Read the Newsletter](#/portal/signup) Research Featured In: ![Featured in CNN, NPR, Bloomberg, Newsweek, and Business Insider](https://storage.ghost.io/c/5e/9d/5e9d7dee-0c07-4c4e-b1fe-effe6ea36348/content/images/2025/12/nomargin_onerow_trustbar_grayscale.png) ![Kevin M. Storer, Ph.D.](https://storage.ghost.io/c/5e/9d/5e9d7dee-0c07-4c4e-b1fe-effe6ea36348/content/images/2026/05/Storer-Headshot-4x5.png) ![Kevin M. Storer, Ph.D.](https://storage.ghost.io/c/5e/9d/5e9d7dee-0c07-4c4e-b1fe-effe6ea36348/content/images/2026/05/Storer-Headshot-1x1.png) ## The Human Infrastructure of AI **Kevin M. Storer, Ph.D.** is a writer, speaker, and public scholar studying the profound impacts of AI-driven work on worker well-being. Grounded in established organizational psychology and his work as the lead author of [the world's first validated model of how organizations succeed with AI-driven work](https://cloud.google.com/blog/products/ai-machine-learning/introducing-doras-inaugural-ai-capabilities-model?ref=kevinstorer.com), Kevin helps leaders maximize the ROI of their AI modernization initiatives by centering worker well-being—not as a standard corporate wellness perk—but as a definitive predictor of productivity, motivation, and retention. [Read Kevin's Full Story](https://kevinstorer.com/about) ## A Crisis of Attribution AI-related job displacement is rarely as extreme as obsolescence and unemployment. Even when workers retain their jobs, AI acts as a structural wedge, introducing a layer of abstraction—opaque reasoning, passive processing, and interpretive pushback—that distances workers from their output, creating a "crisis of attribution" where it becomes increasingly difficult to claim ownership over the final work product. As attribution fades, workers lose the feelings of individuation, contribution, self-connection, and unification that are vital to their well-being. Left unmanaged, this psychological displacement threatens not only the foundations of organizational productivity but also the broader capacity for human flourishing. Keynotes ### Public Scholarship & Speaking Guiding audiences through the organizational and existential impacts of AI-driven work. #### Protecting Well-Being in AI-Driven Work Tailored for organizational leaders. AI mandates often ignore the crisis of attribution and its hidden toll on worker well-being. Learn how to implement the structural policies required to intervene before this shift derails your top talent and your bottom line. #### Finding Humanity in a Post-Work World A visionary exploration of the existential crisis that awaits when human labor is no longer required. Even if the economic problems of AI-related job displacement are solved, we must reorient our sense of well-being around sources of meaning other than our jobs. [View Speaker Kit & Topics](https://kevinstorer.com/speaking) Policy & Culture ### Executive Advisory Successful AI modernization requires more than enterprise licenses; it requires deliberate shifts in organizational policy and culture. I provide expert guidance to leaders navigating this moment of intense transition, helping them establish policies that center worker well-being and maximize AI ROI. [Explore Advisory Services](https://kevinstorer.com/contact) Empirical Data ### Applied Research My advisory and keynotes are informed by over a decade of empirical research. I partner with select organizations on dedicated research engagements to diagnose the human friction of AI modernization, offering specialized expertise in both internal workplace diagnostics and third-party B2B research. [Explore Research Services](https://kevinstorer.com/research) ### Speaking URL: https://kevinstorer.com/speaking/ Last updated: 2026-08-10T13:42:29.000Z # Decode the human stakes of AI modernization. Kevin's keynotes cut through the technical hype to address the profound human transformations required by AI-driven work. By reframing AI as a structural threat to the meaning of work, he equips audiences with the insights needed to center worker well-being, maximize ROI of AI modernization, and protect the broader capacity for human flourishing. [Check Availability](https://kevinstorer.com/contact) [View Speaker Kit](https://kevinstorer.com/speaker-kit/) Kevin Has Been Invited to Speak By: ![Logos of NSF, Google, IEEE, UC Irvine, Clemson University, and Computing Research Association](https://storage.ghost.io/c/5e/9d/5e9d7dee-0c07-4c4e-b1fe-effe6ea36348/content/images/2026/08/speaking_grayscale_onerow.png) ## Keynotes Tailored for executive offsites, conferences, and think-tanks. The Immediate Horizon ### Protecting Well-Being in AI-Driven Work Tailored for organizational leaders, CHROs, and transformation executives. AI mandates often ignore the "crisis of attribution"—the psychological distance created when AI acts as a structural wedge between workers and their final work product. By introducing opaque reasoning, passive processing, and interpretive pushback into workflows, AI makes it increasingly difficult for top talent to claim ownership over their output. When this ability to attribute the final product to their own efforts fades, daily work ceases to generate feelings of individuation, contribution, self-connection, and unification. Because these psychological dimensions are the core foundation of worker well-being—a definitive predictor of organizational productivity—this psychological displacement is an immediate threat to operational and cultural health. This keynote reveals how to enact structural policy interventions to protect your human infrastructure before it derails your top talent and your bottom line. #### Audience Takeaways **Diagnose Friction**Understand why the psychological displacement caused by AI is an immediate threat to operations and culture, not a future macroeconomic policy issue. **Core Conditions**Discover the essential organizational conditions required to sustain human infrastructure alongside technical productivity gains from AI. **Actionable Policy**Learn specific organizational policy and culture strategies to preserve human meaning and attribution, moving beyond corporate wellness perks. The Far Horizon ### Finding Humanity in a Post-Work World A visionary exploration designed for broad audiences, think-tanks, and industry-shaping conferences. The current conversation around AI displacement is focused almost exclusively on economics. However, modern psychological well-being is inextricably linked to the acts of producing and contributing. As AI integration scales toward extreme displacement, it removes the vital friction and mastery that individuals rely on to build self-efficacy and a sense of value. What happens to the broader capacity for human flourishing when labor is no longer required? This keynote confronts the existential crisis that awaits a post-work society. Solving the financial equation of obsolescence does not resolve the psychological void left behind. When the structure of daily work collapses, individuals face a profound loss of individuation and social cohesion. This presentation challenges audiences to look beyond state-level economic interventions like universal basic income and ask a harder structural question: How must individuals and societies proactively reorient identity, well-being, and purpose around sources of meaning other than traditional employment? #### Audience Takeaways **Beyond Wages**Shift the paradigm of AI-related job displacement from financial survival to securing the capacity for human flourishing. **Meaning & Work**Understand how deeply modern psychological well-being is tied to the act of "producing" and contributing to society. **New Framework**Explore how individuals and societies can build meaning and community outside the structures of employment. ## Ready to bring these insights to your stage? Check availability for your upcoming conference, executive offsite, or corporate event. [Check Availability](https://kevinstorer.com/contact) [View Speaker Kit](https://kevinstorer.com/speaker-kit/) ### Contact URL: https://kevinstorer.com/contact/ Last updated: 2026-08-12T09:33:37.000Z # Let's move into the future of work together. If you're experiencing the challenges of AI-driven work and think I can help—whether that's a keynote for your conference, a diagnostic for your organization, a bespoke research partnership, or a conversation for your podcast—I'd like to hear from you. *I work through a fully insured US-based LLC and am a preferred vendor at Alphabet.* ### Contact If you prefer to skip the form, feel free to email me directly at: [kevin@kevinstorer.com](mailto:kevin@kevinstorer.com) ### Connect If you would like to stay in touch, connect with me on: - [LinkedIn](https://www.linkedin.com/in/kevinstorer/?ref=kevinstorer.com) - [Bluesky](https://bsky.app/profile/kevinstorer.com?ref=kevinstorer.com) First Name \* Last Name \* Work Email \* Organization / Event How can I help you? \* Select an option... Keynote Speaking & Events Executive Advisory & Sprints Applied Research / Contracting Media & Press Inquiry Other Tell me about your challenge or event \* Submit Inquiry ### Scholarship URL: https://kevinstorer.com/scholarship/ Last updated: 2026-08-10T12:49:15.000Z # Understanding well-being in the age of AI-driven work. Examining how AI reshapes the relationship between workers and their outputs—exploring the immediate consequences for organizational health and the long-term stakes for the future of society. From weekly executive insights to peer-reviewed empirical research. ## The Newsletter Weekly analysis on the state of AI-driven work and worker well-being—what I'm seeing in the field, what I think leadership is missing, and what I think it means for the future of human flourishing. [ Latest Post Loading... ](https://kevinstorer.com/blog) [Read the Full Archive](https://kevinstorer.com/blog) ## Public Scholarship Empirical research commissioned and published by leading technology organizations. This work has been downloaded over 100,000 times by industry decision-makers. Ameer Abbas, Derek DeBellis, et al. (incl. **Kevin M. Storer**). *DORA AI Capabilities Model*. Google Cloud, November 2025\. — [Read Report](https://dora.dev/dora-aicmr/?ref=kevinstorer.com) Abey Stenman, Bre Arder, **Kevin M. Storer**, Katharine Norwood. *Understanding Builder Intent in the AI Era*. Google Cloud, October 2025\. — [Read Report](https://dora.dev/insights/builder-mindset/?ref=kevinstorer.com) **Kevin M. Storer**, Derek DeBellis. *Introducing the DORA AI Capabilities Model: 7 Keys to Succeeding in AI-Assisted Software Development*. Google Cloud, September 2025\. — [Read Report](https://cloud.google.com/blog/products/ai-machine-learning/introducing-doras-inaugural-ai-capabilities-model/?ref=kevinstorer.com) Derek DeBellis, **Kevin M. Storer**, et al. *DORA 2025 State of AI-Assisted Software Development Report*. Google Cloud, September 2025\. — [Read Report](https://cloud.google.com/resources/content/2025-dora-ai-assisted-software-development-report/?ref=kevinstorer.com) Sarah D'Angelo, Ambar Murillo, Sarah Inman, **Kevin M. Storer**. *Choosing Measurement Frameworks to Fit Your Organizational Goals*. Google Cloud, September 2025\. — [Read Report](https://dora.dev/research/2025/measurement-frameworks/?ref=kevinstorer.com) **Kevin M. Storer**. *Concerns Beyond the Accuracy of AI Output*. Google Cloud, July 2025\. — [Read Report](https://dora.dev/research/ai/concerns-beyond-accuracy-of-ai-output/?ref=kevinstorer.com) Derek DeBellis, **Kevin M. Storer**, et al. *DORA Impact of Generative AI in Software Development*. Google Cloud, March 2025\. — [Read Report](https://cloud.google.com/resources/content/dora-impact-of-gen-ai-software-development/?ref=kevinstorer.com) **Kevin M. Storer**. *How Gen AI Affects the Value of Development Work*. Google Cloud, December 2024\. — [Read Report](https://dora.dev/research/2024/value-of-development-work/?ref=kevinstorer.com) Derek DeBellis, **Kevin M. Storer**, et al. *2024 Accelerate State of DevOps Report*. Google Cloud, November 2024\. — [Read Report](https://dora.dev/research/2024/dora-report/?ref=kevinstorer.com) **Kevin M. Storer**, Derek DeBellis, Sarah D'Angelo, Adam Brown. *Fostering Developers' Trust in Generative Artificial Intelligence*. Google Cloud, September 2024\. — [Read Report](https://dora.dev/research/2024/trust-in-ai/?ref=kevinstorer.com) Derek DeBellis, **Kevin M. Storer**. *AI in the Workplace: Adoption and Impact*. Google Cloud, August 2024\. — [Read Report](https://dora.dev/research/2024/ai-preview/?ref=kevinstorer.com) Derek DeBellis, et al. (incl. **Kevin M. Storer**). *Accelerate State of DevOps Report 2023*. Google Cloud, September 2023\. — [Read Report](https://dora.dev/research/2023/dora-report/?ref=kevinstorer.com) ## Academic Publications Peer-reviewed research published in top computing venues including ACM CHI, DIS, ASSETS, SenSys, and ACM Transactions on Accessible Computing. [View Full Publication History on Google Scholar](https://scholar.google.com/citations?user=pdBPaHQAAAAJ&hl=en&oi=ao&ref=kevinstorer.com) ### About URL: https://kevinstorer.com/about/ Last updated: 2026-08-10T12:46:11.000Z # Tracing the human thread through a technical world. Kevin bridges the gap between rigorous empirical data and existential foresight. His public scholarship and executive advisory work are united by a singular mission: investigating and protecting worker well-being in the age of AI-driven work. Whether guiding enterprise leadership in sustaining their human infrastructure or addressing the long-term psychological displacement of human labor, Kevin ensures that as society transitions into an era of unprecedented technological abstraction, it does not inadvertently abstract away the capacity for human flourishing. [Download Full CV](https://kevinstorer.com/full-cv) [Connect on LinkedIn](https://www.linkedin.com/in/kevinstorer?ref=kevinstorer.com) The Scholar's Journey ## Kevin M. Storer, Ph.D. Dr. Kevin M. Storer is a public scholar and executive advisor specializing in organizational policy and culture. Beginning his career as a software developer, Kevin recognized the inherent potential of coding to drive meaningful change. Yet, within the corporate setting, he encountered a profound structural disconnect: a vast, isolating distance between writing lines of code and witnessing their actual impact. This distance stripped the daily labor of its underlying meaning and established the core friction that would define his career—the gap between technical execution and human contribution. This friction sparked a pivotal realization during his early research designing tools for embedded devices. He discovered that technical workflows only improve when the human experience is deliberately centered. Pursuing this human-first imperative, Kevin immersed himself in deep ethnographic research and critical scholarship at the University of California, Irvine. He rigorously studied knowledge workers, software developers, dyadic collaboration, and organizational culture, honing the methodologies required to measure invisible structural barriers and human friction in professional work. Bringing this critical lens to Google, Kevin applied his expertise across Developer Experience teams. He led research that set strategy and informed critical design decisions for widely loved products built for third-party software developers. Recognizing his specialized authority in understanding enterprise developers, Google elevated Kevin into a public-facing thought leadership role within the DORA research team. Here, his research uncovered a critical phenomenon: [the integration of AI fundamentally alters how software developers ascribe value and meaning to their work](https://dora.dev/insights/value-of-development-work/?ref=kevinstorer.com). AI abstraction was actively scaling the exact disconnect he experienced early in his career into an enterprise-wide crisis of attribution. Charged with guiding the digital transformations of the world's largest companies, Kevin advised a readership of tens of thousands of tech executives annually on the realities of AI-driven work. Today, as an independent advisor, his practice focuses entirely on how AI alters the meaning of work and the organizational interventions leadership must enact in response. Moving far beyond software implementation, Kevin architects the deliberate, top-down organizational policy shifts required to protect worker well-being—framing it as both a definitive predictor of organizational productivity and a foundational prerequisite for human flourishing. #### Credentials & Highlights **Validated Empirical Model**Lead author of the inaugural [DORA AI Capabilities Model](https://cloud.google.com/blog/products/ai-machine-learning/introducing-doras-inaugural-ai-capabilities-model?ref=kevinstorer.com) utilized by top AI providers to drive high-touch client transformations. **10+ Years Applied Research**Extensive research in Human-Computer Interaction from hardware evaluation to enterprise B2B workflows. **Global Media Footprint**Downloaded over 100,000 times and cited in 100+ international news outlets including CNN, NPR, and Newsweek. ### Selected Press - 2025 CNN "Google says 90% of tech workers are now using AI at work." By Lisa Eadicicco. - 2025 NPR "Tech CEOs say the era of 'code by AI' is here. Some software developers are skeptical." By Huo Jingnan. - 2025 Business Insider "Google's senior director of product explains how software engineering jobs are changing in the AI era." By Ana Altchek. - 2025 Newsweek "Despite Using AI, Only a Quarter of Tech Workers Trust It." By Suzanne Blake. - 2025 Observer "Google Study Shows A.I. Writes Code, But Developers Still Don’t Fully Trust It." By Victor Dey. ### Selected Research & Reports - 2025 Introducing the DORA AI Capabilities Model: 7 Keys to succeeding in AI-assisted software development. Kevin M. Storer, Derek DeBellis. *Google Cloud.* - 2025 DORA 2025 State of AI-assisted Software Development Report. Derek DeBellis, Kevin M. Storer, et al. *Google Cloud.* - 2024 How Gen AI Affects the Value of Development Work. Kevin M. Storer. *Google Cloud.* - 2021 "It's Just Everything Outside of the IDE that's the Problem": Information Seeking by Software Developers with Visual Impairments. Kevin M. Storer, H. Sampath, M.A. Merrick. *ACM SIGCHI Conference on Human Factors in Computing Systems.* ## Three Guiding Principles of my Work Investigation ### Empirical Rigor Every insight shared is built on a foundation of rigorous research. I prioritize understanding the ground-truth reality of the worker over standardized metrics, using data to understand the foundational prerequisites for well-being, productivity, and AI ROI. Translation ### Executive Translation Academic research is ineffective if it cannot be operationalized. I specialize in translating complex sociological and psychological data into deliberate, top-down policy interventions for senior executives and transformation teams. Foresight ### Existential Preparedness Beyond immediate organizational implications, my scholarship addresses problems on the far horizon. I am committed to direct, honest, and proactive conversations about how society constructs identity, maintains social cohesion, and finds fulfillment when AI distances humans from the products of their work. ## Looking for specific research capabilities? If you are a recruiter, agency, or enterprise team looking to partner on a targeted applied research contract, view my capabilities and engagement models. [View Research Capabilities](https://kevinstorer.com/research) ### Speaker Kit URL: https://kevinstorer.com/speaker-kit/ Last updated: 2026-08-10T12:57:10.000Z [Download as PDF](javascript:void%280%29;) ## Kevin M. Storer, Ph.D. Speaker Kit [speaking@kevinstorer.com](mailto:speaking@kevinstorer.com) ## Bio 50 Words Dr. Kevin M. Storer is a researcher, speaker, and leading expert on the social dimensions of AI-driven work. Published widely in top computing venues, his research has been downloaded over 100,000 times and cited by over 100 international news outlets. He provides research, advisory, and thought leadership services to leading public and private sector organizations. 100 Words Dr. Kevin M. Storer is a researcher, speaker, and leading expert on the social dimensions of AI-driven work. He is widely published in top computing venues on the subjects of social computing, software development, and system design. His public scholarship on AI-driven work has been downloaded over 100,000 times and cited by over 100 international news outlets including CNN, NPR, Bloomberg, and TechCrunch. As former Senior Researcher at Google and Ethnographic Lead of their DORA Research Team, he developed the theoretical framework for the world's first validated model of the conditions that determine success in AI-driven work. He now provides research, advisory, and thought leadership services to leading public and private sector organizations, including as a preferred consultant at Alphabet and invited member of highly selective international research consortia at the National Science Foundation and the Schloss Dagstuhl - Leibniz Center for Informatics. 250 Words Dr. Kevin M. Storer is a researcher, speaker, and leading expert on the social dimensions of AI-driven work. An ethnographer and critical scholar, he has gathered hundreds of hours of data from workers navigating AI adoption, studying how AI reshapes identity, trust, and labor relations within individuals, teams, and organizations. That research grounds a broader and increasingly urgent project: understanding what happens to societies built around work when work itself fundamentally changes, and guiding people through that transition with clarity and care. He is widely published in top computing venues on the subjects of social computing, software development, and system design. His public scholarship on AI-driven work has been downloaded over 100,000 times and cited by over 100 international news outlets including CNN, NPR, Business Insider, Bloomberg, Newsweek, and TechCrunch. As former Senior Researcher at Google and Ethnographic Lead of their DORA Research Team, he developed the theoretical framework for the world's first validated model of the conditions that determine success in AI-driven work. Kevin now provides research, advisory, and thought leadership services to leading public and private sector organizations, including as a preferred consultant at Alphabet and invited member of highly selective international research consortia at the National Science Foundation and the Schloss Dagstuhl - Leibniz Center for Informatics, convening fewer than 50 researchers worldwide to shape policy, governance, and research agendas for AI-driven work. He holds a Ph.D. in Informatics from the University of California, Irvine. ## Introduction Script For the emcee or session host: "Our next speaker is Dr. Kevin M. Storer, a researcher and leading expert on the social dimensions of AI-driven work. Published widely in top computing venues, his research has been downloaded over 100,000 times and cited by CNN, NPR, Bloomberg, and over 100 other international news outlets. He provides research and advisory to leading public and private sector organizations, is a preferred consultant at Alphabet, and has been invited to highly selective international research consortia at the National Science Foundation and the Schloss Dagstuhl - Leibniz Center for Informatics, convening fewer than 50 researchers worldwide to shape AI policy and governance. Please welcome Dr. Kevin M. Storer." ## Headshots Available in two formats. For high-resolution files, contact [speaking@kevinstorer.com](mailto:speaking@kevinstorer.com). ![Kevin M. Storer, Ph.D. headshot, square format](https://storage.ghost.io/c/5e/9d/5e9d7dee-0c07-4c4e-b1fe-effe6ea36348/content/images/2026/05/Storer-Headshot-1x1.png) 1:1 (Square) ![Kevin M. Storer, Ph.D. headshot, portrait format](https://storage.ghost.io/c/5e/9d/5e9d7dee-0c07-4c4e-b1fe-effe6ea36348/content/images/2026/05/Storer-Headshot-4x5.png) 4:5 (Portrait) ## AV & Technical Requirements | Microphone | Hands-free (lavalier or headset) | | ------------------- | --------------------------------------------------------------------------- | | Presentation | Own laptop, HDMI connection | | Slide format | Google Slides | | Stage preference | Flexible (podium, open stage, or seated) | | Monitor | Confidence monitor preferred | | Recording/streaming | No restrictions | | Accessibility | Happy to provide slides in advance for captioning and/or ASL interpretation | ### Advisory URL: https://kevinstorer.com/advisory/ Last updated: 2026-08-10T12:08:10.000Z # Expert advisory for managing the human infrastructure of AI-driven work. While the mandate to adopt AI is often treated as a standard software rollout, the reality is that AI-driven work fundamentally disrupts how top talent—from software developers to senior management—finds meaning in their work. My advisory practice steps directly into this friction point. Moving far beyond standard corporate wellness perks, I partner with executive leadership to design the deliberate, top-down structural shifts necessary to sustain the human infrastructure required to secure the ROI of AI modernization initiatives. [Schedule a Discovery Call](https://kevinstorer.com/contact) ## The Advisory Practice The conventional consulting approach to AI focuses on change management and prompt engineering. My advisory practice addresses the human roots of AI modernization failure: the crisis of attribution that emerges when AI distances workers from their output. I partner with senior executives and transformation leads to diagnose and repair the human infrastructure that AI disrupts. Targeted Assessment ### Executive Diagnostic Before organizations can enact effective structural shifts, leadership must establish an accurate baseline of their human infrastructure. I conduct targeted assessments to understand how AI modernization initiatives are impacting worker well-being. Moving past standard employee satisfaction surveys, these diagnostics uncover the ground-truth realities of how AI is altering the daily experience of work, identifying hidden friction, and providing the empirical foundation to design precise policy interventions. #### Primary Focus: - **Baseline Assessment:** Establish the current state of worker well-being, motivation, and productivity within affected teams. - **Identify Hidden Friction:** Surface the ground-truth realities and cultural disruptions that employee satisfaction surveys miss. - **Data-Driven Decisions:** Deliver the insights necessary to inform deliberate shifts in organizational policy and culture. Structural Intervention ### Policy Re-Architecture When AI introduces a structural wedge into daily routines, traditional metrics for evaluation and contribution begin to fail. To secure the ROI of AI modernization, organizations must realign their operational structures with the realities of AI-driven work. I partner with leadership to audit existing policies and redesign them for this new era of work. Through targeted structural shifts, I help organizations modernize while protecting the worker well-being that drives productivity and determines the bottom line. #### Primary Focus: - **Policy Audit & Redesign:** Evaluate current operational frameworks and update them to support the integration of AI-driven workflows. - **Structural Alignment:** Restructure performance expectations and team norms to actively prevent a crisis of attribution. - **Protecting the Bottom Line:** Implement the interventions necessary to protect worker well-being as a definitive predictor of productivity. Roadmap Alignment ### Strategic Planning The disruption of organizational norms extends far beyond their initial AI modernization rollout. As AI is integrated it to work, it continuously alters the foundational relationship between top talent and their work product, impacting well-being. I provide strategic counsel to align multi-year AI roadmaps with the organization's human infrastructure. By anticipating how AI integration will impact daily work, I help leadership design long-term strategies to sustain worker well-being and productivity. #### Primary Focus: - **Strategic Alignment:** Map human infrastructure requirements directly onto ongoing technical rollout schedules. - **Proactive Mitigation:** Identify and neutralize emerging crises of attribution before they can derail future phases of modernization. - **Long-Term Resilience:** Secure worker well-being as a definitive, sustained predictor of high performance and operational health. ## The Engagement Model My advisory services are designed for high-impact, low-friction intervention. I do not embed for months or sell bloated implementation teams. I provide the expert diagnostics, the structural frameworks, and the precise guidance leadership teams need to execute top-down policy shifts. Intensive ### Executive Sprints Half-day or multi-day intensive strategy sessions with leadership teams. These high-bandwidth sprints are designed to quickly align executives on human infrastructure requirements, audit immediate policy risks, or rapidly map out strategic roadmaps. Project-Based ### Scoped Engagements Distinct, time-bound engagements focused on a specific organizational challenge. Whether assessing the state of the organizaiton or redesigning a specific set of policies, these bespoke projects have defined timelines and specific deliverables. Ongoing Access ### Advisory Retainers Retained advisory access for senior executives and transformation leads navigating complex AI modernization initiatives. This model provides dedicated expert counsel on the sociotechnical aspects of AI-driven work, like AI's impacts on worker well-being, team cohesion, and organizational culture, which determine productivity and overall ROI on AI investments. ## Ready to build an organization where AI works? Partner with me for retained advisory, scoped projects, or executive sprints. [Schedule a Discovery Call](https://kevinstorer.com/contact) ### Research URL: https://kevinstorer.com/research/ Last updated: 2026-08-10T13:01:27.000Z # Establishing the ground truth of AI-driven work. To build resilient AI workflows, organizations need ground-truth data, not assumptions. Drawing on over a decade of experience designing rigorous empirical research programs, I partner with enterprise leadership, innovation labs, and agencies to investigate the human impacts of AI-driven work. [Abbreviated CV](https://kevinstorer.com/one-page-cv) [Discuss a Contract](https://kevinstorer.com/contact) ## Applied Research Capabilities Drawing on a Ph.D. in Informatics, specialized expertise in Human-AI Interaction, and extensive experience guiding organizational policy for leading technology companies, I bring academic rigor and clarity to highly-complex B2B and organizational challenges. Internal Focus ### Organizational Diagnostics Establishing a ground-truth baseline of how employees, from software developers to senior management, are integrating AI tools into their daily work. This involves deep ethnographic evaluation of internal processes to identify friction that threatens the success of AI modernization initiatives. #### Core Methodologies: - Deep Contextual Interviews - Longitudinal Diary Studies - Workflow Shadowing & Ethnography - Thematic Synthesis for Executives External Focus ### B2B Product & Market Research Evaluating how B2B enterprise customers are responding to AI-augmented products. From early-stage foundational discovery to late-stage evaluation, I design rigorous research programs that reveal how AI tools impact end-users to improve UX and ensure client retention. #### Core Methodologies: - Foundational Discovery Research - Concept & Usability Testing - Persona & Mental Model Development - Cross-functional Workshop Facilitation ## The Contracting Model I take on a limited number of high-impact research contracts to maintain proximity to the ground truth of enterprise AI adoption. I am available for direct enterprise engagements or as an expert sub-contractor for agency partners, providing the empirical research required for data-driven decisions. Project-Based ### Targeted Research Sprints End-to-end execution of a specific applied research study. I handle scoping, study design, recruiting strategy, moderation, analysis, and the final executive read-out. Retainer ### Fractional Research Leadership Ongoing integration with an organization to provide authoritative guidance to inform critical decisions in organizational policy and product design. Partnerships ### Agency Sub-Contracting I partner with research agencies and consultancies to provide specialized support for their most complex enterprise clients. I bring deep domain and methodological expertise in B2B research, developer experience, and AI-driven work to design and execute rigorous research engagements and produce high-quality deliverables, under the partner agency's brand. ## Need research about AI-driven work? Refer to my abbreviated CV to review my history with top tech companies and academic institutions, or contact me directly to discuss an open contract or agency partnership. [Abbreviated CV](https://kevinstorer.com/one-page-cv) [Discuss a Contract](https://kevinstorer.com/contact) ### Full CV URL: https://kevinstorer.com/full-cv/ Last updated: 2026-08-20T09:47:18.000Z [Download as PDF](javascript:void%280%29;) # Kevin M. Storer, Ph.D. Public Scholar · AI & the Future of Work [kevin@kevinstorer.com](mailto:kevin@kevinstorer.com) | [kevinstorer.com](https://kevinstorer.com/) Dr. Kevin M. Storer is a public scholar and executive advisor specializing in the sociotechnical dimensions of AI-driven work. Bridging the gap between rigorous empirical data and executive strategy, his work fundamentally shifts how organizations understand the human infrastructure required for successful AI modernization. During his tenure as Senior Researcher at Google and Ethnographic Lead for DORA, Kevin developed the theoretical framework for the world’s first validated model of organizational AI success. This industry-defining research earned him invitations to shape global AI policy and governance at highly selective consortia hosted by the National Science Foundation and the Schloss Dagstuhl – Leibniz Center for Informatics. Today, his public scholarship has been downloaded over 100,000 times by industry decision-makers and cited by more than 100 international news outlets, including NPR, CNN, and Newsweek. Grounded in a Ph.D. in Informatics and over a decade of research spanning hardware, software, and organizational dynamics, Kevin partners with enterprise leadership to design the deliberate, top-down structural shifts required to sustain worker well-being and maximize AI ROI. ## Education 2021 **Ph.D., Informatics**, University of California, Irvine *Advisor: Stacy Branham* 2017 **M.S., Computer Science**, Clemson University *Advisor: Jacob Sorber* 2015 **B.S., Computer Science**, Bowling Green State University *Minor: Physical Sciences* ## Professional Background 2025 – present **Storer Enterprise Strategy, LLC** — Independent Researcher & Public Scholar 2021 – 2025 **Google** — Senior Researcher 2017 – 2021 **University of California, Irvine** — Graduate Researcher 2015 – 2017 **Clemson University** — Graduate Researcher ## Selected Press \[2025\] **NPR**. *Tech CEOs say the era of 'code by AI' is here. Some software engineers are skeptical.* By Huo Jingnan. \[2025\] **CNN**. *Google says 90% of tech workers are now using AI at work.* By Lisa Eadicicco. \[2025\] **Business Insider**. *Google’s senior director of product explains how software engineering jobs are changing in the AI era.* By Ana Altchek. \[2025\] **Newsweek**. *Despite Using AI, Only a Quarter of Tech Workers Trust It.* By Suzanne Blake. \[2025\] **Observer**. *Google Study Shows A.I. Writes Code, But Developers Still Don’t Fully Trust It.* By Victor Dey. \[2025\] **TechCrunch**. *How Google’s dev tools manager makes AI coding work.* By Russell Brandom. \[2025\] **Digit News**. *AI Adoption Hits 90% Among Developers, Google Report Finds.* By Graham Turner. \[2025\] **The Register**. *Google-sponsored DORA report reframes AI as central to software development.* By Tim Anderson. \[2025\] **InfoQ**. *DORA Report Finds AI Is an Amplifier in Software Development, But Trust Remains Low.* By Matt Saunders. \[2025\] **TechRepublic**. *Google DORA Research: Software Developers Use AI 'Heavily'.* By Megan Crouse. ## Speaking \[S.19\] **National Science Foundation & Institute of Electrical and Electronics Engineers**. San Francisco, CA, USA. *Beyond Code: AI & Trustworthy Software*. February 25, 2026\. \[S.18\] **University of California, Irvine**. Irvine, CA, USA. *Researching in the Spotlight: Public Scholarship & Science Communication*. November 12, 2025\. \[S.17\] **DORA Community Discussion**. Virtual. *DORA 2025 State of AI-assisted Software Development: Report Highlights*. September 25, 2025\. \[S.16\] **DORA Sponsor Preview**. Virtual. *DORA 2025 State of AI-assisted Software Development Report: Preview*. September 22, 2025\. \[S.15\] **Gathr.ai**. Los Gatos, CA, USA. *Generative Artificial Intelligence in Development: Findings from DORA 2024*. December 15, 2024\. \[S.14\] **DORA Community Discussion**. Virtual. *Developers’ AI Adoption and Attitudes*. November 30, 2024\. \[S.13\] **Clemson University School of Computing**. Clemson, SC, USA. *Career Pathways in Computing*. November 15, 2024\. \[S.12\] **National Science Foundation & Computing Research Association**. Virtual. *Promoting Diversity in Graduate Computing*. November 14, 2024\. \[S.11\] **Steckler Center for Responsible, Ethical, and Accessible Technology**. Irvine, CA, USA. *Supporting Blind-Parent/Child Reading through Deinstitutional Design Perspectives in Homes*. March 15, 2021\. \[S.10\] **Google Cloud**. Seattle, WA, USA. *Writing Accessible Technical Documentation*. December 07, 2020\. \[S.09\] **Google Cloud**. Seattle, WA, USA. *Non-Visual Accessibility in Technical Information Seeking*. September 25, 2020\. \[S.08\] **Google Search**. Mountain View, CA, USA. *Understanding Voice Assistant Use in Mixed-Visual-Ability Families*. December 11, 2019\. \[S.07\] **University of California, Irvine**. Irvine, CA, USA. *Pragmatism, Embodiment, and Experience*. October 16, 2018\. \[S.06\] **University of California, Irvine**. Irvine, CA, USA. *Agile Software Development*. February 08, 2018\. \[S.05\] **University of California, Irvine**. Irvine, CA, USA. *Time Management for Software Development Teams*. January 12, 2018\. \[S.04\] **University of California, Irvine**. Irvine, CA, USA. *Human Resource Management for Software Development Teams*. November 14, 2017\. \[S.03\] **University of California, Irvine**. Irvine, CA, USA. *Software Development Teams and Conflict Management*. October 19, 2017\. \[S.02\] **Clemson University**. Clemson, SC, USA. *Developing Software for Error-Prone Devices*. September 08, 2016\. \[S.01\] **Clemson University**. Clemson, SC, USA. *Allocating and Managing Virtual Memory*. April 07, 2016\. ## Publications ### Industry Reports \[I.12\]Ameer Abbas, Derek DeBellis, et al. (incl. **Kevin M. Storer**). *DORA AI Capabilities Model*. Google Cloud, November 2025\. — [Download](https://dora.dev/dora-aicmr/?ref=kevinstorer.com) \[I.11\]Abey Stenman, Bre Arder, **Kevin M. Storer**, Katharine Norwood. *Understanding Builder Intent in the AI Era*. Google Cloud. October 2025\. — [Download](https://dora.dev/insights/builder-mindset/?ref=kevinstorer.com) \[I.10\]Derek DeBellis, **Kevin M. Storer**, Nathen Harvey, Matt Beane, Rob Edwards, Edward Fraser, Ben Good, Eirini Kalliamvakou, Gene Kim, Eric Maxwell, Sarah D’Angelo, Sarah Inman, Ambar Murillo, Daniella Villalba. *DORA 2025 State of AI-assisted Software Development Report*. Google Cloud. September 2025\. — [Download](https://cloud.google.com/resources/content/2025-dora-ai-assisted-software-development-report/?ref=kevinstorer.com) \[I.09\]Sarah D’Angelo, Ambar Murillo, Sarah Inman, **Kevin M. Storer**. *Choosing Measurement Frameworks to Fit your Organizational Goals*. Google Cloud. September 2025\. — [Download](https://dora.dev/research/2025/measurement-frameworks/?ref=kevinstorer.com) \[I.08\] **Kevin M. Storer**, Derek DeBellis. *Introducing the DORA AI Capabilities Model: 7 Keys to succeeding in AI-assisted software development*. Google Cloud. September 2025\. — [Download](https://cloud.google.com/blog/products/ai-machine-learning/introducing-doras-inaugural-ai-capabilities-model/?ref=kevinstorer.com) \[I.07\] **Kevin M. Storer**. *Concerns Beyond the Accuracy of AI Output*. Google Cloud. July 2025\. — [Download](https://dora.dev/research/ai/concerns-beyond-accuracy-of-ai-output/?ref=kevinstorer.com) \[I.06\]Derek DeBellis, **Kevin M. Storer**, Daniella Villalba, Nathen Harvey, Sarah D’Angelo, Adam Brown. *DORA Impact of Generative AI in Software Development*. Google Cloud. March 2025\. — [Download](https://cloud.google.com/resources/content/dora-impact-of-gen-ai-software-development/?ref=kevinstorer.com) \[I.05\] **Kevin M. Storer**. *How Gen AI Affects the Value of Development Work*. Google Cloud. December 2024\. — [Download](https://dora.dev/research/2024/value-of-development-work/?ref=kevinstorer.com) \[I.04\]Derek DeBellis, **Kevin M. Storer**, Amanda Lewis, Benjamin Good, Daniella Villalba, Eric Maxwell, Kim Castillo, Michelle Irvine, Nathen Harvey. *2024 Accelerate State of DevOps Report*. Google Cloud. November 2024\. — [Download](https://dora.dev/research/2024/dora-report/?ref=kevinstorer.com) \[I.03\] **Kevin M. Storer**, Derek DeBellis, Sarah D’Angelo, Adam Brown. *Fostering Developers' Trust in Generative Artificial Intelligence*. Google Cloud. September 2024\. — [Download](https://dora.dev/research/2024/trust-in-ai/?ref=kevinstorer.com) \[I.02\]Derek DeBellis, **Kevin M. Storer**. *DORA Report Preview - AI in the Workplace: Adoption and Impact*. Google Cloud. August 2024\. — [Download](https://dora.dev/research/2024/ai-preview/?ref=kevinstorer.com) \[I.01\]Derek DeBellis, Amanda Lewis, Daniella Villalba, Dave Farley, Eric Maxwell, James Brookbank, Jeffrey Winer, **Kevin M. Storer**, Kim Castillo, Michelle Irvine, Nathen Harvey, Steve McGhee. *Accelerate State of DevOps Report 2023*. Google Cloud. September 2023\. — [Download](https://dora.dev/research/2023/dora-report/?ref=kevinstorer.com) ### Journal Articles \[J.01\]Ali Abdolrahmani, **Kevin M. Storer**, Antony Rishin Mukkath Roy, Ravi Kuber, Stacy M. Branham. *Blind Leading the Sighted: Drawing Design Insights from Blind Users towards More Productivity-oriented Voice Interfaces*. ACM Transactions on Accessible Computing (TACCESS), Volume 12, Issue 4, January 2020, Article 18, 35 pages. \[2 Year Impact Factor: 2.64\] — [Download](https://dl.acm.org/doi/abs/10.1145/3368426?ref=kevinstorer.com) ### Conference Proceedings Articles \[C.07\] **Kevin M. Storer**, Stacy M. Branham. *Deinstitutionalizing Independence: Discourses of Disability and Housing in Accessible Computing*. In Proceedings of the ACM SIGACCESS Conference on Accessible Computing (ASSETS '21), October 18-22, 2021, Virtual Event. \[29% acceptance rate\] — [Download](https://dl.acm.org/doi/10.1145/3441852.3471213?ref=kevinstorer.com) \[C.06\] **Kevin M. Storer**, Harini Sampath, M. Alice Merrick. *"It's Just Everything Outside of the IDE that's the Problem": Information Seeking by Software Developers with Visual Impairments*. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI '21), May 8-13, 2021, Yokohama, Japan. \[26.3% acceptance rate\] — [Download](https://dl.acm.org/doi/10.1145/3411764.3445090?ref=kevinstorer.com) \[C.05\] **Kevin M. Storer**, Tejinder K. Judge, Stacy M. Branham. *"All in the Same Boat": Tradeoffs of Voice Assistant Ownership for Mixed-Visual-Ability Families*. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI '20), April 25-30, 2020, Honolulu, HI, USA. \[24% acceptance rate\] — [Download](https://dl.acm.org/doi/abs/10.1145/3313831.3376225?ref=kevinstorer.com) \[C.04\] **Kevin M. Storer**, Stacy M. Branham. *"That's the Way Sighted People Do It": What Blind Parents Can Teach Technology Designers About Co-Reading with Children*. In Proceedings of the ACM SIGCHI Conference on Designing Interactive Systems (DIS '19), June 23-28, 2019, San Diego, CA, USA. \[25% acceptance rate\] **Honorable Mention for Best Paper (Top 2%)** — [Download](https://dl.acm.org/doi/10.1145/3322276.3322300?ref=kevinstorer.com) \[C.03\]Fatema Akbar, Ayse Elvan Bayraktaroglu, Pradeep Buddharaju, Dennis Rodrigo Da Cunha Silva, Ge Gao, Ted Grover, Ricardo Gutierrez-Osuna, Nathan Cooper Jones, Gloria Mark, Ioannis Pavlidis, Kevin Storer, Zelun Wang, Amanveer Wesley, Shaila Zaman. *Email Makes You Sweat: Examining Email Interruptions and Stress Using Thermal Imaging*. In Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI '19), May 4-9, 2019, Glasgow, Scotland. \[23.8% acceptance rate\] — [Download](https://dl.acm.org/doi/10.1145/3290605.3300456?ref=kevinstorer.com) \[C.02\]Josiah Hester, **Kevin Storer**, Jacob Sorber. *Timely Execution on Intermittently Powered Batteryless Sensors*. In Proceedings of the ACM Conference on Embedded Network Sensor Systems (SenSys '17), November 5-8, 2017, Delft, The Netherlands. \[17.2% acceptance rate\] — [Download](https://dl.acm.org/doi/10.1145/3131672.3131673?ref=kevinstorer.com) \[C.01\]Josiah Hester, Travis Peters, Tianlong Yun, Ronald Peterson, Joseph Skinner, Bhargav Golla, **Kevin Storer**, Steven Hearndon, Kevin Freeman, Sarah Lord, Ryan Halter, David Kotz, Jacob Sorber. *Amulet: An Energy-Efficient, Multi-Application Wearable Platform*. In Proceedings of the ACM Conference on Embedded Network Sensor Systems (SenSys '16), November 14-16, 2016, Stanford, CA, USA. \[17.6% acceptance rate\] — [Download](https://dl.acm.org/doi/10.1145/2994551.2994554?ref=kevinstorer.com) ### Workshop Papers \[W.07\] **Kevin M. Storer**. *A Deinstitutional Perspective on Domestic Accessibility: Three Tenets for Accessible Computing Research in Homes*. Human Computer Interaction Consortium (HCIC '21), June 21-24, 2021, Online Event. \[W.06\] **Kevin M. Storer**. *Social Interaction as Sensory Augmentation?*. Rethinking the Senses: A Workshop on Multisensory Embodied Experiences and Disability Interactions, ACM SIGCHI Conference on Human Factors in Computing Systems (CHI '21), May 8-13, 2021, Yokohama, Japan. \[W.05\] **Kevin M. Storer**, Stacy M. Branham. *Reframing Homes and Families in Accessibility*. Nothing About Us Without Us: Investigating the Role of Critical Disability Studies in HCI, ACM SIGCHI Conference on Human Factors in Computing Systems (CHI '20), April 25-30, 2020, Honolulu, HI, USA. \[W.04\]Shengjie Bi, Ellen Davernport, Jun Gong, Ronald Peterson, Joseph Skinner, **Kevin Storer**, Tao Wang, Kelly Caine, Ryan Halter, David Kotz, Kofi Odame, Jacob Sorber, Xing-Dong Yang. *Poster: Auracle: A Wearable Device for Detecting and Monitoring Eating Behavior*. Proceedings of the ACM Annual International Conference on Mobile Systems, Applications, and Services (MobiSys '17), June 19-23, 2017, Niagara Falls, NY, USA. \[W.03\]Matthew Furlong, Josiah Hester, **Kevin Storer**, Jacob Sorber. *Realistic Simulation for Tiny Batteryless Sensors*. Proceedings of the International Workshop on Energy Harvesting and Energy-Neutral Sensing Systems (ENSsys '16), November 14-16, 2016, Stanford, CA, USA. \[W.02\]Josiah Hester, Travis Peters, Tianlong Yun, Ronald Peterson, Joseph Skinner, Bhargav Golla, **Kevin Storer**, Steven Hearndon, Kevin Freeman, Sarah Lord, Ryan Halter, David Kotz, Jacob Sorber. *The Amulet Wearable Platform: Demo Abstract*. Proceedings of the 14th ACM Conference on Embedded Network Sensor Systems (SenSys '16), November 14-16, 2016, Stanford, CA, USA. \[W.01\]Josiah Hester, **Kevin Storer**, Lanny Sitanayah, Jacob Sorber. *Towards A Language and Runtime for Intermittently-Powered Devices*. Workshop on Hilariously Low-Power Computing, ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '16), April 2-6, 2016, Atlanta, GA, USA. ## Teaching 2019 **University of California, Irvine** — *Digital Embodiments* 2018 **University of California, Irvine** — *Interactive Technology* 2018 **University of California, Irvine** — *Project Management* 2017 **University of California, Irvine** — *Project Management* 2015 **Clemson University** — *Assembly Language* ## Service **Association for Computing Machinery**. Conference on Human Factors in Computing (CHI). Reviewer (2025, 2023, 2020), Papers Accessibility (2020). **Google**. Award for Inclusion Research. Project Sponsor (2025), Reviewer (2024, 2023, 2022). **Association for Computing Machinery**. IMWUT (Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technology). Reviewer (2023). **Association for Computing Machinery**. CSCW (Computer-Supported Cooperative Work). Reviewer (2022). **Association for Computing Machinery**. Conference on Computers and Accessibility (ASSETS). Volunteer Staff (2021), Paper Session Co-Chair (2020). **Taylor & Francis**. Mind, Culture, and Activity: An International Journal. Reviewer (2019). **Association for Computing Machinery**. SIGCHI Working Group on Promoting Accessibility. Member (2019). ### One-Page CV URL: https://kevinstorer.com/one-page-cv/ Last updated: 2026-08-10T13:10:28.000Z [Download as PDF](javascript:void%280%29;) # Kevin M. Storer, Ph.D. Applied Research · AI-Driven Work [kevin@kevinstorer.com](mailto:kevin@kevinstorer.com) | [kevinstorer.com](https://kevinstorer.com/) Dr. Kevin M. Storer is an independent applied researcher and enterprise strategist specializing in the human impacts of AI modernization. As the former Ethnographic Lead for Google’s DORA Research team, he authored the industry's first validated model of the organizational conditions that determine success in AI-driven work. Now an Alphabet-preferred supplier and Principal of Storer Enterprise Strategy, LLC, Kevin delivers high-leverage organizational diagnostics and B2B product research for leading technology companies, translating rigorous ethnographic data into decision-ready insights. Capabilities #### Core Services - **Organizational Diagnostics:** Establishing evidence-based, ground-truth baselines of how a workforce is actually adopting, trusting, and utilizing AI. - **B2B Product & User Research:** Designing foundational discovery and usability studies to map how end-users respond to novel AI-driven features. - **Measurement Frameworks:** Translating qualitative human experiences into rigorous operational metrics to define and prove AI success. - **Strategic Executive Advisory:** Providing independent, high-level counsel to align multi-year technical roadmaps with human infrastructure requirements. Impact #### Selected Outcomes - Led ethnographic research for the DORA AI Capabilities Model, the industry's first validated framework for AI-driven software development. - Translated qualitative insights into public-facing enterprise reporting credited with driving $250M in annual marketing-sourced pipeline for Google Cloud. - Advised select global policy consortia—including the NSF and Schloss Dagstuhl—on the future of AI and work, leveraging foundational research at Google. - Authored public scholarship downloaded 100,000+ times and cited across 100+ international news outlets, including CNN, NPR, and Business Insider. ## Professional Background Storer Enterprise Strategy, LLC 2025 – Present Principal Researcher Independent research and advisory services for organizations exploring AI-driven work. - **Google DORA (2026):** Ethnographic research to build the theoretical framework behind DORA's 2026 developer survey. - **Waymo (2026):** Usability research to refine novel software supporting the expansion of the Waymo Atlanta service area. Google 2019 – 2025 Senior Researcher — DORA Research · Developer X · Cloud · Assistant University of California, Irvine 2017 – 2021 Graduate Research Assistant ## Education - **Ph.D., Informatics** — University of California, Irvine (2021) - **M.S., Computer Science** — Clemson University (2017) - **B.S., Computer Science** — Bowling Green State University (2015) ## Research Methods Mixed-Methods Research · Ethnographic Research · Participant Observation · Content Analysis · Social Media Monitoring · Survey Design · Statistical Analysis · Measurement Framework Design ## Posts ### Zig's AI Ban Isn't Really About Code Quality. URL: https://kevinstorer.com/blog/zigs-ai-ban/ Last updated: 2026-05-06T16:38:36.000Z Last week, a small open source project published the most stringent anti-AI argument I've seen any major project make in public. The project is Zig, a systems language run by a 501(c)(3) foundation that funds a small number of paid maintainers and depends on a wider bench of volunteers. It also happens to be the toolchain underneath [Bun](https://bun.com/blog/bun-joins-anthropic?ref=kevinstorer.com), the JavaScript runtime that Anthropic acquired in December 2025 in its first-ever acquisition. Bun is the toolchain underneath Claude Code. Which makes Zig the foundation beneath a billion-dollar AI coding product, run by an organization that has now formally banned AI from helping build it. Loris Cro, VP of Community at the Zig Software Foundation (ZSF), made the argument in an essay called ["Contributor Poker and Zig's AI Ban"](https://kristoff.it/blog/contributor-poker-and-ai/?ref=kevinstorer.com). The essay frames the ban as a matter of long-term project economics. The project's [Code of Conduct](https://ziglang.org/code-of-conduct/?ref=kevinstorer.com) already places the rule at top level, adjacent to the harassment policy: no LLMs for pull requests, no LLMs for issues, no LLMs for comments on the bug tracker, not even for translation. This is well outside the norm. Most major open source projects have not gone anywhere near this far. For context, open source software is built by two groups of people who are not the same. Contributors are anyone in the world who can write code and submit a change for the project to consider. There is no employment relationship, no formal entry, and no obligation in either direction. Reviewers are the much smaller group with authority to merge those changes, in many cases as part of a core team supported by a foundation (like ZSF) that pays a few of them and relies on volunteers for the rest. Anyone can contribute. Almost no one can review. That asymmetry is what makes reviewer attention, not code, the scarce resource open source projects actually budget around. With this in mind, Cro's argument for Zig's ban on LLM-generated content starts from a frame the essay calls "contributor poker." Open source is an iterated game. The value a contributor brings is not in the first pull request but in the years that follow, as they accrue context, trust, and ownership of pieces of the codebase. Maintainer attention on a first PR is therefore not a transactional cost. It is a bet on a person who, if the bet pays off, returns the investment many times over. As Cro puts it, "you bet on the contributor, not on the contents of their first PR." LLM-assisted submissions break this exchange. There is no strictly-human contributor on the other side of an LLM-generated PR to invest in, and reviewer attention spent on the output yields questionable future return. Cro is also clear about what the project actually receives from LLM-generated PRs: drive-by submissions full of hallucinated APIs that don't compile, ten-thousand-line first PRs from contributors with no prior history, and people who deny using LLMs but were caught running reviewer feedback through one in real time. The essay grants that this is not the only possible result of AI-driven coding. It is, in Cro's words, "clearly a misuse of the tool." But the misuse is overwhelmingly what the project sees in practice. The contributor-poker logic is real on its own terms. Reviewer attention is scarce, and investing it in contributors who will grow into long-term collaborators is rational policy. But quality as an orienting argument doesn't fully explain a blanket ban on LLM use. By Cro's own concession, some LLM-assisted code is indistinguishable in quality from human work and is already in the codebase. Also, if the concern were strictly quality, the response would be better triage, not outright prohibition. While recognizing the limitations of this approach for OSS projects, it is difficult to ignore that a blanket ban isn't calibrated against quality concerns. It's calibrated against something else. My read is that the root of the issue is trust, because trust on teams and in collaborative environments is built in a particular way that AI happens to interfere with directly. Trust between collaborators is built through work product. The mechanism rests on the assumption that the work was performed by the person who hands it over, so the work product reflects that person's head — their taste, their judgment, their patience, their ethics. I can assess the person through the work product, and the running output of those assessments, accumulated over time, is how teams form trust and organize work. That mechanism is so foundational to how we collaborate that our culture polices it with morality. The clearest existing case is plagiarism. We treat plagiarism as an ethical breach, even when potential economic harm is absent, and regardless of the quality of the output. That's because plagiarism severs the work-to-person link the entire trust apparatus depends on. AI threatens that same link by a different route. ZSF's policy isn't taking a moral position and doesn't necessarily need to. But the shape of the policy is the same shape moral language has historically taken around plagiarism – unacceptable in all cases. This is why AI-policy debates often feel so heated: they're debating a valid concern in the wrong terms. Acceptable use policy isn't really about whether the end product is good. It's about whether using an LLM decouples your work from yourself in a way the social fabric of teams and organizations refuses. Most large engineering organizations, including Google, Meta, and Amazon, have already mandated AI tooling for their engineers on productivity grounds. The decision was made without anyone asking what it would do to the trust controls of those organizations. Code review and QA are often cited as controls for concerns about widespread LLM use, but they are quality controls. They don't bear weight on whether teams come to trust each other as collaborators, if managers come to trust their reports as contributors, or if organizational structures can hold if that trust is absent. This same phenomenon problematizes hiring itself. If the work you're hiring someone to do is going to be performed by AI, does evaluating the person separately from that AI predict their job performance at all? Most hiring practice rests on evaluating craft, judgment, and process, on the implicit theory that those qualities are what produce the work. When the work is produced by AI instead, the interview is measuring something else, and most companies haven't reckoned with what. The decoupling Zig is responding to has already happened at the largest companies in software. Most of them haven't noticed. None of them are ready to respond. But, most of the tech workers I know can feel this shift in their organization's culture instinctively, whether they have the words to explain it or not. Zig's response to these issues is the most extreme version. Refuse the decoupling entirely. Most organizations cannot afford that response, will not adopt it, and probably shouldn't – not that they could police it, anyway. After all, Zig can't. But every leadership team is going to face the question Zig is answering, whether they choose to or not. *What are our terms for trusting collaborators and employees whose work may not be theirs in the way work used to be theirs?* That question doesn't dissolve when AI improves. It becomes more urgent. ## Read me in your inbox. New analysis on AI-driven work, every Tuesday. [Subscribe](#/portal/signup) ### Reading Deloitte's "State of AI in the Enterprise 2026" URL: https://kevinstorer.com/blog/deloittes-state-of-ai/ Last updated: 2026-05-30T16:39:31.000Z On January 21, Deloitte published [*State of AI in the Enterprise: The Untapped Edge*](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html?ref=kevinstorer.com), its annual enterprise AI survey, drawn from 3,235 director- and C-suite-level leaders across 24 countries and six industries. Deloitte's key finding is that AI is delivering productivity to most organizations but business transformation to very few, and the gap between those outcomes — what Deloitte calls "the untapped edge" — defines the strategic moment. The report hits the familiar beats of industry research in this space: a near-universal desire for AI to grow revenue, an absence of meaningful proof that revenue is arriving, and self-reported productivity gains that don't quite map to success. 74% of surveyed organizations want their AI initiatives to grow revenue, but only 20% report that they have. 66% report productivity and efficiency gains overall, and the report separately notes that all three transformation tiers — deep transformation, process redesign, and surface-level use — are capturing those gains. Having produced reports of this kind, it stood out to me that the report has the data to test whether those gains differ meaningfully across tiers. I am virtually certain that test was run. And I know how a finding like "deep transformers see X% higher productivity gains" would be presented if it existed — it would headline the recommendation, not surface as a flat qualitative observation that "each tier is capturing gains." That phrasing is what reports produce when the test was run and the differential wasn't there to feature. If that's true, the recommendation to transform deeply has no support in the data. If no difference was found across tiers of transformational depth, the rational read of Deloitte's findings is to do nothing. Whatever you are already doing is evidently getting you the same gain you would get from rebuilding the company around AI. This kind of selective framing threads through the entire report. We are told AI is making organizations more productive but isn't generating revenue. We are told that sovereign AI is a national-security imperative, but building critical enterprise infrastructure on top of foundation models owned by third-party providers is sound long-term strategy. These contradictions are quietly waved past, and they cohere only if you accept the tacit premise that the AI status quo is, on net, good. That premise is the investor class's article of faith, and the industry report genre's task has largely become to find ways to keep believing it, even when the data won't quite cooperate. But the tension between Wall Street's belief in AI's transformative potential and the absence of data to justify that belief isn't strictly delusion. I see it as another instance in which AI's transformative nature requires a ground-up rethinking of basic premises — including the premises behind the research meant to evaluate it. In this case, the industry-standard, survey-style "State of X" report is partly culpable for the lack of meaningful guidance, because surveys are the wrong tool for the work this moment requires. Survey science is well-suited to aggregating stable preferences over known territory: whether 85% of customers liked yesterday's menu item tells you something about how they will feel about it tomorrow. It is not well-suited to charting direction in a moment of structural transformation, where the territory is genuinely new and the respondents are themselves inside the disruption they are being asked to describe. The data are historical — by the time they are collected, coded, and published, the conditions they described have already shifted. And survey science depends on respondents understanding what they are being asked about, which, in a genuinely new domain, is the very thing under contestation. When the report finds that 21% of organizations have a "mature model for governance of autonomous agents," the 21% rests on 3,235 private definitions of mature governance and autonomous agents, in a field that has not yet settled the meaning of either. Asking 3,235 leaders what they think about a fundamentally transformative technology isn't an escape from the ambiguity. They are inside the ambiguity with the rest of us. If the data lack directional clarity, it is because the respondents do. What the moment needs is the opposite of what has long been advocated in tech-sector decision-making: expertise-driven direction in the absence of data-driven direction. Data are confirmatory by nature. They can describe what is and test claims about what is, but they cannot generate what should be. Direction-setting through a structural transformation of labor is generative work, and generative work belongs to expertise and theory, not to aggregation. What we have instead are people who have spent careers thinking carefully about how work, organizations, and labor actually function — researchers in the humanities, critical scholarship, organizational studies, and philosophy of technology, to name a few. Directional questions of this magnitude cannot be answered by aggregating opinions of generic respondents. Industry reports keep failing to find evidence that AI is generating value, and the risk is that to a casual reader the failure reads as evidence of absence. The debate frozen between "AI is good" and "the data shows AI is bad" has missed what the moment actually is. AI is reality; the work in front of us is figuring out how to make it good, and knowing who to ask about that path. ## Read me in your inbox. New analysis on AI-driven work, every Tuesday. [Subscribe](#/portal/signup) ### The WEF's Four Futures All Hide the Same Fact. URL: https://kevinstorer.com/blog/the-wefs-four-futures/ Last updated: 2026-05-18T14:44:04.000Z In January 2026, the World Economic Forum released *Four Futures for Jobs in the New Economy: AI and Talent in 2030*, an analysis organized around a 2×2 matrix crossing the pace of AI advancement (exponential or gradual) with the readiness of the global workforce (high or low). The four scenarios are Supercharged Progress, The Age of Displacement, Co-Pilot Economy, and Stalled Progress. The framework is genuinely useful, and WEF is honest about offering a thought exercise rather than a strategic recommendation. Supercharged Progress is what happens when AI capability advances exponentially and the global workforce keeps pace: an AI-native economy where individual workers oversee fleets of digital agents. WEF attributes the workforce adaptation specifically to a radical redesign of education and training systems. That is the load-bearing precondition. But the academy is structurally the institution least equipped to deliver radical anything on this timeline. That is not a personal failing of the academics involved; the institution is designed to evolve over decades, not to revolutionize itself over years. A recent *Inside Higher Ed* roundtable asked academic leaders how they would meet AI's existential challenge. [Their most concrete proposals](https://kevinstorer.com/blog/ai-didnt-break-the-academy/) were workflow automation and administrative fixes, which are roughly the opposite of radical redesign. What I have argued the academy must recover — "training people to think, judge, and challenge, including by challenging AI itself" — is exactly what Supercharged Progress depends on, and exactly what is not currently being built. The Age of Displacement is what happens when AI capability outruns workforce readiness. WEF imagines mass outsourcing of decision-making to autonomous systems, governance regimes lagging behind agentic transformation, political polarization deepening, and AI control concentrating in a handful of "state-like" companies whose foundational models, compute, and proprietary data make them unaccountable to the public economies in which they operate. But the WEF describes this scenario as a 2030 future. Each of these details of the 2030 scenario is already here, where welfare systems are visibly failing to adapt to AI-driven labor disruption, where the legal frameworks for democratic oversight of autonomous decision-making barely exist, and where foundational-model concentration has already produced a small set of companies whose market position resembles sovereign infrastructure more than competitive enterprise. Co-Pilot Economy is what happens when gradual AI progress is paired with high workforce readiness. AI progress is gradual, the workforce keeps pace through ordinary adaptation, skill floors rise across the labor market, the gig economy expands, and the WEF's framing of AI as infrastructure on the order of electricity feels genuinely apt. That workforce readiness is achievable through gradual adaptation, not the radical educational redesign Supercharged Progress requires. But the precondition for those outcomes is named only in passing. WEF's scenario description states that "the 'AI bubble' burst in the mid-2020s," with capital commitments unwinding, ballooning valuations recalibrating, and frontier AI funding drying up. The report then describes what follows from that market collapse — workers "overseeing hundreds of digital employees" across the labor market. That framing carries a math problem the report doesn't address. A tenfold productivity gain per worker through agent management, in an economy where consumption is not also growing tenfold, looks less like universal abundance and more like 90% headcount reduction. Stalled Progress is what happens when gradual AI is not paired with workforce adaptation. AI applications stay brittle outside frontier firms, automation captures only the most routine tasks, and workers face chronic job insecurity, eroding safety nets, polarization, and declining trust. But Stalled Progress shares Co-Pilot Economy's low AI capability and bubble-burst dynamic, and is hard to distinguish from it at first read. What separates them is workforce adaptation: in Stalled Progress, the workforce never adapts to gradual AI integration, leaving the same low-AI conditions without any relief for workers. Of the four, I read Supercharged Progress as the most optimistic future on paper. But its load-bearing condition is an academic revolution that cannot be delivered in any realistic timeframe. The Age of Displacement reads as the most likely future, because it is the present trajectory and requires no change from where we already are. It is also the most horrifying. Which leaves Co-Pilot Economy as the strangely hopeful one — not because its outcomes are the best on offer, but because, of the scenarios that frame any worker upside, it is the only one whose preconditions do not depend on the academy reinventing itself. But that comes with a cost. Co-Pilot Economy's off-ramp is the AI bubble bursting. That would mean capital commitments unwinding and AI-related investment pulling back across an economy that has increasingly come to lean on AI-related valuations. Hoping for an immediate economic catastrophe in order to avoid a worse long-term one is a strange position. And yet that is where my analysis of WEF's four scenarios lands me. Notably, across all four scenarios — including Co-Pilot Economy, the strangely hopeful one — wage polarization rises in WEF's scenario comparison. The same is true of adjacent social-fracture indicators that recur across the scenarios, such as eroding safety nets, declining trust in institutions, governance gaps, and political polarization. WEF is an economic forum, and the lens it brings to these scenarios is properly economic. What that analysis surfaces, across every cell of the 2×2, is a persistent layer of social fracture the economic framing alone cannot resolve. The report names policy tools that suggest where this leads, allowing that "some governments experiment with AI dividends, wage insurance and universal basic income models." These are projects an economic forum cannot reasonably advance on its own. And I agree that these social issues most likely persist, which is a motivating factor in my decision to devote my career to addressing AI's economic disruption as a social problem. AI's challenges show up most easily as technical problems and, increasingly, as economic ones, and neither framing dissolves the social problem underneath. Whichever future ultimately comes, the four futures WEF presents are not really four. They are the same social problem, in four economic shapes. ## Read me in your inbox. New analysis on AI-driven work, every Tuesday. [Subscribe](#/portal/signup) ### AI Didn't Break the Academy. It Revealed What Was Already Broken. URL: https://kevinstorer.com/blog/ai-didnt-break-the-academy/ Last updated: 2026-05-06T12:52:21.000Z Artificial intelligence is creating an existential crisis in academia, calling into question the financial and philosophical value of education in a world where formerly-niche skills and knowledge are becoming increasingly accessible. That's why *Inside Higher Ed* recently [invited five voices](https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2026/01/05/5-predictions-how-ai-will-shape-higher-ed?ref=kevinstorer.com) to predict what 2026 holds for higher ed. The roundtable features a futurist, a business officer, a learning scientist, an EDUCAUSE researcher, and an ed-tech CEO. Their answers track different concerns, from the AI bubble to scaling and ROI, faculty disillusionment, partnerships, and system fragmentation. Read as a chorus, the predictions orbit a question none of them quite name: whether the academy still knows what it is for. **On Bryan Alexander's bubble dependence.** Alexander frames academia's path as contingent on whether the AI bubble pops. If it does, internal and external pressure to deploy AI slackens. If it doesn't, academic AI efforts continue to expand. Pandora's Box is already opened. AI is deeply embedded in enterprise workflows, university partnerships, and the daily working life of millions of professionals. Once a technology reaches that level of integration, public opinion stops being especially consequential to its trajectory. Meta is the obvious example. Its products were already too integrated into daily life to be abandoned even when there was backlash at the level of Cambridge Analytica. AI has not generated that level of directed backlash yet. The pressure on higher ed to make AI work is unlikely to be alleviated by either a market correction or a public mood shift. In this light, the question isn't really whether the bubble pops. The question is what the academy offers when its educational monopoly is broken — and that break is already underway, regardless of what the markets do. **On Lindsay Wayt's pace problem.** Wayt describes the pace of change as the biggest challenge institutions face, and it exacerbates everything else. I agree. And the observation generalizes well past universities. I write this as someone whose job is depenedent on staying current on AI, and I cannot do that job without the support of AI itself. The pace is genuinely impossible without AI assistants doing research, triage, and summarization in the workflow. The institutions Wayt describes are not piloting AI purely because they want to. They are piloting because they cannot afford not to. The uncomfortable fact is that even when AI shows no measurable return, you cannot simply opt out without losing relevance. That is the position higher ed finds itself in, and it is the position every organization built around knowledge work is in. **On Rebecca Quintana's disillusionment.** Quintana is right that disillusionment is coming, and right that the moment invites broader conversations about the purposes of education. Reading between the lines, I think the question she is gesturing at is sharper than what she names directly. She writes that students are using AI "in ways that do not support their learning and growth." The premise hidden in that sentence is that learning and growth are presently the explicit goals of the system. They may be the values the academy advertises, but the American university has been a credentialing pipeline for the labor market for generations. I got my first computer science degree because it was a job pipeline, not because I was so taken with Python. AI is not degrading the academy's purpose. It is revealing that the purpose was already eroded. The liberal-arts ideal did not survive the conversion of the university into a job-training program with prestige pricing. AI is forcing Quintana's question because the trade — pay tuition, get credential, get hired — is breaking on the employer side. Critical engagement matters more now precisely because the credential is worth less. And I suspect this is the implication she is really driving at, even if it would be uncouth to say so outright in an IHE article. **On Mark McCormack's partnerships.** McCormack argues that ed-tech vendors and academic communities need to build sustained connections grounded in shared governance. I disagree, mildly, from experience. As a private-sector AI researcher who has kept a foot in the academic world for years, I have spent considerable effort looking for ways to help academics with AI work. The academics I talk to often cannot articulate what they want from the partnership, or what they bring to it. Even when a private-sector researcher walks in and asks plainly — how can we help each other — the most common response is a shrug, followed in some cases by a separate lament that Silicon Valley is ruining academia. That is not a personal failing of the academics involved. It is a structural issue. For the first time in modern history, industrial AI research is ahead of academic AI research. Frontier models are being built behind locked doors at Anthropic, OpenAI, and Google. Academia used to chart courses before they were commercially proven, and that head start was the value it offered industry. Now industry is doing the charting and the building. The partnership McCormack describes has to rest on a value proposition academia has not figured out. Academia is being asked, by this moment, to articulate what it is actually for and what it actually offers — not just to Silicon Valley, but to society broadly. Until that work happens, the partnerships will be transactional, if they are established at all. **On Joe Abraham's fragmentation pitch.** Abraham predicts that institutions will use AI to address the fragmentation of their administrative systems — advising, enrollment, financial aid, billing, the LMS — through agentic orchestration and workflow automation. It is the most agreeable take in the roundtable, mostly because it points at something obvious. University administration has been a quiet disaster for as long as universities have existed. To be fair, that is a real and persistent problem. And to his credit, Abraham is actually working on solving it. But notice what gets conceded along the way. In a piece convened to address AI's existential challenge to the academy, the most concrete answer on offer is workflow automation. If "we will fix the billing systems" is the strongest pitch for AI's contribution to the academy in 2026, the existential question has already been answered without anyone needing to ask it. **My own prediction.** As technical and informational skills commoditize, what gains value is precisely what doesn't. Perspective, taste, the capacity to see what is missing from an AI's output. My doctoral dissertation focused on Critical Computing, a discipline within the larger umbrella of Human-Computer Interaction focused on applying critical scholarship and humanistic approaches to technology design. For most of my career, "computing" was the part that carried weight and "critical" was the part employers tolerated. Since AI proliferated, the inversion has happened. The "computing" is increasingly accessible to anyone with a Claude account. The "critical" is what gives me a continued edge — the ability to challenge AI output, to see what is not being said, to argue why one response is better than another when both are technically correct. The disciplines that gain in 2026 and beyond will be the ones that train people to make value judgments on subjective material — the humanities, the social sciences, art history, writing, design. The work of saying "this is a good response and this is a bad one" on questions that have no provable answer is precisely what AI cannot do for you. That is not a retreat into the past. It is an acceleration into a labor economy where unique perspective is a privileged form of value, as I [discussed previously](https://kevinstorer.com/blog/ai-is-coming-for-employment-not-workers/). The academy's path forward is not optimizing operations or scaling pilots. It is recovering an old purpose: training people to think, judge, and challenge, including by challenging AI itself. Academia bemoans an existential crisis. It may actually be receiving a liberatory opportunity. ## Read me in your inbox. New analysis on AI-driven work, every Tuesday. [Subscribe](#/portal/signup) ### AI Is Coming for Employment, Not Workers. URL: https://kevinstorer.com/blog/ai-is-coming-for-employment-not-workers/ Last updated: 2026-05-18T14:44:14.000Z In late December, [TechCrunch surveyed 24 enterprise venture capitalists](https://techcrunch.com/2025/12/29/vcs-predict-strong-enterprise-ai-adoption-next-year-again/?ref=kevinstorer.com) about their AI predictions for 2026\. The survey didn't ask about labor. Multiple investors raised it anyway, unprompted. A [companion piece](https://techcrunch.com/2025/12/31/investors-predict-ai-is-coming-for-labor-in-2026/?ref=kevinstorer.com) followed days later. It cited MIT's estimate that 11.7% of jobs are already automatable using current AI, and assembled a chorus of investor predictions about how aggressively that displacement will unfold over the next twelve months. Two of those investors capture the conversation from different angles. Jason Mendel of Battery Ventures called 2026 "the year of agents as software expands from making humans more productive to automating work itself, delivering on the human-labor displacement value proposition." Antonia Dean of Black Operator Ventures took a different tack: "AI will become the scapegoat for executives looking to cover for past mistakes." One frames displacement as a technology story. The other frames it as a corporate behavior story. They look like different arguments. But they rest on exactly the same foundational assumption. The shared assumption is more foundational than the question of pace. It is that an employment-based economy is the only arrangement available, that whatever happens with AI in 2026 will play out within the structure of employers, jobs, and workers as we know it today. The conversation can debate how aggressively displacement will arrive, which roles will go first, how quickly budgets will shift from labor to AI, precisely because nobody has stopped to ask whether the economy on the other side of all of that still has to be organized around employment at all. We are debating whether we are watching the system die. We are not yet wondering how to rebirth it. The way to begin that exercise is to look at what AI is actually displacing. The current economy runs on a particular kind of arrangement: employment built on fungibility. An employer specifies a role, finds someone with the basic skills to fill it, and treats every candidate who can fill it as essentially interchangeable with the next. The arrangement works because roles are specified in advance, in writing, in a form precise enough that candidates can be evaluated against the spec. AI breaks the arrangement at exactly that seam. If you can specify a role precisely enough to post it on a job board, you can specify it precisely enough to hand it to an agent. What AI is actually displacing isn't workers. It is fungibility-based employment itself: the system that turns most labor into most income in the economy today. Fungibility-based employment is so familiar that it reads as nature. It isn't. Job descriptions, preferred qualifications, the standardized interview, the language of "transferable skills," the org chart of the modern firm: all of these were engineered, deliberately, over the course of the last century, to make labor interchangeable enough to plug into pre-defined slots. The bureaucratic corporation exists in roughly its current shape precisely because it can perform that integration at scale. The fungibility AI now threatens is a design feature of this particular economic system. A different economic system, one not built on the requirement that labor be fungible, is also possible. It just requires deliberate attention to recognize the option, because the current arrangement is so dominant that it disappears into the background. If we take the displacement seriously, and accept that fungibility-based employment is on its way out, three things can happen on the other side. - **Revolt:** workers refuse to absorb the displacement, and political and labor upheaval forces redistribution by force rather than by design. - **Starvation:** displacement arrives faster than any structural offset, consumption-side economics breaks, and a sufficient mass of people who can't eat eventually revolt anyway. - **Restructure:** we deliberately redesign the relationship between labor and income, before the system makes the choice for us. Universal Basic Income is the most well-known restructure proposal in circulation. The premise is straightforward: decouple income from labor entirely, and distribute resources to people regardless of whether they hold a job. The case is usually argued for, and against, as a question of charity. That framing misses the point. The argument for UBI is macroeconomic, not moral. The economy already produces more than its consumers can absorb, and the only mechanism currently closing that gap is wage income. Strip out wages without replacing them, and the consumption vacuum kills the producers along with the workers. UBI, in this reading, is a deliberate consumer-side redistribution ensuring producers have a market to sell into. That makes it worth taking seriously as a transition mechanism. But it isn't the only available restructure, and it isn't the end of the story. A less-debated restructure runs in a different direction: privileging uniqueness over fungibility. The contribution model would invert. Instead of an employer specifying a role and finding someone to fill it, each person would identify what is uniquely theirs to offer (a perspective, an expertise, an identity) and find organizations or other individuals who value it enough to pay for it. In practice this would look like more solopreneurs running AI as their workforce, networks of contracted expertise instead of fungible-headcount hierarchies, and eventually contribution-based mechanisms: paid contributions to shared foundation models, peer-distributed income, structures we have not yet named. This is not a near-term reorg. It is a multi-generational shift in how labor and income relate to each other. UBI and a perspective-based contribution economy are not mutually exclusive. UBI stabilizes the transition. A perspective-based economy becomes possible because of that stability. None of this is inevitable, and the path is long. That is exactly why deliberate planning is urgent rather than optional. Without a stated end goal (a UBI-style decoupling of income from labor, a perspective-based contribution economy, both, or something else entirely), the displacement curve will outrun the redesign, and we will arrive at starvation or revolt by default. We are closer to a more humane economic system than we have ever been. But the only way to get there is to manage the transition slowly and deliberately. The biggest risk is not that AI's impact on workers will be dangerous. It is that the stress of the transitional period becomes too great, and we never arrive at the new economy AI can enable. In the immediate term, organizations will use AI to absorb fungible roles. That move is logical, predictable, and probably already happening in your company. The problem isn't the move itself. The problem is what is left over after it: a recruitment model and a labor-engagement model that no longer match the work that is actually getting done. Recruitment has to shift from "preferred qualifications" (the fungibility plug-in) to "what perspective are we adding to this room." Labor engagement has to shift from full-time employees grinding scope-specific roles to expert consultants engaged at fractional time, brought in for what they uniquely offer. Organizational shape has to shift from interchangeable hierarchy to networks of expertise. Further out, organizations themselves will become more loosely and temporarily constituted, not the permanent-staff entities we are used to today. Human labor doesn't disappear from organizations. It stops being defined by job postings, and organizations stop being defined by their permanent headcount. The VCs in the TechCrunch survey are predicting an outcome: fewer jobs, more agents. That outcome may well arrive. The work of leadership this decade is making sure it isn't the destination. Only a midpoint, on a longer journey to a fundamentally different economy. ## Read me in your inbox. New analysis on AI-driven work, every Tuesday. [Subscribe](#/portal/signup) ### The AI Race Has No Finish Line. URL: https://kevinstorer.com/blog/the-ai-race-has-no-finish-line/ Last updated: 2026-05-18T14:44:21.000Z As 2025 comes to a close, [TIME published its biggest AI developments of the year](https://time.com/7341939/ai-developments-2025-trump-china/?ref=kevinstorer.com): reasoning models that "think," a trillion dollars in infrastructure spending, a government dismantling regulation to accelerate the race, and DeepSeek upending the cost model overnight. Individually, these read as milestones, progress markers in an industry that refuses to slow down. But, taken together, they form a cautionary tale about an economy that is building at extraordinary speed in a direction nobody has confirmed is right. Start with the premise that is driving everything else: AI must get better, quickly. Whether you frame it as a national security imperative, a competitive threat, or a generational investment opportunity, the conclusion is the same. "Better" means more powerful models. Reasoning models are the flagship result of that premise, AI that thinks before it answers, that works through problems step by step, and produces genuinely different output than what came before. But reasoning requires significantly more compute than previous approaches, which requires more data centers, which requires massive capital investment to the tune of a trillion dollars and counting. That investment is now propping up a significant portion of the global economy, from NVIDIA's valuation to the energy sector buildout to construction and real estate. Every link in this chain depends on the first link holding: that "better" necessarily means "bigger and more resource-hungry." Then DeepSeek built a frontier-quality model that is smaller, cheaper, open-weight, and runs locally. One team asked the only obvious alternative question, what if "better" means "more efficient," and half a trillion dollars evaporated from NVIDIA's market cap overnight. The chain didn't break because AI failed to deliver on its promises. It broke because the foundational assumption was never stress-tested. When people talk about an "AI bubble," they tend to focus on capability. What if AI can't actually do what it promises? Hallucinations, unreliable output, overhyped demos. These are what most people point to when they worry the bubble might burst. But DeepSeek exposed a completely separate vulnerability. The threat isn't that AI doesn't work. The threat is that the entire economic structure being built around AI rests on assumptions that nobody bothered to question, because the competitive pressure was too intense for anyone to stop and think. "More efficient" wasn't a hidden insight or a breakthrough. It was the one obvious alternative to "more compute," and nobody in the American system considered it for a second. That is what makes DeepSeek so destabilizing. Not that it was a brilliant move, but that it was an obvious one, and the fact that it wasn't pursued earlier tells you everything about how little room there was in the system for anyone to simply ask whether there might be a better path. The U.S. incentive structure makes this kind of thinking almost impossible. The market rewards growth narratives, and if you're not scaling, you're losing. The government treats AI as a national security race where urgency overrides caution by design. Investors reward speed and first-mover positioning, and pausing to question your assumptions means falling behind the people who didn't. Every actor in the system is optimizing for the same thing, faster and bigger and more, and against the one move that would have prevented the fragility — discernment. This dynamic scales down to organizations. If your reason for adopting AI begins and ends with "our competitors are doing it" or "we can't afford to fall behind," you have inherited the same unexamined assumptions that made a trillion-dollar buildout vulnerable to a single problem reframing. The question isn't whether to invest in AI. The question is whether you can articulate what you're building toward, and whether that thesis survives a challenge as basic as "what if there's another way?" The companies that will define AI-driven work aren't necessarily the ones that move fastest. They're the ones that know where they are going. ## Read me in your inbox. New analysis on AI-driven work, every Tuesday. [Subscribe](#/portal/signup)