『TECHNICALLY JOYFUL - Building Change Capacity in the Age of AI』のカバーアート

TECHNICALLY JOYFUL - Building Change Capacity in the Age of AI

TECHNICALLY JOYFUL - Building Change Capacity in the Age of AI

著者： Gabrielle Kohlmeier - AI Leadership & Change Capacity
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Change is coming faster than ever. Joy helps us keep up — and shape what comes next.


Look closely at the people doing hard things extraordinarily well — creating change that lasts, building what didn’t exist before, turning ambitious ideas into reality — and you’ll notice something: joy is often part of the fuel.


They are technically joyful.


Host Gabrielle Kohlmeier has used joy again and again to build with others what seemed impossible — and create outcomes beyond what anyone thought possible. In Technically Joyful, she shares what she’s learned and brings together people doing hard, consequential things differently: leaders, builders, thinkers, and wonderfully curious humans exploring what they’re seeing, what they’re learning, what isn’t obvious yet, and what the rest of us can use.


We’ll tackle the hard questions. We’ll make the invisible work of change visible. We’ll find signal in the noise. And we’ll get practical about what actually helps people and organizations move — from technology, strategy, and systems to judgment, incentives, relationships, courage, and the wonderfully human messiness in between.


Instead of fear as fuel, we use curiosity, connection, usefulness, awe, and the gentle persistence of joy. Joy gives us energy to engage with hard things — to stay curious longer, see more possibilities, connect more dots, and keep going.

Every episode is designed to leave you seeing something differently, understanding something useful, and feeling more capable of acting — at work, in your organization, and in the much bigger experiment we’re all part of as AI reshapes our world.


Expect hard questions, fascinating people, useful ideas, unexpected connections — and to come out stronger than you went in.


Change is a muscle. Joy helps us build it.


Welcome to Technically Joyful.



Some Questions We‘ll Explore:

  • Why does being a beginner feel so threatening once you’re successful?
  • What happens when the work that disappears was also the training ground?
  • Why do some people seem energized by uncertainty while others freeze?
  • What are we losing when efficiency becomes the dominant measure of value?
  • How do you know when doing nothing is actually the riskier choice?
  • What becomes possible when we stop treating curiosity as frivolous?
  • What does AI reveal about work that was already broken?
  • How do we keep our judgment while changing how we use it?
  • What becomes more valuable — not less — as AI gets better?
  • Why might joy actually make us better at doing hard things?
© 2026 TECHNICALLY JOYFUL - Building Change Capacity in the Age of AI マネジメント マネジメント・リーダーシップ リーダーシップ 経済学
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  • Betting on Joy: Why Hopeful Skepticism Beats Cynicism (Plus Listener Stories with Bonus Guest Paul Kohlmeier)

    Betting on Joy: Why Hopeful Skepticism Beats Cynicism (Plus Listener Stories with Bonus Guest Paul Kohlmeier)
    2026/10/20
    What if staying curious, even when we're skeptical, is one of the smartest ways to navigate change?Cynicism can make us feel sharp, realistic, and prepared for disappointment. But what if our distrust isn’t protecting us, but limiting us - and worse making us duller? In this episode of Technically Joyful, Gabrielle Kohlmeier explores the science of hope and skepticism, drawing on Stanford psychologist Jamil Zaki's research, systems thinking, and her own journey beyond fear-based perfectionism. Gabrielle examines why hope is different from optimism, how our mental models shape what we see, and why staying open to new evidence matters in an increasingly uncertain world.Gabrielle also considers what happens when we begin adapting ourselves to technology rather than shaping technology around human needs. As AI changes how we work, learn, and make decisions, curiosity and joy can help us resist narrowing our thinking, take thoughtful risks, and remain open to possibilities we haven't yet imagined.In a bonus segment, Gabrielle and special guest Paul Kohlmeier read and discuss some delightfully listener stories that reveal the humor and human connection emerging in our interactions with AI. Paul, an AI-native learning architect at ServiceNow, also takes on what staying curious looks like in practice. From AI experiments that didn't go as planned to lessons learned from beginners, they explore how frustration can fuel curiosity, why existing skills matter when learning new technologies, and the importance of asking questions rather than accepting every AI response. Because staying curious doesn't mean believing everything will work out. It means continuing to ask questions, learn from what happens, and recognize that we still have a role in shaping what comes next.Your Transformation RepThink of one assumption you've been carrying about AI or how your team is going to react to it, or is reacting to it.Open whatever AI tool you're using and try this:“Here's an assumption I'm carrying. Help me zoom out. What else might be going on? Whose perspective am I missing? And what's one small, low-stakes thing I could find out whether I'm right?”Then go actually do the small thing. Have the conversation. Try the tiny version. See what comes back.You might have been right. Either way, you'll be working from a fuller picture, and that's the whole point.Episode Highlights:Why cynics are often perceived as smarter, and what the research reveals about skepticism and cognitive performanceThe difference between hope and optimism, and why you don't need a naturally positive outlook to practice hopeHow mental models influence what we notice, believe, and remain open toMoving beyond perfectionism by taking thoughtful risks and learning through experienceThe risk of flattening human thinking to fit technology, rather than shaping technology around usHow frustration, curiosity, and experimentation influence the way we learn with AIWhy beginners can help experts see what they're missing, and how existing skills transfer to new AI toolsThe unexpected humor, personality, and connection that emerge from everyday interactions with AISources & Resources:Jamil Zaki | Hope for Cynics https://www.jamil-zaki.com/hope-for-cynics - The Surprising Science of Human Goodness Huberman Lab | Conversation with Jamil Zaki https://www.hubermanlab.com/episode/dr-jamil-zaki-how-to-cultivate-a-positive-growth-oriented-mindset - How to Cultivate a Positive, Growth-Oriented Mindset, September 2, 2024. Olga Stavrova and Daniel Ehlebracht | The Cynical Genius Illusion https://pubmed.ncbi.nlm.nih.gov/29993325/Exploring and Debunking Lay Beliefs About Cynicism and Competence. Personality and Social Psychology Bulletin 45(2), 254-269 (2019; online 2018). DOI 10.1177/0146167218783195. Cabrera Lab | Mental models and organizing information https://www.cabreralab.science/post/unraveling-the-cloud-how-culture-marketing-builds-shared-mental-modelsUnraveling the Cloud: How Culture Marketing Builds Shared Mental Models. A related primary resource from the lab explaining M=IO and distinctions, systems, relationships and perspectives.Jaron Lanier | You Are Not a Gadget https://www.penguinrandomhouse.com/books/97779/you-are-not-a-gadget-by-jaron-lanier/9780307593146/ABA Commission on Women | The Grit Project https://www.americanbar.org/groups/diversity/women/initiatives_awards/grit-project/Pēteris Zilgalvis | FutureLaw keynote | Reverse adaptation https://futurelaw.ee/speaker/peteris-zilgalvis/https://newsline.haystackid.com/futurelaw-2026-opens-in-tallinn-with-a-sharp-question-who-governs-the-governors/Ways to Connect with GabrielleSubscribe to Technically Joyful on Apple Podcasts, Spotify, and YouTube: technicallyjoyful.comNewsletter: technicallyjoyful.comGK Strategic Advisory: gkstrategic.comLinkedIn: linkedin.com/in/gabrielle-kohlmeier Gabrielle Kohlmeier is a licensed attorney, but nothing on Technically Joyful is legal advice, and no specific results are...
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    42 分
  • The Joy of Making It Work: Leading Through Constant Change | Maureen Naughton

    The Joy of Making It Work: Leading Through Constant Change | Maureen Naughton
    2026/10/13

    What do you hold onto when the technology, possibilities, and even the questions keep changing?

    Maureen Naughton, Chief Innovation & Information Officer at Lowenstein Sandler, has spent her career figuring out how organizations actually change. For Maureen, leading transformation no longer means having the next three years mapped out. It's about knowing what shouldn't change, building strong enough foundations to adapt around it, and staying curious enough to keep questioning what comes next.

    In this conversation, Gabrielle Kohlmeier and Maureen explore what it takes to turn the possibilities of AI into change that actually works. Maureen makes the case for transformation as evolution rather than revolution: start with the problems people actually have, meet them where they are, and use the “art of the possible” to create the energy that helps people move.

    That possibility still needs something solid underneath it. Maureen's premise for AI is simple: it should make us smarter, not dumber. The tools may change how work gets done, but they don't eliminate the need for human judgment, trust, relationships, ethics, quality or client value. In a world where Maureen now thinks in six-month plans instead of three-year plans, those constants become a foundation for navigating uncertainty rather than trying to eliminate it.

    Gabrielle and Maureen also get into the less glamorous work that makes transformation possible: data, process, governance, existing technology and operational design. Because moving faster doesn't always mean chasing the newest thing. Building the right foundation - across data, process and technology - makes it easier to identify where AI can actually add value and where to focus next.

    And underneath all of it is something Maureen genuinely enjoys: getting to figure out what doesn't exist yet. Change can be uncertain, vulnerable and difficult, and still create the energy to ask, How lucky are we that we get to build what comes next?


    Your Transformation Rep


    Let AI Coach Your Thinking

    Pick one decision, tension or problem you're genuinely thinking through. Instead of asking AI, “What should I do?” ask it to coach your thinking:

    “You get five questions. Ask me one at a time. Don't give me advice. Help me notice something that's missing, something that I'm assuming, and what matters most to me in this decision.”

    Answer the questions, then stop and ask yourself:

    Did I notice something I hadn't noticed before?

    The goal isn't to hand the decision over to AI. It's to use AI to create a little more space for your own judgment.



    Episode Highlights:

    • Why transformation can be an evolution rather than a revolution
    • How the “art of the possible” creates energy and momentum for change
    • Why listening and vulnerability can strengthen leadership
    • How to move AI skeptics by starting with problems that matter to them
    • Why Maureen has shifted from three-year plans to six-month plans
    • What remains constant when the tools and delivery mechanisms keep changing
    • Why AI should strengthen human thinking rather than replace it
    • The role of data, process, governance and existing technology in AI transformation
    • What operational redesign means beyond simply adopting new tools
    • Why strategy gives organizations a place to assess, learn and course-correct
    • The joy of getting to create something that didn't exist before



    Ways to Connect with Gabrielle

    Subscribe to Technically Joyful on Apple Podcasts, Spotify, and YouTube: technicallyjoyful.com

    Newsletter: technicallyjoyful.com
    GK Strategic Advisory:     gkstrategic.com
    LinkedIn:     linkedin.com/in/gabrielle-kohlmeier
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    45 分
  • Momentum: How One Step Changes What You Can See

    Momentum: How One Step Changes What You Can See
    2026/10/06

    What builds momentum when something feels too big to start?

    In this solo episode of Technically Joyful, Gabrielle Kohlmeier explores how a manageable, meaningful first step can get us moving, and why the value of that step isn't only where it gets us. It changes what we can see from the new place.

    Gabrielle draws on the unlikely way a Philadelphia Eagles rookie got her interested in American football, a first-week meeting as market general counsel that turned out to be her meeting, a finance friend who finally made accounting stick, and her experience deploying AI across a legal organization at Verizon. Across each story, the pattern is the same: the first step doesn't have to solve the whole problem. It can give us information, relationships, capacity, credibility, and energy that make the next decision possible and better.

    Sometimes overwhelm comes from not knowing what to do. Sometimes it comes from seeing so many interconnected things that need doing that the whole picture feels enormous. Either way, Gabrielle makes the case that you don't need a perfect map of steps one through twenty before you begin. Step one may give you information that changes what step two should be.


    Your Transformation Rep

    Find your step zero.

    Open whatever AI tool you already use, give it the thing that feels too big, and ask:

    “What's my step zero? Give me one physical action I can take, no additional planning required.”

    Once you've got it, ask:

    “What might I know, have, or be able to do after I take this step that I can't do right now?”

    If breaking something into manageable pieces is particularly hard, Gabrielle also recommends Magic ToDo from Goblin Tools, which breaks overwhelming tasks into smaller steps.


    Send your fun, surprising, or tragically awful AI attempts to connect@gkstrategic.com. Some may be featured on the show.



    Episode Highlights:

    • Why the step that works is manageable and meaningful, even when it's tiny
    • Two kinds of overwhelm: not knowing what to do and seeing too much that needs doing
    • Why asking a question can be the first step toward building momentum
    • Becoming capable before becoming self-sufficient
    • Choosing first AI steps that give you data and make the next step smarter
    • Why you don't need a perfect map of steps one through twenty
    • Caroline Kealey's idea to “be a compass, not a roadmap”
    • Brad Stulberg's “motion precedes motivation”
    • Whitney Johnson's S Curve of Learning and why the beginning can feel disproportionately hard
    • Finding your own “rookie” with AI



    Sources & Resources:

    • Caroline Kealey, founder of Results Map, on leading change as "a compass, not a roadmap," from the Change Signal podcast. https://www.resultsmap.com/about/about-caroline/ | https://thechangesignal.com/podcast
    • Brad Stulberg, writer on performance and wellbeing, on motivation following action. https://www.bradstulberg.org
    • Whitney Johnson, *Disrupt Yourself*, and the S Curve of Learning. https://thedisruptionadvisors.com/books/
    • Goblin Tools, Magic ToDo, a task breakdown tool designed mostly to help neurodivergent people with tasks they find overwhelming. https://goblin.tools/ToDo


    Ways to Connect with Gabrielle

    Subscribe to Technically Joyful on Apple Podcasts, Spotify, and YouTube: technicallyjoyful.com

    Newsletter: technicallyjoyful.com
    GK Strategic Advisory:     gkstrategic.com
    LinkedIn:     linkedin.com/in/gabrielle-kohlmeier
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    15 分
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