POPULARITY
14 episodes with ideaflow
2 episodes with ideaflow
2 episodes with ideaflow
2 episodes with ideaflow
2 episodes with ideaflow
In this episode of Crazy Wisdom, host Stewart Alsop talks with Phil Filippak, a software arcanist and knowledgemancer from Ideaflow. The conversation covers a range of topics, including knowledge management, the discipline behind organizing knowledge, personal systems for note-taking, and the impact of AI on programming and game development. Phil shares his experiences with tools like Obsidian and discusses the balance between creative exploration and over-systematization in managing information. You can follow Phil on Twitter at @Blisstweeting (https://twitter.com/Blisstweeting) for more insights.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction to the Crazy Wisdom Podcast00:49 Phil's Journey and Knowledge Management02:17 The Discipline of Knowledge Management05:49 Personal Struggles and Systematization09:43 AI's Role in Knowledge Management16:16 The Future of AI and Programming21:03 Monasteries and the Future of Coding28:03 Navigating Quests Without Markers28:46 Evolution of Game Engines32:02 Creating Games as a Solo Developer34:42 The Balance Between Art and Commerce in Gaming45:00 Knowledge Management in Large Companies52:03 Final Thoughts and Contact InformationKey InsightsThe Role of Discipline in Knowledge Management: Phil Filippak emphasizes that knowledge management is more than just gathering information—it's about organizing it with discipline. This process involves creating orderly structures, either mentally or through notes, to track progress across different areas of interest. Discipline is crucial for maintaining an interconnected understanding of multiple fields.Over-Systematization Can Be a Trap: While using tools like Obsidian to systematize knowledge can be helpful, Phil warns that too much structure can become burdensome. Over-systematizing can make it harder to add new information and can stifle creativity, leading to a reluctance to engage with the system at all.AI's Transformative Role in Programming: Phil discusses how AI is changing the landscape of software development, particularly by assisting with tedious tasks like debugging. However, he points out that AI hasn't yet reached a point where it can handle more creative or complex problem-solving without human intervention, leaving room for the enjoyment and intellectual satisfaction that come from manual coding.Creativity in Game Development is Often Stifled by Commercial Pressures: Large gaming companies, driven by shareholder value, tend to avoid risks and stick to formulas that are proven to sell. Phil notes that this limits experimentation, whereas indie game developers and smaller studios—especially in places like Serbia—have more freedom to innovate and take creative risks.Periodic “Resets” in Personal Knowledge Systems: Phil recommends performing occasional resets on personal knowledge systems when they become too complex. This involves stripping away unnecessary rules and simplifying processes to keep the system flexible and sustainable, helping to avoid burnout from excessive structure.The Idea of a Code Monastery: Drawing on the historical role of monasteries as centers of knowledge preservation, Phil introduces the idea of a "code monastery" where programmers could dedicate themselves to maintaining and refining software. This concept highlights the aesthetic and spiritual satisfaction of combining technical expertise with a disciplined, purpose-driven lifestyle.The Future of Programming and AI: Looking ahead, Phil acknowledges that while AI will likely continue to take over more routine programming tasks, there will always be people passionate about coding for its intellectual rewards. He believes that even in an AI-dominated future, the human element of creativity and problem-solving in programming will remain essential.
In this episode of the Crazy Wisdom podcast, host Stewart Alsop welcomes Cody Hergenroeder, a versatile creator deeply invested in product management. They explore the intricate relationships between symbolic systems and product management, discussing how these domains interconnect within the corporate environment. Cody shares insights on the role of connective tissue in organizations, the nature of memory and knowledge, and the evolving impact of artificial intelligence on society. This episode also touches on AI's role in modern note-taking and the broader implications for knowledge management. For more about Cody's work, visit his LinkedIn.Check out this GPT we trained on the conversation!Timestamps00:00 Introduction to the Crazy Wisdom Podcast00:18 Exploring Product Management and Symbolic Systems01:41 The Role of Connective Tissue in Organizations04:07 The Evolution and Importance of Note-Taking09:06 The Concept of First Brain, Second Brain, and AI as Third Brain13:57 The Impact of AI on Society and Business21:10 Philosophical Musings on Knowledge and Consciousness25:28 Exploring the Concept of Knowing27:20 The Debate on AI Consciousness29:27 The Rapid Evolution of AI32:45 Human Creativity and AI37:45 Building in Public: A New Business Idea45:22 The Future of Music and AI50:00 Conclusion and Final ThoughtsKey Insights1-Interplay Between Symbolic Systems and Product Management: Cody Hergenroeder elaborates on how his background in Symbolic Systems—a field that blends cognitive science, artificial intelligence, and linguistics—naturally led him to product management. He likens product managers to the circulatory system of a company, highlighting their role in connecting various parts of the organization and ensuring smooth operations, much like how symbolic systems integrate diverse fields to create cohesive understanding.2-The Role of Connective Tissue in Organizations: Both Stewart and Cody discuss the metaphor of connective tissue within organizations. Just as connective tissue holds the human body together, product managers serve as the essential link between different departments, facilitating communication and collaboration. This metaphor underscores the critical, often unseen, work that product managers do to maintain organizational coherence and functionality.3-The Evolving Nature of Knowledge Management with AI: Cody touches on the transformative potential of AI in knowledge management, particularly in note-taking and information retrieval. He explains how tools like IdeaFlow are being developed to not only record conversations but also extract and organize key insights, creating structured knowledge bases that enhance both personal and organizational productivity.4-The Concept of the Third Brain: Building on the ideas of the first brain (biological memory) and the second brain (written or digital notes), the conversation introduces the notion of a third brain—AI. This third brain represents a new layer of cognition and information processing, enabling humans to outsource and enhance their memory and analytical capabilities. The discussion reflects on how AI, as this third brain, is reshaping our approach to knowledge and creativity.5-The Dual Nature of Human and AI Cognition: The episode delves into the philosophical aspects of human and AI cognition. Stewart and Cody explore the distinctions between knowing and knowing about, emphasizing that while AI can process and analyze vast amounts of information, it lacks the experiential and conscious aspects of human knowledge. This conversation highlights the complementary strengths of human intuition and AI's analytical power.6-Impact of AI on the Music Industry: Stewart brings up the impact of AI on the music industry, noting how AI-generated music and advanced recommendation systems are changing how music is created and consumed. They discuss the potential for AI to democratize music production, making it easier for new artists to create and distribute their work, while also raising questions about the sustainability of current business models like Spotify's.7-The Intersection of Art, Capitalism, and Technology: Reflecting on the broader implications of technological advancements, Cody and Stewart consider how capitalism and art intersect within the realm of AI and digital innovation. They discuss how economic structures influence the development and dissemination of technology and art, and how AI might accelerate trends that reflect both the creative and exploitative potentials of these systems.
Ready for more ideas about UX for AI and LLM applications in enterprise environments? In part 2 of my topic on UX considerations for LLMs, I explore how an LLM might be used for a fictitious use case at an insurance company—specifically, to help internal tools teams to get rapid access to primary qualitative user research. (Yes, it's a little “meta”, and I'm also trying to nudge you with this hypothetical example—no secret!) ;-) My goal with these episodes is to share questions you might want to ask yourself such that any use of an LLM is actually contributing to a positive UX outcome Join me as I cover the implications for design, the importance of foundational data quality, the balance between creative inspiration and factual accuracy, and the never-ending discussion of how we might handle hallucinations and errors posing as “facts”—all with a UX angle. At the end, I also share a personal story where I used an LLM to help me do some shopping for my favorite product: TRIP INSURANCE! (NOT!) Highlights/ Skip to: (1:05) I introduce a hypothetical internal LLM tool and what the goal of the tool is for the team who would use it (5:31) Improving access to primary research findings for better UX (10:19) What “quality data” means in a UX context (12:18) When LLM accuracy maybe doesn't matter as much (14:03) How AI and LLMs are opening the door for fresh visioning work (15:38) Brian's overall take on LLMs inside enterprise software as of right now (18:56) Final thoughts on UX design for LLMs, particularly in the enterprise (20:25) My inspiration for these 2 episodes—and how I had to use ChatGPT to help me complete a purchase on a website that could have integrated this capability right into their website Quotes from Today's Episode “If we accept that the goal of most product and user experience research is to accelerate the production of quality services, products, and experiences, the question is whether or not using an LLM for these types of questions is moving the needle in that direction at all. And secondly, are the potential downsides like hallucinations and occasional fabricated findings, is that all worth it? So, this is a design for AI problem.” - Brian T. O'Neill (8:09) “What's in our data? Can the right people change it when the LLM is wrong? The data product managers and AI leaders reading this or listening know that the not-so-secret path to the best AI is in the foundational data that the models are trained on. But what does the word *quality* mean from a product standpoint and a risk reduction one, as seen from an end-users' perspective? Somebody who's trying to get work done? This is a different type of quality measurement.” - Brian T. O'Neill (10:40) “When we think about fact retrieval use cases in particular, how easily can product teams—internal or otherwise—and end-users understand the confidence of responses? When responses are wrong, how easily, if at all, can users and product teams update the model's responses? Errors in large language models may be a significant design consideration when we design probabilistic solutions, and we no longer control what exactly our products and software are going to show to users. If bad UX can include leading people down the wrong path unknowingly, then AI is kind of like the team on the other side of the tug of war that we're playing.” - Brian T. O'Neill (11:22) “As somebody who writes a lot for my consulting business, and composes music in another, one of the hardest parts for creators can be the zero-to-one problem of getting started—the blank page—and this is a place where I think LLMs have great potential. But it also means we need to do the proper research to understand our audience, and when or where they're doing truly generative or creative work—such that we can take a generative UX to the next level that goes beyond delivering banal and obviously derivative content.” - Brian T. O'Neill (13:31) “One thing I actually like about the hype, investment, and excitement around GenAI and LLMs in the enterprise is that there is an opportunity for organizations here to do some fresh visioning work. And this is a place that designers and user experience professionals can help data teams as we bring design into the AI space.” - Brian T. O'Neill (14:04) “If there was ever a time to do some new visioning work, I think now is one of those times. However, we need highly skilled design leaders to help facilitate this in order for this to be effective. Part of that skill is knowing who to include in exercises like this, and my perspective, one of those people, for sure, should be somebody who understands the data science side as well, not just the engineering perspective. And as I posited in my seminar that I teach, the AI and analytical data product teams probably need a fourth member. It's a quartet and not a trio. And that quartet includes a data expert, as well as that engineering lead.” - Brian T. O'Neill (14:38) Links Perplexity.ai: https://perplexity.ai Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-Metric-Matters/dp/0593420586 My article that inspired this episode
It doesn't matter who you are or what you do, you're creative. We all are. And the capacity for innovative and creative ideas has never been more important in our work lives and beyond. Our first idea is often not our best. And yet many of us get stuck in the ‘good enough' rut, rather than pursuing something magical.So what does it take to be truly innovative? How can we nurture fertile ground for gamechanging ideas? an environment where ideas flow?I'm sally clarke and today alexis zahner and i are speaking with Jeremy Utley. Jeremy is a world-leading expert on creativity, innovation and entrepreneurship. He's an adjunct professor at Stanford University, a General Partner at Freespin Capital, and the co-author of Ideaflow: The Only Business Metric That Matters. His award-winning teaching has touched more than a million students of innovation. His current research concerns the impact of generative AI on problem solving and innovation. Jeremy is a natural born communicator and this conversation will open your eyes on ideas, creativity and the impact of integrating AI at work, including practical tips on how to harness AI to improve you and your team's ideaflow. Hosted on Acast. See acast.com/privacy for more information.
We are having a Stanford self-improvement sort of weekend. Yesterday, KEEN ON featured a conversation with two Stanford profs on how to acquire a venture capital mindset. Today, Jeremy Utley, the director of education at Stanford's Institute of Design, teaches us how to facilitate our own epiphanies. In his new co-authored book, IdeaFlow: The Only Business Metric that Matters, Utley - who boasts of having been “facilitating epiphanies for over 20 years” - promises to teach us how to radically innovate in the style of disruptive masters like Bezos or Jobs. Trust an evangelical Stanford prof to be in the business of transforming commercial innovation into religion. Not everyone, I suspect, will be quite as keen as Jeremy Utley in becoming personal assembly lines of their own creativity. Jeremy Utley is the Co-Founder of Stanford's Masters of Creativity at the Hasso Plattner Institute of Design at Stanford. He was formerly the Director of Executive Education at the Stanford d.school, where his blend of on-your-feet thinking and penetrating insight have earned him a reputation as a go-to advisor for CEO's and start-up founders alike. The co-author of the book ideaflow (2022), and co-host of the popular Paint & Pipette Podcast, continues to work on invention, discovery and entrepreneurship and demystifies the counter-intuitive techniques that drive productive creativity. Known for his work in Lean Start-Up and Design Thinking methodologies, Jeremy shows their ability to drive product development and consumer engagement at a fraction of typical product development costs. Jeremy is also a General Partner at Freespin Capital, a venture firm that helps legacy companies launch hyper-growth start-ups. He holds a BBA with Honors in Finance from The University of Texas at Austin (2005) and an MBA from the Stanford University Graduate School of Business (2009). Jeremy's book Ideaflow: The Only Business Metric That Matters unpacks why and how innovation is not an event, a workshop, a sprint, or a hackathon; it's a result of mastering ideaflow, a practice that elevates everything else you do. Check out the Ideaflow website at www.ideaflow.design.Named as one of the "100 most connected men" by GQ magazine, Andrew Keen is amongst the world's best known broadcasters and commentators. In addition to presenting KEEN ON, he is the host of the long-running How To Fix Democracy show. He is also the author of four prescient books about digital technology: CULT OF THE AMATEUR, DIGITAL VERTIGO, THE INTERNET IS NOT THE ANSWER and HOW TO FIX THE FUTURE. Andrew lives in San Francisco, is married to Cassandra Knight, Google's VP of Litigation & Discovery, and has two grown children.Keen On is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit keenon.substack.com/subscribe
My guest today asserts that we all want great ideas, but few of us actually understand how great ideas are born. Innovation, he says, is not an event, a workshop, a sprint, or a hackathon. It's a result of mastering ideaflow, a practice that elevates everything else you do. His name is Jeremy Utley, and […] The post 528: The Only Business Metric That Matters is Ideaflow with Jeremy Utley (an Encore Presentation of Episode 446) first appeared on Read to Lead Podcast.
Welcome to the What's Next! Podcast with Tiffani Bova. This week I have the pleasure of welcoming Jeremy Utley to the show. He is the Director of Executive education at Stanford's d.school and an adjunct professor at Stanford School of Engineering. He is also the co-host of the school's widely popular program, Stanford's Masters of Creativity. He's the co-author of Ideaflow: The Only Business Metric That Matters. THIS EPISODE IS PERFECT FOR… anyone who wants to develop their creative muscle. TODAY'S MAIN MESSAGE… everyone has the capacity to be creative, but over time, many lose touch with these instincts. Jeremy brings a refreshing take on how to regain a mindset of exploration in a world that often prioritizes conformity over innovation. He challenges listeners to push beyond the seemingly “bad” ideas, and even the first idea that seems good enough, to unlock new possibilities. WHAT I LOVE MOST… so much of our thinking is about our environment. I love when Jeremy says, “If you want to think outside the box, get out of the box.” He shares the story of Steve Jobs, who, frustrated by the design challenges of the first Macintosh computer, drove to Macy's, bought a food processor, and presented that to his team as the vision for the design. He sought out a new and unexpected input to trigger his imagination and was able to create something new and innovative. Running Time: 21:27 Subscribe on iTunes Find Tiffani Online: Facebook Twitter LinkedIn Find Jeremy Online: Website LinkedIn Twitter How To Fix It Beyond The Prompt Jeremy's Book: Ideaflow: The Only Business Metric That Matters
This week on episode 357, we interviewed Grant Halloran, CEO of Planful, Jeremy Utley, Author of Ideaflow, Kian Gohar, Founder and CEO at Geolab and Chris Deaver, Co-author of Brave Together: Lead by Design, Spark Creativity and Shape the Future with the Power of Co-Creation. DisrupTV is a weekly podcast with hosts R “Ray” Wang and Vala Afshar. The show airs live at 11 AM PT/ 2 PM ET every Friday. Brought to you by Constellation Executive Network: constellationr.com/CEN.
Leveraging AI to unlock new levels of creativity and communication innovationJoin Matt Abrahams with creativity and innovation experts Jeremy Utley and Kian Gohar to explore the transformative potential of AI in the realms of creativity and problem-solving. If you treat artificial intelligence like an oracle, you'll likely be disappointed. But if you treat it like a teammate, Utley and Gohar say you'll be surprised just how helpful a collaborator it can be.Utley, an adjunct professor at the Stanford d.school, and Gohar, a bestselling author, keynote speaker, and futurist, have researched how teams can integrate AI into existing workflows to generate more creative ideas and streamline problem-solving. As they've found, large language models (LLMs) like ChatGPT can be powerful tools for innovation. But without knowing how to implement them, “Most teams leave the vast majority of their innovation potential on the table,” Utley says. In a new white paper, he and Gohar illuminate the path teams can take to use generative AI as a “conversation partner” and transform their brainstorming efforts as a result.In this episode of Think Fast, Talk Smart, Utley and Gohar discuss how innovators can stop viewing AI as a magic 8-ball, and start treating it as a companion — one ready to roll up its sleeves and dig deep for new ideas.Think Fast, Talk Smart is a podcast produced by Stanford Graduate School of Business. Each episode provides concrete, easy-to-implement tools and techniques to help you hone and enhance your communication skills.Episode Reference Links:Jeremy Utley: WebsiteJeremy's book: IdeaflowKian Gohar: Website + GeolabKian's book: Competing In The New World Of WorkFIXITHBR article by Jeremy and Kian: Don't Let Gen AI Limit Your Team's CreativityEp.70 - Keep 'Em Coming: Why Your First Ideas Aren't Always the Best: YouTube / Website Ep.77 - Quick Thinks: AI Has Entered the Chat: YouTube / Website Communicators Kian admires:Peggy Noonan + Declarations at The Wall Street Journal Sam Horn + Tongue Fu!: How to Deflect, Disarm, and Defuse Any Verbal ConflictConnect:Email Questions & Feedback >>> thinkfast@stanford.eduEpisode Transcripts >>> Think Fast Talk Smart WebsiteNewsletter Signup + English Language Learning >>> FasterSmarter.ioThink Fast Talk Smart >>> LinkedIn Page, Instagram, YouTubeMatt Abrahams >>> LinkedInStanford GSB >>> LinkedIn & TwitterChapters:(00:00:00) Introduction to Utley & GoharMatt Abrahams introduces guests Jeremy Utley and Kian Gohar, and their respective books.(00:01:02) Motivation Behind the Study on AI's ImpactAI's impact on creativity and problem-solving, including an experiment involving human ideation with and without AI assistance.(00:03:32) Research Findings on AI and InnovationSpecific practices to effectively use AI in teams for idea generation and the counterintuitive feelings associated with AI-assisted work.(00:04:37) The Counterintuitive Nature of AI AssistanceWhy teams that used AI effectively felt worse about their work, and the importance of adjusting work processes to incorporate AI as a collaborative tool.(00:07:59) The FIXIT Methodology for AI CollaborationThe FIXIT methodology, a five-step process to enhance collaboration with AI.(00:12:29) Enhancing Conversations with AIImproving conversations with AI, including using audio messages for interaction and exploring different large language models for varied inputs.(00:17:45) The Final Three QuestionsJeremy Utley offers a starting point for individuals new to ChatGPT, and Kian Gohar shares two communicators he admires and his three ingredients for successful communication.(00:22:57) ConclusionSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Mastering the Art of Idea Generation: Insights from Stanford's D SchoolJeremy Utley is the Director of Executive Education at Stanford's d.school, author of "IDEAFLOW: The Only Business Metric That Matters," and an incredible innovator. In today's fast-paced business landscape, where creativity and innovation are paramount, Jeremy offers unique insights and strategies to supercharge the innovation process.Jeremy opened my eyes to new ways of thinking and new ways of creating. This episode was powerful. We spoke about idea generation, AI and how we can leverage it in both business and everyday life. Please share this episode with your loved ones.You can purchase your copy of IDEAFLOW here - https://www.jeremyutley.design/ideaflowCheckout Jeremy's website here - https://www.jeremyutley.designListen to the episode mentioned on Jeremy's Podcast with Ed Catmull here - https://podcasts.apple.com/nz/podcast/s3e01-wizard-of-awe-peek-behind-the-pixar-curtain/id1586707064?i=1000628222318Read the HBR study on AI here - https://hbr.org------------------------Most people are downloading this FREE guide to level up their Personal Mastery - https://www.jjlaughlin.com/offers/2wBnEQEH/checkoutIf you would like to help James continue to bring on world-class guests, please consider making a small recurring donation to cover the back end, admin and editing costs. For many years, James has dedicated countless hours to the show and would LOVE to continue bringing you global thought leaders.Thank you for your support. It is greatly appreciated.With much gratitude.Full Transcript, Quote Cards, and a Show Summary are available here:https://www.jjlaughlin.com/blog-----Website: https://www.jjlaughlin.comYouTube: https://www.youtube.com/channel/UC6GETJbxpgulYcYc6QAKLHAFacebook: https://www.facebook.com/JamesLaughlinOfficialInstagram: https://www.instagram.com/jameslaughlinofficial/Apple Podcast: https://podcasts.apple.com/nz/podcast/life-on-purpose-with-james-laughlin/id1547874035Spotify: https://open.spotify.com/show/3WBElxcvhCHtJWBac3nOlF?si=hotcGzHVRACeAx4GvybVOQLinkedIn: https://www.linkedin.com/in/jameslaughlincoaching/James Laughlin is a High Performance Leadership Coach, Former 7-Time World Champion, Host of the Lead On Purpose Podcast and an Executive Coach to high performers and leaders. James is based in Christchurch, New Zealand.Support the show
Today, we're thrilled to welcome a special guest, Jeremy Utley, an academic marvel from Stanford, whose work on AI and idea flows is altering the landscape of business and innovation. In this episode, titled “Jeremy Utley on Getting the Most out of LLMs”, we dive into Jeremy's FIXIT approach for leveraging Large Language Models (LLMs) such as ChatGPT.Jeremy will unpack the nuances of FIXIT, showing us the importance of providing context, iterative interaction, and team incubation while using AI. We'll reminisce about how technology has evolved from cell phone minutes to AI, which is becoming as ubiquitous as smartphones once were.Frank will voice concerns about the technical aspects, like token length, while agreeing with Jeremy's method. We'll examine why AI needs to be seen as a collaborative partner rather than a magical solution. Jeremy will shine a light on his book "IdeaFlow" and share his insights on the psychological barriers hindering the effective use of AI in practice.Expect to hear anecdotal wisdom about the transformative impact of ChatGPT, the importance of prompt engineering, and the potential of AI to refine negotiation skills. Jeremy will even offer examples of how generative AI can offer fresh perspectives on personal and emotional decisions.Don't forget Frank's experiments with creating custom GPTs, such as for our character Bailey. Our lively discussion promises to leave you with a wealth of knowledge on interacting with and extracting maximum value from AI.For all this and much more, stay tuned in as we journey through the possibilities and realities of AI as a tool for amplifying human capacity.And before we kick things off, a quick reminder to check out Audible for the best in audiobooks, and please take a moment to rate and review Data Driven to help us grow and bring you content you love.Now, without further ado, let's get data driven.Show Notes00:00 "Idea flows: the only business metric that matters."05:35 Striving for the best is essential.06:46 Expert hesitations on using AI for knowledge.11:27 Asking for help, ended up with an image.14:41 Convince family to take job offer role-play.16:08 Learning new skills and fostering imagination through technology.19:54 Seek understanding from personal experiences and examples.25:48 Keanu Reeves movie sparks philosophical wonder.28:42 AI prompts emotional epiphanies for life decisions.32:11 Accidental query on creating GPU prompts explanations.36:08 Instructions for using voice mode during coaching.39:50 Ease of use and utility in busy life.43:18 Key to AI success: Team collaboration is essential.47:07 Kids don't care about data plans.50:45 Released groundbreaking technology; implications for all industries.53:18 Excited to buy internet access for chat.54:54 Jeremy Utley wows with innovation and wisdom.
In this week's episode, we revisit our conversation with Jeremy Utley. Jeremy is the Director of Executive Education at Stanford's d.school and an Adjunct Professor at Stanford's School of Engineering. He is the co-host of the d.school's widely popular program, “Stanford's Masters of Creativity.” He is the coauthor of Ideaflow: The Only Business Metric That Matters (Portfolio) with Perry Klebahn. He is also on the teaching teams of d.org, an organizational design course, and Transformative Design, a course that turns the tools of design onto graduate students' lives. One of the most prodigious collaborators at the d.school, Jeremy has taught alongside the likes of Lecrae, Dan Ariely, Laszlo Bock, and Greg McKeown. He holds a BBA with Honors in Finance from The University of Texas at Austin and an MBA from the Stanford University Graduate School of Business.
In today's conversation Judith Germain speaks to Jeremy Utley about generating ideas through creative collisions. Jeremy defines ideas as brain connections and creativity as seeking unrelated connections. Judith agreed that curiosity precedes innovation and environments foster innovation as normal work. Jeremy argued creativity deserves wellness attention like physical metrics. Behaving creatively daily through idea generation, experimentation, and reflection ensures creative resilience. Jeremy revealed top innovators' willingness to share failures and embrace the many ideas needed to produce hits. Practicing divergent thinking daily develops the adaptive mindset for solving emergent problems. Key Takeaways Idea flow (rate of generating and testing solutions) is the most important business metric. Creativity should be considered part of health and wellness. Ideas come from "creative collisions" between existing concepts. Letting your subconscious work on problems overnight can yield creative solutions. Jeremy Utley is a Stanford adjunct and world leading expert on innovation and creativity. Shortlisted for the Thinkers50 Award, he's taught a million students of innovation. You can find out more about our guest and today's episode in this Maverick Paradox Magazine article here. --- Maverick leadership is all about thinking outside the box and challenging the status quo. It's about having the courage to take risks and the confidence to lead in a way that is authentic and genuine. But amplifying your influence as a leader isn't just about having a strong vision or a big personality. It's also about having the right leadership capability and being able to execute on your ideas and plans. The consequences of not having the right level of influence as a leader can be significant. Without the ability to inspire and motivate others, you may struggle to achieve your goals and make a real impact. How Influential Are you? Take the scorecard at amplifyyourinfluence.scoreapp.com and see. Judith's book: The Maverick Paradox: The Secret Power Behind Successful Leaders. Judith's websites: The Maverick Paradox Magazine - themaverickparadox.com The Maverick Paradox Website - maverickparadox.co.uk Judith's LinkedIn profile is here, her Twitter profile (MaverickMastery) is here, Facebook here and Instagram here.
This week I'm covering Part 1 of the 15 Ways to Increase User Adoption of Data Products, which is based on an article I wrote for subscribers of my mailing list. Throughout this episode, I describe why focusing on empathy, outcomes, and user experience leads to not only better data products, but also better business outcomes. The focus of this episode is to show you that it's completely possible to take a human-centered approach to data product development without mandating behavioral changes, and to show how this approach benefits not just end users, but also the businesses and employees creating these data products. Highlights/ Skip to: Design behavior change into the data product. (05:34) Establish a weekly habit of exposing technical and non-technical members of the data team directly to end users of solutions - no gatekeepers allowed. (08:12) Change funding models to fund problems, not specific solutions, so that your data product teams are invested in solving real problems. (13:30) Hold teams accountable for writing down and agreeing to the intended benefits and outcomes for both users and business stakeholders. Reject projects that have vague outcomes defined. (16:49) Approach the creation of data products as “user experiences” instead of a “thing” that is being built that has different quality attributes. (20:16) If the team is tasked with being “innovative,” leaders need to understand the innoficiency problem, shortened iterations, and the importance of generating a volume of ideas (bad and good) before committing to a final direction. (23:08) Co-design solutions with [not for!] end users in low, throw-away fidelity, refining success criteria for usability and utility as the solution evolves. Embrace the idea that research/design/build/test is not a linear process. (28:13) Test (validate) solutions with users early, before committing to releasing them, but with a pre-commitment to react to the insights you get back from the test. (31:50) Links: 15 Ways to Increase Adoption of Data Products: https://designingforanalytics.com/resources/15-ways-to-increase-adoption-of-data-products-using-techniques-from-ux-design-product-management-and-beyond/ Company website: https://designingforanalytics.com Episode 54: https://designingforanalytics.com/resources/episodes/054-jared-spool-on-designing-innovative-ml-ai-and-analytics-user-experiences/ Episode 106: https://designingforanalytics.com/resources/episodes/106-ideaflow-applying-the-practice-of-design-and-innovation-to-internal-data-products-w-jeremy-utley/ Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-Metric-Matters/dp/0593420586/ Podcast website: https://designingforanalytics.com/podcast
Flexing your creativity muscle plays a huge role in wellness that is often overlooked. If you don't consider yourself a “creative” person, you might be surprised by what today's guest, innovation expert and Stanford professor Jeremy Utley, has to say about the discipline of imagination and how it can be used to affect your life and the world in a positive way. Jeremy Utley is one of the world's leading experts in innovation. Over the last 12 years, he served as the Director of Education at Stanford's renowned Hasso Plattner Institute of Design (aka the D School), where his courses have been experienced by nearly a million students worldwide.Jeremy advises corporate leaders on how to embed the methods and mindsets of design into their organization and works with professionals to cultivate a robust personal creative practice. As the co-host of Stanford's Masters of Creativity, he shines the spotlight on creative practice across disciplinary boundaries. On top of blogging and podcasting, he's also the general partner at Freespin Capital and co-author of IdeaFlow, which offers a proven strategy for coming up with great ideas by yourself and with your team. In this conversation, Jeremy shares how to find inspiration, how to know if an idea is “good” through scrappy experimentation, lessons for entrepreneurs from our personal experiences, and how practicing the idea quota can make magic happen. If you ever shut down your creative thinking or you thought you didn't have it within you, Jeremy's here to show you otherwise.We also cover…(02:00) Why Creativity Is Wellness & How To Open Your Mind to Creative Possibilities(28:00) Tips for Building the Discipline of Inspiration(53:00) Why Taking Action Is Key for Creative Problem Solving(01:19:00) One Simple Thing To Do To Get Started With Being CreativeResources:Click here for full show notesWebsite: jeremyutley.designRead: IdeaFlow by Jeremy UtleyTwitter: twitter.com/jeremyutleyLinkedin: linkedin.com/in/jeremyutleyPodcast: The Paint & Pipette Instagram: instagram.com/jdutley Connect with Kelly:kellyleveque.comInstagram: @bewellbykellyFacebook: www.facebook.com/bewellbykellyBe Well By Kelly is produced by Crate Media.Mentioned in this episode:Hiya Health | Get 50% off your first order at HiyaHealth.com/Kelly and give your kids the full-body nourishment they need to grow into healthy, happy adults.BWBK Protein Powder | Grass-fed protein powder OR vegan chocho protein powder with organic monk fruit has exactly what you need to make delicious, blood sugar balancing, nutrient dense smoothies—and nothing...
On this week's Tech Nation, Moira speaks with Jeremy Utley, the Director of Executive Education at Stanford University's d.school, joins Moira to talk about his book “IdeaFlow.” You have no idea how important ideas are in your life, and just how many you'll need! Then in biotech, human clinical trials have begun to treat a particular type of Epilepsy called Temporal Lobe Epilepsy. Dr. Cory Nicholas from Neurona Therapeutics tells us about their big idea and how they have developed an unprecedented cell therapy.
In this episode, we'll discuss all things artificial intelligence (AI) with Justin Grammens – a local educator and AI expert. With much discussion surrounding ChatGPT, Bard, and other AI-related tools, we're diving in to find out what its impacts are and will be on our lives – both at home and at work. What is it? Where does it get its information? How reliable is it, and how is it being used? Join us as we take a look into the future with this local expert. More about Justin Grammens and his work below: Bio: Justin Grammens is a lifelong learner, passionate about Software Development and Artificial Intelligence, and enjoys giving back to the community through non-profit organizations and teaching. He is the Founder and CEO of Recursive Awesome, a management consulting firm helping executives leverage Artificial Intelligence to bring efficiency and automation to their business. He is the Founder and CEO of Lab651, where his team helps companies quickly and efficiently build their custom software applications by guiding them with a proven process and talent to a successful release. He is the owner of IoT Weekly News, a co-founder of AppliedAI and Captovation, the host of the Conversations on AppliedAI Podcast, and an Adjunct Professor at the University of Saint Thomas in their Graduate Programs in Software. A proven leader and builder of technology, Justin speaks regularly at meetups, conferences, and on the Lab651 and AppliedAI YouTube channels. Grammens was named one of the (Real) Power 50 by Minnesota Business Magazine and launched the first-ever Artificial Intelligence conference in Minnesota. He is a mentor to students of all ages through CoderDojo Twin Cities and Macalester College's MacStartups program. Most importantly, he is blessed to have a loving wife and two amazing boys who are the light of his life. - Myself https://justingrammens.com https://www.linkedin.com/in/justingrammens https://twitter.com/justingrammens My Business on Artificial Intelligence Consulting https://recursiveawesome.com My business on Software Development https://lab651.com My Business Using AI Speech Coach https://captovation.ai Applied AI & Emerging Technologies North Conference - https://appliedaiconf.com Monthly Meetup - https://appliedai.mn Podcast - https://podcast.appliedai.mn YouTube Channel - https://video.appliedai.mn Weekly Curated News - https://appliedaiweekly.com Monthly Newsletter - https://mailchi.mp/appliedai/signup LinkedIn - https://www.linkedin.com/showcase/appliedai-mn Emerging Technologies North - https://www.emtechnorth.org/ University of Saint Thomashttps://software.stthomas.edu/about/faculty-staff/biography/justin-grammens/ Resources: Gartner Hype Cycle - https://en.wikipedia.org/wiki/Gartner_hype_cycle Ideaflow - https://www.ideaflow.design/
Have you ever wondered about the essence of an idea? According to Jeremy, there are no entirely new ideas, as our brains cannot create something from nothing. He explains that a new idea is simply a connection between two concepts that haven't been linked before. In other words, it's a novel synthesis of existing knowledge. Sharing with you our exhilarating conversation with Jeremy Utley, centered around his latest and already immensely popular book, "Ideaflow." What makes it even more fascinating is that Jeremy actively participates in start-up initiatives, providing him firsthand experiences of the concepts he describes in the book. Throughout our talk, I relished the multitude of examples he shared from his own experiences and personal life, adding depth and relatability to his insights. --- Send in a voice message: https://podcasters.spotify.com/pod/show/thoughtsparksritamcgrath/message
When we think about the greatest innovators of our time (Benjamin Franklin, Steve Jobs, Frank Lloyd Wright) we often hear about their work ethic. But one thing that all of these innovators have in common is their ability to walk away from the work. They nap, they garden, and they go shopping to give themselves a break from the problem they are working on and look for inspiration in the real world. They gave themselves space to let inspiration come to them, rather than trying to force it. In this episode of unSILOed, Greg talks with Stanford professor Jeremy Utley about his new book (co-authored with Perry Klebahn) Ideaflow: The Only Business Metric That Matters (Portfolio, 2022), which gives readers a strategy to come up with better ideas and determine which ones are worth pursuing. Jeremy Utley is a Director of Executive Education at Stanford's renowned Hasso Plattner Institute of Design (the d.school) and works with leaders around the world to untap their abilities to innovate better and more effectively. Gregory LaBlanc is a lifelong educator with degrees in History, PPE, Business, and Law, Greg currently teaches at Berkeley, Stanford, and HEC Paris. He has taught in multiple disciplines, from Engineering to Economics, from Biology to Business, from Psychology to Philosophy. He is the host of the unSILOed podcast. unSILOed is produced by University FM. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
When we think about the greatest innovators of our time (Benjamin Franklin, Steve Jobs, Frank Lloyd Wright) we often hear about their work ethic. But one thing that all of these innovators have in common is their ability to walk away from the work. They nap, they garden, and they go shopping to give themselves a break from the problem they are working on and look for inspiration in the real world. They gave themselves space to let inspiration come to them, rather than trying to force it. In this episode of unSILOed, Greg talks with Stanford professor Jeremy Utley about his new book (co-authored with Perry Klebahn) Ideaflow: The Only Business Metric That Matters (Portfolio, 2022), which gives readers a strategy to come up with better ideas and determine which ones are worth pursuing. Jeremy Utley is a Director of Executive Education at Stanford's renowned Hasso Plattner Institute of Design (the d.school) and works with leaders around the world to untap their abilities to innovate better and more effectively. Gregory LaBlanc is a lifelong educator with degrees in History, PPE, Business, and Law, Greg currently teaches at Berkeley, Stanford, and HEC Paris. He has taught in multiple disciplines, from Engineering to Economics, from Biology to Business, from Psychology to Philosophy. He is the host of the unSILOed podcast. unSILOed is produced by University FM. Learn more about your ad choices. Visit megaphone.fm/adchoices
Read: https://www.latent.space/p/ai-interfaces-and-notionShow Notes* Linus on Twitter* Linus' personal blog* Notion* Notion AI* Notion Projects* AI UX Meetup RecapTimestamps* [00:03:30] Starting the AI / UX community* [00:10:01] Most knowledge work is not text generation* [00:16:21] Finding the right constraints and interface for AI* [00:19:06] Linus' journey to working at Notion* [00:23:29] The importance of notations and interfaces* [00:26:07] Setting interface defaults and standards* [00:32:36] The challenges of designing AI agents* [00:39:43] Notion deep dive: “Blocks”, AI, and more* [00:51:00] Prompt engineering at Notion* [01:02:00] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space podcast. This is Alessio, partner and CTO in residence at Decibel Partners. I'm joined by my co-host Swyx, writer and editor of Latent Space. [00:00:20]Swyx: And today we're not in our regular studio. We're actually at the Notion New York headquarters. Thanks to Linus. Welcome. [00:00:28]Linus: Thank you. Thanks for having me. [00:00:29]Swyx: Thanks for having us in your beautiful office. It is actually very startling how gorgeous the Notion offices are. And it's basically the same aesthetic. [00:00:38]Linus: It's a very consistent aesthetic. It's the same aesthetic in San Francisco and the other offices. It's been for many, many years. [00:00:46]Swyx: You take a lot of craft in everything that you guys do. Yeah. [00:00:50]Linus: I think we can, I'm sure, talk about this more later, but there is a consistent kind of focus on taste that I think flows down from Ivan and the founders into the product. [00:00:59]Swyx: So I'll introduce you a little bit, but also there's just, you're a very hard person to introduce because you do a lot of things. You got your BA in computer science at Berkeley. Even while you're at Berkeley, you're involved in a bunch of interesting things at Replit, CatalystX, Hack Club and Dorm Room Fund. I always love seeing people come out of Dorm Room Fund because they tend to be a very entrepreneurial. You're a product engineer at IdeaFlow, residence at Betaworks. You took a year off to do independent research and then you've finally found your home at Notion. What's one thing that people should know about you that's not on your typical LinkedIn profile? [00:01:39]Linus: Putting me on the spot. I think, I mean, just because I have so much work kind of out there, I feel like professionally, at least, anything that you would want to know about me, you can probably dig up, but I'm a big city person, but I don't come from the city. I went to school, I grew up in Indiana, in the middle of nowhere, near Purdue University, a little suburb. I only came out to the Bay for school and then I moved to New York afterwards, which is where I'm currently. I'm in Notion, New York. But I still carry within me a kind of love and affection for small town, Indiana, small town, flyover country. [00:02:10]Swyx: We do have a bit of indulgence in this. I'm from a small country and I think Alessio, you also kind of identified with this a little bit. Is there anything that people should know about Purdue, apart from the chickens? [00:02:24]Linus: Purdue has one of the largest international student populations in the country, which I don't know. I don't know exactly why, but because it's a state school, the focus is a lot on STEM topics. Purdue is well known for engineering and so we tend to have a lot of folks from abroad, which is particularly rare for a university in, I don't know, that's kind of like predominantly white American and kind of Midwestern state. That makes Purdue and the surrounding sort of area kind of like a younger, more diverse international island within the, I guess, broader world that is Indiana. [00:02:58]Swyx: Fair enough. We can always dive into sort of flyover country or, you know, small town insights later, but you and I, all three of us actually recently connected at AIUX SF, which is the first AIUX meetup, essentially which just came out of like a Twitter conversation. You and I have been involved in HCI Twitter is kind of how I think about it for a little bit and when I saw that you were in town, Geoffrey Litt was in town, Maggie Appleton in town, all on the same date, I was like, we have to have a meetup and that's how this thing was born. Well, what did it look like from your end? [00:03:30]Linus: From my end, it looked like you did all of the work and I... [00:03:33]Swyx: Well, you got us the Notion. Yeah, yeah. [00:03:36]Linus: It was also in the Notion office, it was in the San Francisco one and then thereafter there was a New York one that I decided I couldn't make. But yeah, from my end it was, and I'm sure you were too, but I was really surprised by both the mixture of people that we ended up getting and the number of people that we ended up getting. There was just a lot of attention on, obviously there was a lot of attention on the technology itself of GPT and language models and so on, but I was surprised by the interest specifically on trying to come up with interfaces that were outside of the box and the people that were interested in that topic. And so we ended up having a packed house and lots of interesting demos. I've heard multiple people comment on the event afterwards that they were positively surprised by the mixture of both the ML, AI-focused people at the event as well as the interface HCI-focused people. [00:04:24]Swyx: Yeah. I kind of see you as one of the leading, I guess, AI UX people, so I hope that we are maybe starting a new discipline, maybe. [00:04:33]Linus: Yeah, I mean, there is this kind of growing contingency of people interested in exploring the intersection of those things, so I'm excited for where that's going to go. [00:04:41]Swyx: I don't know if it's worth going through favorite demos. It was a little while ago, so I don't know if... [00:04:48]Alessio: There was, I forget who made it, but there was this new document writing tool where you could apply brushes to different paragraphs. [00:04:56]Linus: Oh, this was Amelia's. Yeah, yeah, yeah. [00:04:58]Alessio: You could set a tone, both in terms of writer inspiration and then a tone that you wanted, and then you could drag and drop different tones into paragraphs and have the model rewrite them. It was the first time that it's not just auto-complete, there's more to it. And it's not asked in a prompt, it's this funny drag-an-emoji over it. [00:05:20]Linus: Right. [00:05:21]Swyx: I actually thought that you had done some kind of demo where you could select text and then augment it in different moods, but maybe it wasn't you, maybe it was just someone else [00:05:28]Linus: I had done something similar, with slightly different building blocks. I think Amelia's demo was, there was sort of a preset palette of brushes and you apply them to text. I had built something related last year, I prototyped a way to give people sliders for different semantic attributes of text. And so you could start with a sentence, and you had a slider for length and a slider for how philosophical the text is, and a slider for how positive or negative the sentiment in the text is, and you could adjust any of them in the language model, reproduce the text. Yeah, similar, but continuous control versus distinct brushes, I think is an interesting distinction there. [00:06:03]Swyx: I should add it for listeners, if you missed the meetup, which most people will have not seen it, we actually did a separate post with timestamps of each video, so you can look at that. [00:06:13]Alessio: Sorry, Linus, this is unrelated, but I think you build over a hundred side projects or something like that. A hundred? [00:06:20]Swyx: I think there's a lot of people... I know it's a hundred. [00:06:22]Alessio: I think it's a lot of them. [00:06:23]Swyx: A lot of them are kind of small. [00:06:25]Alessio: Yeah, well, I mean, it still counts. I think there's a lot of people that are excited about the technology and want to hack on things. Do you have any tips on how to box, what you want to build, how do you decide what goes into it? Because all of these things, you could build so many more things on top of it. Where do you decide when you're done? [00:06:44]Linus: So my projects actually tend to be... I think especially when people approach project building with a goal of learning, I think a common mistake is to be over-ambitious and sort of not scope things very tightly. And so a classic kind of failure mode is, you say, I'm really interested in learning how to use the GPT-4 API, and I'm also interested in vector databases, and I'm also interested in Next.js. And then you devise a project that's going to take many weeks, and you glue all these things together. And it could be a really cool idea, but then especially if you have a day job and other things that life throws you away, it's hard to actually get to a point where you can ship something. And so one of the things that I got really good at was saying, one, knowing exactly how quickly I could work, at least on the technologies that I knew well, and then only adding one new unknown thing to learn per project. So it may be that for this project, I'm going to learn how the embedding API works. Or for this project, I'm going to learn how to do vector stuff with PyTorch or something. And then I would scope things so that it fit in one chunk of time, like Friday night to Sunday night or something like that. And then I would scope the project so that I could ship something as much work as I could fit into a two-day period, so that at the end of that weekend, I could ship something. And then afterwards, if I want to add something, I have time to do it and a chance to do that. But it's already shipped, so there's already momentum, and people are using it, or I'm using it, and so there's a reason to continue building. So only adding one new unknown per project, I think, is a good trick. [00:08:14]Swyx: I first came across you, I think, because of Monocle, which is your personal search engine. And I got very excited about it, because I always wanted a personal search engine, until I found that it was in a language that I've never seen before. [00:08:25]Linus: Yeah, there's a towel tower of little tools and technologies that I built for myself. One of the other tricks to being really productive when you're building side projects is just to use a consistent set of tools that you know really, really well. For me, that's Go, and my language, and a couple other libraries that I've written that I know all the way down to the bottom of the stack. And then I barely have to look anything up, because I've just debugged every possible issue that could come up. And so I could get from start to finish without getting stuck in a weird bug that I've never seen before. But yeah, it's a weird stack. [00:08:58]Swyx: It also means that you probably are not aiming for, let's say, open source glory, or whatever. Because you're not publishing in the JavaScript ecosystem. Right, right. [00:09:06]Linus: I mean, I've written some libraries before, but a lot of my projects tend to be like, the way that I approach it is less about building something that other people are going to use en masse. And make yourself happy. Yeah, more about like, here's the thing that I built, if you want to, and often I learn something in the process of building that thing. So like with Monocle, I wrote a custom sort of full text search index. And I thought a lot of the parts of what I built was interesting. And so I just wanted other people to be able to look at it and see how it works and understand it. But the goal isn't necessarily for you to be able to replicate it and run it on your own. [00:09:36]Swyx: Well, we can kind of dive into your other AIUX thoughts. As you've been diving in, you tend to share a lot on Twitter. And I just kind of took out some of your greatest hits. This is relevant to the demo that you picked out, Alessio. And what we're talking about, which is, most knowledge work is not a text generation task. That's funny, because a lot of what Notion AI is, is text generation right now. Maybe you want to elaborate a little bit. Yeah. [00:10:01]Linus: I think the first time you look at something like GPT, the shape of the thing you see is like, oh, it's a thing that takes some input text and generates some output text. And so the easiest thing to build on top of that is a content generation tool. But I think there's a couple of other categories of things that you could build that are sort of progressively more useful and more interesting. And so besides content generation, which requires the minimum amount of wrapping around ChatGPT, the second tier up from that is things around knowledge, I think. So if you have, I mean, this is the hot thing with all these vector databases things going around. But if you have a lot of existing context around some knowledge about your company or about a field or all of the internet, you can use a language model as a way to search and understand things in it and combine and synthesize them. And that synthesis, I think, is useful. And at that point, I think the value that that unlocks, I think, is much greater than the value of content generation. Because most knowledge work, the artifact that you produce isn't actually about writing more words. Most knowledge work, the goal is to understand something, synthesize new things, or propose actions or other kinds of knowledge-to-knowledge tasks. And then the third category, I think, is automation. Which I think is sort of the thing that people are looking at most actively today, at least from my vantage point in the ecosystem. Things like the React prompting technique, and just in general, letting models propose actions or write code to accomplish tasks. That's also moving far beyond generating text to doing something more interesting. So much of the value of what humans sit down and do at work isn't actually in the words that they write. It's all the thinking that goes on before you write those words. So how can you get language models to contribute to those parts of work? [00:11:43]Alessio: I think when you first tweeted about this, I don't know if you already accepted the job, but you tweeted about this, and then the next one was like, this is a NotionAI subtweet. [00:11:53]Swyx: So I didn't realize that. [00:11:56]Alessio: The best thing that I see is when people complain, and then they're like, okay, I'm going to go and help make the thing better. So what are some of the things that you've been thinking about? I know you talked a lot about some of the flexibility versus intuitiveness of the product. The language is really flexible, because you can say anything. And it's funny, the models never ignore you. They always respond with something. So no matter what you write, something is going to come back. Sometimes you don't know how big the space of action is, how many things you can do. So as a product builder, how do you think about the trade-offs that you're willing to take for your users? Where like, okay, I'm not going to let you be as flexible, but I'm going to create this guardrails for you. What's the process to think about the guardrails, and how you want to funnel them to the right action? [00:12:46]Linus: Yeah, I think what this trade-off you mentioned around flexibility versus intuitiveness, I think, gets at one of the core design challenges for building products on top of language models. A lot of good interface design comes from tastefully adding the right constraints in place to guide the user towards actions that you want to take. As you add more guardrails, the obvious actions become more obvious. And one common way to make an interface more intuitive is to narrow the space of choices that the users have to make, and the number of choices that they have to make. And that intuitiveness, that source of intuitiveness from adding constraints, is kind of directly at odds with the reason that language models are so powerful and interesting, which is that they're so flexible and so general, and you can ask them to do literally anything, and they will always give you something. But most of the time, the answer isn't that high quality. And so there's kind of a distribution of, like, there are clumps of things in the action space of what a language model can do that the model's good at, and there's parts of the space where it's bad at. And so one sort of high-level framework that I have for thinking about designing with language models is, there are actions that the language model's good at, and actions that it's bad at. How do you add the right constraints carefully to guide the user and the system towards the things that the language model's good at? And then at the same time, how do you use those constraints to set the user expectations for what it's going to be good at and bad at? One way to do this is just literally to add those constraints and to set expectations. So a common example I use all the time is, if you have some AI system to answer questions from a knowledge base, there are a couple of different ways to surface that in a kind of a hypothetical product. One is, you could have a thing that looks like a chat window in a messaging app, and then you could tell the user, hey, this is for looking things up from a database. You can ask a question, then it'll look things up and give you an answer. But if something looks like a chat, and this is a lesson that's been learned over and over for anyone building chat interfaces since, like, 2014, 15, if you have anything that looks like a chat interface or a messaging app, people are going to put some, like, weird stuff in there that just don't look like the thing that you want the model to take in, because the expectation is, hey, I can use this like a messaging app, and people will send in, like, hi, hello, you know, weird questions, weird comments. Whereas if you take that same, literally the same input box, and put it in, like, a thing that looks like a search bar with, like, a search button, people are going to treat it more like a search window. And at that point, inputs look a lot more like keywords or a list of keywords or maybe questions. So the simple act of, like, contextualizing that input in different parts of an interface reset the user's expectations, which constrain the space of things that the model has to handle. And that you're kind of adding constraints, because you're really restricting your input to mostly things that look like keyword search. But because of that constraint, you can have the model fit the expectations better. You can tune the model to perform better in those settings. And it's also less confusing and perhaps more intuitive, because the user isn't stuck with this blank page syndrome problem of, okay, here's an input. What do I actually do with it? When we initially launched Notion AI, one of my common takeaways, personally, from talking to a lot of my friends who had tried it, obviously, there were a lot of people who were getting lots of value out of using it to automate writing emails or writing marketing copy. There were a ton of people who were using it to, like, write Instagram ads and then sort of paste it into the Instagram tool. But some of my friends who had tried it and did not use it as much, a frequently cited reason was, I tried it. It was cool. It was cool for the things that Notion AI was marketed for. But for my particular use case, I had a hard time figuring out exactly the way it was useful for my workflow. And I think that gets back at the problem of, it's such a general tool that just presented with a blank prompt box, it's hard to know exactly the way it could be useful to your particular use case. [00:16:21]Alessio: What do you think is the relationship between novelty and flexibility? I feel like we're in kind of like a prompting honeymoon phase where the tools are new and then everybody just wants to do whatever they want to do. And so it's good to give these interfaces because people can explore. But if I go forward in three years, ideally, I'm not prompting anything. The UX has been built for most products to already have the intuitive, kind of like a happy path built into it. Do you think there's merit in a way? If you think about ChatGPT, if it was limited, the reason why it got so viral is people were doing things that they didn't think a computer could do, like write poems and solve riddles and all these different things. How do you think about that, especially in Notion, where Notion AI is kind of like a new product in an existing thing? How much of it for you is letting that happen and seeing how people use it? And then at some point be like, okay, we know what people want to do. The flexibility is not, it was cool before, but now we just want you to do the right things with the right UX. [00:17:27]Linus: I think there's value in always having the most general input as an escape hatch for people who want to take advantage of that power. At this point, Notion AI has a couple of different manifestations in the product. There's the writer. There's a thing we called an AI block, which is a thing that you can always sort of re-update as a part of document. It's like a live, a little portal inside the document that an AI can write. We also have a relatively new thing called AI autofill, which lets an AI fill an entire column in a Notion database. In all of these things, speaking of adding constraints, we have a lot of suggested prompts that we've worked on and we've curated and we think work pretty well for things like summarization and writing drafts to blog posts and things. But we always leave a fully custom prompt for a few reasons. One is if you are actually a power user and you know how language models work, you can go in and write your custom prompt and if you're a power user, you want access to the power. The other is for us to be able to discover new use cases. And so one of the lovely things about working on a product like Notion is that there's such an enthusiastic and lively kind of community of ambassadors and people that are excited about trying different things and coming up with all these templates and new use cases. And having a fully custom action or prompt whenever we launch something new in AI lets those people really experiment and help us discover new ways to take advantage of AI. I think it's good in that way. There's also a sort of complement to that, which is if we wanted to use feedback data or learn from those things and help improve the way that we are prompting the model or the models that we're building, having access to that like fully diverse, fully general range of use cases helps us make sure that our models can handle the full generality of what people want to do. [00:19:06]Swyx: I feel like we've segway'd a lot into our Notion conversation and maybe I just wanted to bridge that a little bit with your personal journey into Notion before we go into Notion proper. You spent a year kind of on a sabbatical, kind of on your own self-guided research journey and then deciding to join Notion. I think a lot of engineers out there thinking about doing this maybe don't have the internal compass that you have or don't have the guts to basically make no money for a year. Maybe just share with people how you decided to basically go on your own independent journey and what got you to join Notion in the end. [00:19:42]Linus: Yeah, what happened? Um, yeah, so for a little bit of context for people who don't know me, I was working mostly at sort of seed stage startups as a web engineer. I actually didn't really do much AI at all for prior to my year off. And then I took all of 2022 off with less of a focus on it ended up sort of in retrospect becoming like a Linus Pivots to AI year, which was like beautifully well timed. But in the beginning of the year, there was kind of a one key motivation and then one key kind of question that I had. The motivation was that I think I was at a sort of a privileged and fortunate enough place where I felt like I had some money saved up that I had saved up explicitly to be able to take some time off and investigate my own kind of questions because I was already working on lots of side projects and I wanted to spend more time on it. I think I also at that point felt like I had enough security in the companies and folks that I knew that if I really needed a job on a short notice, I could go and I could find some work to do. So I wouldn't be completely on the streets. And so that security, I think, gave me the confidence to say, OK, let's try this kind of experiment.[00:20:52]Maybe it'll only be for six months. Maybe it'll be for a year. I had enough money saved up to last like a year and change. And so I had planned for a year off and I had one sort of big question that I wanted to explore. Having that single question, I think, actually was really helpful for focusing the effort instead of just being like, I'm going to side project for a year, which I think would have been less productive. And that big question was, how do we evolve text interfaces forward? So, so much of knowledge work is consuming walls of text and then producing more walls of text. And text is so ubiquitous, not just in software, but just in general in the world. They're like signages and menus and books. And it's ubiquitous, but it's not very ergonomic. There's a lot of things about text interfaces that could be better. And so I wanted to explore how we could make that better. A key part of that ended up being, as I discovered, taking advantage of this new technologies that let computers make sense of text information. And so that's how I ended up sort of sliding into AI. But the motivation in the beginning was less focused on learning a new technology and more just on exploring this general question space. [00:21:53]Swyx: Yeah. You have the quote, text is the lowest denominator, not the end game. Right, right. [00:21:58]Linus: I mean, I think if you look at any specific domain or discipline, whether it's medicine or mathematics or software engineering, in any specific discipline where there's a narrower set of abstractions for people to work with, there are custom notations. One of the first things that I wrote in this exploration year was this piece called Notational Intelligence, where I talk about this idea that so much of, as a total sidebar, there's a whole other fascinating conversation that I would love to have at some point, maybe today, maybe later, about how to evolve a budding scene of research into a fully-fledged field. So I think AI UX is kind of in this weird stage where there's a group of interesting people that are interested in exploring this space of how do you design for this newfangled technology, and how do you take that and go and build best practices and powerful methods and tools [00:22:48]Swyx: We should talk about that at some point. [00:22:49]Linus: OK. But in a lot of established fields, there are notations that people use that really help them work at a slightly higher level than just raw words. So notations for describing chemicals and notations for different areas of mathematics that let people work with higher-level concepts more easily. Logic, linguistics. [00:23:07]Swyx: Yeah. [00:23:07]Linus: And I think it's fair to say that some large part of human intelligence, especially in these more technical domains, comes from our ability to work with notations instead of work with just the raw ideas in our heads. And text is a kind of notation. It's the most general kind of notation, but it's also, because of its generality, not super high leverage if you want to go into these specific domains. And so I wanted to try to improve on that frontier. [00:23:29]Swyx: Yeah. You said in our show notes, one of my goals over the next few years is to ensure that we end up with interface metaphors and technical conventions that set us up for the best possible timeline for creativity and inventions ahead. So part of that is constraints. But I feel like that is one part of the equation, right? What's the other part that is more engenders creativity? [00:23:47]Linus: Tell me a little bit about that and what you're thinking there. [00:23:51]Swyx: It's just, I feel like, you know, we talked a little bit about how you do want to constrain, for example, the user interface to guide people towards things that language models are good at. And creative solutions do arise out of constraints. But I feel like that alone is not sufficient for people to invent things. [00:24:10]Linus: I mean, there's a lot of directions, I think, that could go from that. The origin of that thing that you're quoting is when I decided to come help work on AI at Notion, a bunch of my friends were actually quite surprised, I think, because they had expected that I would have gone and worked… [00:24:29]Swyx: You did switch. I was eyeing that for you. [00:24:31]Linus: I mean, I worked at a lab or at my own company or something like that. But one of the core motivations for me joining an existing company and one that has lots of users already is this exact thing where in the aftermath of a new foundational technology emerging, there's kind of a period of a few years where the winners in the market get to decide what the default interface paradigm for the technology is. So, like, mini computers, personal computers, the winners of that market got to decide Windows are and how scrolling works and what a mouse cursor is and how text is edited. Similar with mobile, the concept of a home screen and apps and things like that, the winners of the market got to decide. And that has profound, like, I think it's difficult to understate the importance of, in those few critical years, the winning companies in the market choosing the right abstractions and the right metaphors. And AI, to me, seemed like it's at that pivotal moment where it's a technology that lots of companies are adopting. There is this well-recognized need for interface best practices. And Notion seemed like a company that had this interesting balance of it could still move quickly enough and ship and prototype quickly enough to try interesting interface ideas. But it also had enough presence in the ecosystem that if we came up with the right solution or one that we felt was right, we could push it out and learn from real users and iterate and hopefully be a part of that story of setting the defaults and setting what the dominant patterns are. [00:26:07]Swyx: Yeah, it's a special opportunity. One of my favorite stories or facts is it was like a team of 10 people that designed the original iPhone. And so all the UX that was created there is essentially what we use as smartphones today, including predictive text, because people were finding that people were kind of missing the right letters. So they just enhanced the hit area for certain letters based on what you're typing. [00:26:28]Linus: I mean, even just the idea of like, we should use QWERTY keyboards on tiny smartphone screens. Like that's a weird idea, right? [00:26:36]Swyx: Yeah, QWERTY is another one. So I have RSI. So this actually affects me. QWERTY was specifically chosen to maximize travel distance, right? Like it's actually not ergonomic by design because you wanted the keyboard, the key type writers to not stick. But we don't have that anymore. We're still sticking to QWERTY. I'm still sticking to QWERTY. I could switch to the other ones. I forget. QORAC or QOMAC anytime, but I don't just because of inertia. I have another thing like this. [00:27:02]Linus: So going even farther back, people don't really think enough about where this concept of buttons come from, right? So the concept of a push button as a thing where you press it and it activates some binary switch. I mean, buttons have existed for, like mechanical buttons have existed for a long time. But really, like this modern concept of a button that activates a binary switch really gets like popularized by the popular advent of electricity. Before the electricity, if you had a button that did something, you would have to construct a mechanical system where if you press down on a thing, it affects some other lever system that affects as like the final action. And this modern idea of a button that is just a binary switch gets popularized electricity. And at that point, a button has to work in the way that it does in like an alarm clock, because when you press down on it, there's like a spring that makes sure that the button comes back up and that it completes the circuit. And so that's the way the button works. And then when we started writing graphical interfaces, we just took that idea of a thing that could be depressed to activate a switch. All the modern buttons that we have today in software interfaces are like simulating electronic push buttons where you like press down to complete a circuit, except there's actually no circuit being completed. It's just like a square on a screen. [00:28:11]Swyx: It's all virtualized. Right. [00:28:12]Linus: And then you control the simulation of a button by clicking a physical button on a mouse. Except if you're on a trackpad, it's not even a physical button anymore. It's like a simulated button hardware that controls a simulated button in software. And it's also just this cascade of like conceptual backwards compatibility that gets us here. I think buttons are interesting. [00:28:32]Alessio: Where are you on the skeuomorphic design love-hate spectrum? There's people that have like high nostalgia for like the original, you know, the YouTube icon on the iPhone with like the knobs on the TV. [00:28:42]Linus: I think a big part of that is at least the aesthetic part of it is fashion. Like fashion taken very literally, like in the same way that like the like early like Y2K 90s aesthetic comes and goes. I think skeuomorphism as expressed in like the early iPhone or like Windows XP comes and goes. There's another aspect of this, which is the part of skeuomorphism that helps people understand and intuit software, which has less to do with skeuomorphism making things easier to understand per se and more about like, like a slightly more general version of skeuomorphism is like, there should be a consistent mental model behind an interface that is easy to grok. And then once the user has the mental model, even if it's not the full model of exactly how that system works, there should be a simplified model that the user can easily understand and then sort of like adopt and use. One of my favorite examples of this is how volume controls that are designed well often work. Like on an iPhone, when you make your iPhone volume twice as loud, the sound that comes out isn't actually like at a physical level twice as loud. It's on a log scale. When you push the volume slider up on an iPhone, the speaker uses like four times more energy, but humans perceive it as twice as loud. And so the mental model that we're working with is, okay, if I make this, this volume control slider have two times more value, it's going to sound two times louder, even though actually the underlying physics is like on a log scale. But what actually happens physically is not actually what matters. What matters is how humans perceive it in the model that I have in my head. And there, I think there are a lot of other instances where the skeuomorphism isn't actually the thing. The thing is just that there should be a consistent mental model. And often the easy, consistent mental model to reach for is the models that already exist in reality, but not always. [00:30:23]Alessio: I think the other big topic, maybe before we dive into Notion is agents. I think that's one of the toughest interfaces to crack, mostly because, you know, the text box, everybody understands that the agent is kind of like, it's like human-like feeling, you know, where it's like, okay, I'm kind of delegating something to a human, right? I think, like, Sean, you made the example of like a Calendly, like a savvy Cal, it's like an agent, because it's scheduling on your behalf for something. [00:30:51]Linus: That's actually a really interesting example, because it's a kind of a, it's a pretty deterministic, like there's no real AI to it, but it is agent in the sense that you're like delegating it and automate something. [00:31:01]Swyx: Yeah, it does work without me. It's great. [00:31:03]Alessio: So that one, we figured out. Like, we know what the scheduling interface is like. [00:31:07]Swyx: Well, that's the state of the art now. But, you know, for example, the person I'm corresponding with still has to pick a time from my calendar, which some people dislike. Sam Lesson famously says it's a sign of disrespect. I disagree with him, but, you know, it's a point of view. There could be some intermediate AI agents that would send emails back and forth like a human person to give the other person who feels slighted that sense of respect or a personalized touch that they want. So there's always ways to push it. [00:31:39]Alessio: Yeah, I think for me, you know, other stuff that I think about, so we were doing prep for another episode and had an agent and asked it to do like a, you know, background prep on like the background of the person. And it just couldn't quite get the format that I wanted it to be, you know, but I kept to have the only way to prompt that it's like, give it text, give a text example, give a text example. What do you think, like the interface between human and agents in the future will be like, do you still think agents are like this open ended thing that are like objective driven where you say, Hey, this is what I want to achieve versus I only trust this agent to do X. And like, this is how X is done. I'm curious because that kind of seems like a lot of mental overhead, you know, to remember each agent for each task versus like if you have an executive assistant, like they'll do a random set of tasks and you can trust them because they're a human. But I feel like with agents, we're not quite there. [00:32:36]Swyx: Agents are hard. [00:32:36]Linus: The design space is just so vast. Since all of the like early agent stuff came out around auto GPT, I've tried to develop some kind of a thesis around it. And I think it's just difficult because there's so many variables. One framework that I usually apply to sort of like existing chat based prompting kind of things that I think also applies just as well to agents is this duality between what you might call like trust and control. So you just now you brought up this example of you had an agent try to write some write up some prep document for an episode and it couldn't quite get the format right. And one way you could describe that is you could say, Oh, the, the agent didn't exactly do what I meant and what I had in my head. So I can't trust it to do the right job. But a different way to describe it is I have a hard time controlling exactly the output of the model and I have a hard time communicating exactly what's in my head to the model. And they're kind of two sides of the same coin. I think if you, if you can somehow provide a way to with less effort, communicate and control and constrain the model output a little bit more and constrain the behavior a little bit more, I think that would alleviate the pressure for the model to be this like fully trusted thing because there's no need for trust anymore. There's just kind of guardrails that ensure that the model does the right thing. So developing ways and interfaces for these agents to be a little more constrained in its output or maybe for the human to control its output a little bit more or behavior a little bit more, I think is a productive path. Another sort of more, more recent revelation that I had while working on this and autofill thing inside notion is the importance of zones of influence for AI agents, especially in collaborative settings. So having worked on lots of interfaces for independent work on my year off, one of the surprising lessons that I learned early on when I joined notion was that if you build a collaboration permeates everything, which is great for notion because collaborating with an AI, you reuse a lot of the same metaphors for collaborating with humans. So one nice thing about this autofill thing that also kind of applies to AI blocks, which is another thing that we have, is that you don't alleviate this problem of having to ask questions like, oh, is this document written by an AI or is this written by a human? Like this need for auditability, because the part that's written by the AI is just in like the autofilled cell or in the AI block. And you can, you can tell that's written by the AI and things outside of it, you can kind of reasonably assume that it was written by you. I think anytime you have sort of an unbounded action space for, for models like agents, it's especially important to be able to answer those questions easily and to have some sense of security that in the same way that you want to know whether your like coworker or collaborator has access to a document or has modified a document, you want to know whether an AI has permissions to access something. And if it's modified something or made some edit, you want to know that it did it. And so as a compliment to constraining the model's action space proactively, I think it's also important to communicate, have the user have an easy understanding of like, what exactly did the model do here? And I think that helps build trust as well. [00:35:39]Swyx: Yeah. I think for auto GPT and those kinds of agents in particular, anything that is destructive, you need to prompt for, I guess, or like check with, check in with the user. I know it's overloaded now. I can't say that. You have to confirm with the user. You confirm to the user. Yeah, exactly. Yeah. Yeah. [00:35:56]Linus: That's tough too though, because you, you don't want to stop. [00:35:59]Swyx: Yeah. [00:35:59]Linus: One of the, one of the benefits of automating these things that you can sort of like, in theory, you can scale them out arbitrarily. I can have like a hundred different agents working for me, but if that means I'm just spending my entire day in a deluge of notifications, that's not ideal either. [00:36:12]Swyx: Yeah. So then it could be like a reversible, destructive thing with some kind of timeouts, a time limit. So you could reverse it within some window. I don't know. Yeah. I've been thinking about this a little bit because I've been working on a small developer agent. Right. Right. [00:36:27]Linus: Or maybe you could like batch a group of changes and can sort of like summarize them with another AI and improve them in bulk or something. [00:36:33]Swyx: Which is surprisingly similar to the collaboration problem. Yeah. Yeah. Yeah. Exactly. Yeah. [00:36:39]Linus: I'm telling you, the collaboration, a lot of the problems with collaborating with humans also apply to collaborating with AI. There's a potential pitfall to that as well, which is that there are a lot of things that some of the core advantages of AI end up missing out on if you just fully anthropomorphize them into like human-like collaborators. [00:36:56]Swyx: But yeah. Do you have a strong opinion on that? Like, do you refer to it as it? Oh yeah. [00:37:00]Linus: I'm an it person, at least for now, in 2023. Yeah. [00:37:05]Swyx: So that leads us nicely into introducing what Notion and Notion AI is today. Do you have a pet answer as to what is Notion? I've heard it introduced as a database, a WordPress killer, a knowledge base, a collaboration tool. What is it? Yeah. [00:37:19]Linus: I mean, the official answer is that a Notion is a connected workspace. It has a space for your company docs, meeting notes, a wiki for all of your company notes. You can also use it to orchestrate your workflows if you're managing a project, if you have an engineering team, if you have a sales team. You can put all of those in a single Notion database. And the benefit of Notion is that all of them live in a single space where you can link to your wiki pages from your, I don't know, like onboarding docs. Or you can link to a GitHub issue through a task from your documentation on your engineering system. And all of this existing in a single place in this kind of like unified, yeah, like single workspace, I think has lots of benefits. [00:37:58]Swyx: That's the official line. [00:37:59]Linus: There's an asterisk that I usually enjoy diving deeper into, which is that the whole reason that this connected workspace is possible is because underlying all of this is this really cool abstraction of blocks. In Notion, everything is a block. A paragraph is a block. A bullet point is a block. But also a page is a block. And the way that Notion databases work is that a database is just a collection of pages, which are really blocks. And you can like take a paragraph and drag it into a database and it'll become a page. You can take a page inside a database and pull it out and it'll just become a link to that page. And so this core abstraction of a block that can also be a page, that can also be a row in a database, like an Excel sheet, that fluidity and this like shared abstraction across all these different areas inside Notion, I think is what really makes Notion powerful. This Lego theme, this like Lego building block theme permeates a lot of different parts of Notion. Some fans of Notion might know that when you, or when you join Notion, you get a little Lego minifigure, which has Lego building blocks for workflows. And then every year you're at Notion, you get a new block that says like you've been here for a year, you've been here for two years. And then Simon, our co-founder and CTO, has a whole crate of Lego blocks on his desk that he just likes to mess with because, you know, he's been around for a long time. But this Lego building block thing, this like shared sort of all-encompassing single abstraction that you can combine to build various different kinds of workflows, I think is really what makes Notion powerful. And one of the sort of background questions that I have for Notion AI is like, what is that kind of building block for AI? [00:39:30]Swyx: Well, we can dive into that. So what is Notion AI? Like, so I kind of view it as like a startup within the startup. Could you describe the Notion AI team? Is this like, how seriously is Notion taking the AI wave? [00:39:43]Linus: The most seriously? The way that Notion AI came about, as I understand it, because I joined a bit later, I think it was around October last year, all of Notion team had a little offsite. And as a part of that, Ivan and Simon kind of went into a little kind of hack weekend. And the thing that they ended up hacking on inside Notion was the very, very early prototype of Notion AI. They saw this GPT-3 thing. The early, early motivation for starting Notion, building Notion in the first place for them, was sort of grounded in this utopian end-user programming vision where software is so powerful, but there are only so many people in the world that can write programs. But everyone can benefit from having a little workspace or a little program or a little workflow tool that's programmed to just fit their use case. And so how can we build a tool that lets people customize their software tools that they use every day for their use case? And I think to them, seemed like such a critical part of facilitating that, bridging the gap between people who can code and people who need software. And so they saw that, they tried to build an initial prototype that ended up becoming the first version of Notion AI. They had a prototype in, I think, late October, early November, before Chachapiti came out and sort of evolved it over the few months. But what ended up launching was sort of in line with the initial vision, I think, of what they ended up building. And then once they had it, I think they wanted to keep pushing it. And so at this point, AI is a really key part of Notion strategy. And what we see Notion becoming going forward, in the same way that blocks and databases are a core part of Notion that helps enable workflow automation and all these important parts of running a team or collaborating with people or running your life, we think that AI is going to become an equally critical part of what Notion is. And it won't be, Notion is a cool connected workspace app, and it also has AI. It'll be that what Notion is, is databases, it has pages, it has space for your docs, and it also has this sort of comprehensive suite of AI tools that permeate everything. And one of the challenges of the AI team, which is, as you said, kind of a startup within a startup right now, is to figure out exactly what that all-permeating kind of abstraction means, which is a fascinating and difficult open problem. [00:41:57]Alessio: How do you think about what people expect of Notion versus what you want to build in Notion? A lot of this AI technology kind of changes, you know, we talked about the relationship between text and human and how human collaborates. Do you put any constraints on yourself when it's like, okay, people expect Notion to work this way with these blocks. So maybe I have this crazy idea and I cannot really pursue it because it's there. I think it's a classic innovator's dilemma kind of thing. And I think a lot of founders out there that are in a similar position where it's like, you know, series C, series D company, it's like, you're not quite yet the super established one, you're still moving forward, but you have an existing kind of following and something that Notion stands for. How do you kind of wrangle with that? [00:42:43]Linus: Yeah, that is in some ways a challenge and that Notion already is a kind of a thing. And so we can't just scrap everything and start over. But I think it's also, there's a blessing side of it too, in that because there are so many people using Notion in so many different ways, we understand all of the things that people want to use Notion for very well. And then so we already have a really well-defined space of problems that we want to help people solve. And that helps us. We have it with the existing Notion product and we also have it by sort of rolling out these AI things early and then watching, learning from the community what people want to do [00:43:17]Swyx: with them. [00:43:17]Linus: And so based on those learnings, I think it actually sort of helps us constrain the space of things we think we need to build because otherwise the design space is just so large with whatever we can do with AI and knowledge work. And so watching what people have been using Notion for and what they want to use Notion for, I think helps us constrain that space a little bit and make the problem of building AI things inside Notion a little more tractable. [00:43:36]Swyx: I think also just observing what they naturally use things for, and it sounds like you do a bunch of user interviews where you hear people running into issues and, or describe them as, the way that I describe myself actually is, I feel like the problem is with me, that I'm not creative enough to come up with use cases to use Notion AI or any other AI. [00:43:57]Linus: Which isn't necessarily on you, right? [00:43:59]Swyx: Exactly. [00:43:59]Linus: Again, like it goes way back to the early, the thing we touched on early in the conversation around like, if you have too much generality, there's not enough, there are not enough guardrails to obviously point to use cases. Blank piece of paper. [00:44:10]Swyx: I don't know what to do with this. So I think a lot of people judge Notion AI based on what they originally saw, which is write me a blog post or do a summary or do action items. Which, fun fact, for latent space, my very, very first Hacker News hit was reverse engineering Notion AI. I actually don't know if I got it exactly right. I think I got the easy ones right. And then apparently I got the action items one really wrong. So there's some art into doing that. But also you've since launched a bunch of other products and maybe you've already hinted at AI Autofill. Maybe we can just talk a little bit about what does the scope or suite of Notion AI products have been so far and what you're launching this week? Yeah. [00:44:53]Linus: So we have, I think, three main facets of Notion AI and Notion at the moment. We have sort of the first thing that ever launched with Notion AI, which I think that helps you write. It's, going back to earlier in the conversation, it's kind of a writing, kind of a content generation tool. If you have a document and you want to generate a summary, it helps you generate a summary, pull out action items, you can draft a blog post, you can help it improve, it's helped to improve your writings, it can help fix grammar and spelling mistakes. But under the hood, it's a fairly lightweight, a thick layer of prompts. But otherwise, it's a pretty straightforward use case of language models, right? And so there's that, a tool that helps you write documents. There's a thing called an AI block, which is a slightly more constrained version of that where one common way that we use it inside Notion is we take all of our meeting notes inside Notion. And frequently when you have a meeting and you want other people to be able to go back to it and reference it, it's nice to have a summary of that meeting. So all of our meeting notes templates, at least on the AI team, have an AI block at the top that automatically summarizes the contents of that page. And so whenever we're done with a meeting, we just press a button and it'll re-summarize that, including things like what are the core action items for every person in the meeting. And so that block, as I said before, is nice because it's a constrained space for the AI to work in, and we don't have to prompt it every single time. And then the newest member of this AI collection of features is AI autofill, which brings Notion AI to databases. So if you have a whole database of user interviews and you want to pull out what are the companies, core pain points, what are their core features, maybe what are their competitor products they use, you can just make columns. And in the same way that you write Excel formulas, you can write a little AI formula, basically, where the AI will look at the contents of the page and pull out each of these key pieces of information. The slightly new thing that autofill introduces is this idea of a more automated background [00:46:43]Swyx: AI thing. [00:46:44]Linus: So with Writer, the AI in your document product and the AI block, you have to always ask it to update. You have to always ask it to rewrite. But if you have a column in a database, in a Notion database, or a property in a Notion database, it would be nice if you, whenever someone went back and changed the contents of the meeting node or something updated about the page, or maybe it's a list of tasks that you have to do and the status of the task changes, you might want the summary of that task or detail of the task to update. And so anytime that you can set up an autofilled Notion property so that anytime something on that database row or page changes, the AI will go back and sort of auto-update the autofilled value. And that, I think, is a really interesting part that we might continue leading into of like, even though there's AI now tied to this particular page, it's sort of doing its own thing in the background to help automate and alleviate some of that pain of automating these things. But yeah, Writer, Blocks, and Autofill are the three sort of cornerstones we have today. [00:47:42]Alessio: You know, there used to be this glorious time where like, Roam Research was like the hottest knowledge company out there, and then Notion built Backlinks. I don't know if we are to blame for that. No, no, but how do Backlinks play into some of this? You know, I think most AI use cases today are kind of like a single page, right? Kind of like this document. I'm helping with this. Do you see some of these tools expanding to do changes across things? So we just had Itamar from Codium on the podcast, and he talked about how agents can tie in specs for features, tests for features, and the code for the feature. So like the three entities are tied together. Like, do you see some Backlinks help AI navigate through knowledge basis of companies where like, you might have the document the product uses, but you also have the document that marketing uses to then announce it? And as you make changes, the AI can work through different pieces of it? [00:48:41]Swyx: Definitely. [00:48:41]Linus: If I may get a little theoretical from that. One of my favorite ideas from my last year of hacking around building text augmentations with AI for documents is this realization that, you know, when you look at code in a code editor, what it is at a very lowest level is just text files. A code file is a text file, and there are maybe functions inside of it, and it's a list of functions, but it's a text file. But the way that you understand it is not as a file, like a Word document, it's a kind of a graph.[00:49:10]Linus: Like you have a function, you have call sites to that function, there are places where you call that function, there's a place where that function is tested, many different definitions for that function. Maybe there's a type definition that's tied to that function. So it's a kind of a graph. And if you want to understand that function, there's advantages to be able to traverse that whole graph and fully contextualize where that function is used. Same with types and same with variables. And so even though its code is represented as text files, it's actually kind of a graph. And a lot of the, of what, all of the key interfaces, interface innovations behind IDEs is helping surface that graph structure in the context of a text file. So like things like go to definition or VS Code's little window view when you like look at references. And interesting idea that I explored last year was what if you bring that to text documents? So text documents are a little more unstructured, so there's a less, there's a more fuzzy kind of graph idea. But if you're reading a textbook, if there's a new term, there's actually other places where the term is mentioned. There's probably a few places where that's defined. Maybe there's some figures that reference that term. If you have an idea, there are other parts of the document where the document might disagree with that idea or cite that idea. So there's still kind of a graph structure. It's a little more fuzzy, but there's a graph structure that ties together like a body of knowledge. And it would be cool if you had some kind of a text editor or some kind of knowledge tool that let you explore that whole graph. Or maybe if an AI could explore that whole graph. And so back to your point, I think taking advantage of not just the backlinks. Backlinks is a part of it. But the fact that all of these inside Notion, all of these pages exist in a single workspace and it's a shared context. It's a connected workspace. And you can take any idea and look up anywhere to fully contextualize what a part of your engineering system design means. Or what we know about our pitching their customer at a company. Or if I wrote down a book, what are other places where that book has been mentioned? All these graph following things, I think, are really important for contextualizing knowledge. [00:51:02]Swyx: Part of your job at Notion is prompt engineering. You are maybe one of the more advanced prompt engineers that I know out there. And you've always commented on the state of prompt ops tooling. What is your process today? What do you wish for? There's a lot here. [00:51:19]Linus: I mean, the prompts that are inside Notion right now, they're not complex in the sense that agent prompts are complex. But they're complex in the sense that there is even a problem as simple as summarize a [00:51:31]Swyx: page. [00:51:31]Linus: A page could contain anything from no information, if it's a fresh document, to a fully fledged news article. Maybe it's a meeting note. Maybe it's a bug filed by somebody at a company. The range of possible documents is huge. And then you have to distill all of it down to always generate a summary. And so describing that task to AI comprehensively is pretty hard. There are a few things that I think I ended up leaning on, as a team we ended up leaning on, for the prompt engineering part of it. I think one of the early transitions that we made was that the initial prototype for Notion AI was built on instruction following, the sort of classic instruction following models, TextWG003, and so on. And then at some point, we all switched to chat-based models, like Claude and the new ChatGPT Turbo and these models. And so that was an interesting transition. It actually kind of made few-shot prompting a little bit easier, I think, in that you could give the few-shot examples as sort of previous turns in a conversation. And then you could ask the real question as the next follow-up turn. I've come to appreciate few-shot prompting a lot more because it's difficult to fully comprehensively explain a particular task in words, but it's pretty easy to demonstrate like four or five different edge cases that you want the model to handle. And a lot of times, if there's an edge case that you want a model to handle, I think few-shot prompting is just the easiest, most reliable tool to reach for. One challenge in prompt engineering that Notion has to contend with often is we want to support all the different languages that Notion supports. And so all of our prompts have to be multilingual or compatible, which is kind of tricky because our prompts are written, our instructions are written in English. And so if you just have a naive approach, then the model tends to output in English, even when the document that you want to translate or summarize is in French. And so one way you could try to attack that problem is to tell the model, answering the language of the user's query. But it's actually a lot more effective to just give it examples of not just English documents, but maybe summarizing an English document, maybe summarize a ticket filed in French, summarize an empty document where the document's supposed to be in Korean. And so a lot of our few-shot prompt-included prompts in Notion AI tend to be very multilingual, and that helps support our non-English-speaking users. The other big part of prompt engineering is evaluation. The prompts that you exfiltrated out of Notion AI many weeks ago, surprisingly pretty spot-on, at least for the prompts that we had then, especially things like summary. But they're also outdated because we've evolved them a lot more, and we have a lot more examples. And some of our prompts are just really, really long. They're like thousands of tokens long. And so every time we go back and add an example or modify the instruction, we want to make sure that we don't regress any of the previous use cases that we've supported. And so we put a lot of effort, and we're increasingly building out internal tooling infrastructure for things like what you might call unit tests and regression tests for prompts with handwritten test cases, as well as tests that are driven more by feedback from Notion users that have chosen to share their feedback with us. [00:54:31]Swyx: You just have a hand-rolled testing framework or use Jest or whatever, and nothing custom out there. You basically said you've looked at so many prompt ops tools and you're sold on none of them. [00:54:42]Linus: So that tweet was from a while ago. I think there are a couple of interesting tools these days. But I think at the moment, Notion uses pretty hand-rolled tools. Nothing too heavy, but it's basically a for loop over a list of test cases. We do do quite a bit of using language models to evaluate language models. So our unit test descriptions are kind of funny because the test is literally just an input document and a query, and then we expect the model to say something. And then our qualification for whether that test passes or not is just ask the language model again, whether it looks like a reasonable summary or whether it's in the right language. [00:55:19]Swyx: Do you have the same model? Do you have entropic-criticized OpenAI or OpenAI-criticized entropic? That's a good question. Do you worry about models being biased towards its own self? [00:55:29]Linus: Oh, no, that's not a worry that we have. I actually don't know exactly if we use different models. If you have a fixed budget for running these tests, I think it would make sense to use more expensive models for evaluation rather than generation. But yeah, I don't remember exactly what we do there. [00:55:44]Swyx: And then one more follow-up on, you mentioned some of your prompts are thousands of tokens. That takes away from my budget as a user. Isn't that a trade-off that's a concern? So there's a limited context window, right? Some of that is taken by you as the app designer, product designer, deciding what system prompt to provide. And then the
Jeremy Utley is one of the world's leading experts in innovation. As the Director of Executive Education at Stanford's renowned Hasso Plattner Institute of Design (aka "the d.school"), his courses have been experienced by nearly a million students of innovation worldwide. He advises corporate leaders on how to imbed the methods and mindsets of design thinking into their organizations, and works with professionals to cultivate a robust personal creative practice. A self-proclaimed “recovering MBA, spreadsheet junkie, and management consultant,” he now studies innovation in large enterprises and startups. He advises CEOs and senior leadership teams in the United States, Europe, and Asia on growth and innovation strategy, and has led scores of capacity-building initiatives worldwide. He's a prolific blogger and podcaster, and is co-author (alongside Perry Klebahn) of “Ideaflow: The Only Business Metric That Matters.” Jeremy talks with Lou about making “Ideaflow” a practice you incorporate into your daily plans. ***CONNECT WITH LOU DIAMOND & THRIVE LOUD***
What if every time you had a problem instead of feeling anxious, you felt excited? That's how I feel now whenever I face a new problem. Listen to this episode and you will, too! In this amazing conversation with innovation expert, Jeremy Utley, we explore the powerful skill of Ideaflow. This simple, proven approach is absolutely essential for anyone who wants to solve problems better in their life and business. As he says in his new book, "every problem is an idea problem"—you don't necessarily need a better idea, you just need a lot more of them.Jeremy is the co-author of the book Ideaflow and Executive Director of the Stanford d.school, where he researches and teaches ways to design life and business in a better way.EPISODE HIGHLIGHTSWhat is life design?What is Ideaflow?Quantity over quality is the greatest predictor of good ideasHow to break the cognitive bias around ideasYou're a terrible judge of good ideasHow to practice ideaflowACTION STEPs FROM THE EPISODE:Practice ideaflow by giving your subconscious a problem to work on while you sleep and then after your basic morning routine, write down 10 possible solutions to the problem as fast as you can without judging or qualifying. Repeat daily to turn Ideaflow into a skill.Get into the habit of noticing problems.RESOURCES FROM THIS EPISODEVisit Jeremy's website https://www.jeremyutley.design/ Snag Jeremy's book Ideaflow Join the MAGIC MONDAY newsletter and let us help you launch your weekDownload the FREE HABIT TRACKER and start living your best life nowREADY TO LEARN THE MOST IMPORTANT SKILL OF YOUR LIFE?Join The Live Your Best Life CourseMaster the fundamentals of habit building and start living your best life now!>>CHECK OUT THE COURSE
When we think about the greatest innovators of our time (Benjamin Franklin, Steve Jobs, Frank Lloyd Wright) we often hear about their work ethic. But one thing that all of these innovators have in common is their ability to walk away from the work. They nap, they garden, and they go shopping to give themselves a break from the problem they are working on and look for inspiration in the real world. They gave themselves space to let inspiration come to them, rather than trying to force it. In this episode of unSILOed, Greg talks with Stanford professor Jeremy Utley about his new book Ideaflow, which gives readers a strategy to come up with better ideas and determine which ones are worth pursuing.Jeremy Utley is a Director of Executive Education at Stanford's renowned Hasso Plattner Institute of Design (the d.school) and works with leaders around the world to untap their abilities to innovate better and more effectively. Episode Quotes:How you perceive your problems matter02:44: Inspiration is the disciplined pursuit of unexpected input. And every one of those words matters, but being disciplined in your pursuit of input is the way to solve problems. When you think about problem-solving as the big problem, we believe that idea flow can solve the problem of solving problems for good. Because you realize it's actually about how you think about the problem that matters.04:02: The most innovative individuals have this instinct to go and seek input, that drives fresh thinking when they're stuck.39:16:Our default assumption is to think that the majority of ideas we have are good, commercially viable, and successful. The opposite is true.Problems have solutions when you choose to find them30:15: Just because you don't know how to solve a problem doesn't mean it hasn't been solved in the world more broadly. And a lot of times, if you're thoughtful about where you go looking, you stumble upon novel solutions that you never would've seen in your own industry.What is the right way of thinking about idea flow?50:33: When you think about idea flow, it's not a measure of how many good ideas you can generate at any moment. That's an output metric. It's a measure of how many ideas you can generate at any moment and how many ideas are being generated.Show Links:Recommended Resources:Dean Keith Simonton, PhDAstro TellerThe Need For Closure Scale by Arie W. KruglanskiDonald M. MacKinnonA Technique for Producing IdeasDan M. KleinAndrew HubermanScott GallowayPhilippe BarreaudGuest Profile:Faculty Profile at Stanford UniversityJeremy Utley's WebsiteJeremy Utley on LinkedInJeremy Utley on TwitterJeremy Utley on Talks at GoogleHis Work:Jeremy's BlogArticles on MediumThe Paint & Pipette PodcastIdeaflow: The Only Business Metric That Matters
I think most of us have, at some point, wished we had certain information years ago that is available today. That's certainly how I felt after I read the book my guest and I will discuss on this episode. But, according to a brilliant Chinese proverb says, the best time to plant a tree was 20 years ago; the second best time is now. I find comfort in that idea, and I, for one, am very excited to plant trees from the seeds that came from my guest's brilliant work. Jeremy Utley (https://www.ideaflow.design/) is Director of Executive Education at Stanford d.school and an Adjunct Professor at Stanford's School of Engineering. He is also the host of the d.school's widely popular program, "Stanford's Masters of Creativity." Indeed, creativity and ideas make a huge difference, and just how big a difference is reflected the title of the book he co-wrote with his Stanford colleague, Perry Klebahn. The title is Ideaflow: The Only Business Metric That Matters. I loved this book so much that I'm committing to listening to it three times so I can better implement the practices. And I'm not alone in my love. It has an average of 4.9 stars on Amazon and people like Dr. Frederik G. Pferdt, Google's Chief Innovation Evangelist and many other thought leaders have publicly praised the book. So, if the most innovative companies tend to have the best bottom lines financially, and the technology of idea creation described in this book can have positive impacts on virtually EVERY area of your life, I'm guessing you'll want to listen in! Book link: https://tinyurl.com/44jp8975
This week on DisrupTV, we interviewed Miguel Gamino, Chief Experience Officer & Founding Partner at Simplicity and Jeremy Utley, Author of Ideaflow. DisrupTV is a weekly Web series with hosts R “Ray” Wang and Vala Afshar. The show airs live at 11:00 a.m. PT/ 2:00 p.m. ET every Friday. Brought to you by Constellation Executive Network: constellationr.com/CEN.
Joining host Jeff Saviano on today's episode is Jeremy Utley, Director of Executive Education and professor at Stanford's d.school - and a master storyteller. Jeremy has dedicated his career to helping others unleash their innovative potential, changing deeply ingrained behaviors, and discovering better innovation through improved ideaflow, the optimal KPI for innovation. In conversation with Jeff, Jeremy vividly recounts life experiences demonstrating the power of treating every problem as an idea problem. Jeremy's leading innovation practices are brought to life in discussions around his provocative, newly released book Ideaflow, helping innovators with proven strategies to discover breakthrough ideas and unleash new creativity within their organization.
What if I told you, every problem is an idea problem? Today's guest is the co author of Ideaflow alongside Perry Kleiban, former COO of Patagonia. Jeremy has also spent over a decade at Stanford in Education as the Director of Executive Education at the Haaso Platner School of Design. Jeremy Utley graduated the Stanford GSB the same year Earl did, 2009 and we are excited to have him on the podcast for a conversation about why "Ideaflow" is an important way of thinking in today's work environment. I know kung fu moments (8:21) Experience is a Liability The Obsessive Pursuit that characterized the months before Bill Bowerman's waffle maker moment for Nike (13:55) Keep a bug list (15:50 ) Experience is a liability (19:50) Why Jeremy wrote a book (23:17) Purchase your copy of Ideaflow: The Only Business Metric that Matters on Amazon. Want to meet a couple quintessential masters of ideaflow? Check out the free bonus chapter, How to Think Like Bezos and Jobs Join our newsletter and get fresh insights from our guests conversations straight to your inbox!
TOPICS WE EXPLOREWho is Jeremy Utley and how did he find his way to education from business work around the globe?Young people need permission to not knowNot asking a young person what they want to be when they growThe Einstellung effect where we cease to search when we think we have a plausible first answer AND how Ideaflow works to push through this issue of thinking.Connection of creativity and an arm splintWe are not practicing what we are saying we are saying in education? How do we do this type of work of Ideaflow and the space for it?What do we mean by creativity?Don't always evaluate. Simple challenge going, "Oh good! and fill in with a response" Start with a gift of good and the try a gift of bad.What if we couldn't do, then what would we do? Repeat this process for your first new idea to work to get past our first idea.Power of framing a prompt or challenge where it is more on the approach vs. the answer."Curiosity can pull you where discipline and willpower would otherwise have to push"YOUR CHALLENGEShare ideas you gathered from the conversation with us on the socials.What resonated with you?RESOURCE MENTIONED IN SHOWIdeaflow: The Only Business Metric That Matters Blog: jeremyutley.designTwitter: @jeremyutleyStanford's Masters of CreativityLeading Disruptive InnovationLaunchPadAstro Teller who is the head of Google X
About Jeremy Utley: My first guest on The One Percent Project this year is the exuberant Jeremy Utley. Jeremy is a Stanford Adjunct, a celebrated keynote speaker, and co-author of the brilliant book "Ideaflow: The Only Business Metric That Matters.". Jeremy co-teaches two wonderful courses at Stanford, Leading Disruptive Innovation (d.leadership) and LaunchPad, which focus on creating real-world impact with design and innovation tools. Jeremy intended to use his time in Africa and South America as a springboard for a career in economic development when he applied to Stanford's Graduate School of Business, but a contact with the d.school while working at an Indian start-up altered his plans. He learned throughout his time as a post-graduate design fellow that how he worked was more significant than what he accomplished. Today, Jeremy works to assist individuals in transforming their established self-perceptions and learn, like him, that it is possible to contribute creatively to the world without being exceptional. His book “Ideaflow: The Only Business Metric That Matters,” is a product of his journey in helping people develop the skill of generating new problem-solving ideas. Jeremy believes one can cultivate the habit of generating ideas with practice and techniques he explores in his book, much like any other habit. Once that is achieved, innovation flows naturally. The conversation yielded great insights on practising the meta-skill of idea generation, the novel concepts of idea ratio, idea quota, and innovation sandwich to facilitate business innovation in organizations, attracting and retaining innovative people, finding better problems to solve, and much more. Join our No-Spam WhatsApp group Bonus Chapter: How to Think Like Bezos and Jobs? Entrepreneur.com: How to generate 100s of ideas in 1 hour? In this conversation, he talks about: When it comes to creativity, why is quantity more important than quality? Why shouldn't you be judging yourself when coming up with ideas? What does he mean by "every problem is an idea problem"? About Ideaflow What is an Idea Ratio- How do you calculate it? How does Idea flow co-relate with the success of an organisation? How to learn creativity and turn it into a daily activity? How to make a hackathon an effective way to drive innovation? How to run effective brainstorming sessions? How to identify, recruit, and retain innovative people? How to use Ideaflow as a metric for organisations, companies and startups? How is storytelling tied to Ideaflow? What is his counter-intuitive insight from teaching entrepreneurship? What is the nicest thing anyone has done for him?
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #13
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #12
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #11
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #10
In this week's episode, we talk with Jeremy Utley. Jeremy is the Director of Executive Education at Stanford's d.school and an Adjunct Professor at Stanford's School of Engineering. He is the co-host of the d.school's widely popular program, “Stanford's Masters of Creativity.” He is the co-author of Ideaflow: The Only Business Metric That Matters (Portfolio) with Perry Klebahn. He is also on the teaching teams of d.org, an organizational design course, and Transformative Design, a course that turns the tools of design onto graduate students' lives. One of the most prodigious collaborators at the d.school, Jeremy has taught alongside the likes of Lecrae, Dan Ariely, Laszlo Bock, and Greg McKeown. He holds a BBA with Honors in Finance from The University of Texas at Austin and an MBA from the Stanford University Graduate School of Business.
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #9
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #8
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #7
Today I'm chatting with former-analyst-turned-design-educator Jeremy Utley of the Stanford d.school and co-author of Ideaflow. Jeremy reveals the psychology behind great innovation, and the importance of creating psychological safety for a team to generate what they may view as bad ideas. Jeremy speaks to the critical collision of unrelated frames of reference when problem-solving, as well as why creativity is actually more of a numbers game than awaiting that singular stroke of genius. Listen as Jeremy gives real-world examples of how to practice and measure (!) your innovation efforts and apply them to data products. Highlights/ Skip to: Jeremy explains the methodology of thinking he's adopted after moving from highly analytical roles to the role he's in now (01:38) The approach Jeremy takes to the existential challenge of balancing innovation with efficiency (03:54) Brian shares a story of a creative breakthrough he had recently and Jeremy uses that to highlight how innovation often comes in a way contrary to normalcy and professionalism (09:37) Why Jeremy feels innovation and creativity demand multiple attempts at finding solutions (16:13) How to take a innovation-forward approach like the ones Jeremy has described when working on internal tool development (19:33) Jeremy's advice for accelerating working through bad ideas to get to the good ideas (25:18) The approach Jeremy takes to generate a large volume of ideas, rather than focusing only on “good” ideas, including a real-life example (31:54) Jeremy's beliefs on the importance of creating psychological safety to promote innovation and creative problem-solving (35:11) Quotes from Today's Episode “I'm in spreadsheets every day to this day, but I recognize that there's a time and place when that's the tool that's needed, and then specifically, there's a time and a place where that's not going to help me and the answer is not going to be found in the spreadsheet.” – Jeremy Utley (03:13) “There's the question of, ‘Are we doing it right?' And then there's a different question, which is, ‘Are we doing the right “it”?' And I think a lot of us tend to fixate on, ‘Are we doing it right?' And we have an ability to perfectly optimize that what should not be done.” – Jeremy Utley (05:05) “I think a vendetta that I have is against this wrong placement of—this exaltation of efficiency is the end-all, be-all. Innovation is not efficient. And the question is not how can I be efficient. It's what is effective. And effectiveness, oftentimes when it comes to innovation and breaking through, doesn't feel efficient.” – Jeremy Utley (09:17) “The way the brain works, we actually understand it. The way breakthroughs work we actually understand them. The difficulty is it challenges our definitions of efficiency and professionalism and all of these things.” – Jeremy Utley (15:13) “What's the a priori probability that any solution is the right solution? Or any idea is a good idea? It's exceptionally low. You have to be exceptionally arrogant to think that most of your ideas are good. They're not. That's fine, we don't mind because then what's efficient is actually to generate a lot.” – Jeremy Utley (26:20) “If you don't learn that nothing happens when the ball hits the floor, you can never learn how to juggle. And to me, it's a really good metaphor. The teams that don't learn nothing happens when they have a bad idea. Literally, the world does not end. They don't get fired. They don't get ridiculed. Now, if they do get fired or ridiculed, that's a leadership problem.” – Jeremy Utley (35:59) [The following] is an essential question for a team leader to ask. Do people on my team have the freedom, at least with me, to share what they truly fear could be an incredibly stupid idea?” – Jeremy Utley (41:52) Links Ideaflow: https://www.amazon.com/Ideaflow-Only-Business-Metric-Matters-ebook/dp/B09R6M3292 Ideaflow website: https://ideaflow.design Personal webpage: https://jeremyutley.design LinkedIn: https://www.linkedin.com/in/jeremyutley/ Twitter: https://twitter.com/jeremyutley/ Brian's musical arrangement of Gershwin's “Prelude for Piano IIfeaturing the Siamese Cat Song” performed by Mr. Ho's Orchestrotica - listen on Spotify
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #6
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #5
每一個商業問題都是一個創意問題。創意流量可以用來衡量組織整體的創新能力，最終推動市場占有率、利潤和彈性，領導者應該像其他關鍵績效指標一樣密切關注創意流量。
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #4
In this HCI Podcast episode, Dr. Jonathan H. Westover talks with Jeremy Utley about his new book, Ideaflow: The Only Business Metric That Matters. Jeremy Utley (https://www.linkedin.com/in/jeremyutley/) is the Director of Executive Education at Stanford d.school and an Adjunct Professor at Stanford's School of Engineering. He is the host of the d.school's widely popular program, "Stanford's Masters of Creativity." Please consider supporting the podcast on Patreon and leaving a review wherever you listen to your podcasts! This episode is sponsored by/brought to you by BetterHelp. Give online therapy a try at www.BetterHelp.com/HCI and get on your way to being your best self. Check out Ka'Chava at www.Kachava.com/HCI. Check out BELAY here. Check out the HCI Academy: Courses, Micro-Credentials, and Certificates to Upskill and Reskill for the Future of Work! Check out the LinkedIn Alchemizing Human Capital Newsletter. Check out Dr. Westover's book, The Future Leader. Check out Dr. Westover's book, 'Bluer than Indigo' Leadership. Check out Dr. Westover's book, The Alchemy of Truly Remarkable Leadership. Check out the latest issue of the Human Capital Leadership magazine. Each HCI Podcast episode (Program, ID No. 592296) has been approved for 0.50 HR (General) recertification credit hours toward aPHR™, aPHRi™, PHR®, PHRca®, SPHR®, GPHR®, PHRi™ and SPHRi™ recertification through HR Certification Institute® (HRCI®). Learn more about your ad choices. Visit megaphone.fm/adchoices
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #4
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #3
About the book Great Ideas. Methods, not Magic. Innovation is not an event; it's a practice. Don't leave the big ideas to the creatives. Revolutionize your creative process by mastering Ideaflow: the proven strategy that anyone can use to routinely generate and commercialize innovative ideas. Whether you're an entrepreneur, student, C-suite leader, or anything in between, this book will teach you how to unleash creativity and innovation to magnify and accelerate all your other efforts, by simply building it into your daily routine About the authors Jeremy is the Director of Executive Education at Stanford d.school and an Adjunct Professor at Stanford's School of Engineering. He is the co-host of the d.school's widely popular program “Stanford's Masters of Creativity.” Perry is a seasoned teacher, entrepreneur, product designer, chief executive and co-founding member of the Stanford d.school's faculty with over 20 years of experience. Find out more about the book and authors here. Big ideas 1) All problems are idea problems 2) You're not testing enough 3) ElevateSupport my book habit: https://www.buymeacoffee.com/stephsbookshelfSee omnystudio.com/listener for privacy information.
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #2
สร้างทีมทำนวัตกรรมอย่างด้วยเทคนิคระดับโลกจากหนังสือ IdeaFlow #1
Welcome to episode #854 of Six Pixels of Separation - The ThinkersOne Podcast. Here it is: Six Pixels of Separation - The ThinkersOne Podcast - Episode #854. As a dynamic and engaging speaker, Jeremy Utley translates his research into the history of invention and discovery into transformative learning experiences. He co-leads the d.school's Executive Education programs, and co-teaches two celebrated courses at Stanford, Leading Disruptive Innovation and LaunchPad, which focus on creating real-world impact with the tools of design and innovation. One of the most prodigious collaborators at the d.school, Jeremy has taught alongside the likes of Lecrae, Dan Ariely, Laszlo Bock, and Greg McKeown. He has a new book out called, Idealflow - The Only Business Metric That Matters (along with co-author Perry Klebahn). In Ideaflow, they focus on offering their proven strategy for coming up with great ideas by yourself or with your team, and quickly determining which are worthy. Creativity is everyone's business... and this book proves it. Enjoy the conversation... Running time: 58:22. Hello from beautiful Montreal. Subscribe over at Apple Podcasts. Please visit and leave comments on the blog - Six Pixels of Separation. Feel free to connect to me directly on Facebook here: Mitch Joel on Facebook. Check out ThinkersOne. or you can connect on LinkedIn. ...or on Twitter. Here is my conversation with Jeremy Utley. Idealflow - The Only Business Metric That Matters. Free bonus chapter: How To Think Like Bezos & Jobs. d.school. Perry Klebahn. Follow Jeremy on LinkedIn. Follow Jeremy on Twitter. This week's music: David Usher 'St. Lawrence River'.
On this week's Tech Nation, Moira speaks with Jeremy Utley, the Director of Executive Education at Stanford University's school, joins Moira to talk about his book “IdeaFlow.” You have no idea how important ideas are in your life, and just how many you'll need! Then in biotech, human clinical trials have begun to treat a particular type of Epilepsy called Temporal Lobe Epilepsy. Dr. Cory Nicholas from Neurona Therapeutics, tells us about their big idea and how they have developed an unprecedented cell therapy.
JEREMY UTLEY the Director of Executive Education at Stanford's d.school and an award-winning Adjunct Professor at the world-renowned Stanford's School of Engineering. He is the co-host of the d.school's widely popular program, “Stanford's Masters of Creativity.” He is the coauthor of Ideaflow: The Only Business Metric That Matters. LISTEN TO THIS EPISODE IF: You want to understand how great ideas are born You want to learn how to increase your creative output in your professional and personal life You want to learn from a world-renowned professor and one of the top experts in the world on innovation and creativity You want to increase your creativity and become a better leader You want to learn how to generate and commercialize breakthrough ideas
In this episode we sit down with Jeremy Utley of the Stanford d.school to discuss his new book, Ideaflow, which is all about how to create a practice for producing and trading ideas in massive quantities – whether in an organization or as an individual entrepreneur or content-creator – along with a system for sorting the garbage from the gold. We discuss, among many other things, why it is important to focus on input more than output, how to stop obsessing over quality while generating quantity, and peanut butter pumps.Jeremy Utley: https://www.jeremyutley.designIdeaflow: https://www.ideaflow.designStanford d.school: https://dschool.stanford.eduHow Minds Change: www.davidmcraney.com/howmindschangehomeShow Notes: www.youarenotsosmart.comNewsletter: https://davidmcraney.substack.comJeremy Utley's Twitter: https://twitter.com/jeremyutleyDavid McRaney's Twitter: https://twitter.com/davidmcraneyYANSS Twitter: https://twitter.com/notsmartblogBerkeley Alembic Event: https://www.eventbrite.com/e/how-minds-change-with-david-mcraney-tickets-443811591417
Jeremy Utley, director of executive education at Stanford's d.school, joins the podcast to share how anyone can innovate by building their ability to be creative. Jeremy reveals misconceptions about creativity, defines “Ideaflow,” and how your team can achieve it. He shares strategies to discern good ideas from bad ones by creating a lab environment. Jeremy covers other topics from his new book, Ideaflow, including how teams can best develop and select ideas, and the impact of ideating with remote teams. Jeremy's Blog: https://www.jeremyutley.design/blog Ideaflow Website: https://www.ideaflow.design/ Ideaflow: The Only Business Metric That Matters
Too often when faced with a problem we rush to find a single solution – something safe, based on solutions we already know. These comfortable answers are nice, but what if you could create an opportunity to not only solve the problem, but excel? Jeremy Utley and Perry Klebahn are the co-authors of Ideaflow: The Only Business Metric that Matters. It is a book that explores how you and your team can generate a volume of ideas for any situation, by overcoming outdated, stagnant thinking traps. Jeremy Utley is a dynamic and engaging speaker who co-leads the d.school's Executive Education programs at Stanford University. Perry Klebahn is an Adjunct Professor and Director of Executive Education, also at Stanford's d.school. We start our conversation discussing Ideaflow, and how the creation of ideas can be summed up as a complex mathematical equation of ideas over time. Perry explains how, when rushing for a solution, we often fall short because we don't have a volume of ideas. More ideas means more potential directions to overcome a challenge, and even ideas that eventually get discarded may have something to offer our final solution. People often think that generating ideas is a task for a single, individual mind - but that's not the case. In fact, creating ideas with a team can create broader insights,by drawing on a wider pool of perspectives and backgrounds to create ideas that might be outside the experience of a single person.The more, the merrier! Much of the work Perry and Jeremy do is with entrepreneurs and students. For more than 10 years, they've been encouraging their students to use Launchpad, an accelerator which shows teams how to incorporate, develop prototypes, find customers, and prove their proposed offerings are viable. This kind of rigorous idea-testing is invaluable if you want to create solutions that make a real difference. If you want to increase the ideas moving through your organization at all levels this is one episode you won't want to skip! Three Key Takeaways: * To find great ideas, you have to let the unworkable and goofy ideas flow, as well. Don't stifle the process! * In the early stages of business, resist the urge to "lock in" on something just because it is working. Continue to generate ideas and innovate. * To become a better idea generator, ensure that you are working with a wide net of professionals and not the same small team every week.
Search Amazon for the word ‘innovation' in its ‘Business, Finance & Accounting' book section, and you will find more than 60,000 volumes. The trick is finding stuff worth reading in this deep and wide ocean of material. The new book, Ideaflow: Why Creative Businesses Win, by Jeremy Utley and Perry Klebahn is just such a book. I welcomed Jeremy to this episode of TRIUM Connects to discuss the book as well as his general views on creativity and innovation. In the book, Jeremy and Perry argue that we shouldn't think of innovation as an event, a workshop, a sprint or a hackathon…but rather as a more general capability that can be learnt and is relevant to everyone. Their core principle is that you need ideas to solve problems – in contrast to completing tasks where you just need to get on with the work. But, instead of obsessing over quality, successful innovators focus on the generation of many ideas. Volume is key. Once you have a sufficient volume, then you run quick and cheap experiments to gather more information, revise and test again. Jeremy knows what he is talking about. He is one of the world's leading experts in innovation. As the Director of Executive Education at Stanford's renowned Hasso Plattner Institute of Design (aka "the d.school"). His courses [K1] have been experienced by nearly a million students of innovation worldwide. He advises corporate leaders on how to embed[K2] the methods and mindsets of design thinking into their organizations, and works with professionals to cultivate a robust personal creative practice. He also co-hosts the "Stanford Masters of Creativity," program where Jeremy shines the spotlight on exemplars of creative practice across disciplinary boundaries.What makes Ideaflow a great book, and what I really enjoyed in my conversation with Jeremy, is the concrete, actionable innovation practices described and the fact that they are backed up by solid research and evidence. I hope you enjoy the conversation!Cited WorkMcKeown, Greg (2015) Essentialism: The Disciplined Pursuit of Less. Virgin Books.Koestler, A. (2014) The Act of Creation. One 70 Press.Lotto, B. (2017) Deviate: The Creative Power of Transforming your Perception. Weidenfeld & Nicolson.Lucas, B.J. & Nordgren, L.F. (2020) ‘The creative cliff illusion,' Psychological and Cognitive Sciences: Volume 117 (33), pp 19830-19836.Mackinnon, D. W. (1962). The personality correlates of creativity: A study of American architects. In G. Nielson (Ed.), Proceedings of the XIV International Congress of Applied Psychology. Vol. 2. Personality research (pp. 11–39). Munksgaard.Randolph, M. (2021) That will Never Work: The Birth of Netflix and the Amazing Life on an Idea. Endeavour. Hosted on Acast. See acast.com/privacy for more information.
Jeremy Utley, Co-Author of Ideaflow: The Only Business Metric That Matters shares with us how the proactive practice of exercising creative muscles so that the very best ideas can rise to the surface is vital for all to focus on these days. In this thought-provoking episode, we go deep into the concept of cross-pollination and cover several other strategies to discover as we discuss the ‘dumb things' that geniuses just so happen to do to create great ideas and companies. Plus we talk about Jeremy's own career journey and his role at Stanford's d.school. Tune in and get your ideas flowing! On this episode of #TheKaraGoldinShow. Enjoying this episode of #TheKaraGoldinShow? Let me know by clicking on the links below and sending me a quick shout-out on social. Or reach out to me at karagoldin@gmail.com https://www.linkedin.com/in/karagoldin/ https://www.instagram.com/karagoldin/ https://twitter.com/karagoldin https://www.facebook.com/KaraGoldin/ Check out our website to view this episode's show notes: https://karagoldin.com/podcast/308 List of links mentioned in this episode: https://twitter.com/jeremyutley https://www.instagram.com/opco_vc/ https://www.linkedin.com/in/jeremyutley https://dschool.stanford.edu/ To learn more about Ideaflow and Jeremy Utley: https://www.ideaflow.design/
Stanford professor Jeremy Utley joins us on Passion Struck to discuss his new book Ideaflow: The Only Business Metric that Matters, co-written with Perry Klebahn. Brought to you by American Giant (get 20% off using code PassionStruck at https://www.american-giant.com/). Let's face it. We all strive for grand ideas, but very few truly comprehend how novel ideas are born. Innovation is not a one-time event, nor is it a sprint or a hackathon. It comes from the mastery of ideaflow, a technique that advances everything else you do. The simple fact is that ideas matter. That is why innovators focus on inputs instead of outputs. According to Jeremy, innovators don't obsess over quality, they develop quantity. Jeremy Utley is the Director of Executive Education at Stanford's d.school and an Adjunct Professor at Stanford's School of Engineering. He is the co-host of the d.school's widely popular program, “Stanford's Masters of Creativity.” Purchase Idea Flow: https://amzn.to/3SwyXKM (Amazon Link) --► Get the resources and all links related to this episode here: https://passionstruck.com/jeremy-utley-on-mastering-ideaflow/ --► For information about advertisers and promo codes, go to: https://passionstruck.com/deals/ --► Prefer to watch this interview: https://youtu.be/0JZ0btOHz5g --► Subscribe to Our YouTube Channel Here: https://www.youtube.com/c/JohnRMiles --► Subscribe to the Passion Struck Podcast: https://podcasts.apple.com/us/podcast/passion-struck-with-john-r-miles/id1553279283 Where to Follow Jeremy Utley Website: https://www.jeremyutley.design/ Instagram: https://www.instagram.com/stanford/ LinkedIn: https://www.linkedin.com/in/jeremyutley/ Twitter: https://twitter.com/jeremyutley/ -- John R. Miles is the CEO, and Founder of PASSION STRUCK®, the first-of-its-kind company, focused on impacting real change by teaching people how to live Intentionally. He is on a mission to help people live a no-regrets life that exalts their victories and lets them know they matter in the world. For over two decades, he built his own career applying his research of passion-struck leadership, first becoming a Fortune 50 CIO and then a multi-industry CEO. He is the executive producer and host of the top-ranked Passion Struck Podcast, selected as one of the Top 50 most inspirational podcasts in 2022. Learn more about John: https://johnrmiles.com/ ===== FOLLOW JOHN ON THE SOCIALS ===== * Twitter: https://twitter.com/Milesjohnr * Facebook: https://www.facebook.com/johnrmiles.c0m * Medium: https://medium.com/@JohnRMiles * Instagram: https://www.instagram.com/john_r_miles * LinkedIn: https://www.linkedin.com/in/milesjohn/ * Blog: https://johnrmiles.com/blog/ * Instagram: https://www.instagram.com/passion_struck_podcast * Gear: https://www.zazzle.com/store/passion_sruck_podcast
What is one problem you are dealing with in your business right now? What is one problem you are dealing with in your life right now? Well, whatever problems you are facing, this conversation with Jeremy Utley is the answer, because as he says in his new book, Ideaflow, "Every problem is an idea's problem." Today I've invited my friend Jeremy to talk about his research, his insights, developed over many years at the D School, the design school at Stanford University. He and I co-created a class designing life, essentially, and now I'm thrilled to be able to explore these new insights that he's put together to your benefit. By the end of this episode, you will have, in effect, the solution to every problem. Join my weekly newsletter at GregMcKeown.com/1mw Learn more about my books and courses at GregMcKeown.com Learn more about your ad choices. Visit megaphone.fm/adchoices
Jeremy Utley is a Director of Executive Education at Stanford's d.school and an adjunct professor at Stanford's School of Engineering. He is the host of the d.school's widely known program, “Stanford's Masters of Creativity.” Perry Klebahn is an adjunct professor and Director of Executive Education at Stanford's d.school. Previously he served as COO for Patagonia and CEO of Timbuk2. In their new book, IDEAFLOW: The Only Business Metric That Matters, Jeremy and Perry explain that creativity is not reserved for a select few, but can be developed like any other skill and applied to any problem. Together with Martin Reeves, Chairman of BCG Henderson Institute, Jeremy and Perry discuss the concept of “ideaflow”, which can be described as the rate at which one is able to generate ideas. They share various techniques for leaders to master and implement a culture of ideaflow, such as setting up metrics that communicate to their teams that they value creativity, creating evaluation and selection techniques to move ideas forward, and deploying diversity. *** About the BCG Henderson Institute The BCG Henderson Institute is the Boston Consulting Group's think tank, dedicated to exploring and developing valuable new insights from business, technology, economics, and science by embracing the powerful technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand the boundaries of business theory and practice and to translate innovative ideas from within and beyond business. For more ideas and inspiration, sign up to receive BHI INSIGHTS, our monthly newsletter, and follow us on LinkedIn and Twitter.
Jeremy shares his: free bonus chapterJeremy Utley leads some of the most popular courses on creativity and innovation at the d.school of Stanford University. I was delighted to see that he was making his teaching of such popular courses available to a wider audience and chased him for an interview. This is one of his first interviews to talk about his brand new book Ideaflow.In it he discusses the way to have good ideas, and why most of us aren't willing to do what is required. I loved this discussion. Buy Ideaflow here - and find out more about Jeremy and his co-author, Perry Klebahn, here.Sign up for the podcast newsletter here. Hosted on Acast. See acast.com/privacy for more information.
For our penultimate episode of 2022, we interview Perry Klebahn about his soon-to-be-published book Ideaflow: The Only Business Metric that Matters, co-authored with Jeremy Utley. Perry is an Adjunct Professor and Director of Executive Education at the Hasso Plattner Institute of Design--the d.school--at Stanford University. Prior to his role at the d.school, Perry was CEO of Timbuk2 and COO of Patagonia. He's also an avid surfer. Perry posits that innovation is not an event, a workshop, a sprint, or a hackathon, but rather the result of mastering ideaflow. Put simply, ideas / time = ideaflow. When applied, ideaflow is an effective approach to problem-solving that reduces effort, minimizes risk, and magnifies results. To illustrate this concept, Perry shares personal stories of when he has (and hasn't) applied ideaflow to gnarly business problems, and how he, Jeremy, and the d.school team promulgate this approach to their students, startups, and large corporations. "Once you fully understand what ideas are, where they come from, and how to separate the winners from the losers, you can finally put that lightning rod down and start making lightning." Pre-order Ideaflow today and learn more by visiting ideaflow.design/.On Twitter:@SherpaPod@JayGerhart@TheBenReport@jeremyutleySupport the show
What routines and methods do you use to innovate? And how can you design for more creativity and experience idea flow? In this episode, Louka speaks with Perry Klebahn and Jeremy Utley who lead Stanford University's d.school Executive Education. We cover innovation, learning, leadership and the concept of Ideaflow: an experience built on discrete repeatable practices that can increase your creativity and a forthcoming book. Take a listen to hear how you can become more creative in the way you contribute at work, in school and in society and check out https://www.ideaflow.design.On our guests: When it comes to startups, corporations and executive leadership, Perry's seen just about everything. He's a seasoned entrepreneur, product designer, chief executive and co-founding member of the d.school faculty with over 20 years of experience. He also loves math, motorcycles and making things. Perry brought two out of three of those interests to bear when he created a new category of sportswear by way of a high-performance shoe — a snowshoe — for his product design master's thesis. He went on to found the Atlas Snowshoe Company, which remains the leader in snowshoe design and technology. Perry sold Atlas and became the head of Sales and Marketing for the clothing brand, Patagonia in 2000. He then went on to be named the CEO of the iconic bag company, Timbuk2 in 2007. Both opportunities gave him extensive experience in brand turn-around, design and innovation. Despite his years running startups and corporations, Perry's true calling is teaching. He leverages the breadth and depth of his experience as he pushes his students to bring rigor and precision to their fast-paced design work. His students often tell him that, while they were intimidated by him during the course, they're grateful for the pressure he placed on them to exceed their own expectations. Perry is a founding teaching team member for the d.school's startup gauntlet class, Launchpad, the innovation leadership course, d.leadership and the week-long executive education intensive, Bootcamp. He is also on the teaching teams for the personal development course, Designer in Society and the organizational change course, d.org. In every class, Perry guides his students to look back in order to discover what to do next and works from the unshakeable belief that it's always possible to see a problem differently.Perry is an Adjunct Professor and Director of Executive Education at the d.school. He holds a B.A. in Physics from Wesleyan University (1988) and a Master's degree in Product Design from Stanford University (1991).Jeremy never expected to be a designer. On his 10th birthday, his father asked him what he wanted to be when he grew up. Jeremy replied,”I want to be one of the people who carry boxes with handles.” A little over a decade later, Jeremy became a briefcase-carrying management consultant focusing on economic development. Then, in 2008, d.school derailed him completely. His time as a student and a fellow at the d.school showed him that “how” he worked was more important than “what” he did. Today, Jeremy is dedicated to helping others along the same path to becoming a designer. He helps people change their deeply-engrained behaviors and discover, as he did, that it is possible for them to make a difference. He does this through teaching as well as through growing alongside his students to become better in his own life and work every day.Jeremy is the Director of Executive Education at the d.school. He is a graduate of The University of Texas at Austin's Red McComb's School of Business (2005) and the Stanford University Graduate School of Business (2009).
© 2020-2025 Ivy Podcast Discovery LLC
