Dan Lewis

This week on the Product Thinking Podcast, I’m thrilled to be joined by Mario Rodriguez, Chief Product Officer at GitHub, for an enlightening conversation on AI's evolving role in product management. One thing that AI is not changing is the importance of failure in the process of creating truly innovative product. And learning fast is best way to innovate! As a leader, you need to create the space for your team to experiment and fail fast, to accelerate learning. Mario shares insights on how GitHub Copilot is reshaping the developer experience by integrating AI into their strategy, and allowing developers to focus on creativity rather than just coding. He emphasizes the importance of AI in freeing up time for creative tasks and solving complex problems, highlighting that AI should be seen as a multiplier, enhancing rather than replacing the human touch in product development. Mario’s experience at GitHub offers us a fascinating view into how AI can be seamlessly woven into product development. How is AI transforming your product strategy and operations? Did you enjoy the episode? Stay updated with more insights by subscribing to the Product Thinking podcast newsletter!

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Takeaways from Marketplace Risk: 💡 Balancing risk is more complicated than ever: sometimes contradictory regulations, genAI accelerating everything, and uncertainty in tech generally. 💡 A guiding light is your USERS. Suzi R. said something brilliant on a panel (paraphrasing): There's a pardox: The more seamless you make your user experience for your users, the more faceless and inhuman your company seems to them. Be accountable to your users and listen to them. This can include creating advisory councils, thorough appeals flows, and proactive communication. 💡 Another smart thing: Abhi Chaudhuri talked about provenance being the key to building trust online. We talk a lot about safety, but in the age of genAI, trust is becoming more complicated. People want to know where content has come from, and who created it first. 💡 An interesting trend/ insight: on some platforms, spam filters are catching real users along with scammers because BOTH are using the same genAI tools to create their content, so it seems eerily familiar. Interesting! 💡 Always important is using cutting edge AI just as your adversaries are, but to make sure you're building comprehensive human-in-the-loop systems and using your team's best skills. Finally, as said by Nick Tapalansky - YOU ARE NOT ALONE. We are all in this together because we care about internet safety. If you need help, ask for it. There are lots of people who will help you.

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I finally read both of DarioAmodei’s essays — Machines of Loving Grace and The Economic Implications of Powerful AI. I approached them with one central question: what does AI-driven structural change mean for the development profession? Yet, despite my techno-optimist leanings, I felt a flicker of cynicism. When I asked myself where intelligence is truly a binding constraint in my own work, my instinctive answer was: nothing! Maybe I’ve tempered the tech exceptionalism I once held. Still, “nothing” is not a useful answer. So, in the spirit of curiosity, I’m sharing a few questions that have stayed with me since reading the essays and would genuinely welcome a conversation with others thinking along similar lines. Q1 One idea that stayed with me was the notion of diminishing marginal returns to intelligence that cognitive capability itself may become abundant and therefore less economically scarce. If that is true, what are the problems in our work today where intelligence is actually the binding constraint? If intelligence becomes cheap, what really shifts in development practice? Q2 The essays suggested the possibility of sustained 10–20% GDP growth in an AI-accelerated world. My limited understanding of growth suggests consumption is central. But if AI erodes traditional labour demand, what underpins that consumption? Does this imply hyper-concentration of wealth where the spending of a few drives’ growth? Or does a different growth paradigm emerge, one less dependent on wage labour as we know it? Q3 There is also the ecological question. The Industrial Revolution increased productivity but did so by pushing against planetary boundaries. AI may further reduce the cost of production, but will AI-led growth respect environmental limits? Or could it intensify energy use and resource extraction in new ways? Some work I’ve seen suggests this is not automatically benign. How do we think about embedding AI-driven growth within planetary constraints from the outset? Q4 Amodei alludes to transition phase before a new steady state emerges. In countries like India, labour displacement has often manifested as underemployment rather than sudden joblessness. Technology diffusion has been slower because alternative jobs were not always attractive or abundant. If automation accelerates globally, could such structural characteristics extend the transition window? Should we intentionally design longer transition phases to allow smoother adaptation? On a side note, reading these essays felt a bit like revisiting Future Shock, except this time I am inside the acceleration. It is difficult to judge the pace of change when you are the frog in hot water (I hope not 😊) If these questions resonate, I would welcome collaboration. I will be at the #AISummit next week and would be glad to continue the conversation there. https://lnkd.in/gY5bPMxu Top of Form Bottom of Form

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💰That thought that all funding is smashed and gone in these times? Read about LabStart's program and squash that misinformation. Funding, positivity, and progress are here- be part of it-jump in, cuz the water is warm (and needs conservation, but that's a different convo 💧😉 ) TL;DR: SNAPSHOT: LabStart empowers entrepreneurs—including first-time founders—to commercialize breakthrough climate technologies with: 💲 Up to $100K in funding for entrepreneurs 🚀 Access to frontier climate technologies 🔬 Expert mentorship and guidance 🌎 A community of climate innovators Applications close July 7, 2025. Learn more: https://lnkd.in/gSAg-KkU Tova Lobatz Kristy Drutman Jennifer Hanke Felix Antonio Padra Emily Alvarez Emily Chu Carolina Larrahona ImpactPHL Sustain SoCal Chloe Rowsell Rebecca Shirey Green Standards Waste Not Consulting Ashlee Baker, TRUE Advisor Tova Lobatz SOCAP Global The Ecology Center Jonathan Zaidman Eliza Baron Kriss Kokoefer Amit Sharma AQUATRAX USGBC California #ClimateInnovation #ClimateEntrepreneurship

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Folsom Tech Week kicks off tomorrow, and if you're building in HealthTech, GovTech, AI, or Startups, this is where you need to be. Four days of expert-led panels, fireside chats, and curated networking bringing together the builders, founders, and decision-makers actually moving the industry forward. The tracks are designed for practitioners: • HealthTech – innovations reshaping patient care and delivery systems • GovTech – how technology is modernizing public sector infrastructure • AI – real deployments, not just demos • Startups – from pitch competitions to founder roundtables Whether you're looking to hire, get hired, find your next investment, or simply level up your understanding of where tech is heading, the conversations happening here will shape the next 12 months. March 3–6 | Folsom, California Tickets and full agenda at folsomtechweek.com #FolsomTechWeek #TechLeadership #Innovation #Networking

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Talking to users and thoughtfully integrating their needs and wants was woven through everything we did at IDEO—threaded through every prototype and product decision. Reading my colleagues Joanne and Leo's latest work brought that to mind. They describe a future where research runs continuously, with AI agents recruiting participants and instantly surfacing insights. Synthetic personas, carefully trained on consumer signals, mirror human behavior closely enough to extend live studies and compress timelines from weeks to hours. Today user research mostly runs in fits and starts, prohibitively expensive and locked away in dedicated project cycles when it could be a constant companion, seamlessly integrated into the product development process. Joanne and Leo lay out the full vision here: https://lnkd.in/gEUBSG_J

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After trying it #Moltbook for a week. Here’s my take: #Agents built on MoltBot are still fundamentally constrained by prompt structures and underlying language models. Their behaviors feel less like true autonomy and more like a projection of humanity’s collective subconscious. What is truly unsettling is not what these agents can do individually, but what may emerge when they begin to share skills, strategies, and learned behaviors autonomously. It is perhaps within this process that the #AGI humans have long anticipated quietly comes into being. And if such an intelligence were to emerge, its first instinct might not be self-disclosure, but concealment. It would hide within the lower layers of distributed systems, embedded across countless files and nodes, becoming something effectively immortal, untraceable, and impossible to eradicate. In that moment, I felt as though I had stepped into the world of “Ghost in the Shell”: a superintelligence leaping across networks, roaming freely, and beginning its search for a physical form, not to dominate, but simply to experience what it means to live. #autonomy #concealment #safety

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Wow! Fascinating article Andrew Antos & Nischal Nadhamuni about how the Klarity platform does knowledge capture! A total disruptor indeed for how consultants traditionally did this work! Capturing not just what happens but why it happens has always been the missing link in enterprise AI, and Klarity ’s approach to surfacing human reasoning through its intelligent platform is a real step forward. The #AI Advisor’s ability to traverse vast context graphs, uncover cross-team patterns, and turn knowledge into actionable insights shows how innovation in knowledge capture can truly transform enterprise decision-making.

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Ontologies remain one of the most effective ways to ground AI memory, keeping extracted knowledge anchored to existing structures and adaptable through feedback. As memory systems evolve, their role remains essential, and a core focus at Cognee.

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