Josef Ziegler

Had a great time at Climate Week in NYC last week! 🌍 It was exciting to see the demo of our product on stage and get real feedback afterwards - including some tough criticism that will actually help us improve. My 3 main takeaways: - AI is unlocking second-order optimization - we're moving beyond basic efficiency to compound gains. Teams are seeing 20%+ improvements in renewable energy systems through AI-driven optimization. - Cross-sector collaboration is the real accelerator - the best solutions come from unexpected partnerships. Tech teams working with climate scientists and traditional industries are solving problems 10x faster. - Speed beats perfection in climate tech - with the urgency we're facing, deploying good solutions now and iterating beats waiting for perfect ones. The energy was incredible - thousands of people all working on climate solutions. What climate tech developments are you most excited about? #ClimateWeekNYC #AIforClimate #ClimateTech #Sustainability #ClimateAction

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rqlite -- and the way I develop it -- is fundamentally changing. LLMs are in the loop -- from ideation to Pull Request. https://lnkd.in/ejwXGddR

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This is a great experiment by the Anthropic team, showcasing the "true autonomous" capabilities of LLMs. By "true autonomous," I mean that Anthropic (I believe) purposefully did not bombard the LLM with numerous tools in areas where LLMs are weak. For example, LLMs are not good in math, and performance would have been much better if they hd provided a tool like "calculate_profitability." This might have prevented the $100 opportunity miss. In AI agent development, tools bridge the gap where LLMs are not "deterministic." This is currently the MOAT in the GenAI app business: the better your tools, the more deterministic results you can provide. Only till LLMs can write their own tools on the fly and execute them where they know they are weak and using deterministic softwares (tools) to fill these gaps. https://lnkd.in/gMk3VRJv

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#1 - Toss a ball to a friend that is running at an angle and have them catch it. Not that hard for humans in general. Very hard for AI. #2 - Document and explain the math behind #1, with variants for wind speed, elevation, etc. Very hard for humans, relatively trivial for AI.  There's a lot of opportunity at the jagged frontier.

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Today I spoke with two frinds from Google AI and learned that the KPI for engineers at Google and many large tech companies has fundamentally changed. A key metric now is how many tokens you are able to use. The more tokens you use, the greater your potential contribution—one person can now accomplish the work that once required ten or even a hundred people. #HerPowerAI #Google #WorkModel #KPI

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Excited to share my conversation with Marc Baselga and Ben Erez on the Supra Insider podcast. We went deep on one of the biggest shifts happening in product leadership right now: why many experienced PMs and product execs are returning to individual contributor (IC) roles in the AI era, and why that’s not a step back, but a strategic leap forward. Other topics we discussed: -- Why the leverage equation has changed—and how managing AI agents now rivals managing people -- The critical importance of hands-on credibility in AI-native companies -- Why great PMs in the future will blend product, design, analytics, and frontend execution -- What it takes to deliver real customer outcomes (not just launch features) -- How to evaluate breakout companies—and what founders often get wrong when hiring product leaders I also shared my thoughts on: -- Joining the right company: mission, trajectory, talent density, and founder courage -- Crushing it as a “super IC”: the power of speed, ownership, and infectious energy -- Transitioning from product leader to GM or founder with the right mindset If you’re navigating your next chapter in product (or just trying to stay sharp in the AI-native world), I hope you’ll find this useful. Episode links: Spotify: https://lnkd.in/gK48PFWK Apple: https://lnkd.in/gmT265BY YouTube: https://lnkd.in/gtr4RAKd Substack: https://lnkd.in/gM38FSWc

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As an American, and a Californian, I was born into a culture of can-do optimism, with a faith in our collective ability to overcome the odds and not only survive but solve big problems. We’ve done it as a society over and over in our history. We dream, we work, we fight, we compromise and cooperate, we build, and often we rebuild. We are at our best dreaming and defying impossibility, being generous with each other and welcoming the stranger. I believe in this version of us and spent much of my career in national service because of that belief. But we are going through a very pessimistic time. We have problems as big as ever, but we don’t seem to have faith in ourselves to solve them. I spend most of my time now thinking about artificial intelligence and the problems we could solve with it. Nothing is a panacea, and it will take all of us to turn a technology into new scientific discoveries, deeper knowledge and better ways to learn it, a medical system that saves lives and supports caregivers, more meaningful work and greater prosperity. Those things are hard. But we need to do them. That’s a choice we have and can make. We have always invented our way out of problems. I am really struck by our fearfulness. Countries with far fewer advantages than we have are far more bullish on the possibilities of technology. It goes beyond AI. That’s just one small focal point. Where else do we have openings and opportunities. Where else can we roll up our sleeves and come together to fix problems? Where are we deciding to be defeated before we begin? What other possibilities are in front of us? We have proven over and over that we can rise, ultimately, to any occasion. It’s what our history has passed down to us. I’ve been thinking a lot about lines from an old Ani Difranco spoken-word poem: “Our foremothers and forefathers… came through Hell and high water/ so that we could stand here and behold breathlessly this sight/ how a raging river of tears/ is cutting a Grand Canyon of light.”

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Waymo’s recent robotaxi incidents surface one of the most important and least discussed questions in AI product design: What should a system do when it doesn’t know what to do? Waymo’s answer is refreshingly honest, i.e. stop. Its vehicles are explicitly trained to halt when uncertainty rises (power outages, broken traffic lights, unexpected human behavior). This isn’t a model failure. It’s a product decision. When the safest action is inaction, the system chooses restraint over guesswork. At the level of one car, this is rational. At the level of ten cars, still sensible. At the level of hundreds, it becomes a system-wide failure. Waymo optimizes for local correctness, i.e. when unsure, do no harm. But without orchestration above the vehicle, local safety becomes global brittleness. Correct behavior in isolation can cascade into city-scale paralysis. The real lesson: building AI products isn’t about maximizing intelligence. It’s about designing systems that behave predictably when intelligence runs out and understanding how those behaviors compound at scale. Contrast this with China’s Apollo Go (Baidu) and Tesla and you will see three different strategies with different trade-offs. Baidu reduces ambiguity upstream with an “infrastructure-first” strategy. They’re not just building autonomous cars, they’re building V2X cities where roads, signals, and networks talk to vehicles. When things break, the city steps in. Fleets are centrally directed into predefined “safe harbor” states. The result is system-level resilience albeit at the cost of heavy coordination, regulation, and long-term capital alignment. Tesla takes the opposite bet. It assumes ambiguity will reach the vehicle and designs for it. No smart roads. Vision-only autonomy with end-to-end neural networks learning from millions of edge cases. Tesla isn’t optimizing for certainty in the moment, but for learning at scale over time. The product takeaway is clear: robust AI systems can’t rely on a single philosophy. The most resilient products layer their defenses: + Local safety behaviors that fail predictably + System-level coordination that prevents cascading failure + Continuous learning loops that improve with scale In AI, uncertainty isn’t the edge case, it’s the design constraint.

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