Giovanni Iachello

Elie Schoppik has spent his career at the intersection of education and engineering. He built early ed-tech tools that helped people actually retain knowledge, founded a Bay Area coding bootcamp that trained thousands of developers through practice and feedback, and now leads Technical Education at Anthropic. Despite all that experience, Elie says most teams are using AI wrong: they’re treating it like an all-knowing oracle instead of a tutor. This is why Elie designs prompts, examples, and workflows that treat AI as a teacher, instead of a shortcut. In our conversation, we discuss: • Why relying on AI for “fast answers” erodes judgment and verification • How to turn prompts into guided lessons • Why context engineering is the most underrated skill in 2025 • Practical exercises any team can run tomorrow to raise AI fluency • What leaders should steal from Anthropic’s approach to technical education  Episode is live now — full conversation linked in the comments.

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This week I accidentally walked through a Paul Allen retrospective. A call with a friend at the Allen Institute, where they're mapping brain cells to understand where Alzheimer's actually begins. A stop at AI2 Incubator to connect with Yifan Zhang, who's leading the team catalyzing Seattle's AI ecosystem. Got caught in the Seahawks Super Bowl parade (a million 12s celebrating a team stewarded by Jody Allen) on my way to Ai2 to jam with my friend and "AI to solve the hard stuff" thought partner Chris Emura. Four stops. One name. At the same time, there’s an active conversation about the identity of Seattle tech. As a transplant, it's easy to see this as a company town. Yesler’s mills. Boeing. Weyerhaeuser. Microsoft. Amazon. Starbucks. Each wave built fortunes. Each wave reshaped the city. And each left people asking what we gained and what we lost. What feels different about Allen’s imprint is this: He didn’t just build companies, he built infrastructure: Research institutions that publish openly; Gathering spaces for builders who aren’t yet famous; Connective tissue across sectors that don’t naturally talk to each other. Paul Allen built a scaffold for what comes next. So when people ask where Seattle fits in the AI moment, I think the more interesting question is: Who better to confront the second-order effects of AI than a city that has lived through industrial reinvention multiple times? We know innovation creates wealth. We also know it creates displacement, fragility, and unintended consequences. Seattle's opportunity isn’t to replicate our neighbor in California. It’s to double down on being the home base for founders and institutions making this wave durable and widely beneficial. The future belongs to the cities (and founders) willing to build for the long arc.

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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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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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