Chris Nelson

AI made building "Demos" easy. It made building "Products" harder than ever. After 20 years of leading 0-1 execution at places including Google, I’ve seen where the wheels fall off. It’s usually the "Vibe Gap"—the chasm between a cool weekend hack and a reliable, production-ready product. In the 0-1 world, your biggest enemy isn't latency. It’s irrelevance. To ship AI products that actually stick, you need to change your execution rigor: Move from Prompting to Engineering: Deterministic guardrails are mandatory for non-deterministic engines. Beware of "Model Debt": Don't over-optimize a Frontier model before you've validated the user problem. Evals are the Foundation: If you can't measure it, you can't ship it. Period. The goal isn't a system that lasts forever. It's a system that proves it should exist. My 0-1 AI Execution Framework is in the slides below. 👇 Question for the builders: What’s killing your 0-1 AI today: Costs, Latency, or Hallucinations? Let's swap war stories in the comments. #AI #ProductLeadership #Startups #ZeroToOne #TechExecution #Google

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Great guidance from Swami here on how to enable AI-native development (whether at team or organizational level) to succeed. I'd add another - Break the classic roles. The lines between "PM/UX", "SDMs" and "SDEs" needs to blur heavily or dissolve in these teams. Everyone focuses on curating context and refining specifications. That effort enable agents to do the right heavy lifting.

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Would you trust a doctor who gave you a diagnosis but refused to explain why? That's the question on my mind as clinical AI shifts from LLMs, to Agents, and now toward Jev. Jev takes in everything you give it but responds only in decisions. Where today's agents burn time and tokens reasoning back and forth, Jev gets to the choice at a fraction of the cost. In clinical workflows packed with decision points, that's a big deal. Where Jev could shine: • Triage routing and patient prioritisation • Bed allocation and hospital operations • Prior authorisation and claims decisions • High-volume, rule-heavy choices where speed matters Where it gets tricky: • Diagnosis and treatment, where the reasoning is the value • Auditability: regulators and clinicians need a trail • Liability: who answers for a decision with no "why"? • Edge cases that don't fit the patterns it's learned The pattern: Jev makes sense for operational decisions, but the closer you get to the patient, the more the "why" matters. So, would you take a recommendation from an AI that gives you the answer, but not the reasoning? #AIinHealthcare #ClinicalAI #AgenticAI #ExplainableAI #Jev

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Tell your friends! As product manager for both API and MCP, I'm the webinar speaker. I'll use the polyester example noted here and in 20 minutes we will illustrate: - The high level explanation of the differences between MCP and API, in plain English, that you can take with you to any conversation - Plus, of course, the specific implementations of MCP and API at EDITED. You'll leave feeling more comfortable when these come up in conversation, knowing what questions to ask, and when to use which tool for the job. There will also be companion blog posts which I'll be sure to share!

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Last month at the SVWIE Conference at SJSU, I stood before a room of graduate engineers and tech professionals to share a story of two users. 🔹 Maya had a dream vacation planned by AI. 🔹 Carlos had his trip ruined by that same AI. The difference wasn't the model—it was the product decisions made before launch. In my latest Substack, I’m sharing a retrospective of our session, specifically the three "Hard Truths" that came up during our Q&A with the next generation of Silicon Valley leaders: 1️⃣ Responsibility is a Risk Mitigation strategy: If you can’t defend the "Unit Economics of Failure," you can't defend the roadmap. 2️⃣ PMs own the Requirements Architecture: You don’t need to own the model weights to influence a fair outcome. 3️⃣ Explainability is a diagnostic, not a cure: Knowing why a model is biased doesn't fix the bias—that happens upstream. As I shared with the SVWIE forum: AI products that users trust aren't built by better models. They're built by leaders who treat responsibility as a core feature, not a formality. Read the full retrospective and the "3 Hard Truths" here: [Link to Substack in the first comment below] #AI #ProductManagement #Leadership #SVWIE2026 #ResponsibleAI #WomenInEngineering #SJSU #FutureOfTech

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Sharing ZoneBridge widely. I built this from a real gap I felt personally: I needed Fitbit analysis using CDC-aligned intensity logic, and I also needed Fitbit to Strava sync. What ZoneBridge does: * Uses ACSM intensity zones aligned with CDC/AHA guidance * Tracks progress toward the 150-300 weekly target Reports: * Fitness Mins (Fit-Mins) * Total exercise minutes * Syncs Fitbit activities to Strava I also spent time making the README a practical step-by-step guide so setup and usage are straightforward. Run directly from GitHub: uvx --from git+https://lnkd.in/g6QqrqRy zonebridge --help Repo: https://lnkd.in/g6QqrqRy Follow @hemantvirmani for more. ♻️Repost this, if this tool helps someone in your network. #opensource #vibecoded #strava #fitbit #healthtech #HVSays

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Getting laid off is not a character flaw. Lying about it is. If a company finds out you hid a three month gap, they will not worry about the gap, they will worry about your judgment. Tell the truth, rather than creating more problems for yourself in the future. Trust is part of the package, especially If you're going for senior roles.

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