Had a great conversation with a
Derek Ferguson last week and it reinforced something I've been thinking about: Are we solving the wrong layer of the AI?
Most AI coding tools are built around a chatbot paradigm. You prompt -> it responds -> you iterate. But the real bottleneck for Product & engineering teams isn't code generation — it's context.
Three things that stuck with me:
🧠The knowledge graph problem is bigger than we think. Mapping how code connects across a ecosystem — not just explaining what a function does, but showing what breaks if you change it — becomes even more powerful across Product ecosystem. It directly attacks one of the most expensive invisible costs to revenue: time spent re-understanding systems before touching them.
💸Context switching is a revenue problem, not just a developer experience problem. Every time a developer context-switches - there's a real dollar cost. The fix isn't better documentation. It's AI that surfaces the why alongside the what, requirements, prior decisions, tradeoffs, at the moment you need them. The missing link? Connecting your repo to where decisions actually live: Jira, Linear, Confluence, as
Derek called it "decision archaeology." That framing nailed it. (
Atlassian has Launched ROVO Dev – very interesting)
🤖💣 Vibe coding is accumulating invisible debt. We're shipping AI-generated code faster than ever, but AI security reviews don't catch everything. The human-in-the-loop isn't a bottleneck to eliminate. Right now, it's load bearing.
And one thing I'm personally learning and unlearning:
In various, environments, being the person who holds everything together feels like a superpower. In reality, Learning to operate within your role — saying no, delegating with clarity, letting domain experts own their domains — is what scales. The rapidness in environment doesn't go away. Your relationship to it has to change.
Grateful for conversations that push your thinking. If you're working on developer Products, AI Products, or AI product management — I'd love to connect.
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