Weeknotes 513
I did:
Conjecture
Three day week so very busy having lots of conversations about all the new work we’re doing. I love this phase of product work. It’s high energy, high ambiguity, and high fidelity guessing, which is why ‘conjecture’ is my word of the week. Also did this:
- Chatted to product managers about defining outcomes and scoping work to match. It’s been a opportunity for some good product thinking about how to set scope that is deliverable by the deadline but still achieves an outcome.
- Technical proof-of-concept for bringing user behaviour data from websites into our marketing automation platform.
- Thought about ways of building consensus and support for a new product and decided using an existing product to seed usage has a good chance of success.
- Talked about three ways to use data in product work: new opportunities for product development, operational reporting and evaluation, strategic performance analysis.
I read:
Platform Product Management
Nikhil Shrivastava says, “The modern platform PM is no longer responsible only for APIs, SDKs, documentation, and developer experience. Those still matter. But in many commercial platforms, the PM is also designing business systems, operational workflows, monetization architecture, ecosystem incentives, compliance boundaries, and multi-persona product journeys.” I completely agree. Platforms are becoming the cool products, and working on just the technical stuff isn’t enough for platform product managers any more.
Vibe coding is obsolete, product management isn’t
Jeff Gothelf talks about how what Andrej Karpathy, research scientist and founding member of OpenAI, said about agentic engineering is actually just product management. I suppose some of that is true, but only about product management as it is now, not as it will be in the future. It’s a little embarrassing that engineering is changing so quickly and us product people are still hanging on to a soon-to-be out of date idea about what we do.
Seven Myths about AI and Productivity
Fascinating article by Dritjon Gruda and Brad Aeon about the productivity and economic benefits of AI. The conclusion is that its different for different organisations in different situations.
I thought:
Started vs. Finished
What does the number of pieces of work started versus the number finished say about an organisation?
| High finish rate | Low finish rate | |
| High start rate | +: Lots of work gets done. -: Lacks discernment, nothing gets stopped. | +: Good agile decision-making. -: Starting work that never gets finished is wasteful. |
| Low start rate | X | +: Maximum ROI, not much waste. -: Not getting enough done. |
Organisational taxonomies
What organisations call things, what those things mean, who gets to define them, will be increasingly important as AI becomes part of organisational operating systems. AI will need those things clearly defined to treat them consistently.
Opportunity cost is a killer
I’m doing this but could I be doing that instead, or that other thing, or doing more analysis to find things I don’t even know that I could be doing yet.
Judgement is king
“Use AI at the speed of judgement” is the new “ship only as fast as you can learn”.