Framework I shared with my team: - the more ambiguous the product space is, the more human thinking is needed. Do NOT outsource your thinking to AI.
- the more defined the problem is, especially if there are best practices, feel free to use AI to execute faster on the solution.
Given the job market, if you’re in a good spot in your current role, you probably should stay unless
- an opportunity comes up that is legitimately life-changing
- it’s an opportunity to work with a leader who has org + LT respect and can provide you cover if things go south.
This detail from Anthropic S1 tracks with my observations in AI adoption for non-tech companies:
“Claude Enterprise users who don't use Claude Code spend less than $75/month on average, but the top 5% who do use Claude Code spend roughly 20x as much.”
One interesting and kind of heartwarming surprise in becoming a manager again is that I’m leading folks who are at the same stage of their PM journey as my own 10 years ago. One of the tension I’m experiencing is the desire to help them avoid all of the PM pitfalls I experienced
Agentic commerce has to solve for the “how does the agent use and earn my rewards points” problem if proponents truly want it to grow in adoption and repeat use
Here’s an overview video for my side project @usepatternui: design system enforcement for coding agents
If this peaks your interest, install Pattern by adding this line in Claude Code, Codex or any other harness: npx pattern-mcp init
github.com/donaldrichard19-L…
Here’s an overview video for my side project @usepatternui: design system enforcement for coding agents
If this peaks your interest, install Pattern by adding this line in Claude Code, Codex or any other harness: npx pattern-mcp init
usepattern.sh/
As someone building a product similar to Notion in a different space (labor and economic data), I often use Notion as an example of AI making a powerful, but often complicated product more legible to users.
New conference hack: have @meetgranola running while you’re listening to a session
Many companies are miracles, because they survive and even thrive while mired in poor or nonexistent communication between internal teams.
Improvements made on this issue are coordination costs born implicitly by individuals or explicitly in the creation of a function (eg. PM).
Right now is a good time for companies building personal AI assistants to hire some people who actually know, understand and can talk to regular people.
The best approach is start with their user’s outcome (eg. what they must do to stay employed, generate revenue) and work backwards from there
This 100%
Another takeaway after three years of building a company is that the returns to just "being a good person", and not acting transactionally, are higher than I expected.
There are so many transactional, boring, and at times immoral people in the tech industry that if you act with sustained warmth, authenticity, humor, and integrity, great gifts will be laid before you. White pill.
I keep saying SMB through Enterprise AI adoption is downstream of consumer AI adoption and most consumers don’t know what’s possible with today’s AI products.
And SMBs are closer to consumers than they are to midmarket and enterprise businesses.
