Nate Jones

If you have a persistent feeling of fatigue when you post a thought leadership post about a topic like product management consider taking a break from posting to reduce net suffering in this world. If you see signs of fatigue (such as terrible memes) in a thought leader, consider an intervention lol

Know how to tell a story with data! Data doesn't tell a story by itself, so you need to understand how to pattern data, discover trends, clearly articulate a narrative that is more than the data, and finally how to connect the data with the stories of real people (we would call those anecdotes in product management). You'll be able to influence much more quickly if you understand how to build a proposal that has data at the heart of the story.

Look for flywheel effects. Product roles either act as brakes or as gas pedals on your ability to accomplish your goals. It's not product management as a job family that's a driver of personal fulfillment, it's the work culture of the business. If you connect with your colleagues, if you feel energized by the challenge of solving the problems in your space, if you're proud of what you ship, then you're going to bring that energy forward with you into your personal life. Having an energy boost from a healthy work culture with interesting problems enables you to get a work-life flywheel going, and that persistent infusion of energy gives you more momentum on your longterm goals over time. Energizing cultures look different for everyone!

PM's today need to pay attention to two underlying drivers for tech decision-making across the landscape. First, defensibility is a major challenge for PM's as everyone converges on AI. What makes your product defensible in a sea of competitors? How well do you understand the interaction between UX, brand, and product features that together drives a sticky experience? Do you know how to leverage that understanding to position your product correctly in a tightening competitive space? The second driver to keep an eye on is the inherent friction of scaling AI vs. the 2010's era apps. Cost: AI is costly to run and it's difficult to drive down costs with scale. Social: AI does not scale with a social graph like Insta, Snap, etc. No network.

There are a couple of ways to keep your head and make sharp, learnable decisions when you have both feedback and data analytics to assess. First, remember that metrics may be quantitative but they are not unbiased. Your thesis colors the metrics analysis you perform. Always be explicit about what you seek to learn before you conduct an analysis. Open-ended analysis to start discovery rarely works well. If you start with a thesis, you'll be able to see the impact of a problem clearly if your thesis is correct. Second, keep in mind that qualitative anecdotes should be more influential than quantitative metrics as you move from documenting impact to shaping solution. Anecdotes or feedback reveal what really matters to solve. Good luck!

Keep the score and stay curious! Often changes in expectations from leadership are driven by conversations at the board or C-suite level. Get curious about those conversations. Understand the 'why' driving the recommendations or changes of direction you hear from your leader. Be the first to connect the dots with the problem you're currently solving. And keep the score: make it really specific and really clear what will not get done if you change direction now. You should be able to have really clear conversations about what gives way and doesn't ship to customers because direction is changed, and you should be able to articulate how changing direction delays value. Being specific reduces the odds an ask to change direction sticks.