Problem discovery: Cohorts. What is hiding behind your clean distributions? Exploring your data for the cohorts hidden behind top-level analysis is an effective way to discover new kinds of users with surprising needs. Competitive advantages. By understanding the areas you're exceptionally strong in, you can identify opportunities that you're better positioned to fill than other companies. Competitive moats. Moats are a more extreme version of a competitive advantage. Moats represent a sustaining competitive advantage, which makes it possible for you to pursue offerings that others simply cannot. • What do your existing moats enable you to do today? • What are the potential moats you could build for the future? • What moats are your competitors luxuriating behind?

Good goals are a composition of four specific kinds of numbers: 1. A target states where you want to reach. 2. A baseline identifies where you are today. 3. A trend describes the current velocity. 4. A time frame sets bounds for the change. Put these all together, and a well-structured goal takes the form of: "In Q3, we will reduce time to render our frontpage from 600ms (p95) to 300ms (p95). In Q2, render time increased from 500ms to 600ms."

There is a moment in every company's growth when top-level planning shifts from discussing specific projects to talking about goals. This happens recursively across each scope of leadership, as areas of accountability become too broad or complex for their leaders to consistently understand every project's details. This can be a very empowering moment because goals decouple the "what" from the "how," but it can also be a confusing transition for everyone involved: writing clear goals takes a bit of practice.

What I needed was a way to coordinate my approach across teams, both in terms of very specific challenges and in terms of our long-term direction. After experimenting with a handful of different approaches, agreeing on strategy and vision has been the most effective approach that I've found to alignment at scale.

Solution validation: Justify switching costs. What will the costs of switching be for users who move to your solution? Even if folks want to use it, high switching costs may mean that they simply won't be able to. Test with your potential users if they'd be willing to pay the full cost of migrating to your solution instead of their existing planned work.

Solution validation: Write a customer letter. Write the launch announcement that you would send after finishing the solution. Are you able to write something exciting, useful, and real? It's much more useful to test it against your actual users than to rely on your intuition. Find reference users. Can you find users who are willing to be the first users for the solution? If you can't, you should be skeptical whether what you're building is worthwhile. Find the path more quickly traveled. The most expensive way to validate a solution is to build it in its entirety. The upside of that approach is that you've lost no time if you picked a good solution. The downside is that you've sacrificed a huge amount of time if it's not. Try to find the cheapest way to validate.

Problem discovery: Compounding leverage. What are the composable blocks you could start building today that would compound into major product or technical leverage" over time? I think of this category of work as finding ways to get the benefit at least twice. These are potentially tasks that initially don't seem important enough to prioritize, but whose compounding value makes the work possible to prioritize.