Merge pull request #1068 from mbifulco/newsletter/optimal-stopping-problem

feat: newsletter on optimal stopping

Author: mbifulco

src/data/newsletters/optimal-stopping-problem-for-founders.mdx

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+---
+title: "Founder Math: When to Stop Searching and Start Choosing with the 37% Rule"
+tags: [founder, product, startup]
+coverImagePublicId: newsletters/optimal-stopping-problem-for-founders/cover
+date: 2025-08-19
+slug: optimal-stopping-problem-for-founders
+excerpt: "How do you know when to stop searching and just choose? A classic bit of math offers a simple rule."
+---
+
+## The Big Idea
+By using a bit of math called the **optimal stopping problem**, you’ll learn exactly when to stop second-guessing yourself, and start making confident decisions, whether you’re choosing features, hiring teammates, or testing new ideas.
+
+## Two simple rules for decision making
+
+The **optimal stopping problem** comes from probability and decision theory. It describes how find the balance between time and quality when searching for the best option in a given set. The math behind optimal stopping is fairly complex, but the rules are simple.
+
+For our purposes, we can use two simple rules to guide decision making:
+
+- **Rule 1**: If you **know how many options you have**, use the first 37% of them to get a sense of the quality of your options, then choose the next item that is better than all of the previous items.
+- **Rule 2**: If you **don't know how many options you have**, use the square root of the number of options you have seen so far to guide your decision.
+
+These two rules give you a surprisingly effective way to stop overthinking and start **acting**.
+
+## Applied science: hiring engineers
+
+Hiring software engineers is always tricky: posting a job opening online results in a flood of applications, and you're left with the difficult task of sifting through them to find the best fit.
+
+In practice, everyone you interview will _also_ be interviewing with other companies. This means you can't keep applicants waiting around while you interview everyone who applies. Sit on a good candidate for too long, and they'll be snapped up by another company.
+
+It's a problem that is perfect for the 37% rule.
+
+If you post a job and get 20 applicants in the first day, you can plan ahead. That’s where the 37% rule works best: interview the first 7 candidates just to set the bar, then hire the next person who’s better than everyone you’ve seen so far.
+
+If you're hiring on a rolling basis, you don't know how many candidates will apply, the √N rule helps. Say you’ve looked at 16 resumes so far, that’s √16 = 4. Once you’ve seen four solid benchmarks, you’re ready to grab the next standout.
+
+## Applied science: Influencer and Partner Outreach
+Marketing often comes down to people. You might have a long list of influencers, [newsletter authors](https://mikebifulco.com/sponsor), or potential partners you want to reach out to for an ad partnership - but when do you stop searching for more and commit to building a relationship?
+
+If you know the list size in advance (say 30 people), use the **37% rule**: spend time with the first 11 to understand what quality looks like, then commit to the next standout who outshines the rest.
+
+If your outreach is open-ended and you don’t know how many conversations you’ll have, use the **√N rule** instead. After you’ve talked to 16, that’s √16 = 4. Once you’ve met those first four, you’re in a strong position to recognize and act on the next really promising relationship.
+
+"Perfect" partners for influencer marketing don’t exist. Better to stop searching, commit, and grow the relationship once you've found a good fit.
+
+## Conclusion
+This is definitely a _don't take my word for it_ kind of thing - variations of the optimal stopping problem are used all over the place: from options trading, to finding your next high-paying job, this exact kind of logic has been used to make smarter decisions without the analysis paralysis.
+
+So if they can do it, you can too.
+
+## Further reading
+
+If this idea hooked you, there is _loads_ more to learn. I'd recommend starting with these:
+
+- The wikipedia article on [Optimal Stopping Problem](https://en.wikipedia.org/wiki/Optimal_stopping) has a handful of great examples of optimal stopping in action, and links to deeper mathematics from a variety of fields, including computer science, economics, and finance. Definitely worth a dive.
+
+- As you might expect, the Numberphile YouTube channel has a great video on this topic: The [Mathematical Way to Choose a Toilet](https://www.youtube.com/watch?v=ZWib5olGbQ0).