My response fell flat.

“Oh, uhh interesting,” said the hiring manager.

“Yeah, we’d test all kinds of product ideas manually before shipping them to prod,” I repeated.

“Uh huh,” he said — no longer faking interest.

I tried again, now leaning on logic.

“It can take 2-4 weeks of dev effort to get ML models into an app, so we’d send forecasts in emails to make sure people found them useful. It helped us find and fix all kinds of issues before investing a bunch of dev cycles. And when we did release, the feature was super-easy for users to adopt.”

“Well, I’m not sure how that’d work here.”

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I felt like I was speaking a foreign language.

In truth, I was.


In startup product teams, you need efficient (read: scrappy) ways to learn fast and iterate. This is especially true in the seed stage, but continues to be a superpower through the Series A/B stages. Dev resources are tight, the product is young, and you’re still learning a lot about your users.

But, this doesn’t make sense to many late-stage businesses. ‘Manual Experiments’ and ‘Concierge MVPs’ feel unscalable at best and unprofessional at worst. Their relevance, perceived or actual, simply fades as companies grow.

But, here’s the rub.

Much of your team is probably from big companies. They may not know that, “Do Things that Don’t Scale” (another great Paul Graham essay), is actually an advantage.

The language of ‘rapid experimentation’ has a funny accent to most people. It feels slow and unsophisticated. As a startup product leader, your goals is to make it familiar, comfortable, and eventually embraced by the team.

Its a superpower that gives your startup greater speed, more shots-on-goal, and saves precious engineering resources for pre-vetted product investments.

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