Sample frameDay 029·Entrepreneurship · Product

Morning. The minimum viable product is not a small product. It's a learning machine.

Entrepreneurship.

The MVP

5 min read·Apply by lunch

The question

What is the riskiest assumption underneath your current build — and are you learning whether it's true, or just building past it?

The idea

The Minimum Viable Product concept was developed by Eric Ries, drawing on Steve Blank's customer development methodology, and became the centrepiece of Lean Startup (2011). The definition that most builders misunderstand: an MVP is not the smallest version of a product. It is the smallest experiment that tests the riskiest assumption in the business model. Ries's Build-Measure-Learn loop puts learning at the end — but the operational insight is that you must start at the learning end, not the building end. What is the one assumption, if wrong, that makes everything else irrelevant? Test that first, with the minimum possible build. The canonical examples show the range: Dropbox's explainer video was an MVP that tested whether people wanted frictionless file sync before building it. Zappos's manual fulfilment model — photographing shoes at a local store and mailing orders manually — tested whether people would buy shoes online before automating anything. The Wizard of Oz MVP fakes the product with human labour. The Concierge MVP delivers the service manually. The Landing Page MVP tests demand before supply exists. In each case, the goal is the same: invalidate or validate the hypothesis at minimum cost before the big build begins. The pivot vs. persevere decision — Ries's term for the inflection point where Build-Measure-Learn produces a finding — is the most important decision a startup makes and the one most often made too late, because founders conflate product confidence with evidence.

The framework

01
Identify the riskiest hypothesis — start there.
Every business model contains multiple assumptions: customers have this problem, this solution solves it, they'll pay this price, you can acquire them through this channel. Not all of these are equally risky. The riskiest hypothesis is the one whose failure would invalidate the entire business model — not just a feature, but the core premise. The MVP discipline requires you to rank assumptions by risk and test the highest-risk one first, even if testing a lower-risk assumption would be easier or more satisfying. Building a technically impressive product before validating whether anyone wants the core problem solved is the most common and expensive mistake in early-stage product development.
02
Design the smallest possible test — not the smallest possible product.
An MVP is defined by the hypothesis it tests, not by the features it includes. A landing page with a sign-up form tests demand. A prototype with a fake back-end tests UX. A manual service tests whether the outcome is valued before the automation is built. The discipline is to resist adding features that don't contribute evidence toward the hypothesis. Most teams build more than the minimum because it feels safer — a more complete product feels more credible. But every feature that doesn't test the hypothesis is a delay in the learning cycle and a cost that can't be recovered.
03
Measure what validates or invalidates — not vanity.
The Measure step requires defining the metric before you run the test, not after. What specific number, behaviour, or event would tell you that the hypothesis is validated? What would tell you it's invalidated? Vanity metrics — page views, sign-ups, engagement time — rarely answer the question because they don't connect to the hypothesis. Actionable metrics are narrow: did the user complete the core action? Did they return? Did they pay? Did they refer? The difference between a team that learns fast and one that spins in circles is usually whether they defined the success criterion before they started the experiment.
04
Pivot or persevere — make the decision fast and deliberately.
The pivot is not a failure — it's a course correction based on learning. Ries defines a pivot as a structured change in the business model or product direction designed to test a new hypothesis about the product, the market, or the engine of growth. The trap is waiting too long to pivot — continuing to iterate on a hypothesis the data has already invalidated because the team is attached to the original vision. The equally dangerous trap is pivoting too quickly, before the data is conclusive. The discipline: set a decision date before you start the experiment, define the validation criteria in advance, and hold the pivot-or-persevere meeting on schedule regardless of whether the result is comfortable.

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