From the point of view of individual efficiency, working in large batches makes sense. It also has other benefits: it promotes skill building, makes it easier to hold individual contributors accountable, and, most important, allows experts to work without interruption. At least that’s the theory. Unfortunately, reality seldom works out that way.

Getting a startup’s engine of growth up and running is hard enough, but the truth is that every engine of growth eventually runs out of gas. Every engine is tied to a given set of customers and their related habits, preferences, advertising channels, and interconnections. At some point, that set of customers will be exhausted.

Only 5 percent of entrepreneurship is the big idea, the business model, the whiteboard strategizing, and the splitting up of the spoils. The other 95 percent is the gritty work that is measured by innovation accounting: product prioritization decisions, deciding which customers to target or listen to, and having the courage to subject a grand vision to constant testing and feedback.

Do consumers recognize that they have the problem you are trying to solve? 2. If there was a solution, would they buy it? 3. Would they buy it from us? 4. Can we build a solution for that problem?

It is also the right way to think about productivity in a startup: not in terms of how much stuff we are building but in terms of how much validated learning we’re getting for our efforts.

To introduce Five Whys to an organization, it is necessary to hold Five Whys sessions as new problems come up. Since baggage issues are endemic, they naturally come up as part of the Five Whys analysis and you can take that opportunity to fix them incrementally. If they don’t come up organically, maybe they’re not as big as they seem.

Startup, I have always been a bit of a troublemaker at the companies at which I have worked, pushing for rapid iteration, data-driven decision making, and early customer involvement.

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Lo que diferencia una historia de éxito de una de fracaso es que los emprendedores con éxito tuvieron la previsión, la habilidad y las herramientas necesarias para descubrir qué partes de sus planes estaban funcionando de forma brillante y cuáles estaban desencaminadas, y adaptaron su estrategia en función de esto.

That was a good day. In contrast, if I was interrupted with questions, process, or — heaven forbid — meetings, I felt bad. What did I really accomplish that day? Code and product features were tangible to me; I could see them, understand them, and show them off. Learning, by contrast, is frustratingly intangible.