Prototypes are tools used to answer specific questions quickly and cheaply. While the word “prototype” is commonly associated with design, other things first-formed (or “proto”) can be thought of as prototypes too.

This includes startups.

As “first-formed” vehicles, comprised of people and ideas, startups aim to answer two fundamental questions:

  1. Is there a product or service that people want?
  2. Is there a viable business to scale this product?

At Gigster, I saw how challenging it was to answer and focus on answering these questions. The company’s initial software development service had product-market-fit with the Ss of SMBs. However, the small contract sizes put a question mark on how the company could get economies of scale. In response, the company pivoted to serve enterprise clients instead of startups.1 Business viability became better due to the larger contract sizes, but Gigster’s product had to fundamentally change for the new market.2

During this existential time, every single person should have dropped what they were doing.3 Either to idea maze what the new value  and product  Gigster offered to enterprises was, or to idea maze how to scale with SMBs. Getting clarity here was what singularly mattered.

Thinking from a prototyping lens highlights several learnings.

First, there is power in being acutely aware and focused on the questions that need to be answered. Will Wright, a prolific game designer who created The Sims, makes hundreds of prototypes for one game. With all prototypes, small and large, Wright is lucid about the exact questions he is answering. The questions provide a compass. Ultimately, Gigster found a way to serve both enterprises and SMBs. But truly utilizing this compass – and having the meta-understanding that we were all participants in a prototyping process – could have saved years of toil.

Thinking about prototypes also gives a sense of the conditions for successful prototyping. The most conducive environment for figuring things out quickly and cheaply is one with a high learning velocity. In a startup, people are not just working on a product — they’re also collectively building the machine that builds the product. The better a company can adjust to new information and create real value for users, the better the chance to validate its existence before cash runs out. The ideal company is therefore a learning machine, one that continually improves its decision-making and execution abilities.

Funding plays an interesting role in all of this. Money is beneficial because it extends the amount of validation time. But it can be dangerous in the prototyping process.4 Venture capital is especially complicated because companies are expected to demonstrate unnaturally large outcomes at an unnaturally fast pace. This puts downward pressure on intellectual rigor, which requires enormous, deliberate force from the team to counteract.

Whether it’s a lack of awareness or rigor, when it comes to questions of product-market fit or the business’s raison d’être, there is nowhere to hide. These existential questions must be answered. Or else.

Thanks to Veeral and Debo for reading early drafts of this writing. And special thanks to Debo, as always, for letting me work for and work with you.

  1. Aside from the economic viability question, there was another seed of the potential pivot. Gigster was doing work for several enterprise clients, and the customer satisfaction scores on these projects were very high. ↩

  2. The software that a 1000+ company needs is not the standalone, lower complexity web/mobile applications Gigster had refined for startups. ↩

  3. I genuinely mean “every single person” and not just the founders. Ravi Gupta, an investor at Sequoia Capital, once said (paraphrasing him) that it is every person’s responsibility in a startup to know the most important thing that needs to be done. He spoke in terms of company goals, but the sentiment applies here. Every single person, not just the founders, has a responsibility to make progress on the startup’s core, existential questions, one of them being product-market fit. ↩

  4. Even if there aren’t demands that come with money, more time isn’t always a good thing when experimenting and making decisions. Constraints can often be beneficial because they force action and creativity. ↩