Positioning a Machine Learning Company

Positioning a Machine Learning Company

The classic guide for entrepreneurs preparing a pitch is Sequoia’s Business Plan Template. This post aims to be a mere addendum to that in the age of Machine Learning.

Why do investors spend so much time focusing on ‘differentiation’? The job of an investor is to allocate money to its best use. Investors shouldn’t allocate money to a company unless it is crystal clear that the company is the best one to solve a particularly valuable problem.

Investors will independently form views on what problems are particularly valuable (‘markets’), find companies solving those problems, then differentiate between them by asking questions like, “Why you? Why now? Why this feature? Why this technology? Why that set of customers first?” to figure out if you’re the best company to solve those problems.

The question of differentiation is also important for entrepreneurs allocating their time to a company. We all want to get out of the rat race, spending our days in an abundant, green pasture free of competitive pressure.

Entrepreneurs pitching investors preemptively answer questions about differentiation in their investor presentations with graphs, matrices and tables.

Many companies now highlight “Machine Learning” (capitals!) as their source of differentiation. Investors are fairly skeptical of allocating money to those companies, if only because so much money has already gone into the same. So, how do you position your company if you believe that your competitive advantage accrues through machine learning or ‘data network effects’? That is, if you truly believe that your company is collecting unique data and learning over that data to generate a differentiated ‘data asset’. This article provides a few ways to do that from our perspective as investors focused on this new era of computing.

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Say what you don't do Efficacy

The job of an investor is to search through a lot of companies to find the best one solving a particularly valuable problem. Thus, investors have adapted to have very short attention spans. Knowing that investors have short attention spans, entrepreneurs often open pitches with fantastical descriptions of their technology. Perhaps a better approach given the general skepticism around machine learning is to give evidence of efficacy upfront. That is, the proof that your product delivers results some order of magnitude better than the alternatives. This is a fundamental question; it’s not worth discussing your company’s ostensibly panacean machine learning technology if your product doesn’t generate enough of a benefit for customers to justify switching costs.

For example, there are lots of companies that purport to predict crop water stress by analyzing the color spectrum in satellite images of a farm. There are indeed some strong secular trends pushing this technology forward such as cheap satellite imagery and fantastic advances in image recognition. However, one of us here at Zetta has a farming background and can say that the prettiest mid-season pictures in the world don’t matter if the plants are dead at the end of the season. A farmer needs to know that what they’re seeing on the screen is predictive of what’s happening in the real world. While it’s very difficult to build physical models of plant growth based on imagery, it’s reasonable to expect some strong evidence that the image-based predictions of water stress correlate with plant tissue samples.

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Another high level question to answer early in your pitch is whether you’re a horizontal or vertical machine learning startup. For example, Clarifai is a horizontal product and AppDiff is a vertical product. We think that most machine learning-based startups will make vertical products — adding AI to an existing solution — but, if your startup is making a horizontal product, it’s worth articulating how you get sufficient attention and pricing power.



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