Francesco Corea


Artificial Intelligence In Search of Protection — Part III

Reasons behind not looking for patent protection

This is a series of four articles on AI and IP protection:

Part I — Why patenting an AI innovation is different

Part II — The advantages of patenting AI products

Part III — Reasons behind not looking for patent protection

Part IV — AI Patents Landscape

Of course is not all peaches and dandelions here. If from one hand patents increase valuation and attract investors’ attention, some studies seem to suggest otherwise (Smith and Cordina, 2014). Looking at the patents portfolio is a shortcut for less sophisticated investors, but for the best one is nothing more than an extra data point in their complex risk-return simulation modeling.

Many startups deliberatively choose then to not patent any internally generated innovation, and most of the time they do so for one or more of the following reasons:

  • They are too early-stage: the majority of companies working nowadays in AI are relatively young (according to CBinsights, 69% or more of AI deals since 2012 have gone to startups still in the early stages). This often means they have no means, either financial or in terms of resources and time, to file a patent;
  • They might convey too much information: even though a single patent might not say much about your business, a portfolio of them can actually give many more strategic insights to a careful observer (and probably more than what you aim for);
  • The patent landscape is messy: as we already mentioned above, the presence of widespread open-source libraries drastically increases the complexity of filing a patent;
  • Patents are becoming less meaningful: it is extremely easy to violate a patent without even knowing it, and incredibly hard to enforce one. Add to this problem the corollary issue that patent litigation (when you are actually able to recognize a violation, which is not obvious) costs you around half a million (a trademark instead between 300k and 500k) and you will not want to get yourself into that problematic spot at all;
  • Cultural divergence (i.e., they do not believe in it): I might spend many words here, but I rather prefer to quote Tesla announcement of a couple of years ago:
“Tesla will not initiate patent lawsuits against anyone who, in good faith, wants to use our technology. […] Technology leadership is not defined by patents, which history has repeatedly shown to be small protection indeed against a determined competitor, but rather by the ability of a company to attract and motivate the world’s most talented engineers. We believe that applying the open source philosophy to our patents will strengthen rather than diminish Tesla’s position in this regard” (Elon Musk).
  • They have alternative protections: you can easily supply the absence of patents with different traditional and non-traditional moats (by the way, if you haven’t already, go and read Gil Dibner’s post and Jerry Chen’s one of this topic):

i) Data moat: most of the AI software need to be fed with millions of data points. Even if you have access to the algorithm, without data you cannot basically use it. Furthermore, having access to different datasets (or multiple “systems of records”, as Jerry Chen called them) and use them together not only provides you with an exponentially higher value but also increases the likelihood you can deeply personalize your product and tailor it to your customers (which then increases their switching costs toward the competition);

ii) Network effect (also called Metcalf’s law or AI flywheels): the value of the network increases exponentially with the number of the nodes, which in other words means that new customers imply more data, which implies a better algorithm, which in turn implies new customers, and so on so forth. You need though to reach a minimum algorithmic performance (MAP) before the network can attract the first cohort of customers with a strong value proposition;

iii) Talents: AI as a field is dominated by academic-trained talents, which are incredibly expensive and hard to hire and often demands an open approach to research (i.e., open-source software and publications in scientific journals). There are very few strong AI researchers worldwide, and securing one major name and/or a good team can represent a strong barrier for a startup (although you really hit the jackpot if you hire someone with specific domain expertise, which is quickly becoming the real differentiation between succeeding and surviving);

iv) Cost efficiency: many of the most powerful AI-driven businesses today are able to scale while limiting costs related to teams and product development (and let’s not forget that until some time ago the median team size for startups acquired by big incumbents was around 7).

Again, patenting is not a solution that works for everyone, but if you have none of the moats above I would highly recommend you to speak to a patent lawyer and look for some additional IP protection.

Read more on the current AI patents landscape in Part IV

I am always interested in speaking to, learning from or simply connecting with interesting founders working in highly impactful fields like life sciences, energy, and others. If you are one of them, feel free to reach out here!


Smith, J. A., Cordina, R. (2014). “Patenting and the early‐stage high‐technology investor: evidence from the field”. R&D Management 45 (5): 589–605.

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