The Competitive Landscape of AI Startups

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Big tech companies are pouring tens of billions of dollars into research on artificial intelligence (AI). Can small startups hope to enter AI markets and compete effectively?

A recent survey we did of commercial AI startups provides some intriguing insights into how they work, and how and where they compete with larger and more established firms. The picture that emerges is one of a robust, competitive market for startups, but also a market that is restricted in some important ways.

Large incumbent tech firms have several advantages over startups: they can invest huge sums into R&D, they have access to large amounts of data, and they have complementary assets and established markets. Some commentators point to the vast amounts of data available to big technology companies such as Google and Amazon and argue that startups may not be able to compete.

But when it comes to datasets, bigger is not always better.  Google constantly experiments with its search engine to improve its results rather than simply relying on its volume of search data, and Amazon has found that the accuracy of its predictions in e-commerce does not continue to improve by simply adding more products.  In other words, it is not the quantity of the data, but how the company uses it, that provides the competitive advantage. And thus, AI startups can still compete with larger companies even without enormous datasets to work with.

The data that AI startups do use generally comes from their own customers. 80% of the respondents in our survey report using their customers’ data, including data about their customers’ customers and users.  Nearly two-thirds (63%) use third-party public data, including government data, data scraped from the Internet, and public benchmarking data.  Half the firms (51%) also use their own proprietary data, which they almost always use in conjunction with data from other sources; only 6% rely solely on their own proprietary data. Importantly, most of the startups retain secondary rights to customer data; that is, they act as aggregators, collecting data from multiple customers. In this way, they can build large data sets over time.

Startups selling commercial AI applications also play a unique providing solutions to mid-sized companies who can’t afford to develop their own AI. The large companies making big AI investments largely invest for their own use. But these projects often require large fixed investments. Because of this, small and mid-sized companies cannot afford to make comparable investments and they often lack the IT talent to do so in any case. AI startups can meet the needs of these companies. Effectively, the startup’s development costs are shared across all their customers.

Because of the important role that commercial AI applications companies play for mid-sized firms, startups sell disproportionately to them. Almost half of the AI startups sell to midsize firms (51-1000 employees). For comparison, only 26% of employees in the U.S. work at midsize firms.  The startups’ penetration in both smaller and larger companies is proportionately less than would be expected given the distribution of firms in the U.S.

AI startups also sell to a range of industries, including agriculture, law enforcement, government, software and IT, finance, and retail.  On net, this variety suggests that the market for AI is healthy and competitive, with opportunities for newcomers.  Some industries, however, may have higher barriers to entry than others.  Where barriers to entry are low, both large established firms and small startups will focus their efforts and investment on the industries with the greatest opportunities, and therefore one would expect the distribution of large and small firms’ AI investments to look similar.  On the other hand, if larger firms spend proportionately more in some areas, this would suggest the presence of entry barriers.

There are relatively fewer AI startups selling in consumer packaged goods, transportation and logistics, suggesting entry barriers in these industries.  However, as the chart below indicates, there are relatively more startups than larger firms serving retail and financial services industries.  These differences suggest that although AI startups market to all industries, larger firms still dominate in some.  The larger share for startups in retail and financial services likely reflects venture capital funders’ priorities.  The disproportionate share for larger companies in packaged goods and transportation may be related to the apparently large investments needed to enter the market — think, for example, of the investment necessary to compete in self-driving cars — or to the competitive power that the largest firms in those industries have already.

Startup developers of commercial AI applications operate in a competitive market. They compete with the data available to them and meet a market need for AI applications for midsize companies, which, in turn, enables those companies to compete with larger companies that often develop AI applications internally.  Barriers to entry may exist in some industries, but the survey overall points to a robust and competitive market for AI developers.

Source

https://hbr.org/2018/12/the-competitive-landscape-of-ai-startups