Data network effects for an artificial intelligence startup

Data network effects for an artificial intelligence startupShifting attention from product and data collection to network and data sharingPeter ZheginBlockedUnblockFollowFollowingDec 8Photo by NASA on UnsplashArtificial intelligence (AI) ecosystem matures and it is becoming increasingly difficult to impress customers, investors, and potential acquirers by just attaching an .ai domain to whatever you are doing..Data network effects are associated, among other things, with improvement of a product via data collected from its users.Given the importance of data for any AI startup, there is no surprise that data network effects are considered a relevant moat..The wider availability of data and tools to process/label it makes the data collection network effect less appealing, as it takes less time to disrupt the first-mover advantage of someone with a labelled dataset.All above being said, I am not arguing that data collection is not something an AI startup should master, as proprietary data is still a moat..But other types of businesses should think about alternative types of data network effects.A wider view on data network effects — share more data with your networkNew types of data network effects may come from building a wider network of customers and partners around an AI startup and data sharing within that networks..However, applications of network effects to data beyond its collection and direct use to improve a model/product, do not seem to be well covered.A few authors develop the idea of data sharing, but non goes into operational details..The fundamental role of data sharing in the progress of AI ecosystem is covered by Nik Bostrom, for example.How to build a network?Inspired by the literature quoted above, one needs to shift from thinking about an AI startup only as a product (that is improved by data collected from customers/users) to thinking about it as a network, that ignites/manages various types of data exchanges between various types of participants..Below, based on literature on network effects and an analysis of business models behind various AI startups, I explore how to build a data network around an AI startup and what kind of exchanges to launch there.Participants of an AI startup’s network may widely be divided in two groups:1..Vertically, by connecting customers with other elements of their value/supply chains and creating indirect network effects, ‘…when an additional participant of one type increases the value that participants of another type get’.Three kinds of data exchangesWhen directions of data exchanges are understood, the question arise what these exchanges are..Note, that this kind of integration goes much beyond sharing data for transactional purposes.Three types of data exchanges each applied horizontally and/or vertically provide an AI startup with six new opportunities for data network effects (see the data network effects matrix below)..The foundation for these new data network effects is the ability to make data useful by directly/indirectly sharing it across the network, whereas more well-known data network effects (data collection network effects) are based primarily on scarcity of data and challenges associated with collecting it.. More details

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