The artificial intelligence (A.I.) battle has been heating up. IBM, Microsoft, Amazon, Apple, Facebook, and Google all continuously release impressive technologies in the space that are capturing the minds of developers and customers. From a market standpoint, A.I. is positioned to become a pillar of the next generation of software technologies. We can expect all those software giants to capture segments of the A.I. space, however, the most interesting question is who can monetize A.I. at scale first.

Monetizing a technology at scale goes beyond its technical capabilities. Typically, the path to monetization at scale is a combination of different factors such as the following:
  • Leveraging well-established assets like user or customer community as the main distribution mechanism.
  • Expanding the offer across different distribution channels.
  • Providing a compelling value proposition for prospects to become buyers.
  • Building network-effects into the product so that paying customers can attract other paying customers.
  • Nature of the transaction: Large (enterprise), small (consumer).
  • Duration of the sales cycle.

There are other factors that can influence the monetization at scale of a specific technology, but the aforementioned ones must definitely be considered when evaluating a monetization model. In the A.I. space, the picture is pretty complicated as the top vendors already enjoyed a certain level of success and impressive access that can be devoted towards the monetization of A.I..


  • Main A.I. Technologies: IBM Watson remains the grandfather of A.I. technologies and IBM’s main investment in the space.
  • Key Advantages: IBM’s strong enterprise customer base is a unique asset when it comes to the monetization of A.I. technologies.
  • Monetization Models: IBM is likely to monetize A.I. via large enterprise software and services deals.
  • Network Effects: IBM’s strong services partner as well as the Watson developer community are relevant network effects that could play a factor in the monetization of A.I. technologies.


  • Main A.I. Technologies: Microsoft’s main A.I. investments include technologies like Cortana, Microsoft Cognitive Services, and Azure Machine Learning.
  • Key Advantages: Microsoft should be able to leverage its large enterprise customer base as well as consumer presence with technologies like Windows, Office, Skype, and Xbox as monetization channels for its A.I. technology.
  • Monetization Models: Given its presence in both the consumer and enterprise spaces, Microsoft should be able to monetize A.I. via large enterprise deals as well as consumer products.
  • Network Effects: Microsoft’s partner and developer communities as well as the strong user base of products like Office and Skype are strong network effects to be considered.


  • Main A.I. Technologies: Facebook’s main A.I. investments include technologies like M,, and Facebook Messenger Platform.
  • Key Advantages: Facebook Messenger’s strong user base is a unique competitive advantage that Facebook can use when monetizing A.I. technologies. Also, Facebook’s early presence in the VR market with technologies like Oculus can also be relevant.
  • Monetization Models: Facebook is likely to monetize A.I. by leveraging its strong user base using mechanisms like advertising, commerce services, and others.
  • Network Effects: The viral models built in the Facebook and Facebook Messenger platforms are a unique network effect that can be used to commercialize A.I. technologies.


  • Main A.I. Technologies: Amazon’s biggest investments in A.I. technologies include the Alexa platform, consumer devices like Echo and Dot, as well as the AWS Machine Learning platform.
  • Key Advantages: Amazon’s ecommerce user and customer base is a strong channel for the distribution of A.I. technologies. Additionally, AWS’s dominance in the cloud platform space should also help to uniquely position this type of technology.
  • Monetization Models: Amazon is likely to monetize A.I. technologies via its large consumer base as well as enterprise deals via AWS.
  • Network Effects: The AWS developer and partner communities is a strong network effect to consider when evaluating Amazon’s A.I. technologies. Additionally, Amazon’s consumer base has proven to be an extremely strong distribution mechanism.


  • Main A.I. Technologies: Siri remains Apple’s main investment in A.I. technologies today.
  • Key Advantages: Apple’s iOS, iPhone, and iPad customer base could be a unique distribution model for A.I. technologies. Other media properties like Apple Music can also be relevant in this area.
  • Monetization Models: Apple is likely to monetize A.I. technologies leveraging its large iPhone and iPad customer base.
  • Network Effects: Siri, the Apple Store app, and the viral effects built into iOS apps can result in strong network effects when commercializing Apple’s A.I. technologies.


  • Main A.I. Technologies: Google has made significant investments in A.I. across its technology portfolio. Unique technologies like DeepMind, open A.I. platforms like TensorFlow, smart devices like Google Home, mobile apps like Google Assistant, hardware components like the TPU Chip and, of course, the Google Self-Driving Car are some examples of Google’s commitment to A.I..
  • Existing Assets: Google’s dominance in search and advertisement as well as its ownership of the Android mobile OS are unique assets that can be used when monetizing A.I. technologies.
  • Monetization Models: Google is likely to monetize A.I. technologies using its strong consumer and enterprise customer base.
  • Network Effects: Google’s online assets like Search, and AdWords, as well as its presence in the mobile space are strong network effects that can play a role in the monetization of A.I. technologies.

Early rounds verdict: Google wins

The previous analysis clearly highlights the potential of Amazon, Facebook, Apple, IBM, Microsoft, and Google to monetize A.I.. However, we think in the short term Google has a bit of an edge and more options to quickly monetize A.I. at scale. Here are a few points that might help to explain this forecast:

  • Wider A.I. Asset Portfolio: Google has been investing in A.I. across different technology areas such as mobile, cloud, DeepMind, and even chips.
  • Google Search Distribution: We can’t underestimate the power of Google Search as a distribution channel.
  • Consumer and Enterprise Presence: Google is incredibly well positioned to monetize A.I. in both the consumer and enterprise markets.
  • Google Self-Driving Car Project: This is an incredibly unique asset that has no equivalent among the other vendors.

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