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Net-new apps will need to solve the tough problem of determining how much end users will pay for “artificial intelligence” and how to ensure they capture a portion of the value delivered to users.More broadly, it will be interesting to see if our thesis that the value proposition of machine learning will primarily be a revenue generator comes true.These two categories of companies provide a large portion of the value behind intelligent apps, but the key question for this layer will be how to ensure these building blocks can capture a portion of the value they are delivering to end users.
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Also, because of the importance of high-quality, relevant data for machine learning models, we think that industry-specific applications or applications for specialized uses will present the most immediate pockets of opportunity at the Finished Services or application layer.
Today, we see the main categories of use-case-specific applications as autonomous systems, security and anomaly detection, sales and marketing optimization and personal assistants.
We are incredibly optimistic about the future of these interfaces, as these are the ways that humans interact with one another and with the world.
Building Blocks and Learning Services As companies adopt the microservices development paradigm, the ability to plug and play different machine learning models and services to deliver specific functionality becomes more and more interesting.