The intelligent app ecosystem (is more than just bots!)

Application intelligence is the process of using machine learning technology to create apps that use historical and real-time data to make predictions and decisions to deliver rich, adaptive, personalized experiences for users.

We believe that every successful new application built today will be an intelligent application.

The armies of chat bots and virtual assistants, the e-commerce sites that show the right recommendations at the right time, and the latest dating apps, are all built to learn and create continuously improving experiences.

In addition, legacy applications are becoming more and more intelligent to compete and keep pace with this new wave of applications.

Now is an exciting time to be investing in the broader intelligent app ecosystem because several important trends are coming together in application development:

  • The availability of massive computational power and low-cost storage to feed machine learning models
  • The ease of use with which developers can take advantage of machine learning techniques,
  • The adoption of microservices as a development paradigm for applications, and
  • The proliferation of platforms on which to develop applications, and in particular platforms based on “natural user interfaces” like messaging and voice.

We have spent time thinking about the various ways Intelligent Apps emerge – and how they are built.

This Intelligent App Stack illustrates the various layers of technology that are crucial to the creation of Intelligent Apps.

As investors we like to think about the market dynamics of major industry shifts, and the rise of intelligent apps will certainly create many new opportunities for startups and large technology companies alike.

Here are some of our thoughts on the key implications for companies operating at various layers of the intelligent app stack:

“Finished Services”
Applications will define the end user’s experience with machine learning


At the application layer there will be two primary classes of applications: net-new apps that are enabled by application intelligence and existing apps that are improved by application intelligence.

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.

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 also seeing a number of interesting vertically focused intelligent applications especially serving the retail, healthcare, agriculture, financial services, and biotech industries.

The killer apps of the last generation were built by companies like Amazon for e-commerce, Google for search and advertising, Facebook for social, Uber for transportation, and Netflix for entertainment.

These companies have a significant head-start in machine learning and user data, but we believe there will be apps that are built from the ground up to be more intelligent that can win in these categories and new categories that are enabled by application intelligence.

New interfaces will transform applications into cross-platform “macro-services”

Image: Warner Bros. Entertainment

Image: Warner Bros. Entertainment

As we think about how new intelligent applications will be developed, one significant approach will be the transformation of an “app” to a service or experience that can be delivered over any number of interfaces. For example, we will see companies like Uber build “services” that can be delivered via an app, via the web, and/or via a voice interface.

It will also be easier for companies to deliver their services across platforms as they design their apps using a microservices paradigm where adding a new platform integration might be as simple as adding a new API layer that connects to all of the existing microservices for authentication, product catalog, inventory, recommendations, and other functions.

The proliferation of new platforms such as Slack, Facebook Messenger, Alexa, and VR stores will also be beneficial for developers because platforms will become more open, add features that make developers lives easier, and compete for attention with offerings such as investment funds.

Finally, at the interface layer, we see the “natural interfaces” of text, speech, and vision unlocking new categories such as conversational commerce and AR/VR. 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:

Intelligent building blocks and learning services will be the brains behind apps


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. The two categories of companies we see at this layer are the providers of raw machine intelligence and the providers of trained models or “Models as a Service.”

In the first category, companies provide the “primitives” or core building blocks for developers to build intelligent apps, like algorithms and deployment processes. In the second category, we see intermediate services that allow companies to plug and play pre-trained models for tasks like image tagging, natural language processing, or product recommendations.

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.

IBM Watson’s approach to this is to provide developer access to its APIs for free but charge a 30% revenue share when the app is released to customers. Others are charging based on API calls, compute time, or virtual machines.

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