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Weekly overview of AI investment activity and business models - Issue #7

Hi All, I continue to lag behind the AI investment activity. But there are good reasons for that: 1)
Weekly overview of AI investment activity and business models - Issue #7
By Peter Zhegin • Issue #7 • View online
Hi All,
I continue to lag behind the AI investment activity. But there are good reasons for that:
1) I continue to experiment with variables I track and try to look at AI startups from different points of view; Based on your feedback, this time I’ve decreased the number of variables I track, and Instead of eight variables, I track five;
2) I spend time deeper researching of how exactly AI startups build their businesses. The most recent research is on how an AI startup could build/use data network effects. You could find it here. In that post I explore how an AI startup may unlock various data network effects. I explain why it’s important to go beyond the conventional definition of data network effects as a way to collect data from clients for the sake of improving your model/product.
 
This week I look at how AI tech is delivered to end users/customers and what kind of platforms one could build based on it. I cover multiple healthcareAI-powered startups, devops and dataops startups, and a couple of mega investment rounds. 
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If you want to skip my analysis and jump straight to data, go to the end of the note to find a table and a link on a Google spreadsheet, that contains data on featured companies and links to sources. 
Best,
Peter

Newsletter 29.10.2018-04.11.2018 
Key themes
  • Healthcare: multiple companies that build software for diagnosis, surgeries, and other operations have announced investment rounds this week. Interestingly, two mental health/wellbeing startups are among them – Aura Health and Clarigent Health
  • Data science and DevOps is as usually on the roll: Edgeworx (Pic.1) helps to deploy to edge devices, HeadSpin uses machine learning to test mobile apps. Neo4j builds a graph database and Crestle.ai helps to deploy ML models;
Pic.1 Edgeworx's platform
Pic.1 Edgeworx's platform
  • AI mega-rounds: $175M raised by Quanergy that develops a LIDAR and $375M invested in Zume Pizza (Pic. 2).
Pic.2 Zume Pizza's robot
Pic.2 Zume Pizza's robot
Platform perspective 
AI tech is used by companies as a component of platforms of various types:
  • A few startups create platforms to build applications, including ones that provide autonomous agents. Micropsi Industries allows to teach manufacturing robots, while Neo4j develops a graph-database and a platform that are required in many machine learning tasks;
  • Data platforms – AI helps to collect large amounts of data that was hardly accessible before. For example, Clarigent Health uses natural language processing to mine conversations between doctors and patients and to spot early signs of mental health issues. Vantage Robotics specialises on capturing aerial videos;
  • Platforms that connect service providers with customers also benefit from AI. For example, Aura Health (Pic.3) uses machine learing to personalize content that was created by life coaches. New Mobility connects operators of self-driving fleets with riders. 
Pic.3 Aura Health's platform
Pic.3 Aura Health's platform
How AI is delivered to a customer/user
Startups featured in this week’s review clearly demonstrate that it may not be enough to build an algorithm and to package it into friendly software. 12 startups build additional layers around their AI offerings.
  • Hardware is a frequent shell of AI tech. For example, algorithms are locked into an air purifier by Molekule, or in a car by WeRide’s partners;
  • Services is another layer that is required to make machine leaning work. Zume Pizza, for example, does not just build a robot to cook pizza, but delivers it straight to a customer. RedBird relies on a network of drone pilots, that fly drones and generate images, that later are crunched by RedBird’s software; 
  • Software development kits is another way to deliver AI tech to users. An SDK for disease monitoring is created by Eko, EdgeWorx allows developers to run machine learning applications on the edge. 
More examples of AI startups and associated data is below in the Chart 1. 
 
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Chart 1. AI companies that disclosed funding/exits during the week 29.10.2018-04.11.2018, $M
Chart 1. AI companies that disclosed funding/exits during the week 29.10.2018-04.11.2018, $M
Data is here (Google spreadsheet)

***
All data is from open sources and all conclusions/ideas/analysis are built only on publicly available information. For data sources see the Google spreadsheet.
A company is defined as a platform if someone can build up on it. I identify a company as a platform purely based on public sources, so if I misunderstood your startup, please do let me know.
The position of AI tech within a value chain considers only elements of a value chain that are controlled by one company. E.g. if a startup develops a smart air purifier, and manufactures it, then AI tech sits upstream relative to hardware, the purifier itself. If a startup develops software for air purifiers and licenses it to OEMs, then this startup is considered as a ‘Standalone AI startup’. If I misunderstood the value chain of your startup, please do let me know. 
This newsletter does not intend to cover all AI transactions, but covers just limited geographies and the limited number of themes adjacent to industry 4.0, healthcare, smart home/cities/mobility, entertainment, and robotics. 

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Peter Zhegin

I'm a venture partner at Sistema Venture Capital. I look at investments in AI startups and business models behind these companies, so you can keep an eye on your competitors and get inspired by new ideas.

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