Author of article: Armando Viteri, Chair, AI/Machine Learning Special Interest Group
A Thank You
I would like to first thank Ron Weissman of the Band of Angels who has done exhaustive research into the area of AI investing. I have learned much through his presentations.
AI: Hype or Opportunity
As a result of some incredible recent demonstrations in the advancement of AI/Machine Learning, there has been remarkable the resurgence in the interest in investing in startups using the technology. This incredible rise over an abbreviated period can be summarized in one chart:
That is an order of magnitude increase over just 6 years and the interest has continued to grow into 2020. In fact nearly 500 AI startups across 42 countries raised over $8.4B in Q1’20 despite or because of COVID as there are increasing opportunities to deploy this technology in a world very much changed in the last 6 months. To cite just a few examples:
UC San Diego Health developed and applied an artificial intelligence algorithm to more than 2,000 lung X-ray images, helping radiologists more quickly identify signs of early pneumonia in Covid-19 patients [Becker’s Hospital Review]
Mayo Clinic teamed up with the state's health department to create an artificial intelligence-powered tool that can identify zones of greater Covid-19 transmission in southern Minnesota [Becker’s Hospital Review]
Vocalis Health, an Israeli startup company, is working with hospitals and academic institutions to sample voices of confirmed Coronavirus patients through a mobile application; the algorithm would be used for remote diagnosis and monitoring [Reuters]
The Rambam Hospital in Haifa, Israel, has begun a clinical trial of Cordio Medical’s app-based AI system that analyzes speech to diagnose and remotely monitor Covid-19 patients [VentureBeat]
AI startup SparkBeyond will assist Argentina in looking at how the country can allow citizens to return to work and minimize economic impact. The platform will use data from the Argentinian ministry of health, which aggregates travel, demographic and employment data for each citizen, then integrates hundreds of external data sources to create a wider picture of the situation. In addition, SparkBeyond has harnessed millions of open data points to create a dynamic, high accuracy heatmap of Italy that predicts where a Covid-19 carrier is likely to pass [SparkBeyond]
Circolo Hospital in Varese, Italy, has recently deployed six robots to help tend to Covid-19 patients [Reuters]
These examples only cite the uses of AI in healthcare settings during this time of COVID. The business examples of the novel use of this technology continue to proliferate. For example, the Royal Bank of Canada (RBC) was one of the first to spot China's rebound in early March; RBC Elements, an alternative-data and artificial-intelligence team within RBC Capital Markets’ research unit, analyzes alternative sets of data such as energy usage, vehicle congestion, flight information and port traffic, to provide real-time insight into Coronavirus's economic impact [WSJ]
But for seed stage investors this is a case of caveat emptor. Like many new technologies before it AI and Machine Learning technologies will likely follow the same “hype cycle”:
As with any investment in any business it is important to differentiate the hype from the business realities of the fledgling company. Namely:
- Does the company solve a compelling problem in a unique and defensible way?
- Can the company identify the target economic buyer – the person that will actually make the decision to write the check?
- Does the company understand how to reach the target economic buyer?
- Is the market large enough and can the company grow fast enough to make this an investable proposition?
- Is this the right team to do this?
The fortunate part of this equation, despite the hype, is that the new methods that have developed in AI over the past few years do address a broad set of applications and can provide meaningful solutions to a broad class of problems. In fact, an investor can expect that AI and Machine Learning will transform any industry where there is data driven decision making and in which there exists a way to collect and store large scale relevant data. As you can see this is a very broad net which covers, as evidenced in the broad market areas, that patents are being generate in:
And good news for investors is that there is substantial M&A activity in AI. In fact 53% of all acquisitions have happened since 2016:
In fact there is an “arms race” to acquire AI technology among the largest technology companies. This can only spell good news for investors:
And the pace of these acquisitions has continued to increase:
AI acquisitions saw a more than 6x uptick from 2013 to 2018, including last year’s record of 166 AI acquisitions — up 38% year-over-year.
In 2019, there have already been 140+ acquisitions (as of August), putting the year on track to beat the 2018 record at the current run rate.
A Brief History on AI
Artificial intelligence is a broad category of technologies which date back as Alan Turing in the 1940’s. By some accounts we are now in the sixth generation of AI technology which includes:
- Cybernetics – 1940’s with Alan Turing, Claude Shannon and Norbert Weiner
- Birth of AI and the Rise of Knowledge-based Logic – 1950’s
- The Age of Expert Systems – 1960/70’s. A period of great optimism and investment which ultimately did not live up to its early promise.
- The Rebirth of Neural Nets – 1980’s
- Deep Learning Revolution – The period we are in today starting in the mid 2000’s where great strides were made in a variety of previously impossible-to-solve problems.
- Applied Deep Learning – These new methods being applied across a broad range of vertical markets and problems.
Since 2013 these new AI/ML technologies has lead to massive investment leading to a massive explosion in new companies:
I believe that we are just in the beginning stages of the next evolution of AI. While many AI startups have focused on basic infrastructure in areas like machine vision, decision support, industry data resources, cloud infrastructure, middleware, etc, increasingly there are a broad class of startups that are highly verticalized applications of the technology. These range from taking your order at a drive up window through helping you with complex purchasing decisions and doing complex buy/sell arbitrage in a variety of industries including used car sales. For all of these startups the investor needs to take to heart that it is not the “glitz” of the technology but rather the practicality and compelling need of the solution. For this, AI or not, the investor is face with the same age old decision: Is this an investable company?
Here is a small sample of additional resources:
About the Author
Armando Viteri — Chair, AI Special Interest Group, Keiretsu Forum
Mr. Viteri brings to Keiretsu Forum more than 30 years of executive management and expertise in the enterprise computing, software, networking and services market with an impressive record of accomplishments. Mr. Viteri is also CEO and President of Neubloc, LLC, a software product development services provider with 10 development locations worldwide. Prior to Neubloc, Mr. Viteri was most recently President and CEO of RF Code, Inc., an emerging growth RFID solutions business where he successfully raised Tier 1 venture capital financing and delivered premier RFID hardware and software products to market.
Prior to RF Code, Mr. Viteri co-founded and served as president of Pinpoint Corporation, the industry's first active RFID and Real-Time Locating Systems (RTLS) Company, where he pioneered the use of this technology for mission critical applications. Previous to Pinpoint, Mr. Viteri was executive vice president of TriTeal Corporation where he was responsible for creating both domestic and international sales channels and infrastructure, drove the advanced Java™ development organization resulting in three patents, and was responsible for growing direct and OEM sales significantly over a three-year period. Following three private financings, Mr. Viteri played an instrumental role in TriTeal's successful IPO in 1996 and secondary public offering in 1997.
From 1983 to 1993 Mr. Viteri held various sales, marketing, and management roles with Sun Microsystems, Inc. Joining Sun during its start-up phase, he held numerous management roles for multiple disciplines during his tenure by creating, managing, and executing many initiatives that account for the success and growth of the business today. In addition to building and managing highly successful sales and marketing organizations, Mr. Viteri also managed systems engineering organizations.
Mr. Viteri has served on several technology company boards and a variety of industry standards bodies. Mr. Viteri earned a B.S. in Electrical Engineering from the Massachusetts Institute of Technology. He is the regional chair of the MIT Educational Council, president of the MIT Alumni Club of Phoenix as well as a member of the management committee of the MIT Enterprise Forum of Israel.