How has investor appetite in artificial intelligence evolved throughout the years? In this blog post we examine the total investments by year into this sector to help answer that question. The graph below shows the total number of investors in all deals stacked by quarters.
As the graphic demonstrates, investor activity in AI has dipped slightly after reaching a record high. The 5-Year CAGR of AI investor activity from 2013 to 2018 is 31%. In addition, the sector has seen a total of 1,003 investors in all deals through Q2 of this year. This represents 50% of the total investor activity in 2018, and 91% of the investor activity through Q2 in 2018. Taking all these data points together, we can see that investor appetite for AI deals is on a generally upward trend in recent years.
How is the funding environment shaping up for artificial intelligence in 2019? As we pass the mid-year mark, let’s see how the year-to-date metrics compare to the historical trends. The graph below shows AI total funding by year, stacked by quarters.
As the graphic demonstrates, AI has amassed $11B through Q1 and Q2 of this year. This amount represents 56% of the total funding in 2018, and 138% of the funding through Q2 in 2018. The top three funding events in Q2 2019 include a $1.3B round into ByteDance, a $750M round into MEGVII, and a $568M round into UiPath.
A straight-line projection of the completed funding this year would result in $22B, which is 112% of the total 2018 funding. By the same token, a weighted quarterly average projection of 2019 funding would result in $27.3B, which exceeds the total 2018 funding by a whopping 38%. Therefore, based on the mid-year data, AI funding in 2019 is projected to surpass the funding in 2018 and have a banner year.
The artificial intelligence (AI) industry has seen 3,434 investors and $62B total all time funding. Let’s analyze which AI categories have the most number of investors actively financing the startups. The graphic below highlights AI categories based on the number of investors in each category.
As the graphic demonstrates, Machine Learning Applications has the highest number of investors at 1988, with Machine Learning Platforms following behind at 785. Machine Learning Application companies apply self-learning algorithms to optimize specific business operations, while Machine Learning Platform companies build these algorithms that operate based on their learnings from existing data. In addition, the average number of investors across all AI categories is 507.
This blog post examines the different components of the artificial intelligence (AI) ecosystem. We will illustrate what the categories of innovation are and which categories have the most companies. We will also compare the categories in terms of their funding and maturity.
Machine Learning Applications Is The Largest Artificial Intelligence Category
Let’s start off by looking at the Sector Map. We have classified 2497 AI startups into 13 categories. They have raised $60B from 3420 investors. The Sector Map highlights the number of companies in each category. It also shows a random sampling of companies in each category.
We see that Machine Learning Applications is the largest category with 943 companies. These companies utilize self-learning algorithms to optimize vertically-specific business operations. Examples include using machine learning to detect banking fraud or to identify relevant sales leads. Some example companies are Sift Science, SparkCognition, Sumo Logic, and BenevolentAI.
Let’s now look at our Innovation Quadrant to find out the funding and maturity of these categories in relation to one another.
The Pioneers and Disruptors Quadrants Have the Most Artificial Intelligence Categories
Our Innovation Quadrant divides the AI categories into four different quadrants.
We see that both the Pioneers and Disruptors quadrants have the most number of AI categories at 6, each accounting for 46% of all AI categories. The Speech-to-Speech Translation category has the highest average age, and the Recommendation Engines category has the highest average funding. On the other hand, the Context Aware Computing and Virtual Assistants categories are low on both average funding and age.
For this quarter’s funding analysis, let’s examine how average funding event sizes in the artificial intelligence (AI) sector are evolving. The graphic below shows the AI average funding event size over time by quarter.
As the graphic demonstrates, AI average funding event size in Q1 2019 was at $35M. This is an increase of 75% from the $20M in Q1 2018. The average funding size has been on a robust upward trend, with the average funding size last quarter around 3 times larger than it was 5 years ago. The top three funding events in Q1 2019 include a $940M round from Nuro, a $600M round from Horizon Robotics, and a $530M round from Aurora.