How has the exit activity for artificial intelligence developed in the first half of 2019? This blog post explores AI exit metrics through Q2 2019 and compares them to previous years. The graph below shows the number of AI exits by year, stacked by quarters.
Artificial Intelligence Exits Over Time
As the graphic demonstrates, AI has seen a total of 38 exit events through Q2 of this year. This represents 60% of the total exits in 2018, and 119% of the exits through Q2 in 2018. Some of the exit events in Q2 2019 include Cognigo’s acquisition by NetApp, Leap Motion’s acquisition by Ultrahaptics, and PagerDuty’s IPO.
A straight-line projection of the completed exit activity this year would come out to 76 exit events, which exceeds the total exits in 2018 by 21%. By the same token, a weighted quarterly average projection of 2019 exit activity would come out to 75 exit events, which surpasses the total exits in 2018 by 19%. Therefore, based on the mid-year data, AI exit activity in 2019 is projected to increase from the exit activity in 2018.
Given the popularity of our Sector Maps, today we are introducing our Active Investors Map, which shows the most active investors in an emerging technology sector and a sampling of companies that they have invested in. Below you can see our Active Investors Map for the Artificial Intelligence sector.
Artificial Intelligence Active Investors Map
As the above graphic indicates, Y Combinator is the most active investor in the AI sector with 91 investments, followed by Accel with 81 investments and New Enterprise Associates with 73 investments. Rounding out the list is Intel Capital, the most active of corporate investors in the AI sector.
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.
Artificial Intelligence Investors Over Time
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.
Artificial Intelligence Funding Over Time
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.
Artificial Intelligence Investors Per 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.
Artificial Intelligence Logo Map
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.
Artificial Intelligence Innovation Quadrant
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.
Artificial Intelligence (AI) has become one of the hottest sectors in recent years, with its technology promising to revolutionize and automate every industry imaginable. We have been covering this trend, showing a massive increase in AI startup funding. As you can see in the graphic below, AI funding more than doubled from 2016 to 2017. Its funding more than tripled from 2016 to 2018.
AI Funding Over Time
This phenomenon then begs the question – what caused this explosive growth in AI funding? This explainer blog will help you understand the contributing forces behind the growth. It can be attributed to three factors:
The geopolitical battle between the US and China
The AI sector maturing over time
Specific AI functions gaining strong traction
US and China’s Geopolitical Battle for AI Dominance
The first factor contributing to the staggering AI funding jump is the geopolitical battle for AI dominance between the US and China. A plethora of news outlets, such as the Wall Street Journal and Forbes, are reporting on the technology battles between the US and China, especially around AI. Experts in those articles have echoed the sentiment that AI is expected to power the development of future business and national security strategies. Many also voiced the opinion that China may supplant the US as a technology leader as it makes significant headway in AI.
The data from our AI dynamic report reinforces the opinions expressed in the above publications. As you can see in the chart below, the lion’s share of AI startup funding is happening in the US and China, with China overtaking the US in 2018. The US jumped from $3B in 2016 to almost $8B in 2018, while China demonstrated even stronger growth, increasing 8-fold from $1B in 2016 to over $8B in 2018.
It’s also noteworthy that China’s AI funding in 2018 comprised 44% of the entire world’s AI funding, whereas the US’s AI funding comprised 41%. Clearly these two geographies are driving the AI revolution, with China now in the lead.
AI US-China Funding Comparison
We should note a risk factor around the above conclusion. Some news outlets are reporting that 50% to 80% of Chinese companies exaggerate their funding by a factor of 2 to 10 to attract further investments and intimidate competition. While the accuracy of such claims was not further verified, they would certainly encourage us to treat the above conclusion regarding the US-China AI battle with some level of caution and scrutiny.
AI Sector Maturing As Funding Moves to Later Stages
The second contributing factor is the gradual maturation of the AI sector as funding events move to later stages. As demonstrated in the graph below, AI seed financings decreased from almost 70% of funding events in 2013 to below 30% in 2018. In contrast, Series B to Late Stage financings in AI have steadily increased from 15% of total funding events to 35%.
AI Funding Round Count Percentages
This continuous rise in mid to late-stage funding events indicates that the sector is maturing over time. More mature companies require larger funding amounts, which is consistent with the growth in overall funding that we are witnessing. Thus, the explosive funding increase from 2016 to 2018 can be partially explained by the AI sector’s gradual emergence as an established cornerstone in the modern technology landscape.
Specific AI Functions Seeing Massive Funding Increases
Venture Scanner organizes chaotic startup landscapes into understandable groupings. For AI, we have broken the sector down into 13 categories. These categories are defined by a specific technology function, such as Machine Learning or Natural Language Processing. Analyzing the AI categories has revealed the third contributing factor, that a small set of AI functional categories are behind the explosive growth seen in the above charts.
As demonstrated in the graph below, Machine Learning (ML) related categories have seen massive increases in funding, from around $4B in 2016 to around $15B in 2018. Computer Vision (CV) related categories also grew rapidly, from around $1B in 2016 to around $8B in 2018. Other AI categories, such as Smart Robots, NLP, and Recommendation Engines, also experienced large funding growth in 2017 and 2018.
AI Funding Over Time By Category
Machine Learning Platform companies build algorithms that operate based on their learnings from existing data, while Machine Learning Application companies apply these self-learning algorithms to optimize specific business operations. By the same token, Computer Vision Platform companies build technology that analyzes images to derive information and recognize objects, while Computer Vision Application companies utilize this image processing technology in vertically specific use cases.
The fact that Machine Learning (ML) related categories and Computer Vision (CV) related categories are fueling the AI funding growth is consistent with our analysis that the AI sector is maturing as a whole. As specific AI technologies like ML and CV advance, more venture funding is needed to accelerate their development and adoption.
Conclusion: Geopolitical Battles, Sector Maturation, and Technology Advancement Contributed to AI Funding Increase
In summary, our analysis concludes that the top three contributing factors for the funding growth in AI are the geopolitical battle between the US and China, the sector maturing with its funding moving to later stages, and certain AI technology categories gaining significant traction.
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.
Artificial Intelligence Average Funding Event Size
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.
Venture Scanner Insights 