The artificial intelligence (AI) industry has seen $50B in total all time funding. Let’s analyze the investors making bets into AI and identify the most active firms.
The graphic below shows AI investors based on their number of investments into the sector. If an investor participates in two investment rounds in the same company (such as a Series A and Series B), that would qualify as two investments for this graphic.
As the graphic demonstrates, Y Combinator has made the most bets in the AI sector with 75 investments. New Enterprise Associates follows in second place with 64 investments. Examples of companies Y Combinator invested in include Vicarious, Sift Science, Atomwise, and Standard Cognition. Let’s see which investors make their way onto this list in 2019!
Now that 2018 is complete, let’s see how exit activity for artificial intelligence (AI) compares to previous years. The graphic below shows the total annual AI exit events over time.
As the graphic demonstrates, 2018 saw a drop in AI exit activity compared to the previous year. The 58 exit events in 2018 represent a 22% decrease from the 74 exit events in 2017, which was the highest year on record for exit activity. However, AI exits are still on a general upward trend, with a 5-year CAGR of 24% from 2013 to 2018. Let’s see if the AI exit activity in 2019 will jump back up to the 2017 level.
With 2018 now behind us, let’s examine how artificial intelligence (AI) funding compares to previous years. The graphic below shows the total annual AI funding amounts over time.
As the graphic demonstrates, 2018 experienced the highest AI funding on record at $18B. It represents a 24% increase from the previous year’s funding. In addition, AI funding grew at a CAGR of 62% from 2013 to 2018. It’ll be interesting to observe if the funding growth will remain strong in the new year.
We’ve previously highlighted that artificial intelligence (AI) funding has seen explosive growth in recent years. When we take a closer look at the funding trends for each category within AI, we notice two key takeaways:
The Machine Learning Platforms category leads the sector in Q3 funding
The Machine Learning Applications category leads the sector in all-time funding
We’ll highlight these takeaways with some graphics and discussions below.
The Machine Learning Platforms Category Leads AI In Q3 Funding
To start off, let’s review the amount of funding raised this quarter by each category within artificial intelligence.
The above graphic highlights that the Machine Learning Platforms category leads the sector in Q3 funding with $1.9B. The Computer Vision Platforms category follows in second place with $1.6B in Q3 funding.
Machine Learning Platform companies build self-learning algorithms that operate based on existing data. They include predictive data models and software platforms that analyze behavioral data. Some example companies include C3 IoT, DataRobot, Sentient, and AYASDI.
Let’s now investigate how the AI categories’ funding compare with each other historically.
The Machine Learning Applications Category Leads the Sector in All-Time Funding
The graph below shows the all-time funding for the various artificial intelligence categories. The Q3 funding and growth rates of these categories are also highlighted.
As the bar graph indicates, the Machine Learning Applications category leads AI in total funding at $19B. This is more than twice the funding of the next category, Machine Learning Platforms at almost $9B.
Machine Learning Application 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.
In summary, the two machine learning-related categories are leading the AI sector in funding. Let’s see how the the rest of 2018 shapes up for artificial intelligence!
The artificial intelligence sector has experienced explosive funding growth in recent years. This blog post examines the different components of the AI sector and how they make up this startup 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 2,316 artificial intelligence startups into 13 categories that have raised $45 billion. 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 866 companies. These companies utilize self-learning algorithms to optimize business operations in vertically specific use cases. 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 Quadrant Has the Most Artificial Intelligence Categories
Our Innovation Quadrant divides the artificial intelligence categories into four different quadrants.
We see that the Pioneers quadrant has the most artificial intelligence categories with 8. These categories are in the earlier stages of funding and maturity. The Disruptors quadrant has 4 categories that have acquired significant financing at a young age. The Established quadrant has Speech to Speech Translation as its one category. This category has reached maturity with less-than-average financing.
We’ve analyzed the artificial intelligence categories and their relative stages of innovation. Let’s now look at how they stack up against one another in terms of their total funding versus company counts.
Machine Learning Application Startups Have the Most Funding
The graph below shows the total amount of venture funding and company count in each category.
As the above graphic implies, Machine Learning Applications also leads the sector in total funding with $19 billion. Its funding is more than twice the funding of the next category, Machine Learning Platforms with $9 billion. These two categories are related yet have different functions. Machine Learning Application companies apply self-learning algorithms to optimize specific business operations. Machine Learning Platform companies build these self-learning algorithms or their underlying infrastructure.
Conclusion: The Machine Learning Applications Category Leads Artificial Intelligence
As the analysis above demonstrates, the Machine Learning Applications category leads in total companies and funding. We’ll see if any of the other categories catch up during the rest of 2018.