AI Investors With the Most Activity

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.

Artificial Intelligence Investors with Most Investments
Artificial Intelligence Investors with Most Investments

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!

To learn more about our complete artificial intelligence dynamic report, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

AI Exit Activity Slowed in 2018

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.

Artificial Intelligence Exits Over Time
Artificial Intelligence Exits 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.

To learn more about our complete artificial intelligence dynamic report, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Artificial Intelligence Report Highlights  – Q4 2018

Here is our Q4 2018 summary report on the artificial intelligence startup sector. The following report includes a sector overview and recent activity.

To learn more about our complete artificial intelligence dynamic report, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

AI Funding Reaches Record Year in 2018

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.

AI Annual Funding Over Time
AI Annual Funding 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.

To learn more about our complete artificial intelligence dynamic report, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Machine Learning-Related Categories Lead Artificial Intelligence Funding

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.

Artificial Intelligence Latest Quarter Category Funding
Artificial Intelligence Latest Quarter Category Funding

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.

Artificial Intelligence Total Category Funding
Artificial Intelligence Total Category Funding

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!

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Artificial Intelligence Sector Overview – Q3 2018

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.

Artificial Intelligence Sector Map
Artificial Intelligence Sector Map

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.

Artificial Intelligence Innovation Quadrant
Artificial Intelligence Innovation Quadrant

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.

Artificial Intelligence Total Funding by Category
Artificial Intelligence Total Funding by 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.

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact us at info@venturescanner.com.

Artificial Intelligence Startup Highlights  – Q3 2018

Here is our Q3 2018 summary report on the artificial intelligence startup sector. The following report includes a sector overview and recent activity.

To learn more about our complete artificial intelligence report and research platform, visit us at www.venturescanner.com or contact info@venturescanner.com.