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.
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.
To learn more about our complete artificial intelligence dynamic report, visit us at www.venturescanner.com or contact us at info@venturescanner.com.
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:
We’ll highlight these takeaways with some graphics and discussions below.
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 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!
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.
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.
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.
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.
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.
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.
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.
In Q2, AI had the second highest exit activity on record. Now armed with the data through June 2018, we’re performing a mid-year status check on how this year is shaping up.
Based on analysis on our AI research platform, we see that exit activity in the first half of 2018 is slightly down from 2017.
Let’s take a closer look at the number of AI exit events by year.
The above graphic shows 32 exits in the first half of 2018. For the past three years, Q3 and Q4 accounted for 46% of total exit events on average. If that trend holds, 2018 exits finish the year slightly lower than 2017, but higher than 2016. We’ll see if the second half of the year changes this trend!
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.
Here is our Q2 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.
Last quarter we observed that the artificial intelligence sector is maturing. This quarter we are conducting a deeper analysis on our AI research platform to examine funding by category. Our analysis shows two important observations:
We’ll explain these observations with some graphics and discussions below.
To start off, let’s scrutinize the AI funding by category in Q2.
The above graphic shows that both Machine Learning Platforms and Computer Vision Platforms lead the sector in Q2 funding with $1.5B each. Machine Learning Applications and Smart Robots follow in the second and third places with $1.4B and $1B, respectively. It’s also noteworthy that there is a steep drop-off after Smart Robots, as its funding is 3.4 times higher than the next category, Speech Recognition.
So we’ve witnessed how different AI categories stack up in their Q2 funding. But how do these categories’ funding compare with each other historically? Let’s investigate that in the next section.
The graph below shows the all-time funding for different AI categories. The quarterly funding and growth rates of these categories are also highlighted.
The bar graph indicates Machine Learning Applications completely dominates the sector with $17B in total funding. This is more than twice the funding in the next category, Machine Learning Platforms.
In addition, the line graph demonstrates that Computer Vision Platforms saw the highest growth rate in Q2 at 48%.
In summary, we have analyzed the AI funding amounts in different categories. We’ve discovered that Machine Learning Platforms and Computer Vision Platforms lead the sector in Q2 funding. In addition, Machine Learning Applications dominates AI in all-time funding. It’ll be interesting to see if any other AI categories will catch up in 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.
Last quarter we reviewed artificial intelligence exit trends and saw strong growth. We now dig in one level deeper on our AI report and research platform to examine exits by category. We conclude that Deep Learning Applications and Computer Vision Platforms are at the forefront of AI exit activity.
This conclusion was derived from two takeaways:
We’ll illustrate these takeaways with some graphics that show AI exit activity by category.
To help set the stage, the graphic below shows AI exit activity over time. As you can see, the sector’s exit activity experienced strong growth over the past few years.
Let’s examine the exit events for each AI category. Exit events include both acquisitions and IPOs. The below graph highlights the number of AI exit events by category.
This graph shows that the Deep Learning Applications category leads the sector with 71 exit events. Natural Language Processing comes next with 46 exit events.
Deep Learning Applications includes companies that utilize computer algorithms to optimize operations in vertically specific use cases. Examples include using deep learning technology to detect banking fraud or to identify relevant sales leads. Some example companies are Sift Science, SparkCognition, Sumo Logic, and BenevolentAI.
Let’s now see how AI categories compare with one another by acquisition amount.
The graph below shows the acquisition amounts in different AI categories.
We can see from this graph that the Computer Vision Platforms category leads all the other AI categories by far. The total acquisition amount in this category is around $16 billion. Computer Vision Platform companies process images to algorithmically derive information from them and recognize objects. Some example companies in this category include Cortica, Blippar, Kairos, and Clarifai.
Computer Vision Platforms has seen some large acquisitions in recent years. Mobileye was acquired by Intel in March 2017 for around $15 billion. Movidius was acquired by Intel in September 2016 for $400 million. Magic Pony Technology was acquired by Twitter in June 2016 for $150 million.
The acquisition amount in Computer Vision Platforms represents 72% of all AI acquisition activity. It’s noteworthy that its acquisition amount is more than ten times the next category, Deep Learning Platforms. Additionally, Computer Vision Platforms’ acquisition amount is highly concentrated, with 15/16 of the amount coming from the $15 billion Mobileye acquisition.
In summary, we have examined AI exit activity by the number of exit events and acquisition amount. The Deep Learning Applications category leads the sector in the number of exit events. The Computer Vision Platforms category leads in acquisition amount. It will be interesting to see which other categories take the lead in AI exit activity in 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 (AI) has seen a lot of buzz in the news recently. As previously noted, funding into the AI sector grew exponentially in the past few years. We’ve also observed that funding amounts and funding counts have shifted to later stages, indicating that the sector is maturing.
We will now examine the different components of AI and how they make up this startup ecosystem. On our AI research platform, we have classified the companies into 13 categories. This blog post illustrates what these categories are and which categories have the most companies. We will also look at how these categories compare with one another in terms of their funding and maturity.
Let’s start off by looking at the Sector Map for the AI sector. As of March 2018, we have classified 2161 AI startups into 13 categories that have raised $32 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 762 companies. This category contains companies utilizing algorithms that learn and optimize automatically from data. These companies tackle issues in specific use cases such as detecting banking fraud or identifying top retail leads. Some example companies include Sift Science, SparkCognition, Sumo Logic, and BenevolentAI.
We have seen what the different categories making up this sector are and the number of companies in each. What about their funding and maturity in relation to one another? Let’s look at our Innovation Quadrant to find out.
Our Innovation Quadrant divides the AI categories into four different quadrants.
We see that the Pioneers quadrant has the most AI categories with 8. The Pioneer categories are in the earlier stages of funding and maturity. Speech Translation and Speech Recognition landed in the Established quadrant. Both of these speech technology-related categories have reached maturity yet with less financing.
Smart Robots, Recommendation Engines, and Machine Learning Platforms are in the Disruptors quadrant for acquiring significant financings at a young age.
We’ve now seen the AI categories and their relative stages of innovation. How do these categories stack up against one another? Let’s look at the Total Funding and Company Count Graph.
The graph below shows the total amount of venture funding and company count in each category.
As we’ve seen in the Sector Map and the above graphic as well, the Machine Learning Applications category leads the AI sector with 762 companies. The above graphic also shows that Machine Learning Applications leads in funding with almost $16 billion. Some of the best-funded companies in this category include Toutiao ($3B), Argo AI ($1B), Indigo ($359M), and Wecash ($328M).
The funding in Machine Learning Applications is more than 267% of that in the next category, Machine Learning Platforms. These two categories are related yet have different functions. Machine Learning Applications companies apply self-learning algorithms to optimize specific business operations. Machine Learning Platforms companies build these self-learning algorithms or their underlying infrastructure.
From the above analysis, we can see that Machine Learning Applications dominates the AI sector in total funding and company count. It’ll be interesting to see how the AI landscape will change and develop throughout the rest of 2018.
What are your thoughts on this? Let us know in the comments section below.
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.
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