Mid-Year Artificial Intelligence Exits Analysis

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.

2018 Mid-Year AI Exit Activity Lower Than 2017 But Higher Than 2016

Let’s take a closer look at the number of AI exit events by year.

artificial-intelligence-exits-by-quarter
Artificial Intelligence Exits By Quarter

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.

Artificial Intelligence Startup Highlights  – Q2 2018

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.

Machine Learning Categories Lead AI Funding

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:

  • Machine Learning Platforms and Computer Vision Platforms lead the sector in Q2 funding
  • Machine Learning Applications dominates the sector in all-time funding

We’ll explain these observations with some graphics and discussions below.

Machine Learning Platforms and Computer Vision Platforms Lead AI in Q2 Funding

To start off, let’s scrutinize the AI funding by category in Q2.

Artificial Intelligence Current Quarter Category Funding
Artificial Intelligence Current Quarter Category Funding

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.

Machine Learning Applications Dominates AI in All-Time Funding

The graph below shows the all-time funding for different AI categories. The quarterly funding and growth rates of these categories are also highlighted.

Artificial Intelligence Total Category Funding
Artificial Intelligence Total Category Funding

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%.

Conclusion: Machine Learning Categories Are At the Forefront of AI Funding

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.

Deep Learning Applications and Computer Vision Platforms Lead AI Exit Activity

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:

  • The Deep Learning Applications category leads in the number of exits
  • The Computer Vision Platforms category leads in acquisition amount

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.

Artificial Intelligence Exits by Quarter
Artificial Intelligence Exits by Quarter

Deep Learning Applications Leads AI in the Number of Exits

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.

Artificial Intelligence Exits by Category
Artificial Intelligence Exits 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.

Computer Vision Platforms Leads AI in Acquisition Amount

The graph below shows the acquisition amounts in different AI categories.

Artificial Intelligence Acquisition Amounts by Category
Artificial Intelligence Acquisition Amounts by Category

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.

Conclusion: Deep Learning Applications and Computer Vision Platforms Lead AI Exit Activity

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 Sector Overview – Q1 2018

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.

Machine Learning Applications Is the Largest AI Category

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.

Artificial Intelligence Sector Map
Artificial Intelligence Sector Map

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.

Most of the AI Categories Are Pioneers

Our Innovation Quadrant divides the AI categories into four different quadrants.

Artificial Intelligence Innovation Quadrant
Artificial Intelligence Innovation Quadrant

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.

Machine Learning Applications Startups Have the Most Funding and Companies

The graph below shows the total amount of venture funding and company count in each category.

Artificial Intelligence Total Funding and Company Count
Artificial Intelligence Total Funding and Company Count

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.

Conclusion: Machine Learning Applications Category Dominates the AI Sector

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.

Artificial Intelligence Startup Highlights  – Q1 2018

Here is our Q1 2018 summary report on the Artificial Intelligence startup sector. The following report includes an overview, recent activity, and a category deep dive.

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

AI Sector Maturing As Funding Shifts to Later Stage

Last quarter we saw that Artificial Intelligence (AI) funding grew exponentially in 2017. This quarter we are going one level deeper on our AI research platform to examine its funding by round. From our analysis we can conclude that the AI sector is maturing.

This conclusion comes from two takeaways:

  • Funding amounts are shifting to mid and late-stage events
  • Later-stage funding counts increased while early-stage funding counts dropped

We’ll explain these takeaways with some graphics that show AI funding activity by round.

To help set the stage, the graphic below illustrates AI funding amount over time. As you can see, the sector’s funding exploded in 2017.

Artificial Intelligence Annual Funding Amount
Artificial Intelligence Annual Funding Amount

AI Funding Amounts Shifting to Mid and Late-Stage Events

We’ll start off by examining the annual AI funding amounts. The below graph shows recent AI funding amounts in different rounds.

Artificial Intelligence Annual Funding Amount by Round
Artificial Intelligence Annual Funding Amount by Round

We see that AI funding amount for all stages grew from 2012 to 2017. Most notably, Series D funding grew the most.

Let’s look at the AI funding amount by round as a percentage, which shows changes independent of the total size.

Artificial Intelligence Annual Funding Amount Percentages
Artificial Intelligence Annual Funding Amount Percentages

From 2012 to 2017, Seed and Series A funding amount percentages dropped. During the same period, Series B, C, and D funding amount percentages increased.

Specifically, the Seed round funding amount dropped from 15% to 3% from 2012 to 2017. The Series A funding amount dropped from 40% to 20%. On the other hand, the Series B funding amount increased from 26% to 30%. The Series C funding amount increased from 15% to 25%. The Series D funding amount increased from 4% to 20%.

Taken together, these two graphics show that the AI funding amounts in Series B, C, and D increased from 2012 to 2017. The funding amount as a percentage graph also shows that the Seed and Series A funding dropped.

So we have seen the funding amounts graphs indicate a shift to mid and late-stage events. Would the funding event count graphs show the same trend? Let’s examine them in the next section to find out.

Later-Stage AI Funding Counts Grew; Early-Stage Funding Counts Dropped

Let’s now look at the annual AI funding event counts. The below graph shows the AI funding counts in different rounds over recent years.

Artificial Intelligence Funding Count by Round
Artificial Intelligence Funding Count by Round

The above graph shows that Seed round events grew from 93 in 2012 to its peak at 275 in 2015. It then fell from 275 events to 195 events in 2017. On the other hand, the funding event counts for all the other stages grew from 2012 to 2017.

Specifically, Series A funding events grew from 40 to 176. Series B funding events grew from 12 to 84. Series C funding events grew from 4 to 35. Series D funding events grew from 2 to 21. Late Stage funding events grew from 0 to 9.

Let’s now look at the AI funding count by round as a percentage, which can show shifts more clearly.

Artificial Intelligence Annual Funding Count Percentages
Artificial Intelligence Annual Funding Count Percentages

This graph shows that from 2012-2017, the Seed round funding count percentage dropped. In the same years, the funding count percentages for all the other stages increased.

Specifically, the Seed round funding count dropped from 62% to 38% from 2012 to 2017. In contrast, the Series A funding count increased from 26% to 34%. The Series B funding count increased from 8% to 15%. The Series C funding count increased from 3% to 6%. The Series D funding count increased from 2% to 4%. And the Late Stage funding count increased from 0% to 2%.

Combining these two graphics, we can see that the AI funding count for the Seed round dropped from 2012 to 2017. The funding counts for all the other stages grew by various degrees during the same period.

Conclusion: The AI Sector Is Maturing

In conclusion, we have seen that AI funding amounts have shifted to mid and late-stage events. AI funding counts also saw an increase in mid and late-stage events and a drop in Seed events. These observations led us to conclude that the AI sector is maturing. Investors seem to be placing heavier bets into more well-established AI companies.

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.