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.
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...
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.
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 (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...
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 [email protected].
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.
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.
We see that AI funding amount for all stages grew from 2012 to 2017. Most...
The following graphs highlight the exit activity in the Artificial Intelligence sector. The graphics include data through July 2017.
The above graph summarizes the number of exits (acquisitions and IPOs) in each Artificial Intelligence category. The Machine Learning Applications category leads the sector with 4 IPOs and 43 acquisitions. The Natural Language Processing category is the runner-up with 4 IPOs and 29 acquisitions.
The above graph summarizes the number of exits (acquisitions and IPOs) in Artificial Intelligence by year. 2017 currently leads the sector with 1 IPO and 41 acquisitions, with 2016 following behind with 39 acquisitions.
We are currently tracking 1896 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $19B in funding. Click here to learn more about the full Artificial Intelligence market report.
The following graph shows the founding year distribution in the Artificial Intelligence sector. The graphic includes data through April 2017.
The above graph summarizes the number of Artificial Intelligence companies founded in a certain year. 2014 ranks at the top with around 231 companies founded in that year alone. 2013 is the runner-up with 220 companies founded in that year.
We are currently tracking 1844 Artificial Intelligence companies in 13 categories across 70 countries, with a total of $16.5 Billion in funding. Click here to learn more about the full Artificial Intelligence market report.