Artificial Intelligence (AI) has become one of the hottest sectors in recent years, with its technology promising to revolutionize and automate every industry imaginable. We have been covering this trend, showing a massive increase in AI startup funding. As you can see in the graphic below, AI funding more than doubled from 2016 to 2017. Its funding more than tripled from 2016 to 2018.
This phenomenon then begs the question – what caused this explosive growth in AI funding? This explainer blog will help you understand the contributing forces behind the growth. It can be attributed to three factors:
The first factor contributing to the staggering AI funding jump is the geopolitical battle for AI dominance between the US and China. A plethora of news outlets, such as the Wall Street Journal and Forbes, are reporting on the technology battles between the US and China, especially around AI. Experts in those articles have echoed the sentiment that AI is expected to power the development of future business and national security strategies. Many also voiced the opinion that China may supplant the US as a technology leader as it makes significant headway in AI.
The data from our AI dynamic report reinforces the opinions expressed in the above publications. As you can see in the chart below, the lion’s share of AI startup funding is happening in the US and China, with China overtaking the US in 2018. The US jumped from $3B in 2016 to almost $8B in 2018, while China demonstrated even stronger growth, increasing 8-fold from $1B in 2016 to over $8B in 2018.
It’s also noteworthy that China’s AI funding in 2018 comprised 44% of the entire world’s AI funding, whereas the US’s AI funding comprised 41%. Clearly these two geographies are driving the AI revolution, with China now in the lead.
We should note a risk factor around the above conclusion. Some news outlets are reporting that 50% to 80% of Chinese companies exaggerate their funding by a factor of 2 to 10 to attract further investments and intimidate competition. While the accuracy of such claims was not further verified, they would certainly encourage us to treat the above conclusion regarding the US-China AI battle with some level of caution and scrutiny.
The second contributing factor is the gradual maturation of the AI sector as funding events move to later stages. As demonstrated in the graph below, AI seed financings decreased from almost 70% of funding events in 2013 to below 30% in 2018. In contrast, Series B to Late Stage financings in AI have steadily increased from 15% of total funding events to 35%.
This continuous rise in mid to late-stage funding events indicates that the sector is maturing over time. More mature companies require larger funding amounts, which is consistent with the growth in overall funding that we are witnessing. Thus, the explosive funding increase from 2016 to 2018 can be partially explained by the AI sector’s gradual emergence as an established cornerstone in the modern technology landscape.
Venture Scanner organizes chaotic startup landscapes into understandable groupings. For AI, we have broken the sector down into 13 categories. These categories are defined by a specific technology function, such as Machine Learning or Natural Language Processing. Analyzing the AI categories has revealed the third contributing factor, that a small set of AI functional categories are behind the explosive growth seen in the above charts.
As demonstrated in the graph below, Machine Learning (ML) related categories have seen massive increases in funding, from around $4B in 2016 to around $15B in 2018. Computer Vision (CV) related categories also grew rapidly, from around $1B in 2016 to around $8B in 2018. Other AI categories, such as Smart Robots, NLP, and Recommendation Engines, also experienced large funding growth in 2017 and 2018.
Machine Learning Platform companies build algorithms that operate based on their learnings from existing data, while Machine Learning Application companies apply these self-learning algorithms to optimize specific business operations. By the same token, Computer Vision Platform companies build technology that analyzes images to derive information and recognize objects, while Computer Vision Application companies utilize this image processing technology in vertically specific use cases.
The fact that Machine Learning (ML) related categories and Computer Vision (CV) related categories are fueling the AI funding growth is consistent with our analysis that the AI sector is maturing as a whole. As specific AI technologies like ML and CV advance, more venture funding is needed to accelerate their development and adoption.
In summary, our analysis concludes that the top three contributing factors for the funding growth in AI are the geopolitical battle between the US and China, the sector maturing with its funding moving to later stages, and certain AI technology categories gaining significant traction.
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