Statistics related to AI

Following analysis done by AI State Index1, let’s review some statistics related to Artificial Intelligence, that will fully show how important this market is becoming (or already is). It’s crucial to understand that we are still early when it comes to applied AI, and most of those statistics will grow substantially in the upcoming years. The reason for that is most of the cutting-edge research is still far away from day-to-day business applications either because of the costs of the hardware or the required expertise to apply it. I expect the full AI boom to come within the next ten years when every company will need to implement AI elements to be competitive even at the local scale. This will come in pair with the democratisation of AI: the cost and difficulty of implementation of most algorithms will largely decrease. AI applications will be as available as general cloud storage is now.

Looking at Google Trends one can see that “cloud computing” appears in 2008 and then it is replaced by “big data” which starts taking off in 2011. “Machine learning” and “data science” begin to rise together in 2013, which matches the renewed interest in AI after the 2012 ImageNet competition.

Let’s now look at different aspects of the Artificial Intelligence ecosystem.

Research

• Between 1998 and 2018, the share of AI papers among all papers published worldwide has grown three-fold, now accounting for 3% of peer-reviewed journal publications and 9% of published conference papers.
• The number of AI research papers surpassed 35,000 in 2019 worldwide as evaluated by looking at arXiv and AI conferences. Most AI papers are published in North America and China.
• The number of patents related to AI is growing faster than the number of scientific papers. Most of the patents are within the computer vision subdomain and are registered in the US.
• Europe publishes the most AI papers. Papers published by American authors are cited 83% more than the global average.

Business and Funding

Global investments in AI and AI startups continue to rise. From a total of $1.3B raised in 2010 to over $40.4B in 2018 alone, funding has increased with an average annual growth rate of over 48% between 2010 and 2018.

‘State of AI Report in 2019’ claims that the number of AI companies that received funding is also increasing year by year, with over 3000 AI companies receiving funding in 2018. They calculated that between 2014 and 2019 (up to November 4th), a total of 15,798 investments of over $400K have been made in AI startups globally, with an average investment size of approximately $8.6M.

On the other hand, Crunchbase lists 13,650 AI companies as of May 2020, of which 97.8% are active. They have raised $19M on average, the median funding being $2.2M.

VC-driven private investments accounted for about half of total investments in AI in 2019, with M&A and Public Offerings taking the major share of the remaining half. However, private investment accounted for 92% of the number of deals, with M&A making up just over 4% of deals, and Minority stakes and Public offerings (IPOs) together accounting for 3%. These statistics show that most of the money goes to already successful startups.

‘’AI investment2 is growing fast, dominated by digital giants such as Google and Baidu. Globally, we estimate tech giants spent $20 billion to $30 billion on AI in 2016, with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions. VC and PE financing, grants, and seed investments also grew rapidly, albeit from a small base, to a combined total of $6 billion to $9 billion. Machine learning, as an enabling technology, received the largest share of both internal and external investment.’’ AI investments keep on growing in the last years.

Markets and Markets estimate that the AI market will be worth $190 billion by 2025.3 We might hit this benchmark even sooner when you look at the above examples. To add even more examples:

• Open AI had a recent investment by Microsoft of $1 billion.
• SoftBank announces the second Vision Fund, which will be AI-focused and which will have $108 billion to invest.
• SAS is going to invest $1 billion in artificial intelligence over 3 years starting from 2019.

The US federal government is projected to invest around $5 billion in AI R&D in fiscal 2020.

In the fiscal year 2018, the latest year in which complete contracting data is available, US federal agencies spent a combined $728 million on AI-related contracts, an almost 70% increase above the $429 million that agencies spent in fiscal 2017. Since the fiscal year 2000, the Pentagon has accounted for the largest share of AI spending of any federal agency ($1.85 billion), followed by NASA ($1.05 billion), and the departments of the Treasury ($267 million) and Health and Human Services ($245 million).

We can easily extrapolate that investments in AI and AI-related companies will only be growing in the next years as more research will be available for commercialisation. Also time of commercialisation might be shorter due to the growing talent pool and democratisation of AI.

Hiring

Hiring AI talent is hard, as the market is very competitive. Big tech companies have been actively buying AI startups, not just to acquire technology or clients but to secure qualified talent - this is usually called acquihire and is often practised in tech markets.

The pool of experts in machine learning is small, moreover, Microsoft, Amazon, Facebook, Google, and other tech giants have hired many of them. Companies have adopted M&A as a way to grab the top talent - typically those deals are valued at $5 million to $10 million per person on an M&A deal (the lowest is usually $1 million per person). The shortage of talent and the cost of acquiring talent are underlined by a recent report that companies are seeking to fill 10,000 AI-related jobs and have budgeted more than $650 million for salaries. The US alone is opening over 7,000 AI-related jobs in 2019.4 We can look at three aspects of the AI job market.

• growth: the rapid growth in AI hiring is also confirmed by job postings data from Burning Glass that shows the share of AI jobs (% of total jobs posted online) grew from 0.1% in 2012 to 1.7% in 2019 for Singapore. Similarly, in the US, the share of AI jobs increased from 0.3% in 2012 to 0.8% of total jobs posted in 2019.
• demand: machine learning jobs increased from 0.07% of total jobs posted in the US in 2010 to over 0.51% in October 2019.
• salary: compensation of senior engineers at large tech companies is approaching $1,000,000 of which about half is in company’s stocks. At the other end of the spectrum, there’s huge growth in $1.47/hour data labeling jobs.

All in all, hiring might be the biggest bottleneck for organisations to start deploying AI at scale. There are various ways to overcome this problem:

• outsource AI tasks to specialised software houses,
• use existing solutions and adapt them to your needs,
• acquihire whole teams,
• offer a competitive environment for machine learning engineers.

We’ll come back to these issues in the next chapters, while discussing how to use AI in organisations, be that large enterprises or startups.

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