While demand for AI professionals exceeds supply, winners and losers are emerging in the war for talent.
Explore our AI Playbook, a blueprint for developing and deploying AI, at www.mmcventures.com/research.
As AI is woven into the fabric of consumer experiences, and corporate adoption of AI extends from early adopters to the early mainstream, demand for developers who can create AI solutions has surged. In the United Kingdom, job listings for AI roles have increased 485% since 2014 (Indeed). A quarter of
companies highlight that lack of available AI talent is a primary inhibitor in their efforts to adopt AI (Gartner).
Growth in demand is accelerating. In the United States, year-on-year growth in AI-related job postings increased from 20% (2016) to 32% (2017) (Indeed). In the last 24 months, AI-related job postings as a proportion of total postings nearly doubled (Fig. 56).
“In the last 24 months, AI-related job postings as a proportion of total postings nearly doubled.”
In the United States, machine learning has become the top emerging field of employment, with ten times the number of individuals listing it as their profession today compared with five years ago (LinkedIn) (Fig. 57). Data science, more broadly, is the second-from-top emerging field of employment, with more than six-fold growth.
Supply is increasing as:
Source: Google, Udacity
Estimates of the number of global AI developers vary widely, in part depending upon definition. There may be as few as 22,000 highly-trained AI specialists (Element) and up to 300,000 AI researchers and practitioners within broader technical teams (Tencent). AI originated in academia. The advanced mathematics, statistics and computer science required to understand and apply AI required extensive education, limiting the size of the available talent pool. AI developers are highly educated; nearly 60% have a Master’s or Doctoral degree (Fig. 60). AI developers are twice as likely to have a Master’s degree and seven times more likely to have a Doctoral degree than other professional developers (Fig. 61). Two thirds of data scientists believe their university education has been important or very important for their career success (Kaggle).
Source: Kaggle, Stackoverflow
In addition to technical skills, increasingly AI practitioners must have:
The combination of technical, sector-specific, engineering and commercial competencies required from AI professionals continues to limit the size of the talent pool.
Over time, a larger talent pool and more accessible AI tools will alleviate much of the shortfall in AI talent – and enable greater realisation of AI’s benefits.
Governments’ investment in education – in science, technology, engineering and mathematics (STEM) subjects – will be vital for countries to broaden their pools of AI talent. The proliferation of AI courses and resources from universities and technology companies, and market demand, will also boost supply.
However, AI will also become accessible to less specialised developers over time. Development environments for new technologies tend towards higher levels of abstraction over time (few developers program in assembly language today). AI will follow this pattern.
Prior to 2000, developing AI required advanced mathematics, sophisticated programming and the specification of algorithms by hand. Successive developments have reduced the burden on developers:
Today, Google, Amazon and Microsoft offer AI services that require no implementation knowledge of AI. Developers with limited coding experience can upload data and solve simpler classification problems. While there will remain a large core of highly educated AI developers to progress research,
advanced– and domain-specific AI, we expect the technology to become accessible to a greater proportion of developers over time, expanding the pool of developers who can deploy it.
While supply of AI talent is increasing, demand significantly outstrips supply and will continue to do so in the medium term. There are 2.3 roles available for every suitable candidate (Indeed). “There is a mountain of demand and a trickle of supply” (Chris Nicholson, CEO, Skymind). AI professionals themselves cite lack of available talent as their second-greatest challenge (Fig. 62).
A shortage of AI developers is driving high salaries in the market. Data scientists and machine learning specialists are among the best paid professional developers (Fig. 63). At the 20 highest-paying companies, salaries for AI engineers average $224,000 (Fig. 64). Leaders in the field command vast sums.
AI developers’ salaries are particularly high relative to their level of professional experience. Nearly half of data scientists have under two years of professional experience (Kaggle); nearly three quarters have less than ten. Compared with other developers, data scientists enjoy among the greatest salary premium relative to their level of experience (Fig. 65).
Salaries for AI professionals have grown significantly in recent years and continue to increase. Almost all data scientists report increased pay in the last three years; nearly half grew their salaries by 20% or more (Fig. 66).
In the last 12 months, salaries have continued to increase (Fig. 67). This year’s pay dynamic has been more favourable to AI professionals than to many other developers. However, AI professionals are not the only group to enjoy significant year-on-year pay rises. Developers specialising in system administration, embedded applications and enterprise applications all received similar increases. DevOps specialists, who integrate and automate development and operations functions for faster cycles of improvement, are enjoying the greatest average raises.
“Salaries for AI professionals have grown significantly in recent years and continue to increase.”
Source: StackOverflow, MMC Ventures
Despite AI’s potential to reshape sectors ranging from retail to healthcare, technology and financial services firms are absorbing nearly 60% of AI talent (Fig. 68) (Burtch Works). 44% of data scientists are employed in the Technology sector – more than in the healthcare, consulting, marketing, retail, academia and Government sectors combined. Financial services, with a 14% share of data scientists, is a distant second.
Within the Technology sector, the world’s largest technology companies – including Amazon, Apple, Facebook, Google, IBM and Microsoft – are consolidating much of the available talent. Amazon, Microsoft and Apple combined are estimated to be investing $620m in AI talent (Paysa).
of data scientists are employed in the Technology sector – more than in the Healthcare, Consulting, Marketing, Retail, Academia and Government sectors combined.
Source: Burtch Works
Source: The Burtch Works Study – May 2018. N=2,212
The technology and financial services sectors are emerging winners in the war for AI talent – and creating virtuous cycles to extend their leadership. In addition to absorbing the greatest share of data scientists today, technology and financial services companies are planning to increase their investment in AI by the greatest proportion in the next three years (McKinsey Global Institute). Technology and financial service companies are prioritising AI, committing resources and building network effects around people and data to establish and extend leadership in the field.
Conversely, select sectors – including retail and consulting – are lagging, both in their ability to attract AI talent today and in their investment for the future. While many sectors, including retail and consulting, offer numerous prediction and optimisation problems well suited to AI, and large data sets to train AI algorithms effectively, the emerging gulf between winners and losers in the war for AI talent is likely to widen.
The perceived ‘brain drain’ from academia to industry is real – and will have mixed implications. While alternative surveys suggest that up to 15% of data scientists currently work in academia (Kaggle), many are leaving for roles in global technology companies. A three– to five–fold increase in salary, vast data sets for analysis and access to greater hardware resources attract many. Between 2006 and 2014, the proportion of AI research publications including an author with corporate affiliation increased from approximately 2% to nearly 40% (The Economist). Talent has continued to migrate to industry. In the UK, in the last 18 months several leading AI researchers have moved to industry to accept senior roles at Uber, Amazon and Google.
In industry, AI experts are freed from the burden of securing research grants, may innovate faster, and can catalyse AI’s immediate impact on the world. However, their migration has drawbacks – including fewer teachers to train the next generation of practitioners, a concentration of expertise and experience in a small number of companies, and reduced sharing of ideas. National talent working for the public good is becoming overseas resource for private gain – with international implications. The field of AI itself arose from academic experimentation. If we lose the next generation of academics, “in the end, society will suffer” (Maja Pantic, Professor of Affective and Behavioural Computing, Imperial College London).
Competition for AI talent is fierce, not simply because supply is limited. Three quarters of AI developers are content with their current roles, rating their job satisfaction 6 out of 10 or better (Fig. 69).
To optimise hiring and retention, companies should align roles to AI professionals’ primary motivators. To developers, opportunities for learning and professional development, the office environment in which they will be working, and the technologies (languages and frameworks) they will be using are more important than money (Fig. 70).
Large companies seeking to attract AI talent should: take advantage of their ability to pay high salaries and offer job security; highlight the large data sets they have for analysis and the learning opportunities these will provide; emphasise the impact AI developers will have given the companies’ large customer bases; and offer their AI professionals extensive hardware and software resources. Large companies should seek to mitigate likely concerns regarding agility, autonomy and freedom to publish.
Startups and scale-ups cannot, and need not, compete with the pay offered by large companies. Startups should market to candidates: the intellectual and technical challenges they can provide and associated learning opportunities; an engaging office environment; impressive job titles; a greater opportunity to impact product; increased autonomy; faster cycles of innovation; and greater freedom to publish. Startups should address probable concerns regarding pay by highlighting the large, long-term financial rewards they can offer through equity awards.
“Start-ups and scale-ups cannot, and need not, compete with the pay offered by large companies.”
The pathways into AI employment – company websites and technology job boards – prioritised by those entering the field are among the least effective (Fig. 71). People successfully employed in AI highlight that engagement with recruiters, friends, family and colleagues is the most fruitful route into the industry.