The million dollar graduates: making sense of AI talent migration
Looking into the market incentives that drive the AI talent pool.
Artificial Intelligence expertise is hard to recruit, at least on a global stage today. This article identifies key trends in the employment of AI talents and field practitioners and suggests that their uneven distribution is closely linked to scarcity and financial placements. An optimistic outlook is retained, however, in parts due to the unprecedented volume of funding flowing into education.
Seating protocols can reflect real strategic interests at Google. This relatively odd fact was highlighted by Diane Greene, the current CEO of Google Cloud and a board member at Alphabet. Reportedly, in their Mountain View office you will find that the Artificial Intelligence team was conveniently placed at walking distance from the office of Google’s CEO. “Any C.E.O. thinks a lot about where people are sitting — who they can walk around and have casual conversations with”, explained Mrs. Greene.
This is a fairly new treatment. Many experts in the industry will point to the sudden strategic ascension that AI experienced within the past decade and contrast it to the recent period of “AI Winter’’ from the early 1980s until the late 2000s. The change in seats within companies, however, is only one form of migration that AI experts have been seeing. Looking at the prevailing trends and discussions in the field one can not help but notice a high paced geographic and industrial flux.
Geographic Brain drain
AI is making its way to become a general purpose technology — a crucial addition to virtually anything. Countries want it, companies want it, universities want it, activists want it, and skilled individuals have it. There are between 90,000 and 300,000 AI researchers or industry practitioners in the world at the moment depending on which report you read. The problem is clear: the AI wave is limited by the existing talent pool.
In order to satisfy the long-term demand that there will be for AI expertise, everyone is gearing up to teach Machine Learning and Deep Learning to a new generation. However, while a large volume of individuals is being trained to become future AI experts, the current supply is being torn apart by the interested factions.
So-called AI hubs are burgeoning all over the world in an effort by governments and companies alike to center AI talents within their preferred location. Some clear winners are emerging from these efforts as we have pointed out before, and this has concentrated expertise geographically. According to a recent Mckinsey report: in 2016, the United States absorbed around 66% of external investment (VC, PE, and M&A activity); China had a rapidly growing 17%. The San Francisco Bay Area and Silicon Valley ecosystem attracted 40% of global external investment while China is still grooming Beijing and Shenzhen into becoming viable alternatives.
Europe is only now catching up, with the UK, in particular, taking action at a state level to attract talented individuals. In fact, their Tech Nation Visa Scheme aims to deliver 200 “relaxed” visas per year “for applicants with exceptional talent or promise in the digital space”. Moreover, European AI ecosystems are being built, in part, by big tech investments in AI centers in Paris and Berlin which are currently enjoying “exceptional startup activity and VC investment”. The governments of these cities are also contributing financially and carefully crafting attractive environments that would retain European talent from moving to Palo Alto instead. Some countries even expressed their hope to federalize AI completely and would require national expertise for that purpose.
Similar efforts to reduce the outflow of AI talent can be found in regions from the so-called “Global South” that are unable to compete with the high salaries that are being offered abroad. In Africa, some notable efforts have been made in order to give a clear purpose and financial incentives to AI talents. These include the Black in AI and the AI Africa Conference by the Machine Intelligence Institute of Africa. The region has seen its existing expertise focus on regionally specific technology like in agriculture and healthcare where some researchers seek more than just monetary value. Ethiopia, for instance, “has 88 active and individual languages, [and] has been actively developing Natural Language Processing solutions to improve communications”.
Industry Brain drain
Across international and national fluxes of AI researchers and practitioners, you will find a heavy outflow from academia and into the private sector. University executives from the UK’s elite universities have been calling out the unfair competition and cautioning of “national implications” from the exodus. As an example: in 2015, Uber recruited 40 of the 140 staff of the National Robotics Engineering Centre at Carnegie Mellon University. The company simply syphoned a generation of academics by means of financial incentives.
Big technology companies in particular have been seeking out recent graduates and teaching staff, offering 6 to 7 figures salaries in exchange for prompt starts. All the while creating an insurmountable pay gap with academic positions, some corporate labs try to also emulate the inherent freedom of academia by “[taking] on fundamental research” and leaving researchers agenda-free.
The scarcity of talent is best described by a report advancing that ‘companies are currently investing in more than $650 million in annual salaries to fuel the AI talent race with more than 10,000 available positions’. These costs can partly be explained by a particular inter-industry brain drain phenomenon whereby companies adopt merger and acquisitions techniques “as a way to sign up top talent, a practice known as “acqui-hiring,” for sums that typically work out to $5 million to $10 million per person”. Nevertheless, the costs of recruitment are unprecedented and will remain high until the heavy investments that interested parties are making into educating the next generation of AI engineers, bear their fruits.
To list but a few investments in education: Facebook is opening an AI lab in Paris; Google recently invested $4.5 million in the Montreal Institute for Learning Algorithms, a research lab at the University of Montreal; Intel donated $1.5 million to establish a machine learning and cybersecurity research center at Georgia Tech; and NVIDIA is working with the National Taiwan University to establish an AI laboratory in Taipei.
In some respect, one could argue that the current arms race over AI talent leads to positive externalities such as heavy investment in education and cross-border knowledge exchange. Xi Jinping famously pledged to “share results with other countries in the field of artificial intelligence.”
Unfortunately, we will continue to hear the “giant sucking sound of academics going into industry” as Oren Etzioni, who was a professor at the University of Washington and now oversees the nonprofit Allen Institute for Artificial Intelligence, puts it. That is, if no measures are taken. Academic executives have introduced the idea of government incentives and caps: “Beyond getting the companies to pay their taxes…[governments] might have to consider pay caps, a strategy that has reined in corporate salaries in Nordic countries” or even for national football league salaries.
Regardless, experts like Etzioni give hope to believe that some will value educating more than working for high salaries and often impersonal projects, or will simply not compromise on an ethical level. Among those previously described that remain in their home countries in order to improve their respective social welfare, you will also find many who leave their highly paid jobs because of ideological disagreements.
To summarize, the current global AI talent pool is too shallow to satisfy the ambitious projects of all private and public actors alike. Paradoxically, while the demand for skilled workers is making investments in education grow exponentially, with new AI centers and college grants being multiplied across the world, the very institutions of education are being crippled by an exodus of academics and students into the private sector. Universities, non-profits, and even some countries are falling behind while the fight for talent rages between actors that can afford to overspend on short-term talent acquisition frenzies.
SingularityNET stands out from this crowd with programs that have allowed individual ventures and their respective talents to first thrive and also share their input with the world. In our upcoming marketplace, the acquisition of talent by one actor can mean growth for all actors on the market. Indeed, decentralised networks ensure that information can flow between AI developers and their products; cooperation can emerge from information sharing; monetisation is available to a greater number of teams — while inducing network effects; discovery of teams and services is heightened. The DAIA initiative has also accelerated our call for collaboration among smaller actors in the market by offering community guidance to starters and enabling technical alignment between members. Additionally, as the traditional aphorism goes: the absence of evidence does not mean the evidence of absence. SingularityNET is proud to be a core partner of Ethiopia’s first AI lab with an amazing team of local AI researchers working on robots like Sophia. Our culture is differentiating us and attracting a diversity in talent and genuine geniuses lie in wait to be discovered and activated through our platform.
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