Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent
Tech Giants Are Paying Huge Salaries for Scarce A.I.
Talent
Nearly all big tech companies have an artificial
intelligence project, and
they are willing to pay experts millions of dollars to
help get it done.
By CADE METZ OCT. 22, 2017
SAN FRANCISCO — Silicon Valley’s start-ups have always
had a recruiting advantage over the industry’s giants: Take a chance on us and
we’ll give you an ownership stake that could make you rich if the company is
successful.
Now the tech industry’s race to embrace artificial
intelligence may render that advantage moot — at least for the few prospective
employees who know a lot about A.I.
Tech’s biggest companies are placing huge bets on
artificial intelligence, banking on things ranging from face-scanning smartphones
and conversational coffee-table gadgets to computerized health care and
autonomous vehicles. As they chase this future, they are doling out salaries
that are startling even in an industry that has never been shy about lavishing
a fortune on its top talent.
Typical A.I. specialists, including both Ph.D.s fresh out
of school and people with less education and just a few years of experience,
can be paid from $300,000 to $500,000 a year or more in salary and company
stock, according to nine people who work for major tech companies or have
entertained job offers from them. All of them requested anonymity because they
did not want to damage their professional prospects.
Well-known names in the A.I. field have received compensation
in salary and shares in a company’s stock that total single- or double-digit
millions over a four or five-year period. And at some point they renew or
negotiate a new contract, much like a professional athlete.
At the top end are executives with experience managing
A.I. projects. In a court filing this year, Google revealed that one of the
leaders of its self-driving-car division, Anthony Levandowski, a longtime
employee who started with Google in 2007, took home over $120 million in
incentives before joining Uber last year through the acquisition of a start-up
he had co-founded that drew the two companies into a court fight over
intellectual property.
Salaries are spiraling so fast that some joke the tech
industry needs a National Football League-style salary cap on A.I. specialists.
“That would make things easier,” said Christopher Fernandez, one of Microsoft’s
hiring managers. “A lot easier.”
There are a few catalysts for the huge salaries. The auto
industry is competing with Silicon Valley for the same experts who can help
build self-driving cars. Giant tech companies like Facebook and Google also
have plenty of money to throw around and problems that they think A.I. can help
solve, like building digital assistants for smartphones and home gadgets and
spotting offensive content.
Most of all, there is a shortage of talent, and the big
companies are trying to land as much of it as they can. Solving tough A.I.
problems is not like building the flavor-of-the-month smartphone app. In the
entire world, fewer than 10,000 people have the skills necessary to tackle
serious artificial intelligence research, according to Element AI, an
independent lab in Montreal.
What we’re seeing is not necessarily good for society,
but it is rational behavior by these companies,” said Andrew Moore, the dean of
computer science at Carnegie Mellon University, who previously worked at
Google. “They are anxious to ensure that they’ve got this small cohort of
people” who can work on this technology.
Costs at an A.I. lab called DeepMind, acquired by Google
for a reported $650 million in 2014, when it employed about 50 people,
illustrate the issue. Last year, according to the company’s recently released
annual financial accounts in Britain, the lab’s “staff costs” as it expanded to
400 employees totaled $138 million. That comes out to $345,000 an employee.
“It is hard to compete
with that, especially if you are one of the smaller companies,” said Jessica
Cataneo, an executive recruiter at the tech recruiting firm CyberCoders.
The cutting edge of artificial intelligence research is
based on a set of mathematical techniques called deep neural networks. These
networks are mathematical algorithms that can learn tasks on their own by
analyzing data. By looking for patterns in millions of dog photos, for example,
a neural network can learn to recognize a dog. This mathematical idea dates
back to the 1950s, but it remained on the fringes of academia and industry
until about five years ago.
By 2013, Google, Facebook and a few other companies
started to recruit the relatively few researchers who specialized in these
techniques. Neural networks now help recognize faces in photos posted to
Facebook, identify commands spoken into living-room digital assistants like the
Amazon Echo and instantly translate foreign languages on Microsoft’s Skype
phone service.
Using the same mathematical techniques, researchers are
improving self-driving cars and developing hospital services that can identify
illness and disease in medical scans, digital assistants that can not only
recognize spoken words but understand them, automated stock-trading systems and
robots that pick up objects they’ve never seen before.
With so few A.I. specialists available, big tech
companies are also hiring the best and brightest of academia. In the process,
they are limiting the number of professors who can teach the technology.
Uber hired 40 people from Carnegie Mellon’s
groundbreaking A.I. program in 2015 to work on its self-driving-car project.
Over the last several years, four of the best-known A.I. researchers in
academia have left or taken leave from their professorships at Stanford
University. At the University of Washington, six of 20 artificial intelligence
professors are now on leave or partial leave and working for outside companies.
There is a giant sucking sound of academics going into
industry,” said Oren Etzioni, who is on leave from his position as a professor
at the University of Washington to oversee the nonprofit Allen Institute for
Artificial Intelligence.
Some professors are finding a way to compromise. Luke
Zettlemoyer of the University of Washington turned down a position at a
Google-run Seattle laboratory that he said would have paid him more than three
times his current salary (about $180,000, according to public records).
Instead, he chose a post at the Allen Institute that allowed him to continue
teaching.
“There are plenty of
faculty that do this, splitting their time in various percentages between
industry and academia,” Mr. Zettlemoyer said. “The salaries are so much higher
in industry, people only do this because they really care about being an
academian.”
To bring in new A.I. engineers, companies like Google and
Facebook are running classes that aim to teach “deep learning” and related
techniques to existing employees. And nonprofits like Fast.ai and companies
like Deeplearning.ai, founded by a former Stanford professor who helped create
the Google Brain lab, offer online courses.
The basic concepts of deep learning are not hard to
grasp, requiring little more than high-school-level math. But real expertise
requires more significant math and an intuitive talent that some call “a dark
art.” Specific knowledge is needed for fields like self-driving cars, robotics
and health care.
In order to keep pace, smaller companies are looking for
talent in unusual places. Some are hiring physicists and astronomers who have
the necessary math skills. Other start-ups from the United States are looking
for workers in Asia, Eastern Europe and other locations where wages are lower.
“I can’t compete with
Google, and I don’t want to,” said Chris Nicholson, the chief executive and a
co-founder of Skymind, a start-up in San Francisco that has hired engineers in
eight countries. “So I offer very attractive salaries in countries that
undervalue engineering talent.”
But the industry’s giants are doing much the same.
Google, Facebook, Microsoft and others have opened A.I. labs in Toronto and
Montreal, where much of this research outside the United States is being done.
Google also is hiring in China, where Microsoft has long had a strong presence.
Not surprisingly, many think the talent shortage won’t be
alleviated for years.
“Of course demand
outweighs supply. And things are not getting better any time soon,” Yoshua
Bengio, a professor at the University of Montreal and a prominent A.I.
researcher, said. “It takes many years to train a Ph.D.”
A version of this article appears in print on October 23,
2017, on Page B1 of the New York edition with the headline: N.F.L. Salaries for
A.I. Talent.
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