Tech Giants Paying Huge Salaries for Scarce A.I. Talent : Why it matters
An interesting article in New York Times “Tech Giants Are Paying Huge Salaries for Scarce A.I. Talent (link)” is making rounds in social media and among the digirati. Artificial intelligence has fascinated technologists and science fiction writers for decades, but the business world seems to be getting serious about its disruptive potential. Technologies including Deep Blue from IBM, DeepMind from Google or Microsoft’s Chatbots are going beyond press-mentions, and beginning to demonstrate value in solving real world problems.
A.I. also continues to be on “top 10 or “top 25” Digital Startup ideas. Promising AI and Machine learning focused startups are frequently being courted and acquired by the tech oligopoly — Apple, Amazon, Facebook, Google and Microsoft (link). In many cases, executives see it as an opportunity to on-board a pool of talent more than just acquiring a promising technology.
The author, Cade Metz, after discussion with “nine people who work for major tech companies or have entertained job offers from them,” explains how tech giants are paying “Huge Salaries” for AI Talent. A few key points from the article
- In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research
- At the top end are executives with experience managing A.I. projects. Anthony Levandowski, a longtime employee who started with Google in 2007, took home over $120 million in incentives before joining Uber last year.
- 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
- Costs at an A.I. lab called DeepMind, acquired by Google for a reported $650 million in 2014, when it employed about 50 people, illustrates the issue. The lab’s “staff costs” as it expanded to 400 employees totaled $138 million. That comes out to $345,000 an employee.
Some of these are broad generalizations and sound like “in the entire world, fewer than 1,000 researchers are working on the cure for XYZ cancer or ABC disease.” One can discount such hyperbole since the author got most of his inputs and figures from just “nine people.” Still, the premise of the article is still logical and rather straightforward:
“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.”
Let us look at some of the implications of Why and to Whom this matters:
Technology Executives: In a classic case of “Airline Magazine Syndrome,” functional leaders and executives across businesses are beginning to lean on their IS Executives to demonstrate how they leverage Artificial Intelligence, big-data, visualization, robotics and other digitization techniques. Technology vendors are sensing this opportunity and are cleverly rebranding their CRM, ERP and other products as “AI based,” sometimes by just adding cool-new chatbots to the existing platform.
- It is the responsibility of Enterprise Architects and technology leaders to see through the emperor’s clothes.
- Technology leaders can use this opportunity to engage and inform their stakeholders, and help them contextualize relevant user-stories and requirements where they will demonstrate value
Consulting firms: Consulting firms and System Integrators are jumping the AI bandwagon by adding offerings to ‘Digital Transformations.’
- It is necessary for consultants to stay abreast of emerging technologies. However, consultants must also take an objective view of their client’s requirements. While AI holds a lot of promise, in some cases it may just be a solution looking for a problem.
Software Engineers: The article highlights how “companies like Google and Facebook are running classes that aim to teach “deep learning” and related techniques to existing employees.”
- If you happen to be an engineer selected to learn “deep learning,” great.
- Otherwise, you can explore opportunities for self-paced learning on Fast.ai, Deeplearning.ai etc. Keep in mind a certification or training alone may not suffice if your organization is not embarking on an AI based initiative
Startups and entrepreneurs: Many startups and entrepreneurs are looking to carve out a niche in this greenfield space
- If you aspire to be acquired by “the tech oligopoly,” you should focus on innovative application of AI and ML. However, this is much harder than it sounds. Such real world applications of practical value are not easy to visualize.
Students of Computer Science: There are several ‘hot’ and emerging technologies competing for our mindshare, though Big Data, Robotics, Automation, AI and ML stand out.
- As a student of Computer Science, a specialization in AI and machine learning may help you stand out from the crowd.
- A specialization in these technologies will certainly help you land a better job, but don’t be under pipe-dreams of “$300,000 to $500,000” payouts. Those are going to be much harder to come by.
I’m sure this is not the last word on this topic.
Thanks for reading! Please click on Like, or Share, Tweet and Comment below to continue this conversation | Reposted from my Linkedin Pulse article
Originally published at www.mohanbabuk.com on October 26, 2017.