AI Will Put 10–15 Million Jobs At Risk over the next 5 years

AI based automation is coming after industrial jobs, from fast food workers to accountants. While the shift from traditional manufacturing to computer-enabled industry took several decades to almost a century, the tectonic shift from personal computing to billions of smartphones, massive networks, and IoT has unraveled in just a couple of decades.

AI as the next and defining phase of technological evolution is already underway and it largely leverages advanced neural networks that learn, adapt, and respond to situations.

10–15 million US jobs at risk of disruption !

Automation is already all around us. Some cities are seeing front-end automated restaurants like Eatsa gaining in popularity,

Research estimates from CB Insights put around 10 million service and warehouse jobs at high risk of displacement within the next 5–10 years in the US alone. This includes jobs like cooks and servers, cleaners and janitors, as well as warehouse workers. And another 5 million retail workers at a medium risk of automation within 10 years.

So what factors are contributing to it ?

The fundamentals of artificial intelligence were introduced way back in the 1950s. But three broad factors that have redefined what AI can achieve are:

1. Innovation in microprocessors — particularly Nvidia’s GPUs, that are now being widely used in training deep neural networks from being initially used in the gaming industry by hobbyists

2. Availability of large training datasets and access to massive amounts of data (from the internet, IoT devices, etc.),

3. Rapid advances in research from universities combined with a vast leap in the open sourcing of several AI based libraries and frameworks, by the wider developer community and also several large corporations

How are companies applying AI based automation ?

Leading iPhone contract manufacturing company Foxconn pioneered industrial robots called “Foxbots” that were developed internally by the company that can perform up to 20 common manufacturing tasks, effectively contributing to the layoff of over 60,000 workers.

Additionally, it has also invested over $120 million USD into external robotics startups such as Kinova Robotics, and CloudMinds.

Nike and Reebok are also following a similar trend by applying automation to the supply chain and logistics in addition to backing robotics startups such as Grabit .

While robots are still not so perfect at gripping and lifting items in unstructured settings, Amazon and Walmart have several patents for autonomous robots and they are using them to move shopping carts around stores along with drone delivery systems.

Significant impact on the blue-collar labor markets

While majority of AI applications today still require humans in the loop, there are quite a lot of blue-collar jobs at risk. These are based on research estimates using the data available from US Bureau of Labor statistics and by mapping the risk of AI on such sectors, using factors such as tasks involved, current commercial deployment of technology, patent activity, investment activity, technological challenges, and regulations.

Over an estimated time frame of the next 5–10 years, and the relative risk of automation was very high across the following 7 industries :

  • Nurses and health aides (6.9M workers)

White-collar jobs are also facing elimination due to automation

In addition to the jobs and sectors listed above, there is moderated impact of automation on white collar jobs as well in a few sectors.

Startups are developing expertise automation and augmentation software (EaaS) that will replace entry-level white collar jobs in areas like law (automatic document analysis and auditing), media (AI-based news curation and summaries), and even software development (early development phase and debugging).

On the litigation side, natural language processing (text analytics) can summarize thousands of pages of legal documents within minutes — a task that might take a human counterpart several days to complete — while reducing the probability of error.

Europe-based Pinsent Masons, ROSS Intelligence backed by IBM Watson, and several other legal tech AI startups that were applying the concepts to contract reviews, dispute resolution, research assistants etc., witnessed a lot of activity last year. Some more examples of such companies include Casetext, LawGeex, Text IQ

There are several companies also applying AI based concepts to wealth management and financial planning. Private investors have poured over $1.9B into robo-advisors since 2013, spanning 170+ deals and several markets.

Robo-advisors essentially use a combination of algorithms to make risk-adjusted portfolio allocations and machine learning to learn from investor’s behavior to automatically tailor the portfolios during market swings. By automating the human advisor, robo-advisors have significantly lower overhead, and have disrupted the industry by lowering fees and in some cases even eliminating them.

What are the jobs of the future?

A few years ago, Peter Lee, a vice president inside Microsoft Research, said that the cost of acquiring a top AI researcher was comparable to the cost of signing a quarterback in the NFL.” — Wired

The demand for AI talent will continue to grow as AI is integrated into every industry. With big corporations like Google, Amazon, Apple, and Intel increasing their focus on AI initiatives, and while losing top employees competing startups, there is a heated race to grab the best out there.

As mundane jobs are automated, market competition for creative intelligence and the arts will increase. Jobs in industries involving complex human interactions and high emotional intelligence, such as healthcare and education, will be least susceptible to automation.

Summary

The advances in robotics is taking automation well beyond heavy industrial and manufacturing processes into white collar sectors. Startups are automating tasks in marketing, legal, HR, real estate, and financial services. Companies focused on AI software are removing the safety net from many white-collar jobs that were traditionally considered safe from automation.

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