‍“AI is the new electricity!” Electricity transformed countless industries; AI will now do the same.” –Andrew NG, former Baidu Chief Scientist, Coursera co-founder, and Stanford Adjunct Professor.

I see it every day in my profession as an executive recruiter. There is a near desperate demand for a certain set of people – ML Scientist, Product folks who know how to apply and monetize data, Business leaders who have built new age businesses leveraging AI/ML/IOT. Compensation has gone through the roof, acqui-hires are the rage and companies are trying their best to hold on to these profiles. There is a tremendous shift fueled by the pandemic that is well underway - and at a much faster pace than what happened when businesses first moved online and ecommerce was all the rage. Remember the time web developers were hotshot stars? Start-ups and enterprises are now racing ahead at lightning speed to apply artificial intelligence to all workflows via bots and learning platforms. The introduction of OpenAI's ChatGPT has marked a significant milestone in the development and democratization of AI. Which brings us to this - What will happen once they achieve their goals? Would doctors, lawyers, nurses, security staff and many others lose their jobs? From being the most sought after will they become persona non grata? A question that is haunting everybody who is watching the new technology unfold and develop by leaps and bounds! But dig a bit deeper into the past and you are awash with a déjà vu feeling.

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Just take a look at one of the greatest inventions of mankind – the printing press. Sure, it did put the scribes out of jobs but it ended up spawning the rise of print media and created many more jobs. Or consider when the ATM was introduced back in the 1980s – many predicted that the bank tellers would decline sharply. Instead, the number of bank tellers actually increased as the job requirements shifted to more customer-oriented dealings. It’s a different matter altogether that the internet age that swept in a few decades later has altered the bank teller landscape even further. And so, it is with AI, Machine learning, and IOT – while robots will eventually end up doing much of the repetitive and manual work done by humans today, history has shown us time and again that it doesn’t mean that they will take over the workforce.

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According to a recent McKinsey report*,  AI has the power to greatly accelerate economic automation and by 2030 it could represent 30% of the hours worked in the US economy. It is expected that AI and changing consumer habits will result in a significant shift in employment opportunities across various industries, with an estimated 11.8 million workers required to move into different lines of work by 2030. 

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Amid these changes, the so-called ‘soft’ or social and emotional skills and higher order digital prowess will be in high demand in the coming years. Cultivating these unique human features – such as adaptability, social intelligence, complex problem solving, interpersonal skills, curiosity and empathy – will be the key to survival in an automated world where manual skills will not matter much. At the same time, the growing demand for soft skills is blurring the chasm between the hitherto white-collar and blue-collar jobs – the no-collar workforce of tomorrow represents the middle ground wherein man and machines work seamlessly within a culture of human/machine collaboration.

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Lifelong learning is the key and the AI shift will enhance the STEM, creative, business, and legal professionals' work rather than eliminating a significant number of jobs outright. The demand for STEM jobs is anticipated to rise by 23% by 2030 and multiple online learning platforms are steadily increasing, year on year with machine learning, neural networks and deep learning among their top courses, indicating a clear trend in this direction.

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On a similar paradigm, there is also the possibility of the rise of coaching networks as coined by Gordon Ritter. According to him, these networks are far more effective than the traditional educational system – after all sending millions of people back to ‘school’ is just not a practical solution considering the sheer numbers and size of the problem. Coaching networks can be our best defense in the race against AI and machines. The notion is to use machine learning to guide workers in real time by gathering data from a distributed network of workers and identifying the best techniques/practices required for the respective tasks. 

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The need of the hour is a strong emphasis on the need for the ‘new smart’ – that is to amplify our emotional and cognitive traits that distinguish us from increasingly competent machines. This means lifelong learning and unlearning to prepare ourselves with the necessary skills to stay relevant in the workplace of the future. On that note, let me end by saying the labor force has successfully weathered dramatic changes in just the last few centuries. Humans at the end of the day are the most highly resilient, complex and adaptable machine on this planet and we will find ways to thrive in a world of accelerating technological change.

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