Every semester, I ask my students to look at the U.S. Bureau of Labor Statistics Occupational Classification and think of which jobs can be automated using AI. This serves to inspire them to come up with their own startup venures. Quite often, customer service representatives and data-entry keyers come up in the list.

Indeed, generative AI has plenty of potential to automate many aspects of customer service. And other areas of AI, such as computer vision combined with natural language processing, can take over data entry substantially. AI is changing the nature of work—but that isn’t always bad.

Recently, JPMorgan’s CEO predicted a 3.5-day workweek in the future, owing to AI. The more we get machines to work for us, the more time we get back for ourselves.

Machine learning, which is a branch of AI and increasingly being used interchangeably with it, is about teaching machines to learn to behave like human beings—and even outperform them in certain tasks. I have been teaching the subject to graduate and undergraduate students at San Jose State University for the past five years, using my flipped classroom videos, and motivating them in a number of ways, such as asking them to interview subject matter experts from the industry; ask questions that I use in “Ask Me Anything,” a series with renowned researchers; and implement challenging course projects. The students explore the math behind the methods and design interesting machine learning applications.

Most of us already use machine learning in our day-to-day lives, knowingly or unknowingly. Whenever we converse with Siri or Alexa, unlock our mobile phones using facial recognition or laptops using fingerprints, take recommendations from Netflix, or search the internet, we are using machine learning. In several recent and upcoming inventions such as the self-driving car or generative-AI-based ChatGPT, machine learning has the real potential to replace human beings.

States like California have already passed multiple bills limiting the use of AI. I agree that artificial intelligence poses risks, but the opportunities are too compelling to be ignored.

With automation, economies of scale quickly add up, resulting in accelerated progress. Automation already helps with blue-collar jobs such as the bank teller or dishwasher. Artificial intelligence can help in white-collar jobs with data-based decision-making made possible by machine-learning-based data analytics. In that sense, AI is capable of substituting at least partly for our very expensive human intuition.

Jeetu Patel, chief product officer at Box, said recently that AI will impact half a billion white-collar jobs. Given that machines have some amazing qualities, like tirelessness and lack of other human fallibilities, the prospects of AI are huge. Human intelligence has millions of years of evolution behind it. Many paradigms have not yet been considered in the 50 or 60 years that artificial intelligence has been in existence. It can easily be said that we have hardly scraped the full potential of AI.

However, all the above-discussed advantages of AI will not have the desired effect if they work only to fill the coffers of the C-suite and a few others at higher echelons. If the corporate culture does not practice giving back to its employees, their families, their alma maters, and the society at large, all the accelerated efforts will simply be siphoned off into an endless well of corporate greed. Governments and enterprises should strike a delicate balance between the two.