A common sensical take on Generative AI: For what’s it worth!

Spoiler Alert: Expecting a geeky article to understand the complexities of GenAI, this piece is not for you.

Almost blasphemous, not to mention Gen AI in office conversations, meetings, or formal gatherings these days. At the peril of being branded novice or naive, one may choose to have no opinion.  Most organizations though have included Gen AI in their strategic priorities, but nuances and finer details have conveniently been left for some other day.

While I am not Sam Altman or even remotely qualified to talk about large learning models, deep learning or machine learning techniques that power Generative Artificial Intelligence (Gen AI), can certainly try sharing an end user perspective. Had never been so intrigued by any of the technical breakthroughs that I saw in my lifetime as I am now with Gen AI. At the risk of oversimplifying, Gen AI is meant for data analysis, pattern recognition and rendering meaningful information in no time besides we may not have yet fathomed its full potential and repercussions. How many times have we used search engines like Google to compile information from multiple sources to arrive at a logical conclusion or prepare a document. Answer of course is quite frequent but we also wished if someone else could help us with the search job, do analysis and present meaningful insights. That’s to me what Gen AI has done for us.

You may have guessed it by now. Being from Gen X and having witnessed quite a few technological innovations, I once again am at the crossroads though not overly paranoid over the doomsday prophecy that machines will render humans obsolete or deep fakes will steal our identities. The way students, teachers, professionals, researchers have seamlessly integrated Gen AI in their day to day working as they probably did with Google, Insta, LinkedIn etc. does not sound like a death knell for mankind. Shift has been rather swift and seamless. Then where do all the concerns about Gen AI taking over human jobs resulting in massive job losses, Human becoming obsolete, identify thefts, increase in crime emanating from? Do we have any data attributing job losses directly to Gen AI? If yes, what type of jobs were those? How many serious crimes are directly attributed to Gen AI? How much R&D work got reduced due to Gen AI? So, on so forth…all these are the valid concerns but not as catastrophic as they may sound.

Let’s go back to a few of the significant events that can help understand this fear psychosis.

Mass computerization in 80s and 90s:

80s and 90s witnessed massive computerization across the industries and I was fortunate enough to live that journey in a bank. The scenario was quite similar in other industries too that had undertaken this journey. Back then Banking was highly labour intensive that entailed reconciling various ledgers daily, weekly, monthly quarterly and annually very much like a military regimen. Employees would spend nearly 80 percent of the time writing and reconciling ledgers and it was feared that with computerization employees would be left with no work. Massive protests by unions, political class about the potential ramifications of computerization on jobs, humans becoming redundant, increase in crimes erupted but transition was rather smooth both at the human and technology level. Working simultaneously on the computers for the data entry work and updating manual ledgers in parallel (which I realized in my later years in the IT world is called the parallel run.), we were hardly worried about layoffs and job loss. In fact, banks grew anywhere between 20X to 100X over the years both in terms of balance sheet and employee strength.

Internet revolution in 2000s

Be it getting our first email id or chatting on yahoo chat or googling random information, was the stuff we had imagined only in Sci fi movies. Every day felt like a fantasy in the beginning but slowly it pretty much got normalized. Back then there were also concerns about job losses for support staff like typists, stenos, assistants, and the likes. Even researchers, scholars, or historians, who used to spend hours together in libraries to get the required information will be out of job. Yet another school of thought was people will be using internet for all the wrong reasons. Reality however is that MIS has grown much bigger, researchers and scholars have started producing quality contents much faster, physical and Digital libraries co-exist today. Alternate jobs emerged and many found livelihood in this new ecosystem.

Cloud adoption in 2010s

Concerns about cyber security, monopolies and data thefts were some of the initial concerns of the industry and quite genuinely too. But that did not hinder cloud adoption and a fundamental shift in the way we operate. With exponential growth in data and focus on data centricity, not having a cloud strategy is almost unthinkable and would put many organisations out of business if they did not have cloud strategy in place.

What we are observing today with Gen AI is the same paranoia. The biggest lesson that we can learn from all the previous technological breakthroughs is that we cannot undermine the human mind and its capability. If anyone believed that an engineer’s job is to write a code, Teacher’s job is to teach printed books, writers’ job is to compile words, Doctors’ job is just to prescribe medicines then no invention would have happened. Every time it appears that we have reached the zenith of human intelligence, a new breakthrough surprise us all. Every major innovation has the potential to catapult us to the next league where human productivity increases exponentially and so does the pace of innovation.

Gen AI is essentially enabling humans to analyse zettabytes of data (that humans have already created) by leveraging techniques like LLM, ML, deep learning, and few others to generate meaningful insights and patterns much quicker and intelligently. So, the question is to be asked, who is worried about job losses from Gen AI? The ones who used google to the hilt to feign as experts, an entitled class of managers who measure their success by the number of people they manage or the ones who see Gen AI yet another tool to delegate their work. Well, that’s the topic for another day.