At the heart of generative AI are large language models (LLMs), which process massive amounts of information to form what Gupte described as a kind of “artificial quasi-brain”
In a candid and insightful conversation with BW Security World, Sameet Gupte, CEO of EvoluteIQ, demystified the buzz around generative AI, breaking it down in simple terms and offering a clear perspective on its evolution and practical impact.
While Speaking during the conversation , Gupte linked the journey of technology to that of a support system that has always aimed to simplify human life. From solving basic arithmetic to complex equations, the role of computers, he said, has been to help humans do their tasks better.
“The life of technology is pretty interesting,” he said. “The whole existence of computers came in to simplify human life… to solve very complicated problems.”
Gupte explained that over the years, technology has evolved to make humans not only more efficient but also smarter in the way they perform their tasks. Using a journalist’s job as an example, he pointed out how processes—from finding the right person to interview to writing and editing an article—have become more streamlined with the help of digital tools.
“You may have started with a pen and paper. Then came digital recorders, then editing tools, and now you have Teams, Zoom, and transcription software,” he noted. “That’s how technology has been assisting and improving our output.”
According to Gupte, artificial intelligence—especially generative AI—takes this a step further. “Now, processes have started thinking for themselves,” he said. “That’s where the whole AI comes in because there is so much of data coming in.”
He drew an analogy between human learning and how AI learns from data. “How do you know not to touch fire? Because as a child you may have burned your hand once, and you learnt. AI does the same—it learns from data,” he explained.
At the heart of generative AI are large language models (LLMs), which process massive amounts of information to form what Gupte described as a kind of “artificial quasi-brain”. This digital brain is then able to generate responses based on the data it has learned from.
“When you ask a question—what we now call a prompt—you get an output,” he said. “This output is derived from all the data and knowledge available on the internet and elsewhere. That’s what generative AI is.”
However, Gupte was quick to point out that information is not the same as actionable insight. “Wisdom is not useful unless you apply it or unless it tells you what to do,” he said.
He offered a relatable example to illustrate his point: “If you ask how to fly a plane and I tell you, ‘It’s easy once you have a pilot’s licence’, I haven’t really told you how to fly a plane. But if I say, ‘Start the engine, take it to 350 kilometres per hour’, that’s actionable.”
In his view, the true value of generative AI lies in transforming static knowledge into dynamic guidance—moving from just answers to action. As the technology continues to develop, the goal, he implied, should not be to simply marvel at what AI can say, but to see how it can actively support human decisions and tasks in the real world.
By explaining generative AI in such relatable terms, Gupte cuts through the jargon to show that at its core, AI is not magic—it’s the next logical step in technology’s ongoing mission to make life easier and work more intelligent.
