While discussions around India’s AI future often focus on job risks, machines replacing humans, and a world increasingly shaped by algorithms, a more fundamental shift is underway beneath the noise in India. Startups such as Futurense, a Bengaluru-based fast-growing AI skilling and talent company, are working to build a new, AI-enabled education system aimed at nurturing India’s engineering talent for the future. Raghav Gupta, Founder & CEO of Futurense and Co-Founder of 1% Club, in an exclusive conversation with Fortune India, explains why the company is betting on AI education for engineers, how demand for AI talent is evolving, and how an AI-literate youth could reshape India’s education ecosystem, talent pipeline, and economic incentives. Edited exceprts:

Q1: You’re trying to fix a gap. Is the issue a shortage of AI engineers in India, or a shortage of engineers who can deploy AI inside companies? 

Raghav Gupta: From an India standpoint, we are on the application side. We focus on productionising AI, taking it to enterprise-grade deployment. In that sense, an AI engineer is not very different from a good engineer. They are solving core business problems, which today are largely about AI deployment. You’re right: it’s not about AI engineers specifically. It’s about good engineers who understand system-level thinking, product thinking, and can communicate effectively with stakeholders and execute.

I was studying in the US at Emory University in Atlanta in 2019, when Google began publishing work on Transformers, the technology behind ChatGPT. Even then, AI was gaining traction in academic and data science circles. It was clear a new technology wave was coming: one that would challenge many existing jobs in India’s IT industry. I had read an article in 2017 explaining that urban consumption in cities like Delhi, Bengaluru, Pune, and Hyderabad is driven by FDI through IT. Property prices, consumption, everything is funnelled through money coming from abroad.

Q2: So if this industry, built not on knowledge arbitrage but labour arbitrage, changes fundamentally, what happens next?

Raghav Gupta: Humans will never be out of business. They will find ways to remain relevant. That’s where data science, data engineering, and AI/ML became the next frontier of job growth. The US has a massive STEM talent shortage. People there don’t pursue maths or science in large numbers. So new job opportunities will emerge. Maybe not as many as back-office IT jobs, but economically significant.

Earlier, 10 people were employed. Now maybe three will be, but they’ll earn what ten people earned earlier. It’s a winner-takes-all model. That’s the opportunity we saw in 2020. Back then, we called it data engineering. When ChatGPT arrived, AI became mainstream. The core remained the same; the branding changed.

Q3: You interact with companies and universities. What does your student demographic look like, and what are companies really complaining about when hiring AI talent?

Raghav Gupta: There are two sides. About 70% of our business is lateral talent, and within that, 95% is AI: data, cloud, data engineering, data science. AI is the umbrella term. In our undergraduate segment (18-22), UGC norms require teaching physics, chemistry, and basic math in the first two years.  Overall, more than 80% of our portfolio is AI, data science, data engineering, and cloud. Most are graduates working in TCS, Infosys, Wipro, Cognizant, people unhappy with where their careers are going. 

In terms of hiring talent, there are three major gaps. First, people know tools but not systems. Companies don’t want tool users; they want system builders. Second, communication and articulation. In the age of ChatGPT, asking the right questions matters. Many engineers can’t communicate effectively, in English or even Hindi. This is a major bottleneck. Third, AI can hide mediocrity. People use buzzwords, perform well in interviews, but lack fundamentals. Mediocrity gets amplified with AI. The basics, math and logic, are missing.

Q4: How did you define your curriculum and decide where to double down? What’s the hardest part of pushing real-world AI into traditional academic systems?

Raghav Gupta: We have a 'Futurense Leadership Council' with around 65leaders from global tech companies. They continuously tell us what’s happening in industry. Academia can’t keep up with how fast things change. This council has been a game changer. Traditional academicians resist tools. They oppose using AI tools, just as people once resisted computers. That creates friction.

Second, many believe AI is just a buzzword or a bubble. A stock bubble and a technology shift are different. Third, fear and resistance to change. Someone who’s researched for 30 years feels threatened when a younger person can articulate similar ideas using AI.

Q5: Tell me about your AI-native B.Tech course. How do you attract and train quality faculty? How are you different from other ed-tech companies?

Raghav Gupta: With four years, you can mould a person’s mindset. Postgraduate programs don’t allow that. AI-native doesn’t mean more technology, it means focusing on what AI won’t replace: EQ, articulation, teamwork, problem-solving, and human connection. We start this from day one. We also don’t follow traditional UGC sequencing. We start directly with data science because tools allow us to teach faster.

Many professionals who entered IT 15-30 years ago are burnt out. They’ve made money and now want meaningful work. These industry veterans transitioning into academia are our strongest faculty base.

On differential with others, first, most edtech companies are marketing companies, not education companies. My family has been in education for 40 years. Second, our IIT partnerships. Institutions trust us. Third, real engagement with industry leaders through our Leadership Council. We spend 80% of our money on education, not marketing. We’ve never raised money and are profitable. We were doing AI before it became fashionable.