Should India’s software-services behemoths have climbed aboard the artificial intelligence (AI) bandwagon earlier, sinking their capital into large language models (LLMs) and the vast data centre infrastructure required to power them?

That question has recently animated a broader debate over whether Indian IT firms, sitting on mountains of cash accumulated over decades, missed a generational technological shift.

Inevitably, this hand-wringing extends into a familiar lament: Why do Indian companies continually fail to invest sufficiently in research and development?

The autopsies offer some interesting takeaways. India’s Chief Economic Advisor, V Anantha Nageswaran, recently noted that the deficit stems from a large, captive domestic market, a historical leaning toward trading and arbitrage, early financialisation, and third-generation business families losing their entrepreneurial steam.

Veteran investor Shankar Sharma pointed the finger at India’s capital markets in an article written last year.

“This giant machine called the stock market does efficient capital allocation. And hence, it efficiently allocates capital to businesses with high valuation potential. No manufacturing or core research business can ever match up to services in terms of returns on equity and predictability.”

In short, stock markets recoil at risky overseas ventures. Management teams understand this and shape their strategies to satisfy market demands. National technological goals, consequently, take a backseat.

Contrasting India with the United States, Sharma argued that stock-market booms in both nations have bred long-term uncompetitiveness, hollowing out core research and manufacturing.

The harsh reality is that only the state possesses the patient capital required for long-term research. Both democracies, he noted, have starved research in favor of democratic populism.

The Mandate Of The IT Giants

Today, Tata Consultancy Services (TCS), Wipro, and Infosys are sitting on roughly $5 billion each, give or take.

Should these firms have bet their treasuries on AI when the first signs of the opportunity appeared? Did they even spot it? And if they did, what could they have realistically done differently?

Unfortunately, backward-looking, alternate-history debates about what Indian IT companies ought to have done are mostly a waste of time.

In the 2000s, these firms were toasted for creating a whole new class of jobs running into millions eventually, building a whole new industry and democratising wealth.

Consider the famous story of founder and then-Infosys Chairman NR Narayana Murthy’s chauffeur, Kannan, who became a millionaire by virtue of his employee stock options.

In the late 1990s, Murthy declared his ambition to create a thousand millionaires within Infosys by the year 2000. He succeeded, a monumental triumph in a country where "wealth creation" was still viewed with deep suspicion.

Distributed ownership, an alien concept to many traditional business families at the time, became an undeniable force which became part of the compensation and ownership lexicon subsequently.

Since the initial Y2K boom, when Indian coders rescued global computing systems from midnight collapse, these IT firms, now part of a $315 billion industry, have navigated several perilous transitions, including the pivot to digital transformation.

The latest battle with AI is undoubtedly their most challenging, possessing genuine destructive capability. The IT majors argue they are still needed, albeit in leaner numbers, to operationalise and manage AI within large global enterprises.

Skeptics, however, believe this run cannot last.

Yet, while Indian IT giants have arguably eschewed mega-bets on AI, so too have their global counterparts. Accenture and Capgemini are prime examples of software-services majors that have not embarked on mega AI bets.

Because all are doing precisely what their shareholders, boards, and leadership mandate them to do: deliver high-margin software services, capital appreciation, and steady growth.

Broadly speaking, they have executed this playbook flawlessly for more than three decades.

Could they have seen the writing on the wall and destroyed their existing businesses to build anew, akin to Jack Welch at GE?

Perhaps.

But Welch’s track record in the rearview mirror looks far less stellar today than it did at his peak.

The larger question is where true innovation originates.

Does it spring from within and existing large enterprises?

Occasionally.

One can point to Nokia’s century-long evolution, or the transformations of Nintendo which used to produce playing cards, American Express, and IBM.

India, too, has seen trading houses and textile mills evolve into multifaceted conglomerates like the Aditya Birla Group, Reliance, and the Tatas.

But betting on R&D in the context of AI is a fundamentally different wager.

Beyond the LLM Mirage

When Ratan Tata launched the Indica in 1998, it was a major departure for a truck maker, driven by a conviction that India needed an indigenous passenger car.

Similarly, two-wheeler manufacturers like Hero, Bajaj, and TVS leaned on R&D to stretch every possible kilometer out of a liter of petrol in the 1990s.

These are good examples of innovation, but in perfect hindsight, they remain incremental.

Could India's new generation of tech entrepreneurs have been the deep-tech innovators of this century?

The evidence is not encouraging. The most high-profile founders, including some who have since decamped with investor capital, built businesses in edtech, a sector whose very existence is now routinely questioned.

Others are burning vast sums of venture capital on rapid pizza and shampoo delivery.

In these arenas, VCs simply replaced the stock market in dictating capital allocation.

While there are certainly determined entrepreneurs building admirable companies in space tech and defense, they do not yet approach the scale, magnitude, or audacity of what newly minted trillionaire Elon Musk is executing at SpaceX.

The only pertinent question perhaps remaining is whether India is cultivating the right educational ecosystem.

Are the country's future innovators growing up to think differently, ask difficult questions, and target massive structural problems?

Because India has just as much to gain from solving the complexities of indigenous drug discovery and scaling high-quality education for its burgeoning youth as it does from a proprietary LLM bet that may or may not materialise.

Looking at the current state of affairs, plagued by chronically leaked examination papers and an education system structured like a winner-take-all lottery, it is painfully clear that the country has far more pressing work to do.