Artificial Intelligence: Between billion-dollar giants and India’s steady ascent

The global race for Artificial Intelligence (AI) is being driven by a handful of powerful corporations—AMD, Oracle, Intel, Microsoft, OpenAI, and Nvidia. Together they form a multibillion-dollar web of cross-investments, each building the infrastructure and models that power modern AI. Their collaborations and rivalries have created an ecosystem of dazzling capability but dangerous concentration. Around them orbit several smaller firms, each valued in billions, all chasing a share of the AI gold rush.

Yet a new player has challenged this belief that innovation demands astronomical investment. China’s DeepSeek demonstrated that a high-performing AI can be developed at a fraction of the cost of Western systems. Its success, built on efficiency and smart optimization, proved that focus and creativity matter more than funding. The project’s rise has reignited a question: Are we witnessing genuine progress—or the beginnings of another speculative technology bubble?

The present AI boom resembles the dot-com frenzy of the early 2000s and the crypto mania of the 2010s. Start-ups are valued on hype, not profitability; investors chase potential rather than proof. Cloud providers rent GPU clusters at enormous cost, while governments compete for symbolic leadership. Though AI’s promise is real, its over-centralization in a few Western corporations risks creating a “digital oligarchy,” concentrating power over data and discourse in limited hands.

Amid this global frenzy, India has chosen a slower, steadier path—less flashy, but potentially more sustainable. Instead of rushing to replicate Western models, India is building an ecosystem based on distributed growth, linguistic inclusivity, and ethical independence.

Government programmes such as the IndiaAI Mission, Digital India Bhashini, and the National Programme on Artificial Intelligence aim to democratize AI’s benefits. A key focus is on developing Large Language Models (LLMs) that cater to all Indian languages, ensuring access for every citizen. This multilingual approach is vital in a nation where linguistic diversity defines identity. By making AI speak every major Indian tongue, India seeks to close the gap between urban and rural, elite and ordinary.

In parallel, Indian researchers are exploring distributed AGI—a model of artificial general intelligence that relies on shared networks and collective learning rather than centralized data monopolies. This reflects the Indian ethos of Vasudhaiva Kutumbakam—the world as one family—where collaboration replaces competition.

India also recognizes that true AI sovereignty requires control over hardware. Through the India Semiconductor Mission and private efforts by companies like Tata Electronics and Vedanta-Foxconn, the country is laying foundations for domestic chip manufacturing. The objective is not merely self-reliance but resilience—ensuring that India’s AI future is not hostage to global supply disruptions.

Complementing this infrastructure is a vast talent pool of engineers, scientists, and innovators. Indian professionals have long powered global technology; now they are returning that expertise home. Universities such as IITs, IISc, and IIITs are becoming AI research leaders, partnering with global institutions while preserving academic autonomy.

Another strength of India’s approach is its neutral, pluralistic ethos. Many Western LLMs are shaped by political or ideological leanings, often reflecting partisan biases in their training data. India, in contrast, aims to develop AI systems rooted in its composite culture, ensuring fairness and respect for diverse viewpoints. By maintaining independence in data and design, India can avoid importing the cultural polarization that has marked Western AI discourse.

Balancing regulation, innovation, and trust

India understands that laws alone cannot create harmony or trust—a truth as applicable to technology as it is to society. The government’s role is to set ethical standards, promote transparency, and protect data privacy, while letting innovation grow organically. Public–private partnerships are encouraged, but strategic control remains national.

Grassroots initiatives are extending AI’s reach to agriculture, health, and education. Voice-based systems in local languages help farmers access market data, assist patients in rural clinics, and provide students with digital tutors. This bottom-up deployment makes AI a social tool rather than a corporate luxury.

While the Western world races ahead in an expensive contest for dominance, India is moving deliberately—slow, steady, and inclusive. It is building its own AI ecosystem on three pillars: linguistic diversity, ethical neutrality, and indigenous capability. The world’s largest democracy is proving that technological progress need not be partisan, exclusionary, or dependent on foreign capital.

In the end, the success of AI will not be measured by market capitalization but by how deeply it serves humanity. DeepSeek showed that efficiency can rival extravagance; India is showing that purpose can outlast hype.

If this trajectory continues, India’s calm, compartmentalized, and people-centric strategy may well become a model for the world—one where AI empowers society rather than enslaving it to speculation.