The AI flight is taking off, and DeepSeek is the final call for India to show up at the boarding gate. Since its private sector is too risk averse to back research projects with uncertain payoffs, the state will have to step up.
The Chinese startup’s artificial intelligence models, which it began offering last month as open-source licenses, have been built at a fraction of the cost of resource-intensive rivals like OpenAI’s ChatGPT or Google’s Gemini. The US tech industry and Wall Street investors are rightly viewing DeepSeek as a disruptor. Silicon Valley firms are putting down hundreds of billion dollars to fend off the challenge. New York state has banned DeepSeek’s AI assistant from government devices.
India is watching the economic and political contest from the sidelines. That’s dangerously complacent. Unlike in manufacturing or transportation, where it has already ceded a large lead to its neighbor, here’s a race that’s still wide open. But unless the most-populous nation puts its globally acknowledged edge in software programming to work, the moment will pass it by.
DeepSeek is threatening the one comparative advantage the country has assiduously worked toward in the 21st century: code-writing on an industrial scale.
“Let the code write itself,” says DeepSeek Coder. The AI-based programming assistant, which has already made a splash in the development community, is bound to become more capable in the weeks and months ahead. Using such tools, a small, highly skilled section of India’s 5 million code-writers will become tremendously more productive. For the majority, though, this will be bad news.
Homegrown models, if they’re cheaper to license than foreign AI, will get deployed faster. A surge in efficiency across industries will absorb the workers displaced from code-writing jobs. Putting shiny new tools in the hands of the 2.5 million-plus graduates and Ph.Ds minted annually in science, technology, engineering and medicine could lead to breakthroughs that end up lifting the economy. This is the link that policymakers are refusing to see. The $1.2 billion that New Delhi committed last year to the India AI Mission is a fraction of a $24 billion subsidy program for manufacturers.
It’s been nearly two years since Sam Altman was asked at a conference if an Indian startup could build a foundational model, trained on vast datasets and capable of multiple applications, for $10 million. The founder of OpenAI said that such an undertaking would be “completely hopeless.”
It’s only natural for Altman to say that — nobody wants their second-largest market by number of users to become a competitor. But what explains the lack of confidence on the Indian side? Even with DeepSeek calling Silicon Valley’s bluff on costs, Indian tech companies are reluctant to take up foundational work in generative AI because success is not guaranteed. There is some investment going into adapting existing models to handle local languages, but foreign alternatives that will offer the same Indian-language capabilities plus a whole lot more will likely overshadow these efforts, according to Nilesh Jasani of GenInnov, a Singapore-based global innovation fund.
China has ramped up its share of top-tier global AI talent to 47%, compared with 18% for America. India’s figure is just 5% because most of its talent ends up migrating, largely to the US. Multinationals like GE Aerospace are doing sophisticated applied research in India using local engineering talent. But there simply isn’t a supporting environment for deep fundamental research, and no urgency to build one.
This is the situation eight years after a team of Google researchers gave shape to the language-processing architecture behind today’s models. “Attention Is All You Need,” was the title of their paper. Clearly, local tech policymakers weren’t very attentive to the technology’s potential, even though two of the eight scientists involved in that pathbreaking project are Indian-born.
If the public sector has been lacking in attention, the private sector has been short on intent. Mukesh Ambani, the country’s richest tycoon, recently announced that he would build the world’s largest data center. He’s buying Nvidia Corp. chips, which will be crucial for training models.
Infrastructure alone won’t be enough. Breakthroughs in natural language processing will come from hundreds of attempts at model-building, each costing a few million dollars. The software outsourcing companies of Bengaluru, India’s Silicon Valley, should have been at the forefront of this initiative because of the threat GenAI poses to their bread-and-butter activity of code-writing for global corporations. Yet they aren’t keen on taking bold bets. Their current business is still rewarding large shareholders with fat dividend checks and share buybacks, with little new investment. There’s limited appetite for moonshots.
The idea that India is the next China — running just about a decade behind it — was popular around 20 years ago. Back then, people wrote books about “Chindia.” The South Korean financial industry even launched funds covering the two most-populous nations as a single theme.
Two decades later, there isn’t a lot left of that illusion of comparability. While India did attend to its basic infrastructure shortages like electricity and roads, China raised the gap between the two economies by lifting its technology game. Its arc of global dominance, which just 10 years ago was limited to a few industries like drones and solar panels, has extended to electric vehicles, high-speed trains, and now, generative AI.
So when Indian tech companies say that they will profit from growing AI adoption by making customized digital assistants for global corporations, they’re ignoring the inevitable progress of GenAI to AGI, or artificial general intelligence. Models that rival human cognitive abilities will handle most programming tasks on their own. Either OpenAI, or DeepSeek, will get there. Or, another model will succeed. For the South Asian country to not become a permanent importer of AI tools, it needs to own foundational technologies, built with massive government support for research universities and institutions.
Above all, tech policymakers have to shake off their defeatist fatalism and heed the challenge facing them. Attention is everything.
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