What does India need to do to catch up in the AI race?

**SPEAKER_1** (0:01)
Hello folks, you're tuned in Finshots Daily. In today's episode, we explained why the AI race is not just about building the smartest model.
Before we begin, here's a quick word from Team Ditto.
Life can be unpredictable, and if the main earner is not around, the bills won't stop. That's why term insurance is so important. It gives your family a fixed payout, so they can cover school fees, EMIs and daily expenses without stress.
If you buy it early, you can get a one-crore cover for as little as thousand rupees a month, and the premium stays the same for your entire policy term. That's real peace of mind at a very small cost. And if you're not sure which plan is right for you, book a free call with Ditto. No spam, just honest guidance. And we're trusted by over 8 lakh people for their health and term insurance needs.
Now, back to the story.
Last week, NVIDIA unveiled the RTX Park, a new generation of AI chips designed for laptops with up to one petaflop of AI compute. In simple terms, NVIDIA is trying to build serious AI capability out of the data center and onto a developer's desk. With 128GB of unified memory, these systems can run some AI workloads locally, including prototyping and fine-tuning large models. So, the company at the center of the AI revolution is now trying to make powerful AI infrastructure feel almost personal.
And yet, as impressive as NVIDIA's breakthroughs are, they reveal an even more fascinating story. While NVIDIA gets the headlines, there is another player powering almost every major AI breakthrough on the planet. It's not OpenAI, Anthropic, or Google. In fact, it isn't even an AI company, but a small island nation off the coast of China with a population smaller than that of Mumbai. We're talking about Taiwan.
Now, Taiwan didn't bulge at GPT and doesn't even have a single major AI model. Yet, without Taiwan, the entire AI revolution would come to a grinding halt. And that makes you ask, if Taiwan becomes indispensable without building the world's best AI models, what exactly does India need to do to catch up in the AI race? To understand this, let's first discuss what they did right. Taiwan occupies a strange position in the global technology industry. Most people would struggle to name a Taiwanese software company, but if Taiwan's chip industry stopped functioning tomorrow, the AI world would be in a serious trouble. Taiwan's genius was that it did not chase the most glamorous layer of technology, but captured the layer everyone else had to depend on. Chips. The world's most advanced AI chips are designed by NVIDIA, yet they are manufactured thousands of kilometers away by Taiwan Semiconductors, a manufacturing company better known as TSMC, and TSMC is not just another chip maker. In Q424, it controlled about 67% of global foundry revenue. More importantly, it dominates the advanced chip making capacity that companies like NVIDIA depend on. That's what makes Taiwan's story so interesting. Sometimes, it's the country that controls the invisible layer without which the entire system just cannot function. And that brings us to India. Whenever discussions about India's AI ambitions emerge, the conversation usually starts with talent, startups, digital infrastructure, and population scale. These are real strengths not out. India has a large engineering base, a growing startup ecosystem, and public digital rails that already touch hundreds of millions of people. Yet, despite all these strengths, India has not produced a frontier AI model capable of competing with GPT, Cloud, Gemini, or DeepSeek. The instinctive response is to conclude that India is falling behind, but that conclusion only makes sense if we assume the goal is to replicate what countries like the US or China are doing. The reality is that AI resembles a supply chain far more than a single product. At the bottom sits energy, above that sits chips, then comes infrastructure such as data centers, cloud platforms, networking, and compute clusters. Only after that do we arrive at models, and finally at the top, set applications, the tools that businesses and consumers actually use. In this way, the AI race starts to look very different. No country dominates every layer equally. Taiwan is indispensable in chips. China is strong in energy and increasingly competitive in models. The US still leads in frontier models and consumer-facing AI applications. Each country has found a layer where it can build real leverage. This is why India does not need to think of AI race only as contestable and extradited. Smarter question is, which layer of AI supply chain can India become unusually good at? Look, India's position across these layers is uneven. The country trails the US and China in advanced semiconductor manufacturing. It rails them in frontier model research and large-scale computer infrastructure, but it possesses something that many other countries do not, an extraordinary ability to deploy technology at scale. So you could say that India's strongest advantage may be deployment. Over the last 15 years, India has shown that it can take complex social systems and push them across a massive population. UPI and Aadhaar are the strongest examples.