Let's forge our own path amid a foundational tug of war over AI

India is likely to do better by focusing on specific AI applications instead of playing catch-up with big foundational models

- PRAYANK SWAROOP

It's no secret that there's a worldwide tug of war for artificial intelligence (AI) supremacy among leading economies. America has the upper hand by default, China is a disruptor par excellence, France has jumped in and India wants a seat at the top table, too.

Amid all of this, there's one aspect that keeps coming up every now and then—foundational models. In February, Ashwini Vaishnaw, the Union information technology minister, said that India will build and showcase its own foundational AI models by the end of this year. Many took this as a knee-jerk reaction to the hype around Chinese upstart DeepSeek's release of open-for-all AI models. But, all of this begs a question: Does India really need to focus its investments and efforts on foundational models?

Tracing the AI story back to OpenAI's 2022 launch of ChatGPT, the world's first mainstream and popular Generative AI application, one can see why foundational models are pivotal. They are incredibly complex in nature and are trained on vast sets of data to make them generally good at nearly everything. But foundational models have a pitfall—they're not really 'excellent' at anything.

Over the years, advancements in AI research have shown us that while foundational models form the backbone of a GenAI applications, their biggest drawback is their lack of excellence in particular subjects. As a result, algorithms would get their data sources jumbled up and create responses that are often hallucinatory in nature.