India, Dec. 15 -- The Big Artificial Intelligence (AI) hype machine is running in overdrive. The US and China keep unveiling "revolutionary" new models that supposedly think like humans. Each one is bigger, costlier, and noisier than the last, while burning enough electricity to light a city. The machines keep growing, but the progress keeps shrinking. India has become a casualty of the hype, dazzled by OpenAI's trillion-dollar dreams. Every policy discussion now seems to revolve around building an Indian ChatGPT. The reflex is to spend, as if leadership in AI can be bought with chips and data centres. It simply can't. The reality is that progress will come from solving real problems, not from joining the race to build the next giant model. The AI transforming business today has little to do with Silicon Valley's fantasies. The real breakthroughs are coming from smaller, simpler systems that analyse data, spot patterns, and deliver results. The fact is that large language models (LLMs) that are driving this global race have become the bonfires of modern computing. They consume vast amounts of hardware, energy, and money to produce sentences that sound good but are often gibberish. They hallucinate, hide their reasoning, and make disastrous mistakes. Companies that actually use AI, instead of announcing it in press releases, already understand this. They are moving to fine-tuned, open-source models that do specific jobs and run on everyday hardware for a fraction of the cost. That is exactly what my team at Vionix Biosciences figured out. We don't build chatbots or virtual assistants. We build AI that reads the faint light signatures of matter, the optical emission spectra of metals, molecules, and biological samples, to detect contaminants, disease markers, or chemical changes. Our breakthroughs come from physics and chemistry meeting math and computation, a blend of deep science and real-world engineering. This is far from the hype of generative AI. Our models learn from measured data, not scraped text. They see what is physically there and avoid speculation. We run them on NVIDIA A100 and mid-range GPUs that cost a few thousand dollars. The clusters used for large language models can cost millions, while affordable processors give us everything we need and allow us to analyse data continuously. This is where the real value and magic of AI truly lie - in science, mathematics, and data analysis, not in the hype and noise coming out of Silicon Valley. And this is the type of AI development that India needs to focus on. Our chips are hosted on Ola's Krutrim, one of India's leading AI platforms. Systems like this are built for what companies actually need: Secure, efficient, affordable computing. Krutrim and its competitors can become the backbone of India's scientific and industrial AI revolution, a local alternative to the GPU arms race in the West. Further, every result our system produces can be traced back to the light spectrum that created it. In business and science, traceability is everything. If an AI approves a loan, flags a transaction, or detects cancer, we must know why. Without that clarity, the output isn't intelligence; it's blind automation. That is the fundamental defect of today's large language models. They mimic intelligence the way parrots mimic speech, producing sentences that sound convincing but have no grasp of meaning or truth. When they err, there is no way to audit or retrace the steps that produced the output. Romesh Wadhwani, one of Silicon Valley's most accomplished entrepreneurs, made the same point in a recent opinion article in a newspaper. He called it "a losing game" for India to try building its own versions of OpenAI or Anthropic. "India should instead focus on the next wave of small reasoning models, compact and purpose-built systems trained on local data for government, business, and consumer use," he said. "It will allow the country to lead in applied AI rather than chase the capital-intensive race of large language models." The good news for India is that this is exactly what is happening on the ground, outside the venture capital and policy echo chambers, as I have seen firsthand. Institutes such as the IITs, IISc, and BITS are still producing hybrid minds fluent in math, code, and machinery. They move easily between the lab and the laptop, blending theory with engineering in ways that make innovation tangible. This mix of curiosity and technical depth is what gives India its edge. The next generation of AI will be built this way - not in research papers or massive data centres, but in universities and startups that combine science with purpose. So, instead of pouring public money into another language model, India should focus on building a foundation for scientific and industrial AI: Shared data sets, university-startup partnerships, and hardware suited to business needs. As well, India's semiconductor mission should not chase NVIDIA's high-end GPUs. It should design purpose-built chips for smaller, task-specific models, processors that are cheaper, use less power, and can be manufactured in India at scale. That would give its industry a real technological edge. The tech giants can keep chasing size and headlines, but the real breakthroughs will come from applications that combine science with practicality and frugality and jugaad. This is the real AI opportunity for India-and the world....