India's AI Summit Falters as Infrastructure Reality Clashes With Ambition
India's bid to position itself as a global artificial intelligence powerhouse hit a sobering reality check this week when a high-profile AI summit exposed the gap between the country's technological aspirations and its current capabilities, according to attendees and industry observers.
The summit, intended to showcase India as an emerging AI hub capable of rivaling established tech centers, instead highlighted fundamental infrastructure constraints that finance leaders evaluating offshore AI investments have long suspected but rarely seen acknowledged so publicly. For CFOs weighing where to deploy capital for AI development or considering Indian vendors for AI-enabled services, the event offered an unvarnished look at ground-level execution challenges.
The core issue isn't talent—India produces roughly 1.5 million engineering graduates annually and hosts major AI research centers for Google, Microsoft, and other tech giants. The problem is what happens when you try to run AI at scale in a country where power grids strain under load and data center capacity lags far behind demand.
Here's the thing everyone politely dances around in vendor pitches: AI models are absurdly power-hungry. Training a large language model can consume as much electricity as several hundred homes use in a year. Running inference at scale—the actual day-to-day work of an AI system answering queries—requires reliable, uninterrupted power and cooling. India's electrical infrastructure, while improving, wasn't built for this. (Neither was California's, to be fair, but California has fewer scheduled brownouts.)
The summit's struggles reportedly included connectivity issues and demonstrations that didn't quite demonstrate what they were supposed to demonstrate—the kind of technical hiccups that seem minor until you're the CFO who just approved a seven-figure contract based on a proof-of-concept that worked beautifully in controlled conditions.
This matters because India has been actively courting AI investment, positioning itself as a lower-cost alternative to US and European AI development. The pitch makes sense on paper: deep technical talent, English proficiency, established outsourcing relationships, and labor costs that can run 60-70% below comparable US roles. But AI infrastructure isn't just about coders—it's about the physical layer underneath.
The broader pattern here is one finance leaders should recognize: the AI industry is discovering that "move fast and break things" doesn't work when you need 99.9% uptime and your model is making decisions about credit approvals or supply chain logistics. The demo is always better than production. Always.
For companies evaluating Indian AI vendors or considering captive AI centers in India, the summit's difficulties suggest a more nuanced due diligence checklist. It's not enough to verify technical capabilities—you need to understand backup power arrangements, data center redundancy, and what happens when (not if) infrastructure fails. These aren't sexy questions, but they're the ones that determine whether your AI-powered financial close process works in December or whether you're explaining to the board why you missed the filing deadline because of a power outage in Bangalore.
India will likely solve these infrastructure challenges—the country has a track record of building what it needs to compete globally. But "eventually" and "currently" are different states, and CFOs making decisions in 2026 need to operate in the world as it exists today, not as it might exist in three years.
The question isn't whether India can become an AI powerhouse. The question is: what's your Plan B while they're getting there?
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