What is the state of early stage AI investments in India and are investors changing the way they evaluate startups and founders amid raging debates on sovereign models, Indian LLMs as a key focus area, beyond GenAI applications and SaaS tools?

More and more investors are factoring in the influence of GenAI and its future potential impact on sectors and industries when evaluating early stage deals. Even two years into the AI revolution, Indian venture capital in AI is concentrated towards startups in the applications and developer tools layer.

Murmurs of sovereign models and made-in-India large language models might yet influence the course of VC dollars flowing into AI this year, but it’s still too early to call this a significant shift.

However, the evolving due diligence models of VCs in the AI space — and even those outside — has reset expectations from early stage founders and startups. And this includes the potential disruption from sovereign models and Indian LLMs in the future.

Early stage fund managers and VCs are digging deeper into the architecture of the AI stack, dependency on APIs, the flexibility and efficiency of AI models, cost overruns and revenue leaks, and whether AI is core to the startup’s product roadmap. While most early stage investments did not carry the full due diligence weight in the past, this is changing, with technical due diligence becoming very critical.

At the same time, investors are reevaluating how they see the team behind the idea or the product, given the need to be highly conversant with the bleeding edge of AI development. Are teams ready for the next big jump, fund managers are asking as they look beyond the founder.

Inflection Point Ventures’ Ankur Mittal told Inc42, “We now evaluate data strategy, model flexibility, and AI-driven defensibility. Startups that capitalise on proprietary data and thoroughly integrate AI into their value offering outperform those who only use AI as an add-on.”

Instead of focussing on whether they have built their AI models from scratch or integrated third-party solutions, the real criterion should be the impact of technology on end users.

“The first question should always be: What problem does it solve, and how does it enhance customer experience? If a task that once took hours can be completed in minutes, that is a game changer,” Kushal Bhagia of All In Capital told us in an interview last month after the firm launched its second early stage fund.

Once the user impact is established, the focus shifts to technical depth and execution. Companies that create highly personalised AI experiences for specific user segments tend to develop more substantial products.

The All In Capital founder cited the example of an AI-powered skilling startup SuperNova, which enables users to speak English through structured interactions, rather than an open-ended course. This directly helps professionals like hotel employees to upskill for their current job, and it’s a more compelling learning experience than Duolingo or something like that.

In a similar vein, investors think that AI that can redefine existing experiences........

© Inc42