Artificial intelligence has moved decisively from promise to presence in India. Nowhere was this more visible than India AI Impact Summit 2026 in Delhi, an expansive convergence of global technology leaders, consulting firms, startups, policymakers, and investors.
Spending a full day navigating the expo halls offered a revealing snapshot of how India is adopting, building, and positioning itself in the AI era. The experience surfaced both encouraging momentum and important strategic questions for our ecosystem.
The Centre of Gravity: Platforms First, Applications Everywhere
The summit floor reflected a clear structural pattern.
At the core were large technology platforms—Google, Meta, Amazon, Reliance Jio, Adani Group, Tata Group demonstrating AI capabilities across industries at scale. Surrounding them were global consulting firms such as EY and Deloitte, embedding AI into enterprise functions like finance, compliance, HR, and operations.
Beyond this layer was a vast and energetic startup presence, largely focused on applying AI to specific sectors education, healthcare, financial processing, fraud detection, and customer experience.
The message was unmistakable: AI in India is no longer experimental; it is operational.
The Application Surge and Its Limits
Conversations with founders revealed a striking reality: there is no shortage of Indian startups building AI applications. Most leverage a small set of global large language models and focus on vertical use cases.
However, within domains, product approaches often look similar – interfaces converge, workflows overlap, and differentiation remains early. This is natural in the first wave of a technology cycle. Yet it also signals an emerging structural challenge.
As foundational models become more capable, more accurate, less prone to hallucination, easier to integrate, the difficulty of building useful applications falls dramatically. What was once technically hard becomes accessible to many.
The application layer, in other words, risks commoditisation.
When Capability Spreads, Advantage Shrinks
The pavilions of large platform companies made this dynamic visible. With proprietary or multi-model backends, they can rapidly assemble dozens of AI applications across industries.
When the same underlying intelligence is widely available, competitive advantage shifts away from AI itself toward distribution, data, integration, and domain depth.
For startups and investors this has implications.
Application-only AI businesses may increasingly resemble probability bets rather than defensible franchises, unless anchored in deeper moats.
The Economics of Abundance
A common narrative suggests AI will create an unprecedented wave of new wealth and entrepreneurship. This may well occur. But economics remains unchanged: value depends on both demand and supply.
AI dramatically increases the supply of builders. Barriers to creating software intelligence have fallen.
As more teams pursue similar problems:
- competition intensifies
- pricing compresses
- differentiation narrows
History shows that in such environments, large platforms often bundle new capabilities into existing ecosystems, reshaping value capture. Early signs of this pattern are already visible in AI.
Builder Reality Check: Unit Economics Still Matter
Amid the excitement, a practical gap surfaced across many startup conversations: limited clarity on token-level cost economics – the fundamental unit of LLM usage.
At scale, token inefficiency directly affects:
- margins
- pricing power
- deployment viability
Many founders could articulate product value but not token optimisation strategy. As enterprise AI adoption grows, this may become as consequential as cloud cost architecture was in the SaaS era.
In a world where model access is abundant, efficiency – not novelty – may become a primary differentiator.
India’s Strategic Question: Are We Building Deep Enough?
Perhaps the most important reflection from the summit is not about what India is building, but what it is not building at scale.
There were relatively few Indian companies visibly working on:
- foundational model innovation
- advanced neural architectures
- hallucination reduction and reasoning depth
- AI hardware and compute optimisation
- core infrastructure for large-scale deployment
India produces world-class AI researchers and engineers who contribute to global breakthroughs. The open question is whether sufficient domestic capital, patience, and ambition exist to build such depth locally.
The risk is not lack of talent, it is concentration of effort in the easiest layer of the stack.
Where Sustainable AI Value May Emerge
The summit ultimately reinforced a constructive but nuanced view of India’s AI trajectory.
- AI adoption is accelerating rapidly across sectors.
- Entrepreneurial energy is abundant.
- Use cases are proliferating.
Yet durable value may emerge less from selling AI itself and more from:
- deep domain platforms powered by AI
- infrastructure and tooling that enables AI adoption
- hardware and efficiency breakthroughs
- integrated systems where AI is embedded, not marketed
In such models, AI becomes an enabler of value rather than the value proposition alone.
Implications for Investment: The Auxano Lens
From an investment standpoint, these observations sharpen an evolving conviction at Auxano.
As foundational AI capabilities improve and diffuse, the standalone application layer is likely to face structural commoditisation. Sustainable venture outcomes may therefore lie beyond pure-play AI applications, particularly those dependent on third-party models without defensible data, distribution, or infrastructure moats.
Our emerging focus is toward opportunities where:
- AI is embedded within real-economy systems and workflows
- domain depth creates structural defensibility
- technology integration, not model access, drives advantage
- AI acts as an amplifier of value rather than the product itself
In this framing, the question is no longer “Is this an AI company?”
It becomes “Does AI make this business structurally stronger and harder to replicate?”
A Tailwind—and a Test
AI is unquestionably a historic tailwind for India’s innovation economy. But tailwinds amplify both strong and fragile models.
The next phase of India’s AI journey may depend on whether we move beyond applying intelligence toward advancing it, whether we choose depth alongside breadth.
The opportunity is immense.
So is the strategic choice before us.
Author,
Karan Gupta
