Investor and NJF Holdings founder Nicole Junkermann believes the future of artificial intelligence will be shaped less by who has the biggest models and more by who builds systems people are willing to trust. In her view, India’s digital public infrastructure, scale and institutional learning from the first wave of platform technology could give it a distinct long-term advantage in the global AI race.
The global contest in artificial intelligence is often described in muscular terms: compute power, capital deployment, model sophistication and access to elite engineering talent. It is a framing that naturally places the United States and China at the centre of the conversation. One has the world’s deepest capital pools and frontier labs; the other has scale, state support and formidable manufacturing muscle. On paper, it can look like everyone else is playing catch-up.
But Nicole Junkermann, founder of NJF Holdings and an early investor in AI and deep-tech companies, believes that reading of the AI race is incomplete. In her view, the next phase of the industry will not be won by capability alone. It will be decided by something harder to build, harder to replicate and ultimately more valuable: trust.
That distinction lies at the heart of Junkermann’s argument for India. While most commentary around AI leadership focuses on semiconductors, research budgets and model performance, she believes India’s real advantage may lie elsewhere—in its ability to build digital systems that large, diverse populations are actually willing to adopt and rely on.
From capability race to trust race
For the past few years, the AI economy has been dominated by a capability narrative. Which company has the most powerful model? Which country can fund the next generation of compute infrastructure? Which market can attract and retain the best AI scientists? Those questions still matter, Junkermann argues, but they are no longer sufficient.
“Capability in AI is becoming less scarce. The models are getting better everywhere, faster than anyone expected. What isn’t becoming less scarce is trust,” she says. “People and institutions choosing to rely on AI systems, to integrate them into decisions that matter, to build their lives and businesses around them—that’s the harder thing to build. And it turns out India has some significant structural advantages in building it.”
It is an argument that shifts the lens from laboratory performance to real-world adoption. AI can be technically brilliant, but if people do not trust it in contexts that matter—healthcare, finance, education, public services, legal systems—its commercial and social impact remains constrained. In those sectors, adoption is not driven by novelty. It is driven by confidence, transparency and a sense that the system works fairly and reliably.
That, Junkermann suggests, is where the next big contest will be fought.
Why trust matters more than hype
Junkermann’s view is informed by years of investing across technology, life sciences and human behaviour. Through NJF Capital, she has backed companies operating at the frontier of innovation, and through Gameday by NJF Holdings, she has explored how systems create engagement, loyalty and long-term value. The common thread across those investments is a framework she describes as “The Human Code”—the belief that the most durable businesses are built not merely by capturing attention or exploiting efficiency, but by strengthening trust.
In an AI economy, that principle becomes even more consequential. Systems that are embedded into sensitive decisions—whether they are recommending treatments, evaluating creditworthiness, shaping educational pathways or assisting in public service delivery—cannot rely on technical performance alone. They must also be understandable, accountable and dependable.
Junkermann draws an unexpected but telling comparison with sport, an industry she knows well through her investment platform. “Sport is the only commercial system that has built mass trust across cultures, income levels and generations without a regulatory mandate,” she notes. “It does that because the architecture is transparent: the rules are public, the outcomes are unscripted, the consequences of violations are real. AI systems that want to earn the same kind of trust need to think about architecture in the same way. Not compliance. Architecture.”
It is a striking line because it reframes trust not as a communications exercise or a legal requirement, but as a design principle. In other words, the AI systems that endure will not be the ones that merely patch governance onto a finished product. They will be the ones built from the outset with transparency, accountability and user confidence embedded into their structure.
Why India looks different
This is where India enters Junkermann’s thesis in a meaningful way. She argues that India occupies a rare strategic position: it is building its AI future at a moment when the failures and blind spots of the first technology wave are already visible. The West built large-scale digital platforms before issues such as algorithmic bias, data privacy, platform concentration and accountability were fully understood in public discourse. India, by contrast, has the benefit of hindsight.
It also has something many countries do not: a proven track record in building digital public infrastructure that has earned trust at enormous scale.
The obvious examples are UPI and Aadhaar—systems that are often discussed in terms of technical scale, but which Junkermann sees as trust achievements as much as engineering ones. UPI has normalised digital payments across income groups, geographies and generations. Aadhaar, despite debates around privacy and governance, created a digital identity infrastructure of extraordinary reach and utility. What matters in her view is not that these systems were perfect, but that they achieved broad adoption in a country as complex and diverse as India.
“What India has already proved with payments and identity is that it can build digital infrastructure that earns trust across an enormously diverse population. That’s not a small thing. Most countries haven’t done it,” she says. “The question is whether the same instinct gets applied to AI, and I think the conditions are there for it to happen.”
That instinct—building for scale, accessibility and mass usability—could be decisive. India is not approaching AI as a niche enterprise technology layered on top of an already saturated digital economy. It is approaching it at a time when digital adoption is deepening, public infrastructure is maturing and the demand for practical, population-scale applications is rising across sectors.
The investor’s lens: where long-term value will be created
For Junkermann, this is not just a philosophical point about the future of technology; it is an investment thesis. Companies that build trust into their AI architecture from the beginning, she argues, will enjoy lower friction in adoption, stronger retention, healthier regulatory relationships and ultimately more durable economics than those that treat trust as a compliance headache to be managed later.
That is particularly relevant in a market like India, where scale is enormous but diversity of users, languages, use cases and economic realities makes trust even more critical. If India can produce AI companies that combine engineering talent with trust-oriented design, it may not just participate in the AI race; it could shape the terms on which the next phase of that race is run.
And that is perhaps Junkermann’s central point. The future leaders of AI will not simply be the markets that built the most powerful systems. They will be the ones that built systems people chose to trust, institutions felt safe adopting, and societies were willing to integrate into everyday life.
By that measure, India may have more than a fighting chance. It may have a structural edge.
