The Hidden Variables Behind India's AI Confidence
The social network of religion, tight cultures, and why India's AI trust score keeps rising
I researched levels of trust in AI and sentiment across global developers from 2023 to 2025, and discovered that India is the only country where developers not only trusted AI, but actually increased their trust in AI during a time when trust decreased everywhere else.
In the Stack Overflow Developer Survey, one of the largest global surveys of developers from around the world, India is the only country where the highest positive trust response was chosen increasingly by developers during 2023–2025. In the survey, this means that Indian developers selected “Highly trust” at a higher rate each year when asked how much they trusted AI output accuracy. Indian developers who chose “Highly trust” rose from 5% in 2023 to 8% in 2025, while other countries had an overall trust decrease in AI output that fell 19% during this same period.
My finding is that India is the only country with AI trust growth during 2023–2025 in the survey. Let’s explore variables possibly influencing India’s confidence …
The Vishwakarma Tradition and Organization of Knowledge Through Religion
George and Narayan (2022) document the expansion of Vishwakarma worship, devotion to the Hindu deity of craftsmen and engineers, from traditional artisanal settings into factories, engineering schools, and IT firms across India. Technology creation in this tradition is sacred craft. Vishwakarma Puja involves the blessing of tools and machines in the same professional environments where India’s developer workforce is trained and employed.
So why is Vishwakarma relevant? Religion is the greatest organizer of habit, tradition, and network structure for knowledge flow to run through. So, if you have a deity that represents intelligent machines or innovation, you have a network with deep roots from tradition. You also have familiarity, social norms, and identity factors. In addition, India has Yantras; the Ramayana and Mahabharata contain descriptions of Yantras, mechanical automatons created by Vishwakarma and the sorceress Maya, making these among the earliest narratives in any culture of artificially constructed intelligent beings (Mayor, 2018). In sum, the mental model of intelligent machines as created, directed, and honored by human craft pre-existed for AI to travel on.
A culture that has framed intelligent systems as devotional practice across millennia produces a workforce that arrives at AI with reverence rather than apprehension. This cultural orientation is embedded with trust and formed long before workplace AI introductions.
New Behaviors on Old Rails: The BJ Fogg Argument
Now, you didn’t think I could skip BJ Fogg, did you? New habits travel on old habit pathways; put another way. new habits attach themselves to routines already in place. Fogg’s (2009) Behavior Model shows that behaviors with high routine quality are simpler to trigger because they require less cognitive effort. Religious practice in India is embedded in the rhythms of professional and daily life, which includes Vishwakarma Puja. Lord Vishwakarma is the Hindu deity of architecture, engineering, and craftsmanship. Vishwakarma creates an orientation towards new forms of intelligence through the sacred lens of religion and a familiarity through mythology. When a new behavior, like the use and design of AI, can be introduced through an existing behavioral pathway that is already deeply routinized and culturally reinforced, it requires less activation energy. Religion provides an existing social network for AI trust to travel on. Gods and goddesses also anthropomorphize the intelligence of AI into something sacred and of higher ground. Religious community in India, organized around shared practice, shared professional identity through Vishwakarma puja (common at IT and software companies), and shared cultural narrative, provides exactly this kind of dense reinforcement network.
Absorptive Capacity: Indian Developers Are Globally Embedded
One structural feature of India’s developer population adds a final and key layer. WIPO’s Global Innovation Index 2025 ranks India 16th globally for their embeddedness in R&D networks located in the largest multinational corporations. Cohen and Levinthal (1990) established that prior related knowledge determines whether new knowledge can be recognized, assimilated, and applied. India’s R&D is primarily funded outside of India, meaning exposure to frontier AI agendas flows in through global corporate channels rather than domestic venture capital. This means that Indian R&D has exposure to R&D agendas outside of the Indian mental model. This structural proximity of Indian developers to various POVs towards AI means the absorptive capacity required to meaningfully trust AI output is higher in India’s developer population than the domestic R&D investment figures alone would predict.
Why India’s Trust Is Steady: The Gelfand Tight-Loose Index
Gelfand and colleagues’ 33-nation study places India at a tightness score of 11.0, classifying it as an extremely tight culture. Tight cultures are those with strong shared norms, rules, and little tolerance for deviation. A positive orientation toward technology formed across generations in a tight culture does not erode easily under negative media coverage or ambiguous AI outputs. The durability of India’s trust trajectory across three years of global skepticism is consistent with what tight culture theory predicts. In a loose culture the same orientation might soften under pressure. In India it compounds.
What This Is
This is a set of lenses, that when taken together, point toward what technology adoption literature has consistently ignored: the orientation toward intelligent machines that a culture transmits across generations matters. In India, that orientation has been affirming for a very long time.
The views expressed are my own and do not represent the Federal Reserve System or the Board of Governors.
References
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.
Fogg, B. J. (2009). A behavior model for persuasive design. Proceedings of the 4th International Conference on Persuasive Technology. ACM.
Gelfand, M. J., et al. (2011). Differences between tight and loose cultures: A 33-nation study. Science, 332(6033), 1100–1104.
George, K. M., & Narayan, K. (2022). Technophany and its publics: Artisans, technicians, and the rise of Vishwakarma worship in India. The Journal of Asian Studies, 1–19.
Mayor, A. (2018). Gods and robots: Myths, machines, and ancient dreams of technology. Princeton University Press.
Stack Overflow. (2023, 2024, 2025). Stack Overflow Developer Survey. Stack Overflow. survey.stackoverflow.co/2025.
WIPO. (2025). Global Innovation Index 2025. World Intellectual Property Organization. wipo.int/web-publications/global-innovation-index-2025.