Recently, a notable remark from within India’s economic policy establishment observed that if AI-related valuations are subject to market corrections in the advanced economies, then it might be positive for capital inflows in the context of India. His remark should be seen in conjunction with the caution raised in an economic forum that there exists an AI bubble in the US. Naturally, such statements may be seen as defeatist in some ways. However, considering the mainstreaming of AI diffusion across some business-to-business and many business-to-consumer ecosystems, even if the pace is varied across industries, these remarks could make a genuine and unique case of AI Diffusion in late-industrial states, like India.
The aftermath of the AI Impact Summit 2026 was interesting for the Indian tech ecosystem, since realisation loomed among India’s startup founders and managers that building AI in India while has challenges around data, funds, and even working culture, emulating a zero-sum approach that is usually pushed in the Silicon Valley is not helpful, irrespective of whether an AI startup intends to sell their products and services in India or beyond. Because they conflate building trillion-parameter models and massive data centres as a measure of geostrategic competition, the reason they make such flawed assumptions is. Therefore, the infusion of software engineers and domain specialists can pivot new industry segmentations and opportunities, which is shown by countries like Japan, in their approach towards cooperative AI.
Unfortunately, policy reports and sections of a commentariat who argue for and against the “sovereign AI” idea treat the idea as some zero-sum game. This begs us to ask whether in the pyramid of various “layers” of the AI ecosystem, Indian entities are bound to remain interdependent on all or any layers. In fact, there is a “considerable disconnect in awardees, grants, and the promise vs. delivery of public benefit” as pointed out by economic development specialist Smita Srinivas.
For late industrial states like India, a more explicit claim has been made that they lack concentrated private equity capital to build proprietary trillion-parameter models like two major AI labs. the infusion of capital should be furthered in those segments and assets, which create a cycle of successive counterparts of the same segments While from a general R&D perspective it is an issue and one may argue how private and public functionaries support AI and DeepTech initiatives. For instance, a chip design firm in India was acquired by foreign VCs and some commentators argued that it was hurting India’s semiconductor industry and technological sovereignty. Contrary to how they assume, this acquisition made a clear economic case of investing in more Indian talent and resources within India to build more such firms, since the semiconductor economy globally is an interconnected web. However, it does not imply that no sovereign AI ecosystems can be enabled in India at all. Since most facets of geostrategic competition are seen as rather zero-sum games, the conflated assumption is cast upon in the case of AI as well. If we look at India’s industrial AI ecosystem developments, there have been enough developments in the semiconductor and electronics industries which show a positive trend. Data centers is another AI ecosystem industry category which is having a calibrative moment nationwide beyond the efforts in Vizag. This leaves us with AI talent, demand issues around AI products & services and AI technical architecture costs beyond cloud / storage and compute. India has immense talent in the AI and analytics space as well as in non-AI industrial sectors, across service & manufacturing sectors whose potential remains untapped, and unfortunately, trapped. This disconnect, often reflects in distorting which technical research and non-AI technical talent to invest in, how to ensure market trust, examining whether a for-profit approach or a not-for-profit approach is logistically sensible for the AI and DeepTech startups building in India. The Indian AI ecosystem must realise that any AI research and diffusion push cannot be treated as a zero-sum game, when in advanced economies, AI diffusion and evolution cycles are conflated with a geostrategic, zero-sum angle of AI diffusion, which might make some of these advanced economies, in WENA countries, especially in North America, lonelier, especially around data, talent & research cooperation. In fact, the shift from traditional SaaS and BPO-like AI delivery models toward the intersection of AI-enabled industrial engineering can also show new horizons. It is high time these market distortions are avoided.