The mention of “India among the third most competitive AI powers in the world” is based on a ranking produced by Stanford University’s Global AI Vibrancy Tool / AI Vibrancy Index, where a country’s AI ecosystem is assessed not just based on “how many chatbots people are using,” but “how complete the entire pipeline is (research → talent → startups → adoption → policy → infrastructure → public engagement).” In a ranking in 2025, which featured in Indian media, this positions India at No. 3 in this sector, after the USA and China but before a slew of other ‘advanced’ economies.
The Economic Times
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At an headline level, this ranking tells a simple but very potent story: AI is no longer a ‘future possibility’ in India but an ongoing national capability. This tool developed by Stanford University is meant to facilitate comparisons among countries by using indicators which are organized in ‘pillars,’ or so this tool is intended to do, which provides an “interactive visualization to compare AI vibrancy in countries using multiple indicators organized into pillars.”
The “#3” in this ranking is therefore not a ‘vibes check but a composite assessment of a plethora of non-vibes indicators.

Several news articles covering this outcome include their own set of scores. In this set, the US tops the chart with a score of 78.60, China comes a distant second with a score of 36.95, and Indians take third place with a score of 21.59 (scores credited to coverage of a Stanford tool called Global AI Vibrancy). Such scores are important since they provide two different realities in one go. For instance, Indians are part of the top echelons of AI globally, but a major gap exists between leaders and followers.

A way to clarify what “competitive AI power” might mean in this context is to try to understand a bit better what the Global AI Vibrancy tool is trying to assess. According to Stanford, “The Global AI Vibrancy tool enables country-level comparisons and longitudinal analysis of various AI-related indicators. In essence, it attempts to assess a country’s ability to produce strong AI research, transform this research into innovation and enterprises, build and sustain talent, and apply AI widely in various sectors of its economy and government, with support from state-of-the-art infrastructure and capacity.”

One of the most compelling factors for which India scores so high is talent, both in scale and momentum. They have a large pool of engineers, coders, and other STEM-educated people, and they have a diaspora network that gives them access to leading research and companies globally. In descriptions of this ranking, India is noted consistently for being very strong in terms of the ‘talent’ ranking and in being a country with a rapidly developing tech ecosystem.

Ecosystem strength in India is thus a result of not only all these strengths but also because of being pulled in such a manner to a real-world application scale quickly. With AI being considered ‘work’ rather than ‘demo,’ it drives a demand for ‘pipes, models, eval, security & governance,’ helping in an increase in ecosystem strength. In separate reports from 2025 for workplace AI adoption in India, other statistics show very high usage of generative AI among employees in India compared to the global average, thus highlighting how India not only produces talent but utilizes AI extensively. Adoption in itself does not make a country #3 but facilitates learning-by-doing.
The Economic Times

A major factor is also the culture of startups and product development in India. The yardstick for competition is not only research papers but how new companies are being formed, funded, and built scale, and how large companies can quickly incorporate AI into products at a pace sufficient to compete in a global scale setting. Indian startups have, over the last few years, shifted from a purely ‘services’-focused AI to a more productized AI space in vertical SaaS, developer tools, analytics, and automation solutions, customer support solutions, fintech risk solutions, health tech triage solutions, agritech advisory solutions, and language/voice solutions, especially in Indian languages. The significance is not in every startup necessarily becoming globally famous, but in the amount of experimentation in an ecosystem, which is exactly what this index aims to quantify.

The industry structure in India is another factor which is a game changer. As a country with immense IT service capabilities, they can implement AI in industries such as banking, which are slow movers. This adds up to the benefit of AI implementation in a country and makes this skill available not in a handful of research labs but in thousands of implementations. In a way, this leads to a middle class of AI professionals in this country, people who perhaps aren’t publishing research papers but perhaps know how to safely put an AI model out in production, assess a model’s ‘drift,’ and control AI ‘costs.’

Policy and state momentum will also impact these rankings, since state actors influence AI systems through public procurement, digital state infrastructure, and regulation. A digital ecosystem such as that in India, with digital rails such as identity, payments, and a translation of fast digital enablement of government services, can provide a primer to AI implementation in terms of fraud analysis, customer acquisition, complaints handling, translation, and document processing. With large state systems embracing AI, this can increase market viability for local AI providers and raise standards of viability, privacy, and fairness. Stanford’s model ratifies a ranking where governance and ecosystem viability are part of ‘vibrancy’ rather than distinct within it.
Stanford HAI

Nevertheless, it is important to remember that the competition gap in research and compute between US and the REST is a function of frontier R&D and compute—and this is a space in which India has some work left to do. On frontier model development, leading AI research institutions, and access to large compute clusters and semiconductors, the US ecosystem leads the chart. China spends vigorously in developing AI infrastructure in country. Being #3 in a composite ranking is a clear badge of honor in which India is doing superbly, but it does not necessarily mean they are developing the largest frontier models in the world with comparable speed to US, or access to compute investment in magnitude seen in the first two. The way this ranking is to be read is that they have an extremely vibrant AI ecosystem in which they have an outstanding talent and adoption story but still work left to do in research depth in deep tech, access to compute, and supply chains in high-end hardware.

Secondly, research quality and concentration. The challenge for most nations is to raise the numbers of published papers; getting a consistent stream of outstanding, highly cited research, and better yet a contribution to defensible IP and internationally influential products, is a harder goal. The focus of Stanford’s AI ecosystem work (whether in AI Index or other projects) is not research activity but research impact, measured in a more technical way. The challenge for India is to continue scaling up a high-quality research ecosystem, such that not just scale but excellence is achieved in this ecosystem.
Stanford HAI

The third problem is trustworthy AI. As AI finds application in finance, recruitment, law enforcement, healthcare, and public service delivery, errors have stark consequences in reality. A whole ecosystem is spent on model evaluation, audit trails, bias analysis, safety, and governance. Here again, it’s relevant to remember this Stanford hangtag philosophy: The benefits of AI won’t be well-balanced if not planned with a careful thought process concerning AI. Competitiveness is not just speed; it is scaling safely and maintaining public trust in the process, which is a challenge given the scale and diversity of populations in countries such as India.
Stanford HAI
Therefore, how will this #3 ranking affect the Indian economy? First, it means a major influx into the Indian economy of AI build and deployment capacity, rather than being a pure back office center. Then, higher value work such as AI engineering, model operations, AI evaluation, AI security, and domain solutions will migrate into Indian teams. Second, this ranking will boost investor confidence in AI startups in Indian companies because of reduced ‘ecosystem risk.’ Third, this boosts Indian influence in AI standards, cross-border data handling agreements, and digital trade agreements because ecosystem power is a political capital. Additionally, this will bring an increased level of competition to the Indian market. Given AI as a new norm, all industries will require skilled people, with banks requiring AI risk teams, production companies requiring predictive quality and automation, the media industry requiring production and rights management systems using AI, education requiring adaptive learning systems in AI-assisted learning, and governments requiring AI-enabled services to deliver to their citizens. There will be a new trend in the labor market where “AI literacy” will become an in-demand skill, despite one not being a programmer, with a focus on designing these AI prompts, automating workflows, assessment, and control in their respective domains. The Economic Times 1 However, simultaneously—and this is a challenge for India—the country must avoid a possible pitfall. A country might find itself in a situation where adoption scales up in terms of usage without translating into equal ownership. A situation where most of the AI systems being used in an Indian context are not indigenous models built or computed in other countries will have consequences in which value capture can slip outside despite an increase in usage. The distinction between AI usage and AI power will thus require indigenous capability in model-building, compute sharing, and indigenous platform-building in Indian languages. One way to make sense of this report is: India has already demonstrated an ability to scale technology in a country-wide manner in various sectors such as payments, identification, and mobile-first tech, and it is demonstrating this scaling capability in another area: AI. The methodology of AI Vibrancy, from Stanford, is basically assessing if a nation possesses all the elements necessary to keep on moving forward each year. The fact that it is third means it possesses all elements in a sufficient manner to outcompete richer nations in AI in terms of total competitiveness, despite being behind in “frontier” AI. The Times of India 1 Finally, this ranking should be considered a milestone and a checklist. A milestone because being #3 is a mark of international credibility. A checklist because the margin of victory and the structure of the pillars suggest where attention must focus next: enhanced high-end compute, enhanced university and industry research collaborations, better datasets and benchmarks for Indian languages, increased investment in deep tech, enhanced AI safety practices, and enhanced governance to promote innovation but mitigate harm. A third most competitive economy can become a launchpad if the scale superiority can be paired with strength in the frontier.