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Open-source AI can help Indian startups, businesses and public organizations build systems that cost less to enter, can be adapted to local needs and may be hosted closer to sensitive data. A February 2026 Linux Foundation Research report argues that this flexibility is one contributor to India’s AI growth—not its only driver—and cautions that skills, compute access and workforce disruption remain significant challenges.
What the Linux Foundation report says about India’s AI market
The Linux Foundation Research report AI for Economic and Social Good in India: Scaling Inclusive Growth for Entrepreneurs, Creators, and Local Economies, published with Meta in February 2026, presents open-source AI as an enabler of wider adoption. Its argument is that organizations can inspect, adapt and host open models and tools rather than depend entirely on proprietary platforms or external services.
The report combines a literature review with semi-structured interviews with 12 leaders across sectors in India. It is not a census of AI deployments or a controlled comparison of open and proprietary products. The figures it cites come from different underlying sources and years, so they should be read with their original qualifications.
Market estimates and adoption figures
| Measure | Figure | Attribution and qualification |
|---|---|---|
| India AI market/economic estimate | USD 3.2 billion in 2020; USD 6 billion in 2024; almost USD 32 billion by 2031 | Linux Foundation Research, 2026; the 2031 figure is a projection, not a reported current market size. |
| Indian startups using open-source AI | 76% | Reported by Linux Foundation Research in 2026, attributing the underlying figure to the Competition Commission of India; it is not a Linux Foundation survey. |
| Indian startup ecosystem | More than 200,000 startups at the end of 2025; fourth globally for newly funded AI companies in 2024 | Linux Foundation Research, 2026. |
| Indian enterprise AI use | 87% | NASSCOM’s 2024 adoption index, based on a 500-company survey, as cited by Linux Foundation Research in 2026. |
These figures describe different things: market estimates, startup use, ecosystem scale and enterprise adoption. Together they indicate activity, but they do not prove that open source alone caused market growth.
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How open source may help organizations adopt AI
Lower barriers to experimentation
Open models and tools may reduce the initial cost of trying AI and allow a team to choose smaller models when a larger system is unnecessary. That can matter for startups and small businesses with limited budgets. It does not guarantee a cheaper deployment: computing, integration, security, maintenance and skilled staff still carry costs.
Customization for local needs
Organizations can adapt models and interfaces for particular workflows, languages and cultural contexts. The report sees this as relevant in a country with substantial linguistic diversity and a need for services usable beyond major technology hubs. Multilingual systems such as Bhashini and Sarvam AI are cited as efforts intended to reduce language barriers and widen access to digital services.
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More control over data and deployment
When an organization can run a model locally, it may have more control over where data is processed and stored. The report describes this as valuable in settings with sensitive information or restrictions on sending data to outside services. Local hosting is not, by itself, a guarantee of privacy or security; those depend on how a system is configured, governed and maintained.
What “open model” means in this report
The report uses the Generative AI Commons’ Model Openness Framework definition: a machine-learning model whose architecture, parameters—including pretrained weights and biases—and documentation are released under permissive licenses that allow use, study, modification and redistribution. A product described casually as “open” does not necessarily meet that definition; readers and organizations should check what is actually released and what its license permits.
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Examples of applications cited by the report
The report’s examples illustrate possible uses, rather than constitute a comparative evaluation or an independent audit of outcomes.
- Courts: Adalat AI applies AI models and tools to courtroom workflows such as transcription and documentation, aiming to improve throughput and reduce delays. Its co-founder, Arghya Bhattacharya, says: “Open source is the only way this works. We cannot send data outside the country or rely on third-party APIs, so we build on open models, fine-tune them, and host everything in-house.”
- Clinical decision support: Caze Labs’ MeTProAI uses locally hosted models for physician-support tasks, including summarizing standard treatment procedures based on patient details. These tools are described as supporting clinicians, not replacing clinical judgment. Caze Labs co-founder Sanil Kumar says: “For a startup like ours, open source is what makes innovation possible—we can experiment, customize, and use smaller models where large ones are unnecessary, all without the cost structures of proprietary platforms.”
- Agriculture and agroforestry: Farmers for Forests is described as using AI-supported monitoring and computer vision in work with smallholder farmers transitioning toward agroforestry and fruit trees. The Linux Foundation release says the work can increase incomes by up to 3–5x; that is the release’s description of this case example, not an independently established result for farmers nationally.
- Creator economy: The report says AI tools may lower production costs and help creators make culturally and linguistically relevant material.
What open-source AI does not solve on its own
Workforce change and reskilling
The Linux Foundation’s February 2026 release summarizes an estimate that 45–69% of jobs in manufacturing, customer service and retail could potentially be affected by automation by 2030. “Affected” is not the same as eliminated: the figure is about potential exposure, not a prediction that all those jobs will disappear. The report identifies applied AI training and reskilling as important responses.
It cites Skill India Digital Hub as an example of a service that can help people find training centers and jobs in local languages. Sarvam’s head of Edge AI, Tushar Goswamy, argues that skilling and digital empowerment can make AI participation relevant beyond technology companies.
Uneven access and operating capacity
The report also flags unequal access to computing resources, differences in digital literacy and urban-rural divides. Open tools can make adaptation possible, but organizations still need suitable infrastructure, people who can deploy and maintain systems, and safeguards for responsible use. Small and medium-sized businesses may benefit from support that addresses those practical constraints rather than access to software alone.
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What the report recommends
- Invest in applied AI training and reskilling.
- Improve access to localized and multilingual infrastructure.
- Support open models and tools while encouraging secure and responsible AI research.
- Help small and medium-sized businesses adopt AI.
- Measure AI’s economic impact and build multistakeholder policy frameworks.
For a business evaluating an AI system, the useful comparison is not simply “open” versus “closed.” Consider total operating cost and compute needs; where data will be processed; how well the system can be adapted to local language and workflows; what components, documentation and licenses are available; and whether the organization has the skills and governance to run it safely. The report discusses these decision factors but does not provide a controlled product comparison.
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