The Truth About India AI Crisis
India AI Crisis: India boasts 16% of the global AI talent pool and is a major data producer. However, it has not been the trailblazer of a global AI revolution as the U.S. or China are In stark contrast to the thriving IT sector and the digital economy, India is still reliant on AI platforms from abroad and does not have a first-rate local AI model. What’s the reason for this?
The article here explains the main factors behind the AI issue in India and suggests indented measures that might help India to harness its AI power soon.
Why India Has Not Led the AI Revolution: Key Challenges
Acute Talent Shortage and Skills Gap
- For every 10 generative AI jobs in India, only one qualified engineer is available.
- AI roles command sky-high salaries (₹58-60 lakh annually), unlike traditional IT roles.
- Scarcity extends to related fields like cloud computing and cybersecurity, hampering overall AI infrastructure growth.
Infrastructure and High Costs
- India is dependent on expensive foreign AI chips and GPUs (e.g., NVIDIA) for AI model operations.
- By 2025, the computational costs of large AI models are projected to increase by 89%, thus making AI experiments less financially viable.
- Apart from that, the limited access to affordable high-performance computing is a major factor that slows the pace of native AI development.
Lack of a World-Class Indigenous AI Platform
- As opposed to the U.S. (OpenAI’s GPT) or China (DeepSeek), India does not have a proprietary large language model (LLM).
- Local AI startups only concentrate on small-scale applications, which are mostly limited to regional languages and do not have a global user base.
Fragmented AI Ecosystem and “Crab Mentality”
- Indian AI players, investors, and policymakers are frequently compartmentalized in their operations, and they continue to hoard data and resources.
- With the absence of collaboration, the innovation gets suffocated, which is opposite to what happens in the countries that pool their resources into robust national AI ecosystems.
Limited Government Support and Risk-Averse Investments
- India’s AI policies are devoid of provisions for financed structured deep-tech startups.
- Investments are directed to sectors that have been proven like e-commerce where activities are familiar rather than AI innovation.
- Both public and private R&D investments in AI are at a significantly low level.
Educational and Cultural Barriers
- Indian education is based on memorization and exam success, and does not focus much on creativity and problem-solving.
- The traditional way of thinking in this system inhibits the development of new ideas and the taking of risks which are very important for the progress of AI.
Data Accessibility and Diversity Challenges
- To work properly, AI needs large datasets that are ready for AI use and are suitable for local requirements.
- India’s linguistic and cultural diversity makes it very difficult to develop universal AI solutions because the country has 22 officially recognized languages and numerous dialects.
Ethical and Regulatory Gaps
- The fast AI developments are beyond the regulatory and supervisory capabilities of Indian policymakers.
- The absence of enforceable regulations for the ethical use of AI that can ensure correct use of the technology, and the difficulties in determining who is responsible, are some of the issues that arise from this situation.
Why can't India have it's own OpenAI?
— jss (@jsensarma) January 16, 2025
Turns out – answer is not a mystery. Similar ventures have been tried out of India & didn't do well.
Finally penned story of Qubole (early Databricks/Snowflake competitor) & lessons from it that are super-relevant to this question. 🧵
Table of Comparisons: India’s AI Ecosystem versus World Leaders
| Aspect | India | United States | China |
| AI Talent Availability | Severe shortage vs. roles (1:10) | Large pool, top universities | Large investment in talent growth |
| Indigenous AI Platforms | None with global impact | OpenAI (GPT series), Anthropic | DeepSeek, Baidu AI |
| Government AI Support | Limited structured policies | Strong funding, AI strategies | AI-first national approach |
| AI Infrastructure | Reliant on foreign chips, costly | Homegrown AI hardware, cloud | Heavy investment, domestic chips |
| Investment in AI R&D | Low public & private | High, with venture capital support | Massive government-backed funding |
| Cultural Mindset | Risk-averse, focus on degrees | Innovation-driven culture | AI innovation prioritized |
| Data Accessibility | Fragmented, regional diversity | Large, well-curated datasets | Strong data policies and sharing |
Impact of a Talent Shortage in Real Life
Hiring enough skilled engineers proved difficult for an AI startup based in India that wanted to create a conversational AI. Even with high compensation, hiring was still terrible, which delayed product release and development. This is in contrast to U.S. startups, where a larger skilled workforce and strong AI ecosystems allow talent to flow more freely.
Dispelling Myths: Typical Myths Regarding India’s Potential in AI
Myth 1: India lacks the talent to lead the way in AI innovation.
- India has top-notch AI and IT talent, but advancement is hampered by a lack of infrastructure and cooperation.
Myth 2: Government regulations are adequate to support the development of AI.
- In contrast to its international counterparts, India’s AI policies lack clear funding sources and incentives.
Myth 3: AI startups in India are doing well abroad.
- The majority concentrate on specialized, regional applications that lack a global scope.
Practical Measures for the AI Revolution in India
1. Invest more in AI infrastructure and research and development
- Increase public and private investment in AI research.
- Provide reasonably priced supercomputing infrastructure and domestic AI chip manufacturing.
2. Encourage Open Data Ecosystems and Collaboration
- Promote collaborative innovation platforms and data exchange.
- Encourage collaborations between government, academia, and startups.
3. Restructuring Education to Encourage Creativity
- Curriculums should place more emphasis on creativity, problem-solving, and useful AI skills than on memorization.
- To address talent shortages, start nationwide AI training and reskilling programs.
4. Establish a National AI Infrastructure
- Create and market a flagship AI model with Indian origins to compete internationally.
- Assist startups in developing diverse and scalable AI applications.
5. Put in place robust ethical and regulatory frameworks
- Establish transparent AI governance and guidelines for appropriate use.
- Make sure AI outputs are protected from misuse and false information.
In conclusion, action and cooperation are key to India’s AI future.
India’s AI crisis is a result of structural issues such as a lack of talent, poor infrastructure, dispersed efforts, and little government support rather than a lack of opportunity or talent. India must take decisive action by boosting investment, encouraging cooperation, transforming education, creating a flagship AI platform, and putting strong regulations in place in order to prevent further lagging.
India faces a choice as this decade progresses: either continue to use foreign AI technologies or spearhead an indigenous AI revolution that has an international influence. The message is clear for aspiring business owners and legislators: make investments in AI expertise, support innovation, and work together transparently to build an AI ecosystem that can genuinely compete on the global scene.
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