Anupam Mittal, renowned entrepreneur and founder of Shaadi.com, has criticised India’s current approach towards Artificial Intelligence (AI) skilling, arguing that the country is being misled by tech elites into a premature and unstrategic AI learning spree. Speaking at a recent business leadership forum, Mittal stated that while AI is the future, India must focus on realistic, market-driven skills before pushing an unstructured AI skilling agenda.
Key Points From Anupam Mittal’s Speech
- India’s AI Hype Vs Reality
- Mittal highlighted that despite the global excitement around AI, India is still grappling with foundational skill gaps in STEM education, engineering fundamentals, and applied coding standards.
- He said, “Our tech elite is behaving as if India is ready to build the next OpenAI. The harsh truth is, we are not there yet.”
- Blind AI Skilling Push
- The Shaadi.com founder criticised certain corporates and skilling platforms for pushing AI courses without mapping them to real employability or project opportunities.
- “Teaching advanced AI modules without practical ecosystem readiness is like training Formula 1 drivers when roads are not even built,” he added.
- Foundational First Approach
- Mittal stressed that India must focus on fundamental computational thinking, algorithmic logic, and software product design before diving into AI-heavy curriculums.
- He said, “Only when we have a critical mass of strong software engineers, data scientists, and ethical frameworks can AI learning be truly productive.”
- Need For Strategic AI Roadmap
- Highlighting China, the US, and Europe’s AI policies, Mittal urged Indian policymakers to create a structured AI adoption and skilling roadmap aligned with industry needs, research investments, and entrepreneurship ecosystems.
Current State Of AI Skilling In India
| Aspect | Current Status |
|---|---|
| Skilling Programs | Multiple online platforms offer AI/ML courses with low practical project exposure. |
| Industry Demand | Selective demand for AI engineers in top IT firms and startups, not yet mass employability. |
| Research Output | India ranks behind China, USA, UK in AI research citations and patents. |
| Policy Framework | NITI Aayog has AI strategy drafts; implementation pace remains slow. |
| Startup Ecosystem | AI-focused startups rising in healthtech, fintech, and enterprise SaaS but limited global breakthroughs. |
Why Is AI Skilling Gaining Momentum In India?
- Global AI Race: Rapid advances in generative AI, large language models, and computer vision are pushing all major economies to build AI-ready talent pools.
- Employability Perception: AI engineers reportedly earn 1.5-2x compared to traditional software engineers.
- Government Push: Initiatives under Digital India to build AI innovation hubs and academic programs.
- Corporate Demand: Tech giants expanding AI research labs in Bengaluru and Hyderabad to support global AI initiatives.
Anupam Mittal’s Contrarian Perspective
While acknowledging AI’s transformative potential, Mittal warned against:
- Superficial Certifications: Many AI skilling courses focus on theoretical modules without integrated live projects or research exposure, resulting in under-skilled graduates.
- Job Market Mismatch: Entry-level roles for AI engineers remain limited compared to traditional software engineering, backend, frontend, and DevOps roles.
- Ignoring Basics: Students rushing into AI without mastering data structures, algorithms, and core programming lose out in global hiring competitions.
- Overlooking Ethical AI: Lack of focus on AI ethics, responsible deployment, and national security implications in skilling programs.
Industry Reactions To Mittal’s Comments
Several tech leaders resonated with Mittal’s views:
- Rajesh Gopinathan (former TCS CEO): “India needs a pyramid approach in AI skilling, with a strong base in fundamentals before creating AI specialists.”
- Nasscom AI Council Member: “AI is not plug-and-play. Quality projects, internships, and faculty upskilling must precede mass skilling advertisements.”
However, some educators argued that exposure to AI at undergraduate level inspires innovation, even if advanced jobs remain limited currently.
India’s AI Readiness: Comparative Snapshot
| Country | AI Research Rank (Citations) | AI Talent Pool (Est.) | AI Patents Filed (2023) |
|---|---|---|---|
| USA | 1 | 350,000+ | 70,000+ |
| China | 2 | 300,000+ | 68,000+ |
| India | 7 | ~90,000 | ~3,500 |
Source: Industry reports & global AI research databases
What Should India Do Next?
- Strengthen Fundamentals: Introduce computational thinking, coding, and algorithmic logic from school levels.
- Faculty Development: Train educators in AI foundations and industrial applications.
- Applied AI Labs: Promote university-industry joint research labs for hands-on AI model development.
- Regulatory Framework: Formulate AI ethics, data protection, and IP policies for responsible AI deployment.
- Incentivise Startups: Create funding windows for AI startups solving Indian problems in healthcare, agriculture, education, and climate.
Conclusion
Anupam Mittal’s remarks serve as a reality check amid India’s AI skilling hype. While India’s ambition to become a global AI powerhouse is commendable, it must prioritise structured, practical, and employability-driven learning pathways over mere certifications. As global AI innovation intensifies, building robust fundamental skills and aligning them with ethical, scalable applications will be crucial for India to transform AI potential into economic and social dividends.
Disclaimer: This news article is for informational purposes only and does not constitute advice or views for any policy, business, or investment decisions. Readers are advised to evaluate contextually and consult relevant domain experts before drawing conclusions.
