The Illusion of AI Progress in India: Are We Just Repackaging the West?

 🇮🇳 AI Adoption in India: Copy, Paste, and Lose?

As AI advances rapidly across the globe, countries like India are moving swiftly to align with global trends — often by adapting rather than inventing. We fine-tune models like BERT and GPT, deploy frameworks from Hugging Face, and work with tokenization, stemming, parsing, and syntactic tweaks. Techniques like prompt engineering, transliteration, model distillation, and pipeline orchestration using tools like LangChain or Haystack are becoming mainstream. These are meaningful steps, and they contribute to the AI ecosystem. However, much of this work is still built on foundations created elsewhere. While we wrap these efforts in regional branding and localisation, the deeper question remains: are we truly innovating from within, or simply repackaging global models for local use?

But pause. Look deeper.

Are we building AI that thinks like India, or just mimicking models trained on Western culture, Western language, and Western values?

🚨 The Danger of Blind Adoption

While there’s nothing wrong with leveraging global innovation, blind adoption without critical localization creates silent risks:

    • Cultural Erosion: AI trained on non-Indian texts reflects non-Indian perspectives — on ethics, behavior, priorities, and even humour.

    • Tech Dependency: We’re becoming consumers, not creators — reliant on foreign models, libraries, and hardware.

    • Surface-Level Customization: Rebranding a Western model doesn’t make it Indian — it’s lipstick, not roots.

🧭 India’s Lost Goldmine: Our Own Knowledge Systems

We're sitting on a treasure trove of structured, scalable, and time-tested knowledge — yet we continue to train AI on datasets far removed from our civilizational ethos.

Here’s what we should be drawing from:

📚 Vedas & Puranas

Deep explorations into cosmology, linguistics, metaphysics, and moral reasoning. Rich in symbolic language, analogical thinking, and recursive knowledge structures — perfect for training ethical and philosophical AI.

🔢 Vedic Mathematics

Offers computational shortcuts and mental models that are algorithmically efficient — ideal for low-resource edge AI and lightweight computing environments in rural or resource-constrained areas.

🕉️ Sanskrit

A morphologically rich, phonetically precise, and semantically deep language.

    • Excellent for rule-based NLP

    • Enables symbolic AI alongside statistical models

    • Offers clarity for semantic parsing, translation, and logic mapping

📖 Bhāṣyas, Commentaries, and Epics

Dense, multi-layered texts full of nuanced interpretation, debate structures (Purva Paksha–Uttara Paksha), and ethical dilemmas — invaluable for:

    • Contextual reasoning

    • Conversational AI

    • Ethics modeling and value alignment

🧠 Nyāya, Sāmkhya, and Vedānta Darshanas

Ancient schools of logic, categorization, and consciousness studies.

    • Nyāya: Structured reasoning, fallacies, and syllogism — perfect for AI reasoning engines

    • Sāmkhya: Ontological frameworks — helpful for knowledge representation

    • Vedānta: Consciousness-centric models — alternative to Western materialist paradigms

🌐 Panini's Ashtadhyayi (5th Century BCE)

An ancient formal grammar system with production rules akin to modern context-free grammars.

    • Has already inspired early NLP models

    • Could be used to build explainable language models with symbolic+neural hybrid logic

🧘 Yoga Sutras & Ayurveda

Insights into human behavior, psychology, cognition, wellness — critical for:

    • Human-AI interaction

    • Mental health AI

    • Behavioral modeling and affective computing

📜 Itihasa (Ramayana, Mahabharata)

Not just stories — complex simulations of decision-making, morality, duty, and consequence modelling over generations.

    • Source for agent-based learning

    • Dataset for multi-turn dialogues, ethical trade-offs, and social modeling

🔐 The Hardware Trap: Another Layer of Dependency

It’s not just software. AI’s brain — hardware — is also foreign.

Chips today come with lock-ins:

    • Application Sandboxing: You can only run what the chip allows.

    • Hardware-Level Access Control: One-size-fits-West policies.

    • Immutable Configurations: No post-manufacture flexibility.

    • Remote Attestation: Surveillance risks in the name of security.

We may be building "Indian AI" on non-Indian foundations that we neither control nor fully understand.

🕰️ 5 Years or Forever: The Crossroads

The next 5 years are critical. Either we:

    1. Build Indigenous AI Models from Indian texts, languages, contexts, and philosophies.

    2. Design Indian Hardware with flexibility and sovereignty in mind.

    3. Collaborate Across Domains — not just IT, but linguists, historians, philosophers, Sanskrit scholars, policy makers.

Or we go down a path where in 50 years, AI won’t speak India, even if it speaks Hindi.

👥 What’s Needed Now

    • National AI Corpus: Digitize and structure ancient Indian knowledge for model training.

    • India-Centric LLMs: Train models on Sanskrit, regional languages, Indian law, ethics, and logic.

    • Hardware Initiatives: Invest in secure, open, modifiable chip design.

    • Cross-Disciplinary Teams: Move beyond engineers — involve culture, education, history, philosophy.

    • Long-Term Vision: It might take a decade, but shortcuts will cost us centuries.

🧠 AI Shouldn't Just Be Smart — It Should Be Ours

We have a responsibility not just to catch up — but to create AI that carries forward India’s civilizational values. Let's not lose our voice in a chorus of borrowed ones.

Building truly Indian AI won’t be easy, fast, or flashy.

But it will be worth it.