Best Place To Build

Pratyush Kumar, Co-founder, Sarvam AI | "Sarvam means everybody- AI should be for everyone."| Ep. 24

In this conversation with Pratyush Kumar, co-founder and CEO of Sarvam AI, we dive deep into India's AI revolution. As the leader of one of India's hottest AI startups focused on solving Indian language challenges, Pratyush shares insights on building sovereign AI capabilities, the multi-layered approach to AI development, and the vision of making technology accessible to every Indian. Recently selected by the Government of India to build the country's sovereign language model under the India AI mission, Sarvam AI represents the intersection of technological innovation and national strategy. This episode offers a compelling look at how homegrown AI is positioning India at the forefront of the global AI landscape, with valuable lessons for aspiring technologists and entrepreneurs. 00:00:00 - Introduction and Background 00:03:04 - The Birth of AI4Bharat 00:05:07 - How IIT students helped build foundational components 00:08:00 - Birth of Sarvam AI 00:14:51 - The Four Layers of AI Development 00:21:33 - Real-World Applications of AI in India 00:26:04 - Strategic Autonomy in Technology 00:28:43 - Sovereign AI: India's Approach 00:34:40 - AI as a Utility for Everyone 00:38:54 - Technology as an Equalizer 00:41:40 - The Economics of AI Development 00:40:57 - Cost Structure of AI Business 00:42:58 - The Value Loop & Long-term Vision 00:45:05 - Market Dynamics & Competition 00:46:15 - Managing Fast-Paced Growth & Focus 00:47:47 - Indian AI Ecosystem & Academic Integration 00:51:28 - Talent Pipeline & Educational Infrastructure 00:53:22 - National AI Landscape & Government Engagement 00:55:07 - Work-Life Balance & Personal Fulfillment in AI 00:57:20 - AI Integration in Daily Work & Workflows 00:59:06 - Human-AI Relationship & Philosophical Implications 01:03:12 - Sarvam's Roadmap & Closing Thoughts

PKPratyush Kumarguest

May 23, 2025·1h 4m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   Why Indian-language AI matters: diversity, culture, and strategic tech

   Pratyush frames the core problem Sarvam/AI4Bharat set out to solve: building AI that truly works across India’s linguistic and cultural diversity. He argues that for strategic technologies like AI, the country should retain the capability to build key systems itself.

2. 2

   Pratyush’s path into AI: systems engineering to deep learning scale

   He traces his journey from electrical engineering and systems/HPC research into AI during the early deep-learning inflection point. The discussion highlights the shift from algorithm-centric progress to compute-and-data-driven scaling and why that pulled him into foundational work.

3. 3

   AI4Bharat origins: teaching at scale, community experiments, and a pivot to language

   AI4Bharat begins with hands-on deep learning courses that unexpectedly scale to tens of thousands of learners. After a decentralized ‘hacker motion’ doesn’t work well, the team focuses the effort on a few lab-led problems—eventually converging on Indian-language AI as the highest-leverage direction.

4. 4

   Building foundational components at IIT: data pipelines to competitive translation

   Pratyush explains how students helped build critical building blocks such as high-quality web scraping for Indian-language data. Within about a year, the lab produced translation systems competitive with big tech, attracting government and philanthropic support and maturing into a large center of excellence.

5. 5

   From lab to company: why Sarvam AI was created

   The conversation shifts to why a venture-backed company was needed: foundational models and production-grade LLMs require far more compute, capital, and engineering than translation systems. Sarvam is positioned as a contrarian bet on India’s market potential and long-term AI cycle, inspired by DPI scale stories like Aadhaar/UPI.

6. 6

   What foundation models are—and why nations and companies race to build them

   Pratyush defines foundation models as general-purpose systems that can be adapted to many tasks rather than single-skill models. He connects their importance to broad economic value creation and to AI’s emerging status as strategic national infrastructure.

7. 7

   Sarvam’s four-layer ‘full-stack’ view: inference, models, orchestration, applications

   He lays out Sarvam’s stack and why the company spans layers instead of specializing in one. The orchestration layer is emphasized as the glue that makes real systems reliable—especially for voice and reasoning-heavy workflows—while domain experts drive application design.

8. 8

   Data for India: scarcity, ‘culture tokens,’ and code-mixed realities

   Pratyush details why Indic data is harder: many languages have limited digitized text and cultural content often exists in undigitized sources. He also highlights code-mixing and Romanized typing as essential real-world phenomena models must handle to be useful for Indian users.

9. 9

   Real deployments in India: Aadhaar basement stacks, insurance calls, courts, and NITI Aayog

   Concrete case studies illustrate what ‘full-stack’ means in practice: air-gapped sovereign deployments, large-scale voice outreach, and complex data-to-policy reasoning systems. The examples stress reliability, latency, and application-specific design as key to real value creation.

10. 10

    Strategic autonomy and sovereign AI: capability to build, deploy, and scale

    Pratyush frames sovereign AI as the ability to build strategic technology domestically without ‘decoupling’ from the world. He links the concept to national resilience, large-scale deployment capacity (compute/power/app ecosystem), and staying close to state-of-the-art through a long AI cycle.

11. 11

    AI as a utility: open standards, per-capita access, and technology as an equalizer

    The discussion explores how AI could mirror India’s digital public infrastructure pattern: utility-like access with open standards that enables private innovation. Pratyush suggests ‘per-capita AI consumption’ as a future proxy for competitiveness and argues that AI can flatten access gaps between urban and rural users.

12. 12

    The economics of building AI: cost drivers, why funding matters, and the local value loop

    Pratyush breaks down where money goes: data preparation, GPU-heavy training, expensive talent, and productization across the stack. He introduces the ‘value loop’—deploy, learn from usage, improve quickly—and argues that keeping this loop within India is key for long-term economic and strategic returns.

13. 13

    Competition speed, focus, and building amid constant change

    The conversation turns to the emotional and operational reality of the AI race: rapid shifts, noisy news cycles, and strong capital flows. Pratyush emphasizes anchoring on medium-term clarity and democratization goals to avoid being whipsawed by short-term hype.

14. 14

    Ecosystem and talent pipeline: academia-startup integration and a ‘builders vs sellers’ mindset

    Pratyush describes the early signs of an ecosystem effect around IIT Madras but says deeper integration is needed among academia, VCs, and operators. He argues for hands-on building, real feedback loops, and structures that help students (beyond IITM) execute in a world changing every six months.

15. 15

    AI in daily workflows, hallucinations, and the human–AI relationship

    They discuss how AI is reshaping everyday work and the risks of over-reliance, including stacked hallucinations in research workflows. The conversation broadens into philosophy: what remains uniquely human, how engineered systems shape experience, and the need to deliberately steer toward a positive, symbiotic future.

16. 16

    Sarvam’s roadmap: sovereign LLM build, scaling products, closing the state-of-the-art gap

    Pratyush closes with Sarvam’s near-term execution plan and multi-year ambition. The company aims to ship India’s sovereign model, expand product surface area, and steadily reduce the gap to global state-of-the-art while keeping democratization at the center.