Scaling Human Potential in the Age of AI
Detailed Summary
- Moderator welcomed the audience, highlighted that AI Kiran’s community had just crossed the 10,000‑member milestone and introduced the panelists.
- The opening remark set the thematic lens: “What would you do if you weren’t afraid?” – a call to take bold, risky moves when scaling one’s own potential in a rapidly shifting AI landscape.
2. Kirthiga Reddy – From Venture Capital to AI Kiran
| Key Points | Attribution |
|---|---|
| Personal mantra: What would you do if you weren’t afraid? – a prompt for risk‑taking and “starting over” when AI disrupts a trajectory. | Kirthiga Reddy |
| Early career: First‑woman partner at SoftBank, later founded OptimizeGeo (now Optimize.geo) – a generative‑engine‑optimization platform helping brands become relevant in an AI‑driven search landscape. | Kirthiga Reddy |
| Observation: When asked to list 100 women in AI in India, early ChatGPT returned only ten; AI Kiran’s effort grew that list to 250, now 10,000 women actively building AI ventures. | Kirthiga Reddy |
| Vision: AI should be inclusive from the beginning, mirroring previous industrial revolutions that created environmental or social fallout. | Kirthiga Reddy |
| Announcement: AI Kiran will soon unveil a partnership to train one‑million women and youth in AI and automation over the next five years. | Kirthiga Reddy |
3. Lakshmi Pratury – Storytelling, Philanthropy & AI Kiran
| Key Points | Attribution |
|---|---|
| 1994‑1995: Early skepticism around the internet – parallels to today’s AI hype. | Lakshmi Pratury |
| Career arc: Intel → Venture Capital → Philanthropy → founding INK Global Foundation; focus on surfacing unsung innovators worldwide. | Lakshmi Pratury |
| Belief: Every AI revolution offers a chance to “do it right from the start”—designing inclusivity, mitigating mental‑health and social‑media side effects seen in previous waves. | Lakshmi Pratury |
| Quote: “Technology is great, you can’t fight it; you either shape it or get shaped by it.” | Lakshmi Pratury |
| Highlight: AI Kiran’s rapid six‑month progress from a handful of women to a vibrant community, demonstrating the power of network‑effect scaling. | Lakshmi Pratury |
4. Radha Ramaswami Basu – Building iMerit’s AI‑Centred Human‑in‑the‑Loop Model
| Key Points | Attribution |
|---|---|
| Early exposure: First Indian employee at HP, mentored directly by David Packard; later led HP’s medical‑products division in Europe. | Radha Ramaswami Basu |
| 1989: Celebrated $1 M export milestone for Indian software – a historic moment that foreshadowed India’s IT dominance. | Radha Ramaswami Basu |
| iMerit’s mission: Scale human‑in‑the‑loop AI to prevent an “AI divide” across geography and socio‑economic status. | |
| Current footprint: ≈10,000 AI workers globally, ≈3,500 in India, distributed across Calcutta, Shillong, Coimbatore, Hubli, Wysag, Kolkata and other centres. | Radha Ramaswami Basu |
| Four (later five) focus areas: 1. Autonomous mobility & robotics (largest business) 2. Healthcare & medical AI (center of excellence in Wysag) 3. Automotive AI (Coimbatore) 4. Generative AI & small‑model fine‑tuning (Calcutta, Shillong) 5. Precision agriculture & other societal AI (cross‑centre collaborations). | Radha Ramaswami Basu |
| Technical approach: “Tormenting” models – aggressive red‑team testing, reinforcement learning with human feedback, and creation of small, domain‑specific models (vision & language) to serve localized use‑cases such as breast‑cancer screening for Indian women or crop‑failure detection for Indian farms. | Radha Ramaswami Basu |
| Workforce composition: 53 % women; a deliberate diversity target, reinforced with the argument that gender parity is a necessity for responsible AI. | Radha Ramaswami Basu |
| Business health: Cash‑positive, earnings‑positive, 10‑year‑old company that demonstrates AI can simultaneously be profitable and socially inclusive. | Radha Ramaswami Basu |
5. Mihir Shukla – Automation Anywhere’s “Digital‑Worker” Scale
| Key Points | Attribution |
|---|---|
| Book announcement: A Five‑Year Century (pre‑order available) – a manifesto on the pace of AI‑driven change, likening the next five years to a century of transformation. | Mihir Shukla |
| Scale metric: ≈ 500 M digital workers powered by AI on Automation Anywhere’s platform; target to reach 1 B within the next year. | Mihir Shukla |
| Human‑to‑digital‑worker ratio 1:20 – indicating a massive productivity lift across 90 countries and all industry sectors. | Mihir Shukla |
| Emphasis on leadership playbook: preparing executives to harness the “human‑AI partnership” rather than viewing AI as a replacement. | Mihir Shukla |
| View on compute: While compute is a critical enabler, the primary limiting factor is ambition and speed of execution. | Mihir Shukla |
6. Archana Vemulapalli – AMD’s Role in the AI Ecosystem
| Key Points | Attribution |
|---|---|
| Framed AI as a co‑existence model: human intelligence creates AI; AI must be shaped responsibly by human ambition. | Archana Vemulapalli |
| Emphasised the need for infrastructure (hardware, compute) and human ambition to realize AI’s potential. | |
| Called for learning from history: past revolutions (electricity, radio, automotive) produced both benefits and externalities; the same pattern will repeat with AI unless we act deliberately now. | Archana Vemulapalli |
| Stressed the importance of youth engagement, women inclusion, and AI‑Kiran’s Fellows Programme (≈ 250 fellows, including Anurag Hoon). | Archana Vemulapalli |
| Positioning AMD: continue to build the best‑in‑class silicon and software stack to empower partners and drive AI adoption across enterprises. | Archana Vemulapalli |
7. Anurag Hoon – “Heart‑Intelligence” & Music‑Based Community Scaling
| Key Points | Attribution |
|---|---|
| Personal background: grew up in a low‑income Delhi family, scored 52 % in school, left formal higher‑education, learned music, formed a band, later moved to Seattle for marketing. | Anurag Hoon |
| Founded Manzil Mystics – a mobile music school that reaches ≈ 60 000 children across 900 schools via a custom van‑based classroom. | Anurag Hoon |
| Curriculum anchors: Intellectual‑property rights, human‑rights education, and creativity through songwriting based on philosophies of Kabir and Gandhi. | |
| Perspective: AI serves as a tool for learning, but “Heart‑Intelligence” (emotions, senses) remains the core of human development. | |
| Comment on AI‑generated content: royalties flow only for human‑authored works; AI‑generated songs presently do not earn royalties, reinforcing the need to teach IP ownership. | Anurag Hoon |
8. Audience Q&A – Themes & Highlights
8.1 Parenting for an AI‑Automated Future (question from Anupama)
- Anurag Hoon: Emphasised nurturing five senses + nine emotions (sensory & emotional literacy) alongside technology exposure.
- Lakshmi Pratury: Added the need for resilience – learning to fail, survive adversity, and stay happy despite uncertainty.
- Mihir Shukla (via panel consensus): Curiosity and continuous learning are critical; “ask the right questions” is more valuable than any fixed skill set.
8.2 Disrupting K‑12 & Higher Education (question from Salil Pandey)
- Mihir Shukla: Advocate “applied AI” – focus on sector‑specific models (agriculture, healthcare) rather than chasing large‑model races.
- Radha Ramaswami Basu: Emphasised AI‑ready talent, data and infrastructure as a three‑point “investment triangle.”
- Archana Vemulapalli: Call for cross‑disciplinary curricula that blend STEM with humanities, preparing students to ask human‑centric questions of AI.
8.3 Guardrails & Safety for Youth (question from Hemendra)
- Lakshmi Pratury: Stress on responsible AI design from the start, mirroring past revolutions where safety lagged behind adoption.
- Radha Ramaswami Basu: Mentioned human‑in‑the‑loop red‑team testing (“tormenting”) as a technical guardrail.
- Archana Vemulapalli: Highlighted policy‑industry collaboration to set standards for AI use in schools and youth‑focused apps.
8.4 Building Trust & Provenance in the Internet (question from Beena)
- Kirthiga Reddy: Community‑driven verification – AI Kiran members vet and amplify women’s AI work, creating a peer‑reviewed provenance layer.
- Mihir Shukla: Suggests transparent model‑cards and audit trails for AI‑generated content as a systematic way to surface provenance.
8.5 Bridging the Digital Gap for Grassroots Learners (question from unnamed participant)
- Radha Ramaswami Basu: Demonstrated success stories: 700 women in Africa trained for six weeks → 500 jobs within a week; 500 participants in US Mississippi Delta → $120 k AI jobs after six weeks.
- Anurag Hoon: Music‑based outreach shows non‑technical pathways to AI literacy – using creative expression to spark curiosity before technical training.
8.6 Cultivating Wisdom & the Power of the Question (multiple contributors)
- Lakshmi Pratury: The right question is the missing ingredient for AI; gave personal anecdote of a family dinner debate about restoring Patagonia’s ecosystem – a human‑centric, interdisciplinary problem‑solving exercise.
- Archana Vemulapalli: Emphasised “learning how to learn” – fast‑track tools exist; the challenge is to filter what works from what fails.
- Mihir Shukla: Highlighted EQ & arts as essential for developing judgment that pure data cannot provide.
8.7 Closing Remarks (panel consensus)
- Kirthiga Reddy thanked the audience, reminded participants of upcoming AI Kiran announcements (partnerships, training programmes).
- Radha Ramaswami Basu reiterated that inclusive AI is already happening – 53 % women in iMerit, community‑driven AI labs across tier‑2 towns.
- Archana Vemulapalli called for speed, ambition and creativity as the three levers that will decide who shapes the AI future.
Key Takeaways
- Bold, risk‑taking mindsets are essential: Panelists repeatedly cited personal “starting over” stories to illustrate that scaling human potential requires willingness to abandon safe trajectories.
- Community‑first scaling works: AI Kiran grew from 10 women to 10 000 by network‑effects and systematic storytelling, showing that a focused community accelerates representation and impact.
- Human‑in‑the‑loop remains critical: iMerit’s model of “tormenting” AI with rigorous red‑team testing and domain‑expert scholars demonstrates a pragmatic path to trustworthy AI.
- Diversity is a strategic asset: iMerit’s 53 % women workforce and AI Kiran’s women‑centric programs prove that gender parity improves AI outcomes and mitigates bias.
- Compute is not the bottleneck; ambition and execution speed are (Mihir Shukla). Organizations that move fast, even with limited hardware, can capture market leadership.
- Education must shift from pure technical skills to interdisciplinary curiosity: Questions, ethics, emotional intelligence, and arts are viewed as the new “core curriculum” for AI‑ready youth.
- Applied AI over model‑chasing: Building sector‑specific, small‑model solutions (precision agriculture, breast‑cancer screening) delivers tangible societal benefits faster than competing for ever‑larger foundation models.
- Guardrails & provenance are community‑driven: Peer‑verified networks (AI Kiran, iMerit) and transparent model‑cards can restore trust in AI‑generated information.
- Rapid upskilling is possible: Short, intensive training (6‑week programmes) has already lifted thousands from low‑income backgrounds into high‑pay AI jobs, underlining AI’s potential as a social mobility catalyst.
Prepared from the verbatim transcript of the “Scaling Human Potential in the Age of AI” panel at the AI Summit, Delhi, 2026.
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