Flipping the Script: How the Global Majority Can Recode the AI Economy
Abstract
The session opened with a rapid showcase of four Indian AI‑startup pitches, each highlighting niche solutions ranging from AI‑enabled legal dispute automation to high‑performance GPUs, agentic‑AI software for embedded devices, and AI‑driven counter‑drone systems. After the pitch‑fest, the panel of five experts from the Global Majority examined how nations in the Global South can move from AI adoption to agency. The discussion was structured around five thematic pillars – AI literacy & talent, policy & regulatory postures, data sovereignty & multilingual inclusion, trustworthy‑AI governance, and sustainable AI infrastructure. Speakers drew on the report “From Capability to Constraint” (co‑authored with the Global Center on AI Governance) and on concrete South‑South initiatives to outline practical levers—education, public‑private partnerships, sovereign data repositories, low‑compute models, and diversified governance frameworks—that can reshape the AI economy for inclusive, responsible, and climate‑aware growth.
Detailed Summary
1. Opening Pitch‑Fest (≈ 15 min)
1.1 Legal‑AI Startup (Founder Nyanadi)
Problem & Impact – In India, a typical MSME dispute takes ~600 days (down from 1,400 days previously). The startup’s end‑to‑end stack reduces resolution time to 60 days by:
- Pre‑emptive data capture at the enterprise level ( “day ‑ 1” dispute tracking).
- A network of partner law firms across the country.
- AI models that automate ~90 % of litigation argument flow.
Key Industries Served – Lending institutions (2× better recovery), insurers (2‑quarter head‑start on claim prediction), construction arbitration (instant discovery of multi‑million‑page dossiers).
Vision – Treat every societal event as a “question of law”; eventually AI judges could evaluate legal pathways directly.
1.2 GPU‑Innovator (Founder from “Turium”)
Value Proposition – GPUs for inference that deliver 5‑10× better performance‑per‑watt than NVIDIA.
Market View – Inference volume is expected to be ≈10× training volume; the company argues that inference should drive ROI, not the massive $1‑1.5 trillion spent on training over the past five years.
Open‑Source Angle – Open‑source models are closing the gap with closed‑source ones (≈10 % efficiency difference for models < 70 B parameters).
Call‑to‑Action – Visit booth 1.16 (Hall 1) for demos of QN, WAN, and “Huda” models.
1.3 Agentic‑AI Embedded‑Software Platform (Pratik Sharda, Craftify)
Problem – Building firmware and perception pipelines for IoT/edge devices is costly and time‑consuming (multiple drivers, data‑drift, FPGA design).
Solution – Craftify Orbiter, a three‑product suite:
| Product | Function |
|---|---|
| Pypegen | Auto‑generates hardware‑optimized perception pipelines (NVIDIA Jetson, etc.) |
| Firmgen | Generates micro‑controller code (ESP32, SD micro) |
| Agentec | Produces FPGA bit‑streams |
Results – Early adopters reduced development teams from 10 → 2 members and cut 70 % of costs.
1.4 Counter‑Drone Defense (Founder “Armory”)
Context – Recent conflicts (Ukraine‑Russia) show low‑cost drones can inflict $ billion‑scale damage.
Solution – AI engine that processes radio‑signal data to detect drones out to 5 km and jam them at 3 km.
Traction – ₹ 100 crore of MoD orders secured; Indian defence budget earmarks $ 2 bn over three years for such systems.
1.5 AI‑oT OS Platform (Vashat Patel, UbicEdge)
Premise – India is a “physical AI laboratory” (1.4 bn people, massive infrastructure) but suffers from a coding gap among field engineers.
Stack –
- Samus – Cloud AI engine.
- Cleon – Hardware platform.
Outcome – From 15‑20 use‑cases to >5,000 deployments; 25,000+ active installations.
1.6 Transition to Panel
The moderator thanked the founders, announced the closing of the pitch‑fest, and invited the jury and audience to assemble for the award ceremony. After a brief logistical interlude, the panel discussion began.
2. Panel Discussion – “Re‑coding the AI Economy”
The panel was anchored by Kazim Rizvi (moderator) and featured Reggie Townsend, Rachel Adams, Philip Thigo, and Vrushali Sawant. The conversation followed the structure of the underlying report From Capability to Constraint and was divided into five thematic segments.
2.1 AI Literacy & Talent Development
| Speaker | Main Points |
|---|---|
| Reggie Townsend (SAS) | • AI literacy must move beyond awareness to capacity building (ability to question AI decisions). • Multi‑stakeholder public‑private‑academic‑civic partnerships are essential. • Analogy: Electricity – citizens need a basic functional understanding, not deep engineering. • Curriculum should cover data provenance, consent, algorithmic basics without demanding technical depth from every citizen. |
| Kazim Rizvi (moderator) | Reinforced the “technology‑for‑technology’s‑sake” warning; called for fluent AI users across professions (farmers, artists, etc.). |
| Implications | • Governments should act as conveners, industry as creators, academia/civil society as translators. • Training programmes should target poets, fishermen, teachers as well as data scientists. |
2.2 Policy Structures & Scaling National Initiatives
| Speaker | Highlights |
|---|---|
| Philip Thigo (Kenya) | • African governments were dragged into AI policy (data protection, AI strategies). • Negotiated the first UN AI resolution (safe, secure, trustworthy AI for sustainable development). • Pushed the Global Digital Compact to bind public‑private actors. • Emphasised a science‑first panel (independent, globally representative) to feed African AI research. • Stressed that AI fluency must be a policy priority – not just regulation but citizen empowerment. |
| Key Insight | AI sovereignty = self‑determination; no “new colonialism” of intelligence. |
2.3 Data Sovereignty, Multilingual Inclusion & Infrastructure
| Speaker | Core Arguments |
|---|---|
| Rachel Adams (Global Center on AI Governance) | • Global‑north frameworks dominate current responsible‑AI standards. • Global‑majority nations are originating new norms (sovereignty, societal impact, public value). • Examples: India’s digital public infrastructure (Aadhaar, UPI, ONDC) for equitable AI; Senegal’s women‑centric AI policy. • Resource constraints foster innovation (agile public‑procurement, PPPs). |
| Vrushali Sawant (SAS) | • India’s Bhashini AI initiative: building large‑language models (LLMs) for >20 constitutionally recognised languages, plus hundreds of dialects. • Need for a national AI data repository (sector‑specific data trusts for health, finance, agriculture) mirroring digital public‑infrastructure governance. • Emphasised compute sovereignty – leveraging small‑language models (SLMs) to match limited compute budgets while still delivering high performance. |
| Data Gap Illustration – 3 bn people in the Global South are offline; only 35 % internet penetration versus 80 % in advanced economies. Africa holds < 1 % of global data‑center capacity. | |
| Implications | • Prioritise multilingual training data and local data trusts. • Adopt edge‑AI and SLMs to reduce compute and bandwidth needs. |
2.4 Governance: Balancing Trustworthy AI with Innovation
| Speaker | Summary |
|---|---|
| Reggie Townsend | • Governance is not a speed‑bump; it embeds decades of learning to avoid repeat mistakes. • Proposes layered regulation: (i) Speed‑ticket‑style fines, (ii) Licensure for high‑risk systems, (iii) Prohibited‑use bans. • Emphasises defining purpose first – AI should serve societal goals, not become a “digital god”. |
| Philip Thigo | • Africa’s 2024 Continental AI Strategy (60 bn figure). |
| Rachel Adams | • Pluralistic global governance is desirable; a single monolithic standard would ignore diverse political‑economic realities. • Supports a layered approach: shared baseline norms (accountability, transparency) plus local divergent frameworks. • Calls for middle‑power coalition to inject Global South perspectives into multilateral standard‑setting. |
| Kazim Rizvi | • Highlights the need for “friction as a feature”: deliberate governance can guide AI toward purposeful outcomes. |
| Takeaway | • Effective governance hinges on purpose‑driven design, multi‑layered oversight, and inclusive standard‑setting that respects resource‑constrained contexts. |
2.5 Sustainable AI & the Second‑Mover Advantage
| Speaker | Key Points |
|---|---|
| Kazim Rizvi | • The Global South can avoid the industrial‑revolution‑style polluting path by adopting low‑carbon AI from the outset. • Three guiding principles: Efficiency, Fragility, Inclusion. • Cites China’s DeepSeek as an example of high‑performing models built with reduced compute. |
| Vrushali Sawant | • Emphasises edge‑AI, hardware optimisation (right‑sized GPUs/TPUs), and sustainable‑by‑design procurement. • Stresses that democratising compute access (through partnerships) prevents a technology divide between Global North and South. |
| Reggie Townsend | • Reiterates that governance can embed sustainability: define carbon budgets, incentivise frugal model architectures, require lifecycle assessments. |
| Policy Insight | • The Global South’s second‑mover status allows it to set low‑carbon standards, adopt SLMs for domain‑specific tasks (e.g., crop‑disease detection), and shape mobile‑first AI ecosystems. |
2.6 Closing Remarks
Kazim Rizvi summarized the discussion, reiterating that the Global South holds a unique opportunity to become a leader in trustworthy, resilient, and sustainable AI. The panel thanked the audience, the venture ecosystem, and the organizing staff, and officially closed the session.
3. Announcements & Miscellaneous
| Item | Details |
|---|---|
| Ankur Capital Fund | Invested ₹ 32.7 crore in two startups (unnamed in transcript). |
| Antler Innovation Fund | Total investment ₹ 21.8 crore across multiple startups (including the counter‑drone venture). |
| Venue & Booth Information | Turium’s GPU showcase located at Hall 1, Booth 1.16. |
| Jury & Audience Movement | Several instructions to assemble outside MR6 for award announcements (logistical transition). |
| Report Reference | “From Capability to Constraint” – QR code displayed for attendees to download. |
Key Takeaways
- AI literacy must be a multi‑stakeholder, purpose‑driven curriculum that equips all citizens—not just technologists—to question AI‑driven decisions.
- Policy leadership in the Global South should prioritize sovereignty (data, compute, talent) and fluency, treating AI as a tool for sustainable development rather than an end in itself.
- Multilingual data ecosystems (e.g., India’s Bhashini, sector‑specific data trusts) are essential to avoid representational bias and to build locally relevant LLMs.
- Governance should be layered and purpose‑centric, using a mix of regulatory levers (speed‑tickets, licences, prohibitions) while encouraging private‑sector self‑governance and public‑private partnerships.
- Implementation gaps—infrastructure, cross‑ministerial coordination, and realistic financing—remain the biggest hurdles for African AI strategies.
- Sustainability by design is achievable through edge AI, efficient hardware, small‑language models, and a mindset that treats “friction” (governance) as a beneficial feature.
- The second‑mover advantage enables Global South nations to adopt low‑carbon AI architectures, avoid the high‑compute waste of early adopters, and set new global standards for responsible AI.
- Pluralistic global governance—shared baseline norms plus locally tailored frameworks—offers resilience and avoids the dominance of a single “gold‑standard”.
- South‑South collaboration (e.g., sharing multilingual model recipes, joint data trusts) is a concrete pathway to build a value‑sharing AI economy.
End of summary.
See Also:
- ai-beyond-moonshots-a-playbook-for-many
- accelerating-indias-ai-growth-a-blueprint-for-indias-ai-success
- sovereign-ai-infrastructure-for-bharat-and-global-south
- ai-innovators-exchange-accelerating-innovation-through-startup-and-industry-synergy
- inclusion-for-social-empowerment
- ai-for-impact-skilling-inspiring-and-empowering-the-next-gen
- pathways-for-equitable-ai-compute-access
- democratizing-ai-resources-and-building-inclusive-ai-solutions-for-india
- thriving-with-ai-human-potential-skills-and-opportunity
- ai-for-inclusive-economic-progress-the-public-services-ai-stack