India's White Paper Calls AI Infrastructure a Public Utility: What Government Teams Should Do Next
India's new white paper from the Office of the Principal Scientific Adviser urges a strategic pivot: treat AI infrastructure-compute, datasets, and models-as shared public utilities, not private assets. The goal is clear: broaden access, reduce concentration, and accelerate public-interest outcomes across the country.
The paper warns that limited access to AI inputs will widen the digital divide between large firms in major cities and everyone else. The remedy is a Digital Public Goods and Digital Public Infrastructure approach that makes AI resources affordable, trustworthy, and easy to use for startups, researchers, public institutions, and MSMEs.
Why this matters for government teams
AI capacity is consolidating. Without intervention, smaller regions and agencies get priced out or locked out. A utility model puts equity, reach, and affordability at the center-so state departments, universities, and public-sector partners can build real solutions, not pilots that stall.
What "AI as a Public Utility" looks like
- Shared compute: Subsidised GPU/TPU access via national or state nodes; fair-use quotas; credits for research and public projects.
- Open, trusted data repositories: Curated datasets with quality checks, privacy protections, and clear licensing for safe reuse.
- Public model hubs: Reusable base models and fine-tunes for priority sectors (health, agriculture, education, justice), with transparent benchmarks.
- AI commons: Interoperable datasets that reflect India's linguistic diversity and local needs, enabling inclusive applications.
- DPI/DPG alignment: Common standards, APIs, and governance frameworks, much like Aadhaar and UPI did for identity and payments.
Immediate actions for ministries and states
- Map demand: Identify top workloads needing compute, data, and models across departments and institutions.
- Pilot shared compute: Stand up a regional compute cluster or procure credits; set allocation rules and SLAs.
- Stand up data exchanges: Launch sector data sandboxes with privacy-by-design and audit trails.
- Back Indian languages: Fund collection and annotation for low-resource languages; engage universities and domain experts.
- Create public model hubs: Host baselines and evaluation harnesses; require documentation and intended-use guidelines.
- Standardise procurement: Publish model RFPs for compute, storage, data services, and security controls to speed adoption.
- Protect rights: Enforce privacy, safety, and content provenance; require data minimisation and impact assessments.
- Build skills: Train civil servants and public researchers on data stewardship, evaluation, and safe deployment.
Governance: access, pricing, and fairness
- Tiered access: Prioritise public-interest projects; define quotas for research, startups, and departments.
- Transparent pricing: Subsidies for academia and social impact; caps to prevent resource hoarding.
- Open standards: Interoperable formats, model cards, data documentation, and audit-ready logs.
- Risk controls: Pre-deployment testing, bias checks, red-teaming for safety, and redress mechanisms.
Risks to anticipate (and how to reduce them)
- Vendor lock-in: Use open formats, portability requirements, and multi-cloud strategies.
- Regional inequality: Allocate nodes across states; monitor usage and rebalance capacity.
- Data misuse: Strong access controls, purpose limitation, and continuous audits.
- Cost overruns: Budget caps, usage dashboards, and cost-per-outcome metrics.
- Quality drift: Scheduled evaluations, dataset refresh cycles, and external benchmarking.
- Process delays: Pre-approved frameworks, rapid RFP templates, and lean governance boards.
What success looks like in 12-24 months
- Operational compute pools in multiple regions with clear SLAs and real usage by public institutions.
- Open datasets for key sectors and languages with documented lineage, quality scores, and licenses.
- Model hubs hosting evaluated baselines that agencies can fine-tune safely.
- Dozens of state or ministry pilots graduating into production services with measured impact.
- Procurement cycle times reduced; cost per training or inference job tracked and trending down.
- Regular public reports on access, performance, inclusion, and safety audits.
Fit with India's DPI playbook
The paper builds on what already works: common infrastructure, shared rails, and clear governance. Identity and payments set the pattern; AI can use the same logic-open standards, credible institutions, and broad reach.
For context on digital public goods, see the Digital Public Goods Alliance overview. For the payments rail referenced, learn more about UPI via NPCI's product page.
Next steps for policymakers
- Nominate a lead agency to coordinate compute, data, and model workstreams with states and academia.
- Publish a 180-day roadmap with budgets, sites for regional nodes, and sector priorities.
- Launch two flagship pilots per sector (health, agri, skilling, justice) using shared infrastructure.
- Set public KPIs and quarterly reporting for transparency and course correction.
Capacity-building is essential. If your department is planning training for AI literacy, governance, or evaluation, explore role-based options here.
The message is simple: access wins. Treat AI like a utility, and you widen who gets to build-and who benefits.
Your membership also unlocks:
