Stop working in silos: scientific temper is India's key to AI autonomy

To tackle AI for strategic autonomy, India must stop working in silos

AI will not secure India's strategic autonomy by itself. It amplifies whatever you already have - strong science, competent institutions, and a culture that questions dogma. Without those, the tech becomes a shiny distraction.

The core shift is cultural. India needs scientific temper, institutional reform, and a clear break from sectarian politics that fractures talent and trust. Then connect AI to hardware, data, and doctrine. No silos.

What AI changes in national security

AI compresses the time between signal and decision. It can tilt outcomes in cyber defence, electronic warfare, logistics, targeting, and information operations. It also expands scientific discovery and industrial design.

We've already seen credible proofs. The AlphaFold database mapped protein structures at scale, enabling new molecule discovery and new antibiotics from small teams. AlphaFold Protein Structure Database is a case study in open science that moves the needle for bio, materials, and medicine.

The missing piece: scientific temper

India's education system still rewards rote learning, hierarchy, and deference. That blocks original work. Real research needs dissent, replication, and the freedom to be wrong without career suicide.

Stop treating ancient myths as literal science. Encourage critique in classrooms and labs. Incentivise publication in tough journals, open datasets, and reproducibility audits. Reward people who change their mind with new evidence.

Fix the money: from loans to risk capital

Loans for R&D are a bad fit. Research fails often. Debt expects predictable cash flows. That mismatch kills exploration.

Switch to equity-style funding and prizes. Three moves can work now:

• Let NPS/EPF contributors allocate up to 5% into approved deep-tech venture funds with strong governance and long lock-ins.
• Create mission prizes (Rs 50-200 crore) for verifiable milestones: SAR-on-a-chip, radiation-hard MCU, biothreat detection specificity, onboard edge-AI for micro-sats.
• Offer matching grants for open-source toolchains (EDA, compilers, simulators) and research software engineering teams inside universities.

Mobilise the private sector without coddling it

Indian industry loves safe margins. That's fine for cash flow, bad for frontier work. Change the incentives.

• Advance market commitments: pre-buy domestically built components that hit test benchmarks (sensors, EW payloads, radiation-hard memory, GaN power stages).
• Tax breaks tied to verifiable IP generation, not headcount or capital spend.
• Fast-track testing ranges, regulatory sandboxes, and export approvals for dual-use tech with clear audit trails.

Chips: aim for national capability, not just domestic factories

A fab with a local address is insufficient if the supply chain can be switched off abroad. Domestic capacity helps with logistics; national capability secures access under stress.

Build across the stack: lithography, deposition, etch, metrology, optics, resists, gases, ultrapure materials, packaging, and test. Yes, it's ambitious. But the path is modular: multiple startups per component, strict benchmarks, and shared pilot lines.

• Stand up a "Sovereign Silicon" program with a component-by-component roadmap and open test facilities.
• Public procurement that favours domestic IP over local assembly.
• Licensing where sensible; indigenous alternatives where licensing blocks exist. Keep a living dependency map and de-risk annually.

If you want a quick primer on the bottleneck everyone talks about, see EUV lithography from ASML. ASML: All about EUV

AI is not a silo: integrate with hardware and data

Useful AI is fused into systems. Think drones plus comms plus countermeasures, not just a model. Think SAR satellites plus ground processing plus tactical delivery, not just an image classifier.

• Unmanned swarms: onboard inference, jam-resistant links, distributed tasking, and field-repairable airframes.
• Space ISR: low-cost SAR constellations, on-satellite filtering, and sub-hour tasking-to-tip workflows.
• Biosecurity: AI models for protein design paired with strict lab protocols, sequence screening, and trained oversight.
• Energy systems: AI-assisted control for fission and fusion experiments, grid stability, and predictive maintenance.

Institutions that learn

Set up mission-driven research units with five-year charters, not forever-institutes with vague mandates. Build a career track for research software engineers and lab technologists at pay parity with faculty.

• Tenure tied to a mix of publications, reproducible code/data, and tech transfer.
• Rotating fellowships between DRDO/ISRO/MeitY, startups, and universities.
• Compute and data commons with allocation based on peer review and transparent cost accounting.

Governance that keeps pace

Regulate outcomes, not tools. Put safety where it matters: pre-deployment testing, incident reporting, and recall authority for models embedded in critical systems.

• National red-teaming centre for AI in defence, bio, and cyber.
• Privacy-preserving analytics (federated learning, secure aggregation) for health and citizen data.
• Clear export controls that protect genuine edge tech without strangling startups.

Drop sectarian politics if you want world-class science

Science needs trust networks, free movement, and talent density. Sectarian divides scare off collaborators, investors, and returning researchers.

Open labs to the best minds, irrespective of identity or ideology. Keep the focus on measurable research and fielded systems.

A 24-month action plan

• Replace loan-based R&D with equity-style funding, prizes, and AMCs.
• Launch "Sovereign Silicon": component-level challenges, pilot lines, open EDA stack.
• Fund three integrated demo programs: drone swarms, SAR constellation, secure comms stack.
• Stand up the national red-teaming centre; mandate incident reporting for safety-critical AI.
• Create a public compute-and-data commons with allocation via peer review.
• Install research software engineer and lab technologist career tracks in top 50 institutions.
• Enable NPS/EPF optional allocations into audited deep-tech funds.
• Reform promotion criteria: code, datasets, and reproducibility count as first-class outputs.
• Open test ranges and regulatory sandboxes for dual-use systems with strict audit trails.
• Annual dependency audit for chips, optics, materials; publish and de-risk.

Skills and upskilling

Most teams lack a shared baseline on modern AI workflows, tooling, and deployment. Fix that with structured learning and hands-on projects that map to your mission.

If you're building capability in-house, you can browse training options by role and tech stack here: AI courses by job

Pick the target, then build the stack

Arjuna focused on the bird's neck because someone else picked the target. Today, we must decide the target, the forest, and the season - then align funding, talent, and factories.

No more silos. Scientific temper, national capability in critical hardware, AI embedded into real systems, and institutions that learn. That's the play for strategic autonomy - and it's achievable if we start now.