The Lab Is Not The Finish Line

The Lab To Capability Problem

For decades, much of Indian academic research has worked inside a grant-driven model. Success was measured by the ability to secure funding and generate publications. That system built a strong knowledge base and expanded the national academic pool.

The next phase must judge research by conversion.

Dr Akhilesh Gupta, former Secretary of the Science and Engineering Research Board and former Senior Adviser at the Department of Science and Technology, places the test beyond publication. His vantage point is institutional: the grant, the lab, the mission and the gap between scientific output and national capability. Indian research cannot remain publication-led. It must be judged by outcomes: advancement in Technology Readiness Levels, intellectual property generation, industry adoption, and direct contribution to national missions.

"The ANRF vision seeks to reorient this paradigm toward mission-driven research, where the focus shifts from outputs to outcomes," Gupta notes.

The lab cannot be the end point. A paper proves knowledge. A patent protects it. A prototype tests it. A pilot exposes it to use. Industry adoption turns it into capability.

Research that fails to move into technology, industry and public use leaks into reports before it becomes capability.

Publications build knowledge. Translation builds power. The next research system must reward conversion, not activity alone.

TRANSLATION BUILDS POWER

The Exit Discipline

A serious research system is also judged by how it handles underperformance. Historically, public-funded systems have often struggled to discontinue or restructure underperforming missions due to institutional inertia, reputational anxiety, or procedural rigidity.

A serious research system must normalise exit as part of excellence, rather than treat it as failure.

The system requires strict stopping rules: mid-term independent technical audits, rigorous peer reviews, and sunset clauses. Red-flag dashboards can track TRL stagnation, cost overruns, milestone delays and weak industry interest. When predefined thresholds are breached, the system must trigger active course correction.

This includes fail-fast funding pools, where resources released from discontinued projects are rapidly redeployed to more promising deep-tech frontiers.

Innovation needs ambition. It also needs stopping rules. A mission that cannot progress should release talent, capital and institutional attention for stronger bets. Failure becomes expensive when the system refuses to name it.

The Industry Trust Gap

India has to move closer to systems where private industry carries a much larger share of R&D funding. In many OECD economies, private industry accounts for more than 70 per cent of R&D funding. India's private sector contributes roughly 36 to 40 per cent of gross expenditure on R&D.

The barrier is trust. Industry will not enter hard science unless risk has structure and value has a path.

The ANRF funding architecture recognises this gap. Its ₹50,000 crore blueprint for 2023–28 includes ₹14,000 crore in Central Government budgetary provision. The remaining ₹36,000 crore is expected from non-government sources including industry, public sector enterprises, philanthropy, foundations and international bodies.

That structure is not merely a funding target. It is a forcing function: Indian research scale will depend on whether public ambition can mobilise non-government capital into high-risk scientific missions.

The hook for industry is not moral appeal. It is de-risking.

Public funding absorbs early-stage uncertainty, making high-risk research more attractive to industry. Industry avoids high-risk deep-tech research when risk lacks architecture and outcomes lack a clear path to market. Public funding should make the first risk investable.

The partnership model needs co-owned IP, matching capital, fiscal incentives and sector-specific deep-tech funds for semiconductors, biotechnology and clean energy.

The next partnership model is not grantor and beneficiary. It is co-investor and co-creator. Capital enters deep tech only when risk is shared and value is visible.

SHARED RISK MAKES VALUE VISIBLE

The Deep Tech Clock

The global success of digital platforms has created a misleading expectation of speed. Software startups scale rapidly, but deep technologies such as semiconductors, quantum materials and advanced energy systems operate on fundamentally different timelines.

They require high scientific validation, complex experimentation and immense capital requirements. Gestation periods of 10 to 20 years in these sectors are not exceptions. They are the norm.

A policy that demands software-speed returns from science-depth sectors will misread progress and kill hard technologies before they mature. The policy shift discussed in the interview recognises longer deep-tech horizons, including a 20-year window for deep-tech startups.

A 20-year horizon is not indulgence. It is realism.

"In deep tech, time is not a constraint—it is a strategic resource," Gupta states.

This reality necessitates patient capital: funding models combining public and private capital that are willing to wait for long-term outcomes without immediate profitability.

Patient capital is not slow capital. It is capital matched to scientific reality and institutional continuity.

PATIENT CAPITAL MATCHES SCIENCE

The Translation Network

Translation needs networks, not islands.

While premier institutes drive high-end output, a large majority of India's research base sits in state universities and affiliated colleges. Initiatives like PAIR connect anchor hubs to state-level nodes, ensuring talent is not limited by institutional postcode.

At the same time, IP locked in academic silos must move to market through standardised policies, fast-track licensing and technology transfer offices. A scientist should not lose the market because administrative paperwork moved slower than the technology.

The PhD outcome also needs to harden. A thesis alone cannot remain the only marker of research maturity. The stronger outcome is a patent, prototype or commercial pathway.

Translation is not a corridor between elite institutions. It is the national network through which research becomes capability.

NETWORKS CONVERT RESEARCH INTO CAPABILITY

The Trust Infrastructure

Science communication is often misunderstood as public relations. In deep tech, it is core infrastructure.

Science that cannot be explained struggles to win capital, policy cover and public trust. It allows industry to see the risk, investors to understand the horizon, policymakers to defend the bet and citizens to trust the mission.

Communication is not decoration. It is part of the trust infrastructure.

TRUST MOVES SCIENCE

The Readiness Test

A serious capability blueprint must measure readiness honestly. When assessing India's industrial readiness to absorb emerging technologies from the National Quantum Mission and the Cyber-Physical Systems Mission, Gupta provides a candid rating of 4 to 5 out of 10.

India has strong theoretical capabilities and scientific momentum. But knowledge leadership is not deployment power.

The gaps are physical and industrial: limited precision manufacturing ecosystems, weak lab-to-market pipelines, and industries that are still early in absorbing frontier technologies. The next phase must shift decisively toward industry-led pilot deployments, real-world sandboxes and national standards.

Science creates the possibility. Industry readiness decides deployment.

This readiness test must also incorporate climate consequence. Gupta argues for a Green TRL framework, evaluating technology not only by its maturity, but by its lifecycle carbon footprint and resource intensity.

Whether in green hydrogen, carbon capture or climate-resilient agriculture, economic growth and net-zero commitments must reinforce each other. Green TRL asks the harder question: the technology may be ready for deployment, but at what environmental cost?

READINESS DECIDES DEPLOYMENT

The Ownership Shift

The most persistent policy myth is simple: increase funding, and innovation will follow.

"Innovation is not a budgetary outcome; it is an ecosystem outcome," Gupta asserts.

Many countries have experienced the paradox of rising R&D expenditure without proportional gains in breakthrough technologies. Money placed into a misaligned system reinforces inefficiencies.

Innovation does not come from how much the system spends. It comes from how intelligently the system converts.

This requires incentive structures that reward patents over mere publications, risk-taking cultures that accept managed failure, institutional autonomy paired with strict accountability and agile decision-making.

Funding signals intent. System design produces capability.

India is putting more money into research, building national missions and adding deep-tech infrastructure. The test now is whether that infrastructure converts science into deployable capability.

Funding must become missions. Missions must become technology. Technology must become industry adoption.

The lab is not the finish line. It is the beginning of translation.

For Gupta, this is not an abstract systems argument. After nearly four decades in India's science ecosystem, the question that still holds his attention is climate-resilient agriculture at scale. That choice is revealing. It is where atmospheric science, artificial intelligence, biodiversity, water stress and farmer livelihoods meet.

It reminds the system why conversion matters: research must eventually reach the field, the factory, the market and the citizen.