India's edge lies in recognising hidden value, says investor
As AI lowers the cost of building software, knowing what to build becomes the real competitive advantage, writes Gayatri Panda, investor and founder of the Indian Tech Society, London. She argues India's proximity to large-scale problems in electricity, cooling, agriculture and logistics is a source of information, not just a constraint. The International Energy Agency expects Indian electricity demand to grow 6.4% a year on average between 2026 and 2030, adding over 570 terawatt-hours of annual consumption.
Source
Technology · read the original report ↗
Desk check · compared with the source
What the desk checked (5)
- India's electricity demand is forecast to rise by an average of 6.4% a year between 2026 and 2030. — Figure appears in source, attributed in context to International Energy Agency projections; not independently verified.
- The IEA expects India to add more than 570 terawatt-hours of annual consumption in that period, with cooling accounting for over a fifth of demand growth. — Attributed to the International Energy Agency in the source.
- India has a ₹1 lakh crore Research, Development and Innovation scheme for high-risk, high-impact technologies. — Stated in source as government policy; no document or date cited.
- The ₹10,000 crore Startup India Fund of Funds 2.0 includes deep technology and innovative manufacturing among its priorities. — Stated in source without a supporting citation.
- Advantage in technology is shifting from the ability to build to knowing what to build, due to AI lowering barriers. — Author's opinion, explicitly marked as personal views.
Analysts’ view opinion
Gayatri Panda's argument reframes where advantage sits once AI makes building cheap and commonplace: not in writing code, but in knowing which problem is worth solving. Her case is that India's proximity to large-scale problems — electricity, cooling, agriculture, logistics — functions as information, not just as a development constraint. Crucially, she concedes the other half: hard problems alone do not produce important companies without an investment and institutional ecosystem able to recognise unfamiliar value.
- In the software era the ability to build was itself scarce; AI lowers that barrier, letting small teams prototype faster and shifting advantage upstream to problem selection.
- When thousands of founders draw on similar underlying models and tools, differentiation comes from understanding something about the world that others have overlooked.
- AI looks weightless but its consequences are physical — data centres need electricity, cooling, land and transmission — pulling technology's centre of gravity toward energy and infrastructure.
- The IEA forecast of 6.4% average annual demand growth from 2026-30 and over 570 terawatt-hours of added consumption reads, from an entrepreneur's angle, as a market for generation, storage, grid intelligence, efficiency and cooling — and potentially an export market, since other countries face similar constraints.
- The binding constraint is capital behaviour: grid technologies, new industrial processes and advanced materials need pilots, demonstration facilities and patient or infrastructure-style finance, which is what the ₹1 lakh crore RDI scheme and ₹10,000 crore Fund of Funds 2.0 appear aimed at.
What to watch — Watch whether domestic investors, corporates and institutions actually redirect capital toward deep tech, industrial and energy technologies that do not behave like fast-return software businesses.
This is a personal opinion piece, not evidence that the approach will produce globally significant Indian deep-tech companies, and the story establishes nothing about how the funding schemes are performing in practice.
Deep dive
Research brief · 8 facts · 3 dates · exam-readyThe brief
Context
This is an opinion piece by Gayatri Panda, investor and founder of the Indian Tech Society, London, written after she judged early-stage startups at STEP San Francisco. Her argument is that as AI lowers the cost of writing software and building products, the scarce advantage shifts from the ability to build to knowing what to build. She contends India's closeness to large, hard problems — electricity stress, extreme heat and cooling, congested cities, agriculture, water, logistics, healthcare access, industrial inefficiency — is a form of information, not merely a development constraint. The uncomfortable corollary, she says, is that capital tends to follow familiar patterns, so India risks holding valuable knowledge about future problems while funding the models of the past.
Key facts
- India's electricity demand is forecast to rise by an average of 6.4% a year between 2026 and 2030.
- The International Energy Agency expects India to add more than 570 terawatt-hours of annual electricity consumption during 2026-2030.
- Cooling alone could account for more than a fifth of India's electricity demand growth, per the article.
- India's Research, Development and Innovation scheme is worth Rs 1 lakh crore and is intended to support high-risk, high-impact technologies.
- The newer Startup India Fund of Funds 2.0 is worth Rs 10,000 crore and lists deep technology and innovative manufacturing among its priorities.
- The author judged startups at STEP San Francisco, where founders compressed years of work into three-minute pitches.
- The author calls the risk of overlooking ideas that do not resemble past successes 'pattern blindness'.
- The piece argues grid technologies need pilots, new industrial processes need demonstration facilities, advanced materials need years of development, and energy technologies may need infrastructure finance rather than another venture-capital round.
Timeline
- A few days before publicationThe author judges early-stage founders pitching at STEP San Francisco, where AI dominates conversations.
- PresentGovernment policy begins to address the funding mismatch through the Rs 1 lakh crore RDI scheme and the Rs 10,000 crore Startup India Fund of Funds 2.0.
- 2026 to 2030IEA-forecast period in which Indian electricity demand grows 6.4% a year on average, adding over 570 TWh of annual consumption.
Who has a stake
- Early-stage Indian founders — Cheaper building tools lower entry barriers, but funding may still favour familiar, US-proven models over obscure industrial or deep-tech ideas.
- Investors and venture capital — Risk of pattern blindness; may need to judge tacit knowledge and problem proximity, not only whether a business can scale.
- Government of India — Deploying the Rs 1 lakh crore RDI scheme and Rs 10,000 crore Fund of Funds 2.0 to finance technologies that do not behave like software businesses.
- Power-system operators, factory engineers, farmers — Holders of unrecorded, experiential knowledge about where systems fail — treated in the piece as an underpriced asset.
- Deep-tech and energy startups — Need pilots, demonstration facilities and possibly infrastructure finance rather than conventional VC rounds.
- Other developing and urbanising countries — Technologies engineered to work affordably under Indian conditions could become export markets for shared constraints.
Why it matters
If AI commoditises the act of building software, national advantage shifts to problem selection — and India sits closest to some of the world's hardest problems at the largest scale. The 570 TWh of additional electricity demand the IEA projects for 2026-2030 is simultaneously an infrastructure challenge and a market for generation, storage, grid intelligence, efficiency and cooling. Whether India captures that value depends less on producing familiar unicorns than on building institutions that can recognise unfamiliar value early.
UPSC angle
Prelims pointers
- IEA projection: Indian electricity demand to grow 6.4% a year on average, 2026-2030, adding over 570 TWh of annual consumption.
- Cooling could account for more than a fifth of India's electricity demand growth in that period.
- Research, Development and Innovation (RDI) scheme: Rs 1 lakh crore, aimed at high-risk, high-impact technologies.
- Startup India Fund of Funds 2.0: Rs 10,000 crore, with deep technology and innovative manufacturing among priorities.
- 'Pattern blindness' — term used in the article for investors overlooking ideas unlike past successes.
- Author: Gayatri Panda, investor and founder, Indian Tech Society, London; views personal.
Mains framing
The article's central claim is that AI is collapsing the cost of building software, so competitive advantage migrates upstream from execution to problem selection — from 'can this scale?' to 'what does this founder understand that we have not recognised?'. India's structural difficulties — stressed electricity systems, extreme heat and cooling demand, congested cities, industrial inefficiency, agricultural productivity, water, logistics and healthcare access — are real constraints but also unrecorded information held by those living inside failing systems, a tacit knowledge the innovation economy prices poorly until it becomes intellectual property or a company. Technology's centre of gravity is also shifting to physical dependencies: data centres need power, cooling, land and transmission, while electrification needs grids and storage, making the IEA's projected 6.4% annual demand growth and 570 TWh addition both a challenge and a market for storage, grid intelligence, efficiency and cooling technologies exportable to countries facing the same constraints. The risk is a mismatch: capital follows familiarity — American-proven models, recognised universities, fashionable AI categories — so India could hold the best information about future problems while allocating money by past patterns. The suggested way forward is a funding architecture suited to non-software timelines (pilots, demonstration facilities, long materials development, infrastructure finance), of which the Rs 1 lakh crore RDI scheme and Rs 10,000 crore Fund of Funds 2.0 are partial acknowledgements, plus institutions, corporations and investors able to back entrepreneurs outside conventional networks — borrowing Silicon Valley's confidence without importing its definition of innovation.
Key terms
- Pattern recognition (in investing)
- The practice of judging founders, markets and companies by their resemblance to past successes.
- Pattern blindness
- The author's term for the failure to see promising ideas precisely because they do not resemble earlier winners.
- Tacit knowledge
- Unrecorded, experience-based understanding of how systems fail, held by operators, engineers and farmers rather than in documents or data.
- International Energy Agency (IEA)
- The body whose forecast of 6.4% annual growth and over 570 TWh of added Indian electricity consumption for 2026-2030 the article cites.
- RDI scheme
- India's Rs 1 lakh crore Research, Development and Innovation scheme meant to support high-risk, high-impact technologies.
- Startup India Fund of Funds 2.0
- A Rs 10,000 crore fund that explicitly includes deep technology and innovative manufacturing among its priorities.
Practice questions
- As AI reduces the cost of building software, does competitive advantage shift from engineering capability to problem selection? Discuss with reference to India's technology ecosystem.
- "A constraint of sufficient scale eventually becomes a market." Examine this in the light of the IEA's projection that India will add over 570 TWh of annual electricity consumption between 2026 and 2030.
- Why do deep-tech, grid and advanced-materials ventures require a different funding architecture from conventional software startups? Evaluate India's RDI scheme and Fund of Funds 2.0 in this context.
Grounded only in the source report — figures and dates are the source's, not inferred.