India’s startup funding winter nearing a turning point, says Artha VC’s Anirudh Damani
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India’s startup ecosystem may be approaching an inflection point after nearly two years of correction, with early signs of recovery emerging—albeit at a slower and more disciplined pace than the previous funding cycle.
That is the view of Anirudh Damani, managing partner at Artha Venture Fund, who tracks early-stage market trends through a proprietary dataset spanning over a decade of venture activity.
In an exclusive fireside chat with Fortune India, Damani describes his analysis centered at “graduation rate”—the proportion of startups that move from seed to Series A funding within a three-year window.
“Historically, this number has been around 13–14%. Over the past two years, it has consistently remained below that,” Damani said.
The latest data point stresses the extent of the slowdown. The graduation rate has dropped to about 5.8–6%, implying that only one in sixteen startups is able to raise a Series A round within three years—less than half the historical average.
Sharp slowdown in deal activity
The decline is not limited to funding outcomes but extends to overall deal activity.
At the peak of the funding cycle in 2021–22, the ecosystem was witnessing as many as 350–370 deals per month. That number has now fallen to roughly 60–70 deals per month, indicating a sharp contraction in funding velocity.
“From doing over a dozen deals a day, the market has moved to barely two deals a day,” Damani said.
Investors, he added, have become more selective, while funding cycles have lengthened. At the same time, the scarcity of capital has led to an increase in startup shutdowns, with only the more resilient business models surviving.
Early signs of recovery, but a different cycle ahead
Despite the slowdown, Damani believes the market may be nearing the bottom of the cycle.
“We are beginning to see some recovery, particularly in later-stage transitions. The graduation rates from Series A to B and beyond are moving closer to historical medians,” he said.
However, the next funding cycle is unlikely to mirror the previous one. Capital allocation is expected to shift away from broad-based consumer plays towards more specialised and capital-intensive sectors.
“These include deep tech, vertical AI, semiconductors, specialty chemicals and space technology,” he said.
Policy clarity remains a key bottleneck
Even as capital availability improves, Damani pointed to policy inconsistency as a structural constraint, particularly for deep-tech investments.
The issue, he said, lies in the mismatch between investment horizons and administrative timelines.
“Venture capital operates on a 10–20 year horizon, while policy and bureaucratic cycles tend to be much shorter. That creates uncertainty, especially in sectors like semiconductors and space,” he said.
He stressed the need for clearer, long-term policy frameworks and single-window clearances to support capital deployment into emerging sectors.
Beyond policy, Damani argued that the next phase of growth in deep tech will require the government to play a more active role as a buyer.
“The government has to become the first customer. Funding alone is not sufficient,” he said.
He said that a fixed portion of public procurement—particularly in sectors such as defence and infrastructure—should be allocated to startups, a model that has been instrumental in building deep-tech ecosystems in countries such as the United States and Israel.
India’s long-term investment case remains intact
Despite near-term volatility in both public and private markets, Damani remains constructive on India’s long-term growth prospects.
“India is one of the few economies where multiple sectors are expanding simultaneously, and where capital can be deployed at scale,” he said.
Unlike other emerging markets that are dominated by a handful of large firms, India’s growth is more distributed across sectors and companies, offering a broader investment opportunity set.
He added that the depth of India’s domestic capital base has improved the market’s resilience.
“Twenty years ago, the level of foreign outflows we are seeing today would have led to a 5% market correction. Today, domestic investors are confident and are able to absorb that pressure,” he said.
Micro VC model gaining prominence
Damani also noted a structural shift within venture investing itself, with smaller, more agile funds gaining relevance.
“In a market where sector cycles change every few years, micro VCs are better positioned to adapt. Deploying smaller amounts of capital early often leads to better capital efficiency,” he said.
He cautioned against excessive funding at early stages, noting that overcapitalisation can be detrimental to startups by reducing discipline and distorting business models.
Capital allocation remains a weak link for founders
While acknowledging that founders are now more mature and better prepared than in previous cycles, Damani said capital allocation remains a key area of concern.
“Venture capital is the most expensive form of capital. If founders are not able to generate returns on equity that exceed their cost of capital, the business becomes unsustainable,” he said.
He added that many startups continue to misuse equity capital for functions such as working capital or inventory, which could be financed more efficiently through debt.
Rethinking the ‘Bharat’ opportunity
On the demand side, Damani said founders often misread India’s non-metro markets.
“The mistake is not ignoring Bharat, but approaching it with a price-first strategy. India is a value-conscious market, not a price-conscious one,” he said.
He pointed to strong demand for premium products, even outside major cities, as evidence that consumers are willing to pay for perceived value.
Separating AI hype from sustainable models
On artificial intelligence, Damani offered a straightforward framework for evaluating business viability.
“The key question is whether the customer is willing to pay,” he said.
He expects enterprise adoption to lead the cycle, with businesses more likely than consumers to pay for AI solutions that either improve productivity or increase revenue.
Sustainable AI businesses, he added, should demonstrate strong unit economics and gross margins in the range of 60–80%.