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Informist, Tuesday, May 12, 2026

 

By Shakshi Jain

 

NEW DELHI – A moment of reckoning awaits companies involved in development of artificial intelligence systems, which will make them more selective of where AI is applied, said Vishal Sikka, founder of Vianai Systems, Inc. "I think the reckoning first of all will come because the system right now is in a complete state of imbalance. This cannot be sustained," he said in an online interactive session hosted by Nirmal Bang Institutional Equities late last month.

 

The former chief executive of Infosys Ltd. said the ecosystem is propped up because of substantial venture capital and market froth, however, the true cost of tokens (the fundamental blocks of text or data that AI models use to process information and generate responses) will compel stakeholders to reassess if the benefit of the progress is ahead of the cost involved. "So, that will raise the bar of where you will apply AI," Sikka added.

 

Agreeing with him, Pritesh Thakkar, a research analyst from Prabhudas Lilladher Pvt. Ltd., said "At this level definitely they have a lot of money on the table to invest...they're not even concerned about their profitability in at least the near to medium term. But definitely at one point of time they'll realise that the money invested, they also need a return on that, and definitely when it is backed by private equity...the concern would mount."

 

A senior research analyst from HDFC Securities said infrastructure costs continue to be high and a meaningful monetisation of investments made so far will begin only with large-scale enterprise adoption – only when net spending of enterprises goes up, and fresh funding is released for AI projects, will come the desired boost in place. "This is why the global giants are tying up with existing vendors such TCS, Infosys, and the Accentures of the world, to penetrate the enterprise layer." 

 

World over, industry watchers are concerned that while investments into AI companies are on the rise, profitability seems out of sight for many. With global AI funding surging to $229 billion in 2026 alone, capital continues to flow towards foundation models, infrastructure, and platform-scale bets, where upfront research and development, and compute costs are tremendous, said Neha Singh, founder and chief executive officer of data intelligence platform Tracxn Technologies Ltd. "This results in lower near-term profit visibility despite higher valuations, as companies prioritise scale and defensibility over immediate returns," she added.

 

While global behemoths like OpenAI and Anthropic PBC lead the enormous, general-purpose foundational model segment, they are understood to be "deep in the red" due to sky-high compute and training expenses involved in the process. As of early 2026, the global AI industry is clocking fast revenue growth, but at the cost of channelling billions in investment to finance infrastructure and inferencing. 

 

Meanwhile, in India, the story is somewhat peculiar and more nuanced.

 

India was irrefutably late to the AI party even though the ecosystem in the country is now progressing fast, industry players said. Further, India has far fewer AI companies building large language models compared to its developed counterparts. Indian companies are, instead, focusing on building smaller, more efficient, and specialised AI models, often dubbed as vertical AI in industry parlance. These are industry-specific and specialised applications rather than horizontal foundational models.

 

Profit visibility for Indian startups building foundational large language models remains relatively lower compared to global leaders, largely because India is still developing the compute infrastructure, enterprise monetisation depth, and ecosystem advantages that currently exist in the US and China, industry watchers said. Infrastructure remains one of the key bottlenecks, with India's AI compute capacity still significantly behind leading global AI markets.

 

"Over the long term, India's competitive advantage is likely to emerge more strongly in applied AI and verticalised enterprise deployment rather than frontier-model dominance." Singh said.

 

In fact, margins continue to be a hurdle for established IT services companies as well, which are largely carrying out "enterprise plumbing services" using AI, according to analysts. While AI-led deals are coming at better pricing, companies are choosing to pass on the productivity benefits to clients in the near term, resulting in a margin jerk, they said.  

 

The global giants are in a compute-intensive arms race requiring tens of billions of dollars for graphics processing units, data centres, and model training infrastructure, whereas Indian startups are largely pursuing smaller-scale, application-oriented, or Indic-language-focused models, Singh said, adding that as a result, India's ecosystem today is less capitalised but potentially more capital-efficient. "Companies such as Sarvam AI, Krutrim, Swades, etc have relied heavily on backing from investors like Lightspeed, Khosla Ventures, Peak XV Partners. Domestic participation has primarily come from angel-led capital, Indian corporates like (Reliance) Jio or government entities, and select local VCs, while large Indian institutional investors remain relatively absent from the category," Singh said.

 

The government is promoting development of indigenous large language models and sector-focused small language models through the INR 103.72-billion IndiaAI Mission. Under the programme, 12 organisations have been selected to develop sovereign AI, including the likes of Tech Mahindra, Sarvam AI, Soket AI, Gnani AI, and Avatar AI. These initiatives focus on multilingual, open-source models, tailored for Indian languages and public services. 

 

Venture capital firms are keen on investing in AI startups across domain areas because they do not want to miss out, however, building a large language model demands substantial capital and time, and India hasn't seen that patient capital come in, said Shilpa Maheshwari, managing director - strategy and finance, Aavishkaar Capital.

 

India today has a relatively small funding base at a cumulative $6 billion compared to $524 billion in the US, $34.7 billion in China, and $17.8 billion in the UK, according to data sourced from Tracxn. Funding towards Indian AI startups rose about 22% year-on-year in 2025 with 106 companies raising $888 million through 121 rounds. Of this, $16 million was pumped into seed stage funding, $312.4 million in early stage, and $438.8 million towards late stage.

 

In 2024, investments into the Indian AI startup ecosystem had risen 71% on year, with 133 companies garnering $729.6 million across 166 rounds. A majority of these investments were channelled towards early stage funding at $348.5 million, followed by late stage at $228.6 million and $152.6 million in seed rounds. In 2023, about 98 AI startups raised a total of $427.3 million through 111 rounds of funding.

 

"Investor openness toward Indian startups building LLMs has improved moderately over the last three years, although funding appetite remains highly selective and concentrated among a small number of perceived category leaders," Singh said. She added that investors are increasingly willing to back Indian AI companies when there is a clearly differentiated thesis around areas such as Indic language capabilities, sovereign AI infrastructure, enterprise workflow integration, or cost-efficient deployment models. This helps explain why companies such as Sarvam AI and Krutrim have been able to attract meaningful capital despite India's relatively early-stage AI ecosystem, she said.

 

According to Singh, investor behaviour in India continues to differ materially from the US market, where frontier AI labs have attracted aggressive long-duration capital on strategic and infrastructure-led expectations. In India, investors generally remain more disciplined, with greater emphasis on monetisation visibility, enterprise adoption, operational efficiency, and lower burn profiles before committing large-scale capital deployments, she said.

 

In the past two-three years, the thought process of the funding ecosystem has evolved to ask difficult questions, prompting founders to come with some line of sight on revenue and growth, though timelines differ, Maheshwari said.

 

When seen from the broader lens, Indian AI startups show moderate but tangible profit visibility, supported by disciplined capital deployment and business model choices. "With ~271 profitable companies out of 1,688 active (~16%), India demonstrates a meaningful conversion to profitability despite a relatively small funding base," Singh said. However, she added that Indian startups may achieve profitability earlier but profit pools tend to be narrower driven by application-layer economics, while global leaders pursue capital-intensive dominance with infrastructure or technology-led differentiation, but significantly larger profit potential.  End

 

US$1 = INR 95.62

 

Edited by Vandana Hingorani

 

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