Indian AI Startups Face Funding and Cultural Hurdles: A Reality Check
Published April 7, 2025
The Ambition vs. Reality of Deep Tech in India
Amid ongoing discussions about India’s burgeoning startup scene and the government’s push for advanced technological innovation, specifically in Artificial Intelligence (AI), Vinay Borhade, a prominent entrepreneur in India’s AI startup sector, recently provided a stark assessment. His remarks, initially shared in a LinkedIn post, challenge the prevailing narrative and highlight significant obstacles to AI advancement. Borhade’s comments came in response to Commerce Minister Piyush Goyal’s critique of Indian startups, which he accused of lacking the necessary ambition to drive meaningful innovation.
Borhade’s blunt assessment strikes at the heart of the issues facing AI startups in india, pointing to systemic and cultural challenges that frequently enough overshadow the potential for genuine innovation. His perspective resonates with many in the Indian tech community who feel the investment landscape is not conducive to long-term, deep-seated research.
Funding Disparities and Investor Mindsets
A core issue, according to Borhade, is the misalignment between investor expectations and the time-intensive nature of AI progress. Deep tech like AI and IoT demands time and effort for real innovation. Yet, fundraising favors flashy ideas over substance—leaving serious research underfunded,
Borhade wrote, echoing a broader sentiment among entrepreneurs who feel underserved by the current investment climate.
This sentiment echoes concerns within the U.S. tech sector, where similar debates occur regarding venture capital’s focus on rapid scaling and immediate returns versus nurturing foundational research. For example, companies developing new battery technologies or advanced materials often struggle to secure funding compared to those with more easily monetizable software applications.
The problem isn’t unique to India; however, its specific context magnifies the challenges. The difference in resources available to U.S.AI startups compared to their Indian counterparts is significant. According to Tracxn data, funding for AI startups in India plummeted nearly 80% in 2023, dropping to $113.4 million from $554.7 million in 2022. While U.S. funding also experienced fluctuations, the overall investment volume remains considerably higher.
This is further compounded by a perception that investors often lack a fundamental understanding of AI, leading to unrealistic expectations. Borhade added, Investors often don’t get AI. They’re fixated on swift returns,not the long-term potential of good research.
This mirrors concerns in the U.S., where some investors chase AI hype without fully grasping the underlying technology and its limitations. This can lead to misallocation of resources and pressure on startups to prioritize short-term gains over sustainable development.
Such as, a U.S.-based AI startup developing advanced medical diagnostics might face pressure from investors to release a product prematurely, even if it hasn’t undergone rigorous clinical trials. this could have severe consequences for patient safety and erode public trust in AI-driven healthcare solutions.
The Reality on the Ground: MSMEs and Digital Adoption
Borhade’s assessment extends beyond funding challenges to encompass the practical limitations of implementing AI solutions within Indian Micro, Small, and Medium Enterprises (MSMEs). He paints a picture of an ecosystem ill-equipped to leverage AI’s transformative potential. Even interested clients are so cost-conscious that freelance web devs out-earn AI engineers.
This highlights a disconnect between the promise of AI and the willingness of businesses to invest in it.
“MSMEs still run on manual processes with zero digital footprint. Basic data science or dashboards? A tough sell,” he wrote. This lack of basic digital infrastructure presents a significant hurdle to AI adoption. Similar challenges exist within the U.S., particularly among smaller businesses in rural areas, where internet access and digital literacy can be limited.
Consider a small, family-owned manufacturing business in the Midwest. While AI-powered predictive maintenance solutions could considerably improve their efficiency and reduce downtime, the initial investment in sensors, data infrastructure, and AI expertise may be prohibitive. this scenario parallels the challenges faced by Indian MSMEs, highlighting the need for accessible and affordable AI solutions tailored to specific business needs.
Cultural Resistance and the “Jugaadu” Mindset
Borhade argues that the issue is both economic and deeply cultural. The ‘jugaadu’ mindset reigns—clients take pride in saying, ‘I know my business better than you’ or ‘I don’t need yoru AI to decide how to run my business.’”
the term “jugaadu” refers to a resourceful, frequently enough improvisational approach to problem-solving. While it can be an asset in certain contexts,it can also foster resistance to adopting new technologies and processes,especially if they challenge established ways of doing things.
This isn’t entirely foreign to the U.S.context. The “if it ain’t broke, don’t fix it” mentality can be prevalent in certain industries or regions, where businesses are hesitant to embrace new technologies due to perceived risks or a lack of understanding of their potential benefits.
One counterargument to Borhade’s point is that this “jugaadu” mindset can also foster innovation by forcing startups to develop creative and cost-effective solutions tailored to the specific needs of Indian businesses.However, Borhade contends that this approach often prioritizes short-term fixes over long-term strategic investments in AI.
Policy Implications and the Path Forward
Borhade also pointed to the government’s emphasis on policies and compliance, stating that these become a dead end not worth the rant”
when the market itself resists AI adoption. This suggests that top-down approaches to promoting AI innovation might potentially be ineffective without addressing the underlying cultural and economic barriers.
His argument underscores the need for a more nuanced approach to fostering AI innovation, one that combines policy support with initiatives to address the practical challenges faced by startups and businesses. This could include providing funding for AI education and training programs, creating incubators and accelerators that focus on deep tech, and promoting collaboration between academia, industry, and government.
Borhade concludes by emphasizing the untapped potential of Indian AI startups, posing the pivotal question: Indian AI startups have potential. The question is: will the ecosystem catch up?
This question underscores the urgency of addressing the challenges highlighted in his post to unlock India’s full potential in the global AI landscape.
Recent Developments and Addressing Counterarguments
Since Borhade’s initial post, there have been some developments. The Indian government has launched several initiatives to promote AI adoption, including the National AI Portal and the AI Skill India mission. However, it remains to be seen whether these initiatives will be sufficient to overcome the challenges identified by Borhade. One potential counterargument to Borhade’s concerns is that the indian startup ecosystem is still relatively young and that it will take time for investors and businesses to fully understand and embrace AI. while this may be true, Borhade’s post serves as a crucial reminder that addressing the underlying structural and cultural barriers is essential for ensuring India’s long-term success in the AI race.
Investment in Indian AI Startups
| Year | Funding (USD Millions) | Change |
|---|---|---|
| 2022 | $554.7 | – |
| 2023 | $113.4 | -79.5% |
