Will the BARBIE Founders’ Strategy Work for India’s AI Startups?

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On a typical evening in Bengaluru, people swap startup stories over filter coffee, discussing late-night pivots, missed payrolls, and improbable breakthroughs. Interestingly, many of these stories follow a familiar arc: a young Indian goes abroad for higher education, learns from global leaders, and then returns to build something in India.

Piyush Bansal of Lenskart, Anupam Mittal of People Group, and Aadit Palicha of Zepto—all come from different sectors and backgrounds, but share a familiar story.

The Indian venture ecosystem refers to them as the BARBIE founders, which stands for Bachelors Abroad Returned to Build in the Indian Ecosystem. The term was coined by Sajith Pai of Blume Ventures and later popularized by the widely read blog It’s a BARBIE World, which put numbers to what many investors had sensed intuitively. 

The analysis looked at about 300 Indian unicorn founders and found that over 10% fit the BARBIE profile. This is a much higher number than founders from even the top Indian institutions. 

The statistics are striking. About 3.7% of BARBIE founders have gone on to build unicorns, compared to 2.7% from IIT Delhi, 2.0% from IIT Bombay, 2.1% from IIT Kanpur, and 1.6% from IIT Madras. In absolute terms, this cohort of a few hundred founders has created 11.5% of all active unicorns in India.

These numbers raise an important question, especially now that India wants to stand out in the field of artificial intelligence. Does the BARBIE approach really help India’s AI startups, or is it merely a simple label for complex founder narratives?

Imported Global Mindset

For Madhav Krishna, founder and CEO of Vahan.ai, the answer lies less in credentials and more in mental models. Krishna studied artificial intelligence at Columbia University, an experience he credits with shaping his thinking about building a company.

“Spending time abroad helped shape how we, at Vahan.ai, think about risk, failure, and innovation,” he says. “There’s a mindset in startup ecosystems like Silicon Valley, of not being afraid to fail and constantly pushing boundaries, that you absorb simply by being there.”

Krishna is careful not to overstate the value of foreign experience. He points out that India has strong technical education and a growing AI talent pool. Still, global ecosystems offer a different way of thinking.

“The global experience brings different mental models: a deep focus on craft, intensity of effort, and a willingness to experiment,” he says. “That combination has helped us test the limits of what can be built in our space.”

For AI startups, this matters. The challenge is rarely just about building models; it’s about navigating imperfect data, fragmented markets, and users who don’t behave like textbook case studies. Global ambition can push founders to try more, but local experience decides if their ideas last.

When Frameworks Don’t Travel

That tension becomes even sharper in domains like sustainability, governance, and social impact, where AI startups increasingly operate. Vivek Shankaranarayanan, co-founder of Impactree.ai and an MBA from London Business School, says his biggest learning abroad wasn’t in the classroom or through his network, but in how systems are designed.

“Working on consulting assignments in Europe exposed me to a very different operating logic, proactivity by design,” he says. “Systems are built to anticipate failure modes, scrutiny, and structured decision cycles. That mindset became foundational while building in sustainability and social impact, where credibility and trust matter as much as capability.”

However, this experience also showed the limits of using global methods.

Many Western sustainability frameworks, Shankaranarayanan notes, do not fit Indian realities. “Instead of importing frameworks wholesale, we had to deconstruct them, separate the intent from the structure, and then re-engineer them for India. Global exposure gave us a reference architecture, but building here forced us to translate standards into something usable, not just reportable.”

The IIT/IIM Counterfactual

If the BARBIE founders offer a global perspective, what do founders from IITs or IIMs bring to the table? Dr Srinivas Padmanabhuni, co-founder of testAIng.com and a PhD in computer science from the University of Alberta, says the main difference is in networks, not ability.

“Global exposure brought diverse perspectives, while the Indian context helped tailor solutions locally,” he says. “But founders who start straight out of IIT or IIM may lack global exposure while having much stronger local networks.”

This trade-off repeatedly surfaces in AI startups, especially those working with Indian enterprises or the government. Local trust, relationships, and understanding of regulations can be as important as advanced research.

Padmanabhuni is quick to caution against over-reading the BARBIE advantage. “Global exposure can accelerate scaling, but it’s not the sole factor,” he says. “Team strength, resilience, and adaptability play a much bigger role.”

Execution Beats Labels

Ankush Sabharwal, founder and CEO of CoRover, sees BARBIE as a metaphor, not a formula. Sabharwal studied AI and machine learning at Stanford and took leadership programs at MIT Sloan, but he does not believe in simply copying Silicon Valley ideas for India.

“The key takeaway isn’t the label, it’s what we learn from it,” he says. “We should think big, stay aware of global trends, but balance that with a situational approach. You take the first small step in your specific context to solve a real problem.”

Sabharwal continues to lean on mentors abroad, but he draws a clear line between learning and imitation. “I don’t aspire to replicate their approaches,” he says. “I want to understand their thought process and adapt it to our context.”

Would CoRover be different if it started from an  IIT or an IIM? Sabharwal doubts it. “Every part of your background, education, social circle, customers, and partners shapes outcomes,” he says. “Rather than ranking institutions, we should recognise that different experiences lead to different approaches.”

Access vs Outcomes

This distinction between access and execution is where the BARBIE idea starts to break down. Shankaranarayanan says that international experience does offer one tangible advantage: easier early access to global capital and networks.

“But that advantage is shrinking fast,” he says. “Indian founders today have strong global alumni bases, international accelerators, and returning founders who’ve flattened that gap.”

He adds that the label fails when it’s used to explain outcomes. “You can’t build something meaningful for India unless you deeply understand on-ground realities. That only comes from building here, not observing it from outside. If BARBIE explains anything, it explains access, not execution.”

So, Does BARBIE Work?

For India’s AI startups, BARBIE is most effective when viewed as one component of a broader strategy, not a standalone strategy. Global education can raise ambition, increase risk-taking, and set higher standards for founders. India, on the other hand, demands honesty about scale, limits, and adoption.

The most successful founders are not those who rely only on their education. They’re the ones who learn fast, listen to customers, and know when to take risks and when to be careful. BARBIE might explain how some founders start out strong. But what ultimately matters is execution, understanding the context, and staying grounded long after the foreign degree has faded into the background.