Natasha Malpani explains how she backs founders amid intense competition & evolving models
Boundless Ventures is founder-first, prioritising mindset, resilience and storytelling over pedigrees
She argues defensibility come from verticalised products that fuse infra & applications into real-world systems
AI's Infra, Application Layers are Converging: Boundless Ventures' Natasha Malpani
Natasha Malpani explains how Boundless Ventures evaluates early-stage AI startups without traditional metrics and breaks down her "founder-first" framework for identifying AI outliers in a crowded market
Natasha Malpani, Founder and General Partner at Boundless Ventures
Summary
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In a conversation with Outlook Business, Natasha Malpani, Founder and General Partner at Boundless Ventures, breaks down how early-stage investing in AI actually works on the ground today. At a time when multiple start-ups are chasing the same use cases and model capabilities are improving at an unprecedented pace, she explains how her firm evaluates founders, forms conviction without relying heavily on traditional metrics and supports companies through uncertainty.
Malpani also addresses some of the biggest questions shaping the AI ecosystem right now. It ranges from whether consumer or enterprise AI will dominate, to what really makes a start-up defensible as intelligence becomes more accessible. Drawing from her experience investing across sectors like consumer AI, deep tech, and robotics, she offers a clear view on where the next set of breakout companies could emerge, especially in the Indian context.
In AI, especially at the application layer, there are multiple players competing for any use case. So, in an environment like this, how do you decide that the player that I am betting on will win, since at an early stage there aren’t enough growth figures or track record metrics that you can consider?
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I mean, that's the job, right? First is sourcing: if thousands of companies approach you, how do you find the outlier teams? Second is selection: how do you decide what to invest in? Third is portfolio management: how do you support founders to achieve the best outcomes?
In terms of selection, there is no single right answer. Every investor has a different framework. At Boundless Ventures, we focus on multiple aspects. First, we are founder-first. We spend significant time understanding founders, their background, psychology, thinking, and past actions. We do not rely only on standard metrics like education or work experience. Our portfolio includes founders who are former CROs of hospitals, CMOs of companies like Ola Electric, as well as university dropouts with no work experience. There are founders from IIT Kanpur and IIT Bombay, people who have worked at Unacademy or spent a decade at ISRO, and others who started companies in college. There is no single visible pattern, so we focus on mindset.
We look for belief, the ability to shape the world, a unique vision, and resilience, the capacity to improve after setbacks. We also assess whether founders combine technical depth and customer understanding with the ability to sell. Selling is storytelling: convincing employees, investors, and customers at every stage. At the early stage, founders have very little and must create their own reality, so we look for people who can do that.
Second is the market. We spend time at the frontier because we are a specialized fund, an AI-native, cross-border fund that invests early, doubles down at each stage, and focuses on consumer AI, vertical AI, and deep tech. We engage with universities and labs, including Stanford, IISc, and IIT Madras, to understand developments in areas like physical AI, robotics, space tech, and world models. This is how we form market views.
As a result, our portfolio is differentiated, spanning areas like space, travel, AI security, and healthcare, along with emerging work in fields such as immunology and world models.
Finally, there is traction. At our stage, we are not focused on unit economics like a growth investor. However, raising money purely on an idea is no longer sufficient. Since anyone can build an MVP today, we expect founders to have built one and demonstrate early validation.
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Your investment portfolio is widely diverse. It varies from Agentic AI start-ups to health and fashion-tech to robotics start-ups. You’ve invested in start-ups ranging from productivity apps to autonomous aircrafts. Which sector, according to you, will have the highest leverage in an AI-native world?
As a cross-border fund, we evaluate companies globally, which gives us a clear view of different markets. In India, we are particularly bullish on consumer AI and full-stack physical AI.
One contrarian view we hold is that AI infrastructure and the application layer are converging. Many of our consumer companies are not just building on top of models like ChatGPT or Claude; they are also solving for memory, context, security, and governance. They are effectively building both infrastructure and applications. Similarly, companies like Alter, which focus on AI agent access, security, and identity control, are also building infrastructure alongside applications.
We believe the winners at the application layer will have infrastructure built into them. AI cannot be deployed in the real world without reliability, and that reliability must be engineered. Having access to powerful intelligence alone is not enough. The most successful outcomes come from systems that are reliable, trustworthy, and dependable. The same principle applies to AI, intelligence must be translated into real-world impact.
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You have been quite vocal about India leading the Consumer AI wave, as displayed on your website and your linkedin. However, the general view is that Enterprise AI will be the flag bearer of the AI tech industry and there is not yet a visible substantial money-making route for Consumer AI. What is your view on that and do you think India should or can position itself in the direction of a consumer AI builder nation?
The consumer versus enterprise debate is a false one. There is opportunity in both segments. In consumer AI specifically, I am bullish. We have an exclusive program with Anthropic that we believe will help surface the next generation of AI breakouts.
Consumer AI is still very early globally, with few clear winners. You could argue that ChatGPT and Claude are the primary consumer AI breakouts. It is notable that foundational model companies themselves are among the largest consumer-facing players. However, at a vertical level, across sectors like health or travel, there are no clear breakout winners yet.
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But aren’t both OpenAI and Anthropic now pivoting toward enterprise?
No, no, nothing like that. They'll go off to all markets. Claude began with an enterprise focus and is now moving into consumer, while ChatGPT started as a consumer product and is now expanding into enterprise. At that scale, the distinction becomes artificial. Once a company has sufficient capital, strong technology, and a viable business model, it can move across segments. The same is true for companies like Microsoft, Apple, and Tesla, which operate in both consumer and enterprise markets.
However, at the start-up stage, founders must make choices. It is not practical to target both segments simultaneously. Globally, consumer AI is still early, with few vertical breakouts so far. That said, this is likely to change. The absence of clear winners today does not mean they will not emerge.
In consumer AI, the path to success remains similar to traditional consumer tech. Companies must deeply understand their users, build around their workflows, and create sticky products with strong habit loops and retention. What AI adds is the ability to leverage context and memory. The more a product understands a user, the more personalized and engaging it becomes. This level of depth and personalization was not possible before generative AI, and it can make products extremely difficult to switch away from.
This also changes how sectors like education evolve. Future education companies may not resemble traditional models. With generative AI, it becomes possible to fully personalize learning, understanding a student’s current knowledge, identifying gaps, adapting to their learning style, and tailoring both content and format. This level of customization can significantly improve outcomes, and that is where the opportunity lies.
In that case, what business model would these consumer AI companies then follow?
The same as consumer tech, Users will pay for usage, engagement, outcomes, or subscriptions. Strong products can monetize effectively, as seen with Pocket FM and AstroTalk. Across content, education, and astrology, companies have demonstrated that Indian consumers are willing to pay for the right product.
This is what makes India interesting: a large population combined with a proven willingness to pay for high-quality offerings. In AI-driven learning, especially language learning, there are already strong breakouts like SpeakX and Supernova. Monetization is not the core challenge; it is already validated.
The real challenge lies in the infrastructure layer. This is why strong technical teams matter. AI enables more creativity, products can be multimodal, explore new formats, and deliver experiences that were not possible earlier. However, technical depth is now essential. In the past, consumer companies could rely on strong product sense, psychology, and marketing while outsourcing technical work. That is no longer viable. Without capabilities like memory and context, even a strong product will struggle. The product must continuously improve, and that depends on technical excellence.
In consumer AI, what do you think is the hardest problem to solve? Generally, it is believed that retention is still the hardest problem. What kind of user behavior would convince you that a product has crossed the novelty stage?
In consumer, there are three key buckets: distribution, retention, and monetization. Distribution is about how many people know you exist. Retention covers stickiness and habit formation, how often users return, how much they rely on your product, and how strongly they associate with it. Monetization focuses on willingness to pay and how the business scales.
The core challenge is building stickiness. With the explosion of content and apps, the real competition is for user attention. You are not just competing with another app; you are competing for a person’s limited time. The goal is to earn mindshare, getting users to depend on your product.
That is what we look for. Across our companies, whether it is Shram, Glide, or our edtech investments, the focus is on capturing user mindshare and building strong user affinity. The approach varies by sector. What works in travel differs from education, and both differ from proactive agent-based products like those Shram is building.
In India, Seed + early-stage start-ups account for most of the total tech funding, with seed stage having the most number of rounds and early having the most amount, and since Boundless Ventures operate in the same domain, what do you think is the reason behind it? Is it because there are a very limited number of start-ups who have matured to the growth or late stage or is there something deeper here?
Structurally, if you break down funding rounds, it is the opposite. Growth rounds, by nature of funding size and quantum, are larger, so more money goes into them. However, if you look at the number of rounds rather than the amount of funding, there are far more rounds at the seed stage. This is true anywhere in the world. More people start up, and there is always a funnel determining how many graduates to follow-on funding.
Sometimes founders decide that a certain number of rounds is enough; not every start-up needs to go to Series D, E, or F, depending on the business model. In other cases, they do not progress because outcomes do not materialize. It is a tough funnel globally, and the bar for raising Series A, B, C, and beyond is only getting higher. As more companies enter, competition to advance increases, and at every stage, the bar for raising money rises.
At the same time, business models are changing due to AI, so not all companies need to raise capital. That is one level of the answer. The second is that there are many more seed funds than growth funds, which is simply a function of how the market operates, similar to the rest of the world.
In a market where model capabilities are improving so quickly, what actually becomes defensible for a start-up: data, distribution, product workflow, or brand?
No, it's exactly the opposite. The companies are back. The better the models become, the better it is for application-layer companies. The core thesis is that models will continue to improve, compute will get cheaper, and intelligence will become commoditized. We already have access to extremely powerful models, and that capability will only expand.
The question then is how companies at the application or physical AI layer bring that intelligence into the real world in a trustworthy, reliable, personalized, and sticky way. Foundational model companies are unlikely to solve highly specific, vertical problems. For example, a company like OpenAI or Anthropic is unlikely to focus on niche use cases such as imaging the earth’s oceans like Piersight, building specialized robotic systems like Armatrix, or creating highly tailored AI mentorship platforms for specific exam preparation.
Even in areas like travel, where models may understand user preferences and context, vertical companies differentiate through proprietary data, specialized workflows, and deeper integration. These companies access or generate unique datasets that foundational model providers either cannot access or do not prioritize. They also build the pipelines and infrastructure required to continuously improve their systems, capabilities that are outside the scope of foundational model companies.
The overarching thesis is that as foundational models improve, specialized companies benefit. They build verticalized solutions, leverage unique data, and create differentiated products on top of increasingly powerful underlying intelligence.