Theses

This one predates AI and will outlast it. Consumer applications for Indians is its own lane, separate from AI for Indians, because most of what breaks here has nothing to do with model quality. It's the same mistake global playbooks keep making: treating India as a market to enter rather than a product to design from scratch.

Monetization is the first place the playbook fails. Subscription pricing tuned for a US willingness to pay doesn't survive contact with a price-sensitive, cash-flow-aware user. The apps that actually won here monetized around the edges of engagement instead: commerce take-rates, lending, ads, a small cut on a transaction the user was already going to make. Dream11, CRED, Meesho didn't out-subscribe anyone, they built revenue into behavior that was already happening.

Distribution doesn't run through the app store either. Discovery in India is social, not algorithmic search. A reseller in a WhatsApp group, a family member's recommendation, a regional-language creator on Moj or Josh carries more weight than a category ranking. Vernacular content apps like Kuku FM and Pratilipi grew by going where the audience already was, not by winning an English-language app store category no one in their user base was browsing.

Retention follows the same logic. Habit loops built for a single user on a personal device assume a kind of privacy and consistency that doesn't hold for a shared family phone. The products that stuck were the ones designed for interruption, for someone else picking up the phone mid-session, for trust that's earned through a community or a reseller network rather than a streak counter.

I'm looking for founders who start from how Indians actually discover, pay for, and stay with a product, not from a global consumer template with the currency symbol swapped. The constraint is the product brief, not an afterthought once growth stalls. Early stage, same conviction on team and problem as the rest of this list.

The localised version of the last thesis is a different business, not a smaller one. India's professional services market for small businesses is enormous, fragmented, and runs almost entirely through a network of local CAs, company secretaries, and compliance agents most people have never heard of outside their own city.

Global mid-market buyers self-serve. Most Indian MSMEs don't, and won't, no matter how good the interface is. They pay for a relationship they trust, usually a CA who's filed their returns for a decade, not a dashboard. Any AI-native firm here has to keep that trust layer, even while replacing the manual work behind it.

The actual opportunity is the gap underneath the trusted relationship. GST filings, MCA compliance, labour law paperwork are rule-based, repetitive, and exactly the kind of work AI does well. AI doing the compliance work, a local human doing the sign-off and the relationship, is the right shape here, not a pure self-serve product.

Most small businesses in India don't avoid compliance because they don't understand it. They avoid it because a CA relationship is expensive and slow relative to what a tiny business can pay. A firm that cuts that cost by five to ten times doesn't just take share from existing CAs, it brings in businesses that were informal by default.

I'm looking for founders who've actually sat inside this ecosystem, who understand why a local agent is trusted and a SaaS login isn't, and are rebuilding the economics around AI without assuming the buyer behaves like a global self-serve customer. Same lane as AI for Indians, applied to compliance instead of consumer products.

Professional services is the next place this plays out. Law, audit, tax, and consulting firms sell judgment by the hour, and every AI copilot pointed at them so far just makes the existing associate cheaper to run, not the firm structurally different.

Incumbents can't rebuild themselves around AI, because the billable hour is the business. A partner who halves the hours a matter takes has just cut their own revenue. That's not a technology gap, it's an incentive gap, and it's why this has to be built outside the incumbents, not sold into them.

AI-native here means a different leverage ratio, not a faster associate. A handful of senior judgment-holders plus AI systems doing the drafting, research, and first-pass review, priced on the outcome instead of the hour. The firm looks smaller on the org chart and serves more clients than the one it's replacing.

The constraint that never goes away is liability. Professional services carry malpractice risk, regulatory exposure, and a client who needs someone accountable when it's wrong. The winning firms design for that from day one, human sign-off, credentialed review, insurance, rather than skipping past it and hoping regulators don't notice.

I'm looking for founders willing to own the service, the liability, and the client relationship, not sell software to the firms that already exist. The wedge is usually narrow: one workflow, done at a fraction of the cost and time, at equal or better quality, before the firm expands into the next one.

The second lane from the earlier thesis deserves the same treatment. AI for the world, built in India is not a talent-arbitrage story dressed up in a new label. It's a different claim entirely.

The old model rented out engineers to someone else's roadmap. This one is founders who own the product, the brand, and the customer relationship, and simply chose to build the company from India. That distinction is the whole thesis. A services mindset optimizes for utilization. A product company optimizes for the customer's next best experience.

The advantage people reach for first is cost, and it's real, but it's not the interesting part. Cost buys runway. What you do with that extra runway is what separates a discount competitor from a category leader: more iterations per dollar, a team that can afford to rebuild something twice before a well-funded rival ships once.

The teams worth backing already have a decade of building for consumer scale inside companies like Flipkart, Swiggy, and Razorpay, not just a computer science degree. That maturity has to travel outward, though. Built in India does not mean designed for India. Once the customer is global, the UX, support hours, pricing psychology, and payment rails all have to fit that customer, not the founder's own defaults.

Zoho, Freshworks, Postman, Chargebee already proved the pre-AI version of this works. I'm looking for the AI-era version of the same founders: technically deep, globally fluent from day one, and treating the India base as an engine for speed and craft, not an excuse to ship less of it.

The first lane from the last thesis needs its own space. AI for Indians is not one product decision, it's four or five, and most founders only make one of them.

Everyone gets language first. Support Hindi, add a few regional languages, call it done. That's not what real usage looks like. People don't speak in clean Hindi or clean Tamil, they code-switch mid-sentence, drop in English nouns, and shift dialect depending on who they're talking to. A model trained on textbook translations breaks the moment it meets a real voice note.

Language is the visible constraint. Bandwidth and device class are the invisible ones. Most of the next wave of users are on a shared, low-RAM Android phone with inconsistent 4G, not a laptop on fiber. Every design decision, from model size to how much you round-trip to a server, has to assume that from day one, not retrofit it after a demo works on a founder's iPhone.

Trust here isn't a UI pattern, it's a track record. People pay for what a neighbour or a WhatsApp forward vouches for, not what a landing page claims. And the price point isn't a discount off the US price, it's a different unit of value entirely: pay per use, pay per minute, pay inside a bundle someone already trusts, not a monthly subscription card.

India has already produced the proof: Jio, UPI, Meesho didn't win by being cheaper versions of a global product. They won by being built for constraints global products never had to solve. That's the bar for AI here too. I'm looking for founders who start from language, device, trust, and price as the design brief, not as a localization checklist bolted on after product-market fit in English.

Most conversations about AI in India collapse into one bet. They shouldn't. There are two, and they call for different products, different teams, and different economics.

The first is AI for Indians. Language is the wall here, not intelligence. A model that reasons well in English and stumbles on a voice note in Bhojpuri hasn't solved the problem for the next 500 million users online. Trust, price, and bandwidth matter as much as accuracy. The winning products here won't look like a chat app with a translation layer bolted on. They'll be built assuming low bandwidth, shared devices, and a first internet experience that is voice and vernacular, not text and English.

The second is AI for the world, built in India. This is not the old outsourcing story with a new label. Indian teams have shipped globally-used products before, in payments, developer infra, and SaaS. The same depth of engineering, now applied to model work and applied AI, at a cost structure competitors can't match, is a real edge. The founders worth backing here aren't running an offshore delivery arm for someone else's roadmap. They own the product and the customer, and India is where they chose to build, not where they were forced to.

What ties the two together is capital efficiency. Small teams, tight loops between building and shipping, and a default to doing more with less compute and less headcount. That instinct travels well in both directions: serving a price-sensitive Indian user and out-executing a well-funded competitor come from the same discipline.

This is the lens I'm underwriting through: founders solving a real problem in one of these two lanes, with product decisions that follow from who they're actually building for, not from what's trending. Early stage, conviction on team and problem over traction.