India is treating the AI wave like the SaaS wave. And that’s going to backfire. The SaaS wave was a very specific kind of win. We didn’t win because our products were always elegant. We won because we had two blunt arbitrages the market rewarded for years: we were cheap, and we could throw people at a problem until it looked solved. Even if the UI was clunky, even if onboarding was painful, even if the product needed hand-holding, buyers tolerated it because the pricing was absurdly good. And when the product didn’t do something smoothly, we didn’t redesign the system, we staffed it. Price + labor covered a lot of sins. AI kills both of those advantages. Cheap labor stops being special when software itself becomes labor. And “we’ll throw a team on it” stops sounding like execution and starts sounding like “we don’t have a product, we have a staffing plan.” In AI, one great builder with the right workflow and model can do what ten people used to do. Headcount is not your moat anymore. It’s often your drag. That’s why treating AI like “SaaS 2.0” is dangerous. If your plan is wrappers, services disguised as product, competing on price, and bragging about team size, you’re walking into a market where “good enough” is already the baseline. Everyone has access to strong models. Everyone can ship a decent demo. Everyone can integrate an API. “We’re like X but cheaper” doesn’t land when X can get cheaper too, and when users can try five alternatives in fifteen minutes. AI rewards different things: taste, judgment, and depth. Taste isn’t “pretty UI.” It’s whether the product feels inevitable. Whether the workflow is clean. Whether the defaults are smart. Whether it’s fast to value. Whether it’s trustworthy. In SaaS, you could compensate for weak taste with bodies. In AI, bodies don’t fix taste, they just hide it briefly (and badly!), until the user churns. Depth is the other requirement. Generic AI is already good. You don’t beat it by being “also generic.” You beat it by being specific in ways the general model can’t cheaply imitate: better data, better evaluation, better reliability, and a real understanding of the domain you’re serving. In AI, shallow understanding gets exposed in production and there’s no coming back. So how should India think about this race instead? Our advantage isn’t cheap labor anymore. It’s context. India is one of the hardest markets in the world: multilingual, convoluted, high-volume, constraint-heavy. If you can build AI that works reliably here, you can build AI that works in a lot of the world. But context becomes an advantage only if you turn it into product excellence, not discounting. For founders: if you remove “we’re cheaper” and “we’ll provide support” from your pitch, what’s left? What do you know that the general model doesn’t? What’s the one workflow you can make feel like magic by removing friction, not by adding features? For builders: your edge is no longer “I can execute.” Execution is getting commoditized. Your edge becomes judgment. The ability to simplify, to choose, to build reliability, to make the experience feel clean and human. For investors: stop underwriting AI like SaaS. Big teams, services revenue, feature checklists, and shiny demos are weak signals now. Look for evaluation discipline, domain depth, reliability engineering, and founder taste. Reward being right, not being busy. AI isn’t our SaaS sequel. We’re running the very real risk of becoming world’s best b team again. This could be our inflection point. But only if we acknowledge what it needs.