In the MVP development process, AI-powered minimum viable products (MVPs) don't need large datasets initially. Startups can begin with small, high-quality datasets or even use pre-trained models to power the MVP. This allows quick deployment and testing with early users, focusing on core features rather than perfecting the AI. As the product grows, data accumulates, enabling improvements to the AI model. Pre-trained models and transfer learning help reduce data requirements during the early stages. Therefore, MVP development services can support AI-powered products without the need for large datasets upfront. Conclusion AI-powered MVP for startups can be built with small datasets, validating core features and evolving over time with more data. Startups can launch faster and refine the AI model as the product grows.