Artificial Intelligence (AI) is increasingly adopted by early-stage technology startups as a means to improve efficiency, reduce costs, and accelerate product development under conditions of limited resources and high uncertainty. Despite growing interest, empirical evidence on the concrete operational and economic effects of AI adoption in early-stage startups remains fragmented. This thesis investigates the role of AI in enhancing efficiency and cost performance in early-stage tech startups through a mixed-methods approach that combines literature review, comparative case study analysis, and computational validation. Two startups were examined: a fintech software startup that incrementally augmented human teams with AI tools, and a game technology startup designed around AI-supported workflows from inception.