As AI reshapes how startups build products and run businesses, the conversation is moving beyond productivity hacks to a deeper question: what does it mean to be AI-native? That question took center stage at the Snowflake x AWS Mixer on July 3, where a panel explored ‘How AI-Native Startups Are Scaling Revenue Without Scaling Teams’. Moderated by Shivani Muthanna, Senior Director – Content Partnerships, YourStory, the panel featured Sandipan Mitra, Co-Founder and CEO, Hungerbox; Kaushal Singh, VP, Tech, Jar; Joshua Gautham, Deputy COO (Acting CTO), The Reward Store (A Vananam Company); Sanat Kumar Mohapatra, Director, Product Engineering, Aurigo Software Technologies; and Shobhit Gupta, Principal, Avataar Ventures. The discussion was followed by a live demonstration from Snowflake showing how agentic AI can move beyond analysing data to reasoning over it and triggering actions through intelligent workflows. Every startup today claims to have an AI strategy. But the panel argued that becoming AI-native is less about branding and more about solving problems that were previously impossible. For Hungerbox, the shift came from moving away from abstract strategies to addressing specific customer needs. “Today we feel that slowly we are now being able to use AI rather than being used by AI,” Mitra said. Hungerbox’s cafeteria platform now recommends meals by combining ordering history, consumption patterns, ratings, dietary preferences, and nutritional information. On the operations side, AI helps food partners forecast demand, reducing wastage by predicting daily requirements. Internally, Hungerbox’s 40-45 member engineering team has become nearly 1.5 times more productive. For fintech platform Jar, AI’s biggest impact has been on customer confidence. Handling nearly three million daily transactions means even small uncertainties can affect trust. Jar introduced AI-powered voice support at critical transaction moments, allowing customers to speak to an AI agent in their preferred language. For users in Tier II and Tier III cities, this reassurance has been crucial. Internally, AI has shortened development cycles. Tasks that once took days can now be completed in under an hour, enabling faster validation, MVPs, and iteration. Singh cautioned, however, that in transaction-heavy systems, human oversight remains indispensable. Gautham explained how The Reward Store focused first on internal bottlenecks. By rebuilding workflows with AI integrations, the company reduced friction between engineering, product, and operations teams. AI also transformed customer support by connecting banking systems, transaction histories, and internal knowledge, enabling support teams to instantly determine reward eligibility without lengthy back-and-forth. With every company now claiming to be AI-first, investors are looking beyond labels. “If you’re AI-first, don’t tell me you’re AI-first. Show me you’re delivering an outcome that wasn’t possible, say four years ago,” Gupta sa
