{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"AI Thoughtmakers","title":"The Missing Backend : Why AI Prototypes Fail in Production","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/a8dc99bc\"></iframe>","width":"100%","height":180,"duration":1028,"description":"AI can generate impressive apps in minutes. But why do so many AI-powered prototypes fail when real users start using them?\n\nIn this episode of AI Thoughtmakers, we sit down with Manuinder Sekhon, Tech Lead I at GeekyAnts, to discuss why many AI-generated applications struggle in production despite looking polished on the surface.\n\nFrom authentication gaps and database bottlenecks to observability, scalability, and backend reliability, we explore the invisible engineering work that separates a demo from a real product.\n\nKey topics covered:\n • Why AI-generated prototypes often collapse under real user traffic\n • The hidden 90% of engineering work founders don't see\n • Common backend issues in AI-generated applications\n • Why observability and monitoring are essential for production systems\n • The risks of shipping AI-built products too early\n • How AI coding tools are changing startup expectations\n • The difference between validating an idea and launching a product\n • What founders should do before building with AI tools\n\nSubscribe for more conversations on AI, engineering, product development, and the future of software.\n\nConnect with the Speakers\nLinkedIn -   / manuindersekhon  \n\nConnect with Prem:\nLinkedIn -   / premgoswami  ","thumbnail_url":"https://img.transistorcdn.com/9ucptEYd5kVPtNA_zR1pGGq70YIZjxQEgWQXPqsHHDg/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8yNGQz/ZmI1YWRiYWMxMTM3/YmQyNDA4YTQ2ZTY4/N2RjNi5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}