{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Concept To Cloud","title":"Your Users Don't Care If It's AI - They Just Want Results","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/718b6d71\"></iframe>","width":"100%","height":180,"duration":1179,"description":"Tom Barber challenges the AI hype cycle, arguing that users care about outcomes, not architecture. Learn why slapping an 'AI-powered' label on everything is the wrong approach, and discover how to thoughtfully integrate LLMs into products without falling into common pitfalls like dependency on unstable APIs or unnecessary chatbot interfaces.\nShow Notes\nEpisode Overview\nTom Barber returns with a critical examination of AI integration in modern software development, challenging teams to focus on user outcomes rather than jumping on the AI hype train.\nKey Topics Covered\nThe AI Marketing Problem\nWhy 'AI-powered' labels are often meaningless marketing\nThe difference between machine learning (which has existed for decades) and modern LLMs\nExamples of invisible AI: spam filtering, fraud detection, map rerouting\nUsers grade products on consistency, not on the impressiveness of the underlying model\nEngineering Considerations for LLM Integration\nChoosing the right model for your specific use case (Opus, Sonnet, GPT-4, etc.)\nTradeoffs between cost, speed, and inference quality\nBuilding evaluation systems and fallback paths\nManaging latency budgets and graceful degradation\nHandling API outages from providers like Anthropic and OpenAI\nThe risks of depending on frontier models that can be deprecated\nTrust and Transparency\nAI as a potential trust liability\nManaging user expectations around hallucinations\nThe importance of data provenance and quality (garbage in, garbage out)\nWhen and how to disclose AI usage to users\nThe ethical obligation to be transparent when AI makes consequential decisions\nProduct Strategy\nWhy you can't charge an 'AI tax' on top of existing pricing\nPricing based on outcomes, not on the technology stack\nHow to use LLMs to deliver genuine efficiency gains\nReducing user overhead and friction through thoughtful AI integration\nBeyond Chatbots\nWhy chatbots may be the most inefficient way to interact with LLMs\nThe challenge: How to integrate LLMs without forcing...","thumbnail_url":"https://img.transistorcdn.com/PxMdoqfN_29mQkukm_nn1W_IIYV1IIAUO08NXqUzges/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8wNTI4/MmI4OTJkZTVkNjZi/YTExNjA5ZTFlYjRm/M2U0NC5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}