{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Engineering Evolved","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.Show NotesEpisode OverviewTom 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.Key Topics CoveredThe AI Marketing ProblemWhy 'AI-powered' labels are often meaningless marketingThe difference between machine learning (which has existed for decades) and modern LLMsExamples of invisible AI: spam filtering, fraud detection, map reroutingUsers grade products on consistency, not on the impressiveness of the underlying modelEngineering Considerations for LLM IntegrationChoosing the right model for your specific use case (Opus, Sonnet, GPT-4, etc.)Tradeoffs between cost, speed, and inference qualityBuilding evaluation systems and fallback pathsManaging latency budgets and graceful degradationHandling API outages from providers like Anthropic and OpenAIThe risks of depending on frontier models that can be deprecatedTrust and TransparencyAI as a potential trust liabilityManaging user expectations around hallucinationsThe importance of data provenance and quality (garbage in, garbage out)When and how to disclose AI usage to usersThe ethical obligation to be transparent when AI makes consequential decisionsProduct StrategyWhy you can't charge an 'AI tax' on top of existing pricingPricing based on outcomes, not on the technology stackHow to use LLMs to deliver genuine efficiency gainsReducing user overhead and friction through thoughtful AI integrationBeyond ChatbotsWhy chatbots may be the most inefficient way to interact with LLMsThe challenge: How to integrate LLMs without forcing users to type everythingAsking...","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}