{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Runpoint: AI Business Transformation Podcast","title":"10 Hard Questions • This Week in AI","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/9eae99ae\"></iframe>","width":"100%","height":180,"duration":2550,"description":"Two builder-operators break down the last two weeks in AI using 10 Tyler Cowen–style questions. We get into Sora 2’s cameo culture, whether “thinking” models are worth the latency, agents that actually help, model choice for client work, the energy/compute wave, and why open-weights like DeepSeek matter (or don’t) for practitioners.\nWhat you’ll get\nPractical takes from people shipping client projects\nWhere Claude 4.5 vs GPT shines (coding vs writing)\nWhen to use “extended thinking/deep research” vs fast models\nReal talk on agents, meeting schedulers, and workflow design\nEnergy, nuclear, and why AI ≈ infrastructure\nOpen-weights vs ecosystems: where the moat really is\nChapters\n00:00 – Cold open & intro\n00:26 – Who’s Tyler Cowen and why this format\n02:00 – Q1: Sora 2, IP, and the “cameo economy”\n06:44 – What we’re doing in this episode (format explainer)\n08:11 – Q2: GPT apps & the VibeCoder value prop (workflow architect vs app builder)\n17:03 – Q3: “30-hour agents” & autonomy myths (Claude, Replit Agent)\n22:57 – Q4: When to use thinking models vs fast models (and deep research)\n29:04 – Q5: SB-53 AI transparency—useful or compliance theater?\n30:24 – Q6: Picking models for clients: capability, brand, or last best output?\n37:01 – Q7: Agents that actually help (Lindy scheduling, weekly pain points)\n41:34 – Q8: Compute, energy, and nuclear—should builders be optimistic?\n46:50 – Q9: DeepSeek R1 costs & the real moat (ecosystems > raw perf)\n49:33 – Wrap-up & feedback ask\nLinks & mentions (non-sponsored)\nTyler Cowen / Marginal Revolution\nAnthropic Claude 4.5 (coding + writing)\nOpenAI GPT-5 (auto/fast tasks), Deep Research modes\nLindy meeting agent\nReplit Agent 3 (autonomous build experiments)","thumbnail_url":"https://img.transistorcdn.com/J8t_zr9ARjI2f2cdFABJsSzRDnvqZ_RmHIhyQWPPIXs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9jZjVl/ZmRjZDdmZjBiNDY3/ZjNjMWQxY2ZkMDY3/Y2FiNS5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}