In Episode 12, Pete and Andy explore common AI myths and misconceptions, diving deep into interface design, the productivity vs creativity paradigm, and how work might evolve to resemble play in an AI-enabled future.  

Key Discussion Points:  

*Reflections on Guest Episodes (00:00-03:20)*

• Dynamic of having guests vs. just the two hosts

• Preference for discussion format over structured interviews

• Organic conversation flow versus scripted content

*The "I Trained the Model" Myth (03:20-10:30)*

• Misconception between fine-tuning vs. adding context/documents

• Most "training" is actually just attaching PDFs or system prompts

• LLMs should handle interface, not factual recall

• Context engineering as the superior approach over model training

*Small vs. Large Language Models (10:30-16:30)*

• The "Ferrari for grocery shopping" mentality - overusing frontier models

• Small language models as the better choice for repetitive commercial workflows

• Cost and speed advantages of smaller models for specific tasks

• Modular approach: using right-sized models for different pipeline steps

*Interface Design Myths (16:30-27:30)*

• Chat as the default AI interface limiting potential

• Need for adaptive interfaces suited to different working styles

• Microservices architecture finally becoming economically viable with AI

• Moving beyond monolithic "big model for everything" approach

*Flow State and Adaptive Interfaces (27:30-39:00)*

• Spreadsheets as example of adaptive, durable tools

• Visual vs. text-based collaboration preferences

• The ramp-up/ramp-down challenge when returning to complex projects

• Multiple input/output modalities for different contexts

*Human Collaboration Patterns (39:00-48:00)*

• Engineers gravitating to whiteboards for collaboration

• The canvas as shared workspace vs. individual thinking space

• Voice, visual, and collaborative interfaces serving different needs

• Balancing real-time interaction with persistent documentation

*Creativity vs. Productivity Paradigm (48:00-58:00)*

• AI as creative enabler rather than just productivity booster

• The scary prospect of agency - having to decide what to work on

• Embodied human experience as irreplaceable for insight generation

• Examples from Rory Sutherland: mirrors in elevators, train comfort over speed

*The Future of Work as Play (58:00-1:08:00)*

• Moving from medieval peasant schedules to office work and back to leisure

• Work resembling exploration and experimentation

• The role of craft and embodied skills in an AI world

• Victorian gentlemen as preview of future leisure class

*Error Tolerance Double Standards (1:08:00-1:14:00)*

• Unrealistic expectations for AI accuracy vs. human error rates

• Need for same systems and processes, just faster iteration cycles

• Human mistakes tolerated due to context; AI mistakes seen as fundamental flaws

Key Insights:  

"It's not the job of the model to know stuff... the best way to get good factual core from these things is context engineering." 

"Why are you in a hurry? Take your time. Be really comfortable. We'll get rid of the plebs." - On reframing problems 

"The future's here, it's just not evenly distributed" - Applied to leisure and creative work 

 *Bottom Line:* AI myths persist because people experience AI through limited interfaces and apply unrealistic error expectations. The real opportunity lies in modular, adaptive systems that enable work to become more play-like, with humans focusing on embodied creativity and meaning-making while AI handles decomposed tasks.