More The AI Native Dev episodes
Published 13 Jan 2026
Duration: 4355
An AI-focused podcast discusses the evolving dynamics of AI development, shifting from knowledge-based systems to intelligence-driven approaches, with a focus on AI agents enhancing productivity through context management.
In this special milestone episode, Simon Maple and Guy Podjarny celebrate 1 million views by looking back at the chaos of 2025 and forecasting the hig...
The podcast examines the progression of AI development, highlighting a transition from knowledge-focused strategies to intelligence-driven methodologies. It emphasizes the growing significance of AI agents in boosting productivity by effectively managing context, enabling them to perform complex tasks through structured information and automation. The discussion forecasts that by 2026, open AI models will be deeply integrated into everyday workflows, with agent-based systems becoming more prevalent. The shift from traditional prompt engineering to context-driven AI interactions is explored, alongside challenges in developing reliable agent behaviors and evaluating their performance. Managing knowledge to direct AI performance remains a critical factor in this evolution. Industry trends also come into focus, including the adoption of platforms like Slack in development processes, the expansion of solo-founder startups, and rising competition in AI models and tools. Looking ahead, the podcast notes advancements in AI agents in 2025 and anticipates a focus in 2026 on optimizing agent use, assessing return on investment, and refining context management to enhance AI effectiveness.
12 May 2026 "AI Doesn't Stand for Artificial Intelligence" Venkat Subramaniam's Take Will Change How You Think About It
AI in software development requires balancing its speed and automation with human accountability for safety and ethics, emphasizing rigorous quality practices, community-driven adoption, critical thinking, and oversight to ensure AI complements rather than replaces human judgment.
5 May 2026 The Creator of Spring Thinks You Can't Code Serious Software With AI
Integrating AI into enterprises via HTTP calls and existing infrastructure requires balancing language agnosticism, deterministic frameworks like GOAT, Java/Kotlin over Python for reliability, and prioritizing explainability, human oversight, and alignment with business logic over overreliance on AI for simple tasks.
28 Apr 2026 What OpenAI, Stripe & ElevenLabs Devs Do Differently Now | AI Native Dev
The text examines challenges in integrating AI into software workflows, highlights AI-native practices like Stripe's Minions automating code tasks, emphasizes balancing human oversight with automation, and explores future trends in agent-native engineering, specialized models, open-source tools, and ethical considerations in AI-driven development.
21 Apr 2026 Logan Kilpatrick on Who Ships AGI, DeepMind and the Problem With More Software
Software developers must adapt to AI's complexity by bridging tools with real-world applications, mastering context engineering, shifting toward high-level design and system architecture, and navigating fragmented ecosystems as AGI emerges as an integrated system rather than a standalone model.
14 Apr 2026 Everything 100 Episodes Revealed About AI Native Dev
AI in software development shifts from code-centric practices to context- and specification-driven approaches, with humans prioritizing decision-making and oversight while AI handles implementation, but challenges like non-human-readable code, alignment with team practices, and contextual accuracy remain critical.