More The AI Native Dev episodes
Published 6 Jan 2026
Duration: 4399
The podcast explores AI's integration in software development, discussing tools, strategies, and challenges for improving productivity, collaboration, and decision-making.
What does it take to make AI work inside engineering teams? This high-stakes compilation episode with Ian Thomas (Meta), Wesley Reisz (ThoughtWorks),...
The podcast covers the growing role of AI in modern software development, with a focus on tools like Cursor and Cloud Code that improve developer productivity by offering AI-assisted coding features. It delves into concepts such as metaculture and engineering empowerment, stressing the need for evidence-based decisions and cooperative efforts between developers and large language models (LLMs) using frameworks like Ripper 5. The discussion also addresses the limitations of legacy software architectures and potential strategies for migrating to more modern, efficient systems, including the use of semantic layers and query engines.
Further, the podcast explores how AI contributes to faster and more accurate testing, code generation, and system validation. Key considerations include the importance of context, customization based on rules, and the value of community-driven knowledge sharing. Topics such as model selection, benchmarking, and the ongoing evolution of AI assistants are also examined, highlighting the trade-off between the speed and depth of AI outputs. Additionally, the impact of AI on team workflows, productivity metrics, and the transition toward value-based AI adoption in companies like Meta are discussed, alongside practical methods such as spec-driven and behavior-driven development.
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.