Episode Details

In this episode of the Crazy Wisdom Podcast, I, Stewart Alsop, talk with AJ Beckner about how AI is reshaping the terrain of software development—from the quiet ritual of mate-fueled coding sessions to the radical shift in DevOps, tool use, and what it means to build software in a post-LLM world. AJ shares insights from his time in the Gauntlet AI program, reflecting on how platforms like Cursor, Lovable, and Supabase are changing what’s possible for both seasoned engineers and newcomers alike. We also explore the nuanced barbell dynamic of skill disruption, the philosophical limits of current AI tooling, and how rapid prototyping has morphed from a fringe craft into a mainstream practice. You can find more about AJ on Twitter at @thisistheaj.

Check out this GPT we trained on the conversation!

Timestamps

00:00 — Stewart and AJ kick off with mate culture, using it as a metaphor for vibe coding and discussing AJ’s caffeine stack of coffee and yerba mate.
05:00 — They explore how AI coding reshaped AJ’s perspective, from URBIT and functional languages to embracing JavaScript due to LLMs’ strength in common corpuses.
10:00 — AJ breaks down why DevOps remains difficult even as AI accelerates coding, comparing deployment friction across tools like Cursor, Replit, and Lovable.
15:00 — They outline the barbell effect of AI disruption—how seasoned engineers and non-technical users thrive while the middle gets squeezed—and highlight Supabase’s role in streamlining backends.
20:00 — AJ dives into context windows, memory limits, and the UX framing of AI’s intelligence. Cursor becomes a metaphor for tooling that “gets it right” through interaction.
25:00 — Stewart reflects on metadata, chunking, and structuring his 450+ podcast archive for AI. AJ proposes strategic summary hierarchies and cascading summaries.
30:00 — The Gauntlet AI program emerges as a case study in training high-openness engineers for applied AI work, replacing skeptical Stanford CS grads with practical builders.
35:00 — AJ outlines his background in rapid prototyping and how AI has supercharged that capacity.
40:00 — The conversation shifts to microservices, scale, and why shipping a monolith is still the right first move.
45:00 — They close with reflections on sovereignty, URBIT, and how AI may have functionally solved the UX problems URBIT originally aimed to address.

Key Insights

1. AI reshapes the value of programming languages and stacks: AJ Beckner reflects on how large language models have flipped the script on what makes a programming language or stack valuable. In a pre-LLM world, developers working with niche, complex systems like URBIT sought to simplify infrastructure to make app development manageable. But now, the vast and chaotic ecosystem of mainstream tools like JavaScript becomes a feature rather than a bug—LLMs thrive on the density of tokens and the volume of shared patterns. What was once seen as a messy stack becomes a fertile ground for AI-assisted development.
2. DevOps remains the bottleneck even in an AI-accelerated workflow: Despite the dramatic speedups in building features with AI tools, deployment still presents significant friction. Connecting authentication, deploying to cloud services, or managing infrastructure introduces real delays. Platforms like Replit and Lovable attempt to solve this by integrating backend services like Supabase directly into their stack, but even then, complexity lingers. DevOps hasn’t disappeared—it’s just more concentra