#334: The debate over whether AI saves developers time misses a fundamental truth: coding was never the hardest part of software development. Writing code is mechanical work - the real challenges have always been understanding problems, designing solutions, communicating with stakeholders, and navigating organizational complexity. AI is now forcing a reckoning with this reality, pushing developers at every level to reconsider what skills actually matter.

The traditional separation between architects who design and developers who implement is breaking down. AI enables a return to something like pair programming, where the person thinking through problems can now work alongside a fast executor without the old bottleneck of slow human typing. This shift means developers need stronger communication skills - the ability to explain technical decisions to non-technical stakeholders and translate business requirements into technical direction. For juniors, the opportunity is unprecedented: you can upskill faster than ever in the history of software, but only if you balance building things with actually understanding how they work.

Darin and Viktor explore what this means for developers at every career stage, from juniors who should focus on fundamentals and end-to-end understanding, to seniors who are becoming more like editors and supervisors of AI-generated work. The developers who will thrive are those who combine real experience with a willingness to embrace change - and that combination has always been the winning formula.

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#333: Pete Hunt, CEO of Dagster and early React team member, explores the evolution from Facebook's early React development through trust and safety infrastructure at Twitter, to building modern data orchestration tools. The conversation reveals how similar infrastructure problems plague every industry - whether you're launching rockets or managing porta-potties, the core challenges remain consistent: late data, quality issues, and mysterious errors that require both automated solutions and human oversight.

The discussion dives into the technical realities of scaling systems, from the microservices complexity trap to the current AI adoption wave. Hunt shares candid insights about leadership challenges, including how well-intentioned technology recommendations can backfire, and why most data projects fail despite sophisticated multi-agent orchestration. The conversation touches on career advancement pressures that drive unnecessary complexity and the importance of focusing on actual user adoption rather than technical sophistication.

This episode features Pete Hunt in conversation with hosts Darin and Viktor, covering everything from regular expression nightmares to the future of data infrastructure and the lessons learned from building products that people actually use.

Pete's contact information:

X: https://x.com/floydophone

LinkedIn: https://www.linkedin.com/in/pwhunt/

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#332: AI adoption in enterprise software development is accelerating, but operations teams are lagging behind. While application developers embrace AI tools at a rapid pace, those on the ops side remain skeptical—citing concerns about determinism, control, and a general resistance to change. This mirrors previous technology waves like containers, cloud, and Kubernetes, where certain groups initially pushed back before eventually adapting. The prediction for 2026: AI will not see widespread adoption in operations despite its growing presence elsewhere in the software lifecycle.

The bigger challenge facing organizations is not just adopting AI but transforming entire processes to take advantage of it. Improving just one piece of the software delivery pipeline—like development speed—only creates bottlenecks elsewhere. Companies cannot hand developers AI tools while keeping everything else the same and expect transformational results. The future points toward a world where experts bring their own AI agents to companies: personal toolsets trained on their experience and best practices that integrate with organizational systems.

Perhaps the most provocative insight centers on the value of writing code itself. The argument: writing code is the easiest and least valuable part of software development. The real cognitive load comes from thinking through requirements, architecture, and design. Developers who simply translate instructions to code without deeper engagement may find themselves in real danger as AI continues to advance. Darin and Viktor explore these predictions and more as they look ahead to what 2026 might bring for DevOps, platform engineering, and the evolving role of developers.

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