Dan Luu argues that while LLMs have improved at code generation, relying on them without human oversight produces broken software. The real problem isn't LLM capability—it's that people declaring "programming is solved" are shipping products that don't work and fooling themselves about quality standards.

• LLMs fail consistently on out-of-distribution problems, nuanced requirements, and tasks with hidden constraints—exactly what real software projects contain. They perform well only on narrow, well-defined tasks.
• When developers outsource thinking to LLMs and walk away, the software has serious bugs: infinite loops, broken functionality, overfitting to tests, and logic errors that code review catches immediately.
• The gap between "works for me" personal projects and shipping commercial products matters. People claiming LLMs solve programming often confuse the former with the latter, or ship low-quality code while ignoring obvious defects.

The article discusses how the rise of AI tools, particularly LLMs, has affected software engineering and data work. While some engineers are concerned about the declining quality of code, data professionals find value in these tools for generating quick, low-maintenance solutions. It emphasizes the need for careful evaluation of the new data generated by these systems.

• Fast, disposable code from LLMs isn't a problem for data work the way it is for engineers building long-term systems, since data scientists often throw away code after one use anyway
• LLMs are genuinely useful for building data extraction tools (e.g., pulling usable data from messy PDFs), unlocking previously inaccessible data sources with minimal effort
• Using LLMs to generate synthetic data is risky and shouldn't replace real human data, especially in user experience research
• As LLMs improve at generating and explaining code, data professionals need less hand-holding from software engineers for low-risk coding tasks, shrinking the traditional divide between the two roles

This article explores how advancements in software design, particularly through LLMs, shift the focus from using standard libraries to generating custom code. It highlights the implications for dependency management and emphasizes the need to understand the problem being solved rather than just the mechanics of coding. The author compares this shift to the evolution of 3D printing in manufacturing.

• LLMs flip the standard-library calculus: the default question becomes "is this worth a dependency?" instead of "is there a library for this?", since custom code is now cheap to generate.
• Unlike 3D printing, LLM-generated code has no inherent quality penalty versus hand-written code—if it's correct, it performs identically, unlike physical printed parts that sacrifice strength/precision.
• Design cost (understanding the actual problem/business logic) doesn't disappear even as coding cost collapses—LLMs don't grasp constraints on their own, so that burden stays on developers.
• The tradeoff shifts from dependency/version management to maintenance and vulnerability-tracking responsibility for more bespoke, less shared code.

In a podcast discussion, predictions for the tech industry in 2026 are shared, highlighting the undeniable improvement of LLMs in writing code, advancements in coding agent security, and the potential obsolescence of manual coding. Other predictions include a successful breeding season for Kākāpō parrots and the implications of AI-assisted programming on software engineering careers.

• LLMs will keep getting undeniably better at writing code through 2026, shifting more programming work to coding agents
• Security around coding agents will become a major focus as they gain more autonomy and access to systems
• Manual hand-coding may start becoming obsolete for significant portions of software engineering work, reshaping the profession
• Kākāpō parrots are predicted to have a successful breeding season in 2026