You’ve been coding for three years, but you still panic when Stack Overflow is down. Your AI coding assistant handles most debugging, but when it fails, you’re lost. The code you committed yesterday works, but you couldn’t explain how or why if your life depended on it. You’re not alone, and it’s not entirely your fault.
Across the industry, organisations are making what appears to be a rational decision: hire junior engineers, add AI tools, and watch productivity soar. The simple mathematics seems compelling when you compare the annual cost of a junior engineer plus AI tools against a senior engineer with the same AI investment. This calculation is creating a generation of engineers with years of experience but minimal problem-solving depth. More critically, it’s building a false economy that will compound for years, creating operational fragility that only becomes apparent when systems fail at 2 AM.
In March 2024, Neil Hollis (@neilpaints on Instagram) stood on stage at Adepticon, holding the Golden Demon award for best single Warhammer 40,000 miniature. His Aeldari Exodite, a dinosaur-riding warrior crafted from converted parts, was undeniably stunning and absolutely deserved the win. The paint job was masterful, the conversion work inspired. Yet within hours, the miniature painting community erupted into fierce debate. The controversy wasn’t about his brushwork or colour choices. It was about the backdrop, a lush prehistoric landscape that Hollis had generated using AI.
The software engineering job market has entered an unprecedented state of paralysis. Senior engineers with decades of experience find themselves competing for junior roles, whilst companies post job advertisements that sit unfilled for months. Behind closed doors, engineering leaders whisper about AI replacing their teams, and CFOs question whether they need to hire engineers at all.
This pattern is particularly evident across the UK and Australian markets, where I observe similar contractions in engineering hiring through my consulting work. The phenomenon extends to other developed economies including the United States and European markets, suggesting this isn’t merely a regional correction but a global strategic miscalculation driven by AI expectations.
This article presents a systematic approach to decision documentation that transforms how engineering teams maintain institutional knowledge and make decisions. You’ll learn how to implement pull request workflows for team decisions, create accessible documentation hubs for technical and non-technical stakeholders, and build sustainable processes that reduce repeated discussions whilst improving decision quality. The framework addresses the critical challenge of context erosion in engineering teams and provides practical templates and workflows you can implement immediately.
Engineering leaders face a unique cognitive burden: brilliant at generating solutions and spotting opportunities, yet often overwhelmed by their own creativity. This article explores how to apply engineering principles to idea management, transforming creative overflow from a source of stress into a systematic advantage. Learn practical frameworks for reliable idea capture, evaluation methods like RICE scoring and the Eisenhower Matrix, and how AI tools can enhance your thinking process. Discover how to build your “external brain” — a systematic approach to managing ideas that reduces mental overhead whilst ensuring your best concepts receive the attention they deserve.
