AI isn’t going to take your job, it’s going to change your job. There are things you do as part of your job now which will go away, but new parts will be added.

I recently built a production-ready application entirely with AI assistance, 100% written by the AI - not as a proof-of-concept, but as a functional tool designed to solve complex workflow problems.

It wasn’t magic, it was work.

• AI-assisted development accelerates both coding speed and error propagation, with risks like cascading mistakes and design flaws magnified by AI’s context limitations, akin to Knight Capital’s $500M human-error-driven collapse.
• Real-world failures (e.g., Knight Capital) stem from process gaps, not technology; AI intensifies this by compressing multi-year human error patterns - like developers Alice, Bob, and Charlie inheriting undocumented code under time pressure - into hours or days.
• AI lacks persistent context, acting as a “new developer” per request, which forgets safeguards, amplifies poor documentation, and misses architectural pitfalls unless explicitly guided by experienced oversight.
• Defensive coding practices (e.g., explicit error checks, copy-before-move for files) are critical since AI won’t self-advocate for safeguards - users must enforce quality control as architects and reviewers.
• While AI democratizes development, inexperienced users often overlook foundational pitfalls, making seasoned judgment essential to prevent small oversights from escalating at machine speed.

Ever feel like you’re whispering into a megaphone when talking to AI? You’re not alone - and the fix is simpler than you think.

Are you curious about how AI can enhance your work, but feel overwhelmed by the technical jargon? If so, you’re in the right place!

When you last interacted with AI, did it seem like a mysterious magic box, or did you feel empowered to mold it to your needs?

In this primer, we’re going to remove the magic, and help you understand it as a tool you can manipulate.