Control systems for fleets in Constant Motion

As AI has entered into work environments as a productivity booster, I've seen polarizing views on how much creative influence AI should have in our work. Some use AI for everything, vibe coding their way through a project until they've lost touch with how it is designed and actually works. Others resist using AI, trading off speed and efficiency and often reinventing the wheel when solutions already exists.

I spent years building everything by hand, ground up. It taught me to think critically through problems, then visualize and research the best solution. This process created a solid foundation of engineering skills. However, the approach is tedious, and it requires remembering every intricate detail about a project: a blessing and a curse when a project grows and requires scale.

For the past few years, I have settled into a middle ground of using AI for what it excels at, productivity and acceleration. I set the vision and direction, and plan exactly what I want the architecture to look like.  AI then challenges and refines my plan, executes deep research on feasibility, and runs extensive test cycles to ensure performance.  I collaborate with AI throughout the full project lifecycle to create, refine and harden the final product.

Expectations keep climbing on both speed and quality,and I've found that AI raises quality first and foremost. When working with AI, it helps us design systems right the first time and we spend less time recursively debugging. Speed then follows quality, for those who know how to effectively and responsibly use Artificial Intelligence.