That was a nice read and a great analysis, Lynette.
The finding about the productivity gap between junior (40%) and senior developers (7%) matches what I found after analyzing 2,260 documented AI use cases.
The most successful implementations weren't just using AI for code generation. Companies like Factory and Bito achieved 2-4x faster development by completely restructuring workflows around AI agents. They're saving developers a full workday per sprint by reducing context switching, not just autocompleting code faster.
Your point about AI being like "extremely novice coders" is spot on. In my dataset, the winners were companies that embraced this reality of using AI for volume and speed while keeping humans for architecture and quality control. Interestingly, some of the most creative coding AI applications came from non-tech sectors like banks automating compliance code, and healthcare organizations building data pipelines.
That was a nice read and a great analysis, Lynette.
The finding about the productivity gap between junior (40%) and senior developers (7%) matches what I found after analyzing 2,260 documented AI use cases.
The most successful implementations weren't just using AI for code generation. Companies like Factory and Bito achieved 2-4x faster development by completely restructuring workflows around AI agents. They're saving developers a full workday per sprint by reducing context switching, not just autocompleting code faster.
Your point about AI being like "extremely novice coders" is spot on. In my dataset, the winners were companies that embraced this reality of using AI for volume and speed while keeping humans for architecture and quality control. Interestingly, some of the most creative coding AI applications came from non-tech sectors like banks automating compliance code, and healthcare organizations building data pipelines.
If you're curious about broader AI adoption patterns beyond coding, check my write-up and dataset here: https://abbasmahdi.substack.com/p/i-analyzed-2260-ai-use-cases