Almost half of commercial code is now AI-generated. Code churn has doubled and the engineers who built these systems can't always explain how they work anymore.
This is the part of AI adoption many organizations still underestimate.
Technical debt used to be mostly about maintainability of code.
But AI systems can also create “cognitive debt” inside teams:
people relying on generated outputs they no longer fully understand, verify, or reason through deeply.
The scary part is that the system can appear productive on the surface while understanding quietly erodes underneath.
That’s why I think the real AI discussion is less about intelligence itself and more about dependency, optimization pressure, and institutional behavior.
In The Hitchhiker’s Guide to the Galaxy, Douglas Adams mocked philosophers demanding “rigidly defined areas of doubt and uncertainty” when Deep Thought threatened to produce actual answers. Much modern AI criticism follows the same pattern. An expanding class of ethicists, commentators, and professional explainers confidently denounce systems they barely understand, because fear, ambiguity, and cultural panic are easier to monetise than technical competence or intellectual clarity.
This matters for software buyers evaluating AI coding tools. Technical debt from AI-generated code is a real implementation cost that rarely shows up in vendor demos. Worth building into your evaluation criteria.
Well written. AI may speed up production, but unless teams deliberately protect architecture, ownership, and explanation, velocity can eventually become fragility.
I think the underrated part is that code review was never just QA. It was also how teams quietly taught each other judgment. You’d see why someone avoided a certain pattern, why they split logic one way, why they refused the “cleaner” abstraction because they knew it would become cursed in six months. A lot of that knowledge never made it into docs because it lived in review friction.
This article has a negative tone and suggests blindly following an AI tool for the development of code has pitfalls largely created by prompt-based code generation, without really understanding what the code is doing. It has several pointers of disaster, but does not offer any best practices to avoid this pitfall.
I wish it had more of a positive solutioning, rather than just a doomsday.
https://shapeofcinema.substack.com/p/the-actual-hand-on-the-trigger?r=8dbojf
This is the part of AI adoption many organizations still underestimate.
Technical debt used to be mostly about maintainability of code.
But AI systems can also create “cognitive debt” inside teams:
people relying on generated outputs they no longer fully understand, verify, or reason through deeply.
The scary part is that the system can appear productive on the surface while understanding quietly erodes underneath.
That’s why I think the real AI discussion is less about intelligence itself and more about dependency, optimization pressure, and institutional behavior.
I explored a related idea here:
https://www.lifeofqa.com/p/ai-vs-human-is-the-wrong-debate
In The Hitchhiker’s Guide to the Galaxy, Douglas Adams mocked philosophers demanding “rigidly defined areas of doubt and uncertainty” when Deep Thought threatened to produce actual answers. Much modern AI criticism follows the same pattern. An expanding class of ethicists, commentators, and professional explainers confidently denounce systems they barely understand, because fear, ambiguity, and cultural panic are easier to monetise than technical competence or intellectual clarity.
https://jbsections.substack.com/p/academics-denouncing-aino-technical
This matters for software buyers evaluating AI coding tools. Technical debt from AI-generated code is a real implementation cost that rarely shows up in vendor demos. Worth building into your evaluation criteria.
Well written. AI may speed up production, but unless teams deliberately protect architecture, ownership, and explanation, velocity can eventually become fragility.
I think the underrated part is that code review was never just QA. It was also how teams quietly taught each other judgment. You’d see why someone avoided a certain pattern, why they split logic one way, why they refused the “cleaner” abstraction because they knew it would become cursed in six months. A lot of that knowledge never made it into docs because it lived in review friction.
This article has a negative tone and suggests blindly following an AI tool for the development of code has pitfalls largely created by prompt-based code generation, without really understanding what the code is doing. It has several pointers of disaster, but does not offer any best practices to avoid this pitfall.
I wish it had more of a positive solutioning, rather than just a doomsday.