I've been running AI models for my dev shop since early 2025 for privacy, reliability, latency, and cost.
I made this image to show my developers the progression of commercial AI vs what we are running on a 3 GPU setup.
I don't think AI model intelligence is a problem today, however, the processes that people generally chose is where the problem starts, and it's how you end up with slop.
Because commercial AI systems are so slow, most people are using agentic coding processes which have the side effect of removing you from the process. Insights that would naturally steer the outcome don't occur. Flaws in your plan that you'd normally notice during the process are also waiting for you in the end product. Problems with the code are also waiting you at the end of the process, and later, since this way of working discourages both that.
So with these processes you are kicking multiple cans down the road and probably never picking them up because that would make you a 2x developer instead of 10x developer.
Instead of this, we use human in the loop processes with high visibility, inside the human work environment. When the AI model makes changes, those changes are highlighted for easy and fast review. Design problems are immediately noticed. No cans get kicked down the road and we consistently end up with human-grade or better product from this process.
We never produce slop because awareness and participation on the programmer's side is as high as it was without AI. This process is slower but it results in shorter, more relevant to the task, and better code quality.
Maintaining architectural and codebase quality is critical in applications we work on; the negative effects of poor code / design choices compound quickly in a large and business critical app. And that's mainly what we work on!
Agentic processes can shine in areas where the code has low criticality / responsibility / isolated context. In these cases, substituting clanker brain for human brain usually works out OK.
The codebases we work on are similar to large corporate codebases. We watched large corporations hose their codebases with agentic processes and then decide to dial it back. They are gradually starting to adopt our ways. Amazon and Microsoft are good examples of this.
In conclusion, i'd say it's not about how big the artificial intelligence is, but it's how you use it!