Models Are Hitting Diminishing Returns Within Software Engineering

For the creds: I'm a distinguished engineer at a hyperscaler and work in the space. We've seen Claude's Fable 5 release recently, and I've been having a go at it. Thus far, I wouldn't be able to tell if you did a blind test which model I was using. If you had put Opus 4.6, 4.7, 4.8 and Fable in my Claude Code setup, based on the work I do and how I work, I wouldn't be able to tell which is which. The reasoning is pretty straightforward, in that I never 'one-shot' a project. Since I need to understand every component inside and out, I work in small chunks - and I'm not alone. Moreover, models have had access to the Internet's wide suite of information such as API docs, best practices, etc for a while - which added 'intelligence' of a certain flavour to the models outputs. So when you look at how software engineers in industry work, we work in singular abstractions, test those abstractions and move on. I can almost do this today with local Gemma 4 models. This is also true for system architecture asks, where understanding every component is pretty crucial. And Fable still hallucinates on this. Example: Fable got the AWS ALB/ECS draining behaviour completely wrong, and confidently so. The only reason I was able to catch it is that I was already familiar with how those two pieces work together. So anyways, in short, we're hitting an asymptotic limit here. I'm not getting more value from every model release anymore, and the way I work isn't changing. Having spoken to my colleagues who are heavy AI enjoyers, my views also track with their own experiences as well. Anecdotally, by this time next year, I believe there will be local models you can run on a 128GB MacBook Pro that will provide 90% of the value Claude adds to my software engineering work today. I can already see this with the current suite of open source models.