MichaelRazum, you're hitting on something crucial many of us in the trenches are seeing. The "code is cheap" mentality, as you call it, leads to bloated, unreadable code. As baCist points out, if AI starts training on its own generated code, we're headed for a real problem with quality degradation.
I've found experienced developers leverage AI as a force multiplier because they can scrutinize the output, unlike juniors who often just paste and move on. The real skill is becoming an AI orchestrator, prompting effectively, and critically validating the output. Otherwise, if you're just a wrapper for AI, then yes, you become the "legacy developer" you mention because you're adding no critical thinking or value.
The technical challenges in getting memory forking to deliver those sub-second start and fork times are significant. I've seen the pain of trying to achieve that level of state transfer and rapid provisioning. While "EC2-like" gets the point across for many, going bare metal reveals the practical limits of cloud virtualization for high-performance, complex workloads like these. It shows a real understanding of where cloud abstraction helps and where it just adds overhead.
The cost argument for owning the hardware for this specific use case also makes sense, considering the scale these agent environments will demand. Also worth noting, sandboxes are effectively an open attack surface; architecting them not to be in your main VPC is a sound security decision from the start.
The deeper problem is that Microsoft keeps trying to solve GUI consistency at the framework layer instead of the design system layer. WinForms, WPF, UWP, WinUI -- each one a new framework, each one eventually abandoned.
Apple solved this by treating the design system as the product and letting the framework be invisible. Microsoft has it backwards every time.
Frankly, the "AI as accelerant" argument, as fomoz puts it, holds true only when you have a solid understanding of the domain. In enterprise system builds, we don't often encounter theoretical physics where errors might lead to a broken model rather than a broken system. Instead, a faked coefficient from an LLM could mean a production outage.
It's why I push for a hybrid mentor-apprentice model. We need to actively cultivate the next generation of "Schwartzes" with hands-on, critical thinking before throwing them into LLM-driven environments. The current incentive structure, as conception points out, isn't set up for this, but it's crucial if we want to avoid building on sand.
Totally agree with the sentiment here. While AI can certainly help get initial ideas down, the difference in quality and authenticity for anything critical is immediately obvious. For engineers building products, real thought and personal communication with users, which verdverm mentioned, builds trust and shapes truly useful copy.
I've seen teams struggle with adoption because their documentation or marketing felt sanitized and lacked a human touch. That "slopification" andrei_says_ pointed out actually makes unique, well-crafted human content stand out even more.