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Two reasons: 1. Product decisions require far more knowledge than coding and architecture decisions, and that knowledge is harder to come by. It often requires research, talking to customers, understanding the organization of the company, the company's mission, the company's employees, their performance, stakeholders, the competitive landscape, funding, growth/sales/marketing strategies and budgets, customer buying patterns, company strategy, etc. Not to mention the code, the coding backlog, etc. Companies have to compete in a hostile, competitive, capitalistic market, which means they're trying to beat other companies, which means a seemingly very good product decision can actually be bad solely because other people are doing it better. Imagine if your perfectly functional code failed to compile simply by nature of the fact that better programmers exist. 2. Product design requires taste, and taste is partly a function of novelty, but there aren't that many LLMs, so everyone's using the same few LLMs, therefore everyone's getting the same designs, therefore the designs look like clones of each other rather than novel thoughtful design, therefore they fail the taste bar. In addition, LLMs simply weren't trained on good vs. bad visual or product design nearly as much as they've been trained on good vs. bad, functional vs. non-functional code.
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I see these more fundamentally as arising from whether a good feedback loop is possible or not. The same is fundamentally true for human experts; you need validation that your work was good or not, or you cannot improve. With code, it's fairly easy to have unit or end to end tests. That can be relatively objective and deterministic, and pretty quick. Architecture is one layer removed, and far harder to test. You're stuck with things that are at least an order of magnitude to prove autonomously; will it scale in terms of performance, will the abstractions be a good fit for future requirements, etc. Testable, but much harder. Design; well, there's possibly some metrics by which you could measure UX (attention for the viewer, contrast ratios, number of actions needed to achieve something, how far the mouse had to travel), but really the majority of it is subjective. "How does the human feel when viewing this" - I suspect almost by definition needs either a human, or a very good model of it. I don't think we have the latter, making this pretty hard.
Thank you, that is an excellent explanation, I couldn't have phrased it better myself.
> It often requires research, talking to customers, understanding the organization of the company, the company's mission, the company's employees, their performance, stakeholders, the competitive landscape, funding, growth/sales/marketing strategies and budgets, customer buying patterns, company strategy, etc. You're relying on that information not being in the training data, because you are working with third-party generic models, so your expertise is safe.
Awesome, thanks. Also to jwr.