No objective way to know if a different AI model, reasoning level, or tool would actually be better — permanent uncertainty drives endless trial and anxiety

Summary

Users face a growing hierarchy of AI models, reasoning levels, and tools with no reliable quality benchmark, so they cannot tell whether a poor answer is caused by their choice or by the tool itself. Responsibility shifts onto the user: they ask, doubt, switch settings or tools, compare outputs without objective measures, and repeat — a perpetual upgrade/trial cycle that consumes hours and creates fear of choosing incorrectly. Existing directories and vendor UIs show more options but provide no task-based, outcome-anchored recommendation or comparison that tells someone which capability level or tool actually fits their goal and when a cheaper/simpler option is 'good enough.' This is unsolved and high-pain because it turns AI from a cognitive-load reducer into a source of decision fatigue, wasted spend, and eroded trust. A recommendation engine that maps a plain-language task to the right tool/model level — and justifies the choice — would directly eliminate this permanent uncertainty.