I lean towards your viewpoint as well. Their assumptions (axioms, postulates?) are highly controversial, while the actual principles seem quite sound to me.
The only issue I can see is with #5. I would argue for decision making, you absolutely need a single metric, otherwise the process collapses into bickering over which measure is more important at the time (often for political or interpersonal reasons). The point is a bit vague on what exactly is being evaluated (product quality, which means what?). For launching products or running A/B tests, aim for a single metric as your decision framework. If you must have more than one, then be explicit about the tradeoffs in a flowchart: e.g., "if X is > 0, we launch. If x <= 0, but y > 2%, we launch, otherwise no launch".
The only issue I can see is with #5. I would argue for decision making, you absolutely need a single metric, otherwise the process collapses into bickering over which measure is more important at the time (often for political or interpersonal reasons). The point is a bit vague on what exactly is being evaluated (product quality, which means what?). For launching products or running A/B tests, aim for a single metric as your decision framework. If you must have more than one, then be explicit about the tradeoffs in a flowchart: e.g., "if X is > 0, we launch. If x <= 0, but y > 2%, we launch, otherwise no launch".