Pure, disinterested A/B testing where the goal is just to find the good way to do it, and there's enough leverage and traffic that funding that A/B testing is worthwhile is rare.
More frequently, A/B testing is a political technology that allows teams to move forward with changes to core, vital services of a site or app. By putting a new change behind an A/B test, the team technically derisks the change, by allowing it to be undone rapidly, and politically derisks the change, by tying it's deployment to rigorous testing that proves it at least does no harm to the existing process before applying it to all users. The change was judged to be valuable when development effort went into it, whether for technical, branding or other reasons.
In short, not many people want to funnel users through N code paths with slightly different behaviors, because not many people have a ton of users, a ton of engineering capacity, and a ton of potential upside from marginal improvements. Two path tests solve the more common problem of wanting to make major changes to critical workflows without killing the platform.
More frequently, A/B testing is a political technology that allows teams to move forward with changes to core, vital services of a site or app. By putting a new change behind an A/B test, the team technically derisks the change, by allowing it to be undone rapidly, and politically derisks the change, by tying it's deployment to rigorous testing that proves it at least does no harm to the existing process before applying it to all users. The change was judged to be valuable when development effort went into it, whether for technical, branding or other reasons.
In short, not many people want to funnel users through N code paths with slightly different behaviors, because not many people have a ton of users, a ton of engineering capacity, and a ton of potential upside from marginal improvements. Two path tests solve the more common problem of wanting to make major changes to critical workflows without killing the platform.