Many large deployments of AI have carefully engineered solutions to problems (ie self driving cars). In these systems, it is important to have discussions about how these systems should react in morally ambiguous situations. Having an agent react "appropriately" sounds similar to the Turing test in that there is a "pass/fail" condition. This leads me to think that the current mindset of most AI researchers falls into "Conservative anthropomorphism".
However, there is growing interest in Continual Learning, where agents build up knowledge about their world from their experience. This idea is largely pushed by reinforcement learning researchers such as Richard Sutton and Mark Ring. Here, the AI agent has to build up knowledge about its world such as:
When I rotate my motors forward for 3s, my front bump-sensor activates.
and
If I turned right 90 degrees and then rotated my motors forward for 3s, my front bump-sensor activates.
From knowledge like this, an agent could eventually navigate a room without running into walls because it built up predictive knowledge from interaction with the world.
In this context, lets ignore how the AI actually learns and only look at the environment AI agents "grow up in". These gents will be fundamentally different from humans growing up in homes with families because they will not have the same learning experience as humans. This is much like the nature vs nurture argument.
Humans pass their morals and values on to children through lessons and conversation. As RL agents would lack much of this interaction (unless families adopted robot babies I guess), we would require different ways of judging their moral worth and thus "Post-human fundamentalism".
Sources: 5 years in the RL academia environment and conversations with Richard Sutton and Mark Ring.