There is also a difference between learning and playing. During play, the human operated at ~20 watts on computation while the machine ran at a rate of anywhere from 26,000 watts to 260,000 watts, depending on how efficient the TPUs are (and assuming 10x as the ideal case). The human is also
learning new things about Go as it plays, planning complex muscle firing programs, filtering audio and managing attention, working on subconscious goals, running complex vision tasks, all while running its autonomic subsystem.
Low power is also still important due to issues of heat and energy availability. Low power also implies high efficiency which is important for several reasons.
The human brain is estimated at 20 watts (when people talk about computing systems they tend to not include the power needed for all the auxiliary infrastructure needed to keep it networked and cooled); it's also estimated that beyond 4 hours a day, learning effectiveness drops precipitously.
If we take the case of Go, you can take a 4 year old human and have a professional player by 13. This is about 950 megajoules spent by the brain while learning Go. For the machine, if you look at the learning part (self play, value and policy on 50 GPUs for several weeks) the estimate on energy spend is about 30,000 megajoules. The policy network is itself ~20,000 MJ, while the full AlphaGo system playing on a single GPU and 48 CPUs is just a strong amateur.
But this is not even an apples to apples comparison since the brain is not spending all of its energy on learning Go. In fact, learning how to play Go is very far from the most difficult thing the brain is learning how to do.