That's an extremely dogmatic position. There are certainly situations where it is not useful or even accurate to proceed under the null hypothesis, but the converse is also very much true. Checking Wikipedia, I see they also describe the scenario as "accept or fail to reject".
If you want to argue for why "accept" is materially different from "fail to reject", feel free to do so - but I suggest that the chasm is by no means wide.
If you claimed to accept the null in an intro statistics class, you'd probably be failed.