It disagrees with every other data point I have, so I'm very skeptical with both the methodology (which is opaque) and the conclusion.
From all of my experience at Kaggle (we run machine learning competitions), with our community, and from being close to programming competition sites & understanding their communities, doing great at competitions is an unambiguously positive signal.
(It's worth noting that doing great at competitions is only a positive signal - the lack of competitions is by no means a negative signal).
Many of our customers have found that their best hires have come from competitions. In a lot of cases, this surfaces candidates that would normally be completely overlooked because they don't fit the "top tier CS school" mold that recruiters commonly overfit to.
Several companies have had a successful recruiting strategy built on poaching our top users (https://www.kaggle.com/users).
Peter Norvig's criticism that "programming contest winners are used to cranking solutions out fast and that you performed better at the job if you were more reflective and went slowly and made sure things were right" is specific to programming competitions with very short time durations (vs. the machine learning competitions that I'm used to running, which typically last months and incentivize solutions that generalize well).
However, we've seen that many programming competition winners also do well on machine learning competitions, and the same qualities that aid in competitive programming (creativity, efficiency, tenacity, fluidity with tools, and the ability to build something that works) help win machine learning competitions.