His memo absolutely fails to mention the biases women deal with on a daily bases. It fails to mention how unjust such things can be and how they must make his female colleagues feel. And that such things are well supported by research.
That failure makes his audience significantly less receptive, and puts them on the immediate defense. He should spend some serious time reading "Difficult Conversations" and other books of it's ilk. As much as he wants to avoid feelings, feelings always matter. Hence the justified accusation of his memo being "tonedef".
And you are not considering the context in which it was written, or are missing key statements like:
> For the rest of this document, I’ll concentrate on the
extreme stance that all differences in outcome are due to
differential treatment and the authoritarian element
that’s required to actually discriminate to create equal
representation.
For him it is clearly table stakes that women are discriminated against. He doesn't feel the need to argue it because he, and the Google culture which he is addressing, finds it so imminently obvious.
Something he states. Multiple times. Throughout the document.
> Sure, he says that innate traits are only part of the cause. But the rest of his essay implies they are the primary cause.
The weight [of the research on the matter](
http://emilkirkegaard.dk/en/wp-content/uploads/Men-and-thing...) (a significant amount of it) indicates that interests are a primary driver in the discrepancy:
Therefore, from the inter-individual perspective,the
individuals who pursue STEM careers are more likely to
be male than female. For example, assuming that
individuals within the highest 25% of a population
interest distribution are likely to make occupational
choices consistent with an interest type, the number of
women entering the engineering occupation, then, is only
19.5% of the number of men entering the field. This
percentage is very similar to the actual female–male
ratio of individuals employed in engineering. In science
and mathematics interest distributions, the female–male
ratios in the upper 25% asymptote are 0.60 and 0.64,
respectively. However, the actual female–male ratio of
individuals employed in the field of physical sciences
is only about 0.40 and, in mathematics, it is about
0.45. This discrepancy between interest data and real
employment composition indicates that there may be
reasons other than sex differences in interests that can
account for gender disparity in science and mathematics.
A few observations though:
1) Google tends to pull a lot from Science and Math, not just engineering, in the "tech world". Dalmore is one such individual. This indicates the ratio should be much more skewed to an even makeup.
2) Even without that Google is still pretty "male" compared to what the research indicates.
3) Research on interests is largely still post social, so we don't really have a good feeling for the biological vs social components that drive the interest rates we see today. We know some pre-social effects exist, but we don't really have a good grasp on the magnitude of such effects. His memo fails to make this differentiation.