Women are underrepresented in CEO positions, although perhaps not for the reasons people think.
The average age of a CEO is 55. Many are far older. You get to that point by being in management positions within an industry for decades. Outside of fringe cases, it takes a long time to become a CEO.
Obviously, that filters out some women due to them choosing family life over chasing job position above all else, as well as things such as in the past there being an even greater disparity in the difference between maternity and paternity leave than there is today (and it's still not great today either!), as well as past sexist attitudes in having women in managerial roles.
IMO, there being fewer women in CEO positions is an indicator of sexism in the past, not sexism in the present.
Nowadays there are far more women in managerial positions, it's not seen as weird anymore in the slightest, and that will naturally translate to more CEOs. It will just take time for that influx of managerial-position women to reach the CEO-level.
Will it be 50/50? Eh, probably not. The fact that women give birth means there will always be a not insignificant amount of women that take a significant amount of time out of work and prioritise family life to a greater extent than men.
LLMs are an interesting tool to fuck around with, but I see things that are hilariously wrong often enough to know that they should not be used for anything serious. Shit, they probably shouldn't be used for most things that are not serious either.
It's a shame that by applying the same "AI" naming to a whole host of different technologies, LLMs being limited in usability - yet hyped to the moon - is hurting other more impressive advancements.
For example, speech synthesis is improving so much right now, which has been great for my sister who relies on screen reader software.
Being able to recognise speech in loud environments, or removing background noice from recordings is improving loads too.
As is things like pattern/image analysis which appears very promising in medical analysis.
All of these get branded as "AI". A layperson might not realise that they are completely different branches of technology, and then therefore reject useful applications of "AI" tech, because they've learned not to trust anything branded as AI, due to being let down by LLMs.