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Silicon Valley Singles Are Giving Up On the Algorithms of Love (washingtonpost.com)
The Washington Post: Melissa Hobley, an executive at the dating app OkCupid, hears the complaints about the apps [being unable to find good matches] regularly and thinks they get a bad rap. Silicon Valley workers "are in the business of scalable, quick solutions. And that's not what love is," Hobley said. "You can't hurry love. It's reciprocal. You're not ordering an object. You're not getting a delivery in less than seven minutes." Finding love, she added, takes commitment and energy -- and, yes, time, no matter how inefficiently it's spent.
"You have a whole city obsessed with algorithms and data, and they like to say dating apps aren't solving the problem," Hobley said. "But if a city is male-dominant, if a city is known for 16-hour work days, those are issues that dating apps can't solve." One thing distinguishes the Silicon Valley dating pool: The men-to-women ratio for employed, young singles in the San Jose metro area is higher than in any other major area. There were about 150 men for every 100 women, compared with about 125 to 100 nationwide, of never-married young people between 25 and 34 in San Jose, U.S. Census Bureau data from 2016 shows. That ratio permeates the economy here, all the way to the valley's biggest employers, which have struggled for years to bring more women into their ranks. Men make up about 70% of the workforces of Apple, Facebook and Google parent Alphabet, company filings show.
"You have a whole city obsessed with algorithms and data, and they like to say dating apps aren't solving the problem," Hobley said. "But if a city is male-dominant, if a city is known for 16-hour work days, those are issues that dating apps can't solve." One thing distinguishes the Silicon Valley dating pool: The men-to-women ratio for employed, young singles in the San Jose metro area is higher than in any other major area. There were about 150 men for every 100 women, compared with about 125 to 100 nationwide, of never-married young people between 25 and 34 in San Jose, U.S. Census Bureau data from 2016 shows. That ratio permeates the economy here, all the way to the valley's biggest employers, which have struggled for years to bring more women into their ranks. Men make up about 70% of the workforces of Apple, Facebook and Google parent Alphabet, company filings show.
While your description is pretty much spot on you say it's a mess for both genders and I don't really see how. Women certainly can make romantic advances if they like someone, but if they just sit back they get plenty offers and can pick and choose. Sure they complain about not finding Mr. Right and all the guys looking for a one night stand, but if you look at the standards in the lower end of men where the choice is between a girlfriend or no girlfriend... eh. Anything with a pussy and a pulse can get a boyfriend. If they want, as more women than men seem happy with artificial substitutes.
Live today, because you never know what tomorrow brings
In fact, the traditional belief that men are more promiscuous than women can't actually be correct, due to basic maths. if you have e.g. 100 men and 100 women, and each man has dated 3 women on average, then each woman must have dated 3 men on average as well, out of mathematically necessity.
The same with cheating. If some men are cheaters, they must be cheating with someone, which implies female cheaters they're cheating with. Or, if they somehow are only cheating with single women, then some other single men must be getting *zero* partners to make up the difference. e.g. if married men are more promiscuous than married women, then single men must be less promiscuous than single women, which is a result that would seem to be contradictory to common sense: what's more likely is that married men and married women are equally likely to be cheaters.
"male/female pairings" are a *shared* statistics that counts for +1 for both genders, so any statistic that purports to show a big gender difference in something that by necessity involves *both* genders is clearly based on bad data, e.g. people are lying in surveys.
http://www.nytimes.com/2007/08/12/weekinreview/12kolata.html