Who Y Combinator Companies Want

For a blog article under a data subdomain, there's surprisingly few numbers and quantitative analysis, which is disappointing. And one matrix of survey data as the only visualization.

I get that startups do not want to reveal their competitive advantage, but there has to be some give-and-take. Taking an analysis on blind faith alone is frustrating.

I like the ontology of programmers. Are there any other efforts to do this? Anything data driven or externally validated?

Given what a big role personas and demo/psycho-graphics play in marketing, I'm surprised that I haven't heard more about then in the context of hiring and managing.

Well, consider that there are well documented academic studies (that I can't be bothered to look up right now) showing that most hiring processes are no better than throwing darts at a board with regards to retention rate, employee success, and every other measure of success of the HR process. The only thing that is remotely effective are IQ scores, and even then it's only a weak correlation.

So regardless of what the companies want, it's near impossible to accurately judge someone in an interview process. Even if you know what you want, it's very difficult to assess how someone is going to perform in a job through an interview process. The main benefit of interviews IMO is to help managers "buy-in" on hiring decisions.

> "We’ve seen that most engineers only have the stomach for a limited number of interviews. Investing time in the wrong companies carries a high opportunity cost."

I suspect in addition to not having the "stomach" for an unlimited number of interviews, they don't have the vacation days to burn for them. Let's say you are working already and have 10 days a year vacation (pretty standard). With these ridiculous all-day interviews, you have the ability to waste that vacation on a maximum of 10 companies, and that's if you choose not to take any "real" vacation at all during the year.

In an environment like today, where every company is ostensibly hiring yet overly picky, a job candidate potentially will waste their entire PTO balance on interviewing with companies serious about interviewing but not serious about hiring.

Because ML/AI are feature enablers. They make a good product better, but they won't make a product successful. It's a signal that people are more interested in solving technical problems than solving business/product problems.