Comment by jxub | Hacker News Reader

Comment by jxub | Hacker News Reader

I suspect there may be some sort of hype in the AI area regarding salaries. Sure, there are a couple of rockstars, but I suspect that competition is really intense and compensation in ML area has an even more acused power law distribution than general vanilla software engineering that doesn't face as much pressure from maths, stats and other grads that look for a career to apply their quantitative skills.

I think that in the real world paradoxically math skills are easier to find than solid software design and development abilities. It may stem from the fact that the first one is taught quite well in school while the other one is more about individual learning and sometimes a contrarian stance to the system (can be reflected even a somewhat childish "I'll learn Haskell because the OOP and Java suck!") which is harder to find and therefore, more valuable.

In my experience with AI start-ups, if you have a rockstar CV in your deck you get funded. If you don't, you don't. It is very difficult for non-experts to evaluate the competence of budding AI teams. The best heuristic, track record, thus prevails, which in turn attaches a lot of value--from the company's perspective--to that single CV.

I'd argue this line as well. A lot of the earlier stage startups who don't need this talent are trying to secure it because it offers them that halo of prestige to grease the wheels of fundraising.

A $400k salary is nothing if it makes it easier to unlock $XXm in funding.

I’d laugh if I wasn’t crying. I narrowly escaped high school where the popular kids and “school celebrities” won. All that studying in university, all those labs, grinding that entry level job, building my skills, grad school, more hard work... and at the end of the day the popular kids inevitably win again.

I don't understand the comparison. Aren't the highly salaried AI experts also people who undertook a lot of hard work to get there?

Reply on news.ycombinator.com