Hearing from past coworkers and friends who have been on the job hunt at some point within the last 5 years has been interesting with regards to what is scrutinized in a candidates profile.
During the hiring spree it seemed like other than ensuring that a candidate was able to (or was able to be trained to) operate at the average expectations of a role's level.
With the demand for software engineers dipping below the supply, recent anecdotes have painted a different picture.
Some of the rejection reasons I've heard for candidates that have otherwise passed all rounds:
* They don't have enough experience in our stack (need to be able to hit the ground running)
* Their experience in our stack, while of a suitable duration, was 2 or 3 jobs ago, so it's not "recent enough" to hit the ground running
* They have worked too many jobs within a timeframe
* They've worked too few jobs in their career
While these of course vary company to company, the former 2 are interesting in this context in that I wonder how an automated candidate <-> requisition matcher would go about gathering data to make it's decision. Will the stack you work in now cement you into that stack for a longer period of time? Will lack of blog posts or other publicly scrapable details about your knowledge or abilities with certain technology impact your matching score?
Related to the above, if those get gamified, I wonder how that might affect a surge of slightly altered blog posts to make one look more experienced with a technology. And when will the SaaS startup get created that will auto-post technical blogs copied/altered from other existing tech blogs.
It will be interesting to see how this all progresses.
the optimal solution to job advertising/search is people putting a damn ad in the paper. All technology applied to the problem has made it worse, for everyone
Am I alone in thinking that this sounds scary (at least the ominous intent is palpable) - once OpenAI has some sort of metrics-based evalaution platform they present to managers, they can shoehorn in their AI agent 'applicants' to positions (and show hiring managers that 'John' the agent is as good as 'Peter' the human)
I know this is going to get ridiculed since we are in an AI winter as of a month ago, but if (and when) significantly more capable agents hit the market (not AGI, but not dummies like the current ones), the only way OpenAI will have the market to themselves (or convince investors that it does), is to control more of the value chain.
This is a great move if you think there’s a vast number of jobs that can be completed by AI almost all the time but you don’t think that AI will be able to close the gap enough to replace employees altogether. If this is the case, then the winning move is to abstract the work behind an interface that can farm out tasks to both human and artificial agents. That way, people who need the work done will come to you, and you can charge less than humans on average but more than AI agents on average. To do this, you need a strong supply of knowledge workers ready to do gig work and a strong supply of people wanting to spend money to get knowledge work done.
17 comments
[ 6.7 ms ] story [ 36.8 ms ] threadDuring the hiring spree it seemed like other than ensuring that a candidate was able to (or was able to be trained to) operate at the average expectations of a role's level.
With the demand for software engineers dipping below the supply, recent anecdotes have painted a different picture.
Some of the rejection reasons I've heard for candidates that have otherwise passed all rounds:
* They don't have enough experience in our stack (need to be able to hit the ground running)
* Their experience in our stack, while of a suitable duration, was 2 or 3 jobs ago, so it's not "recent enough" to hit the ground running
* They have worked too many jobs within a timeframe
* They've worked too few jobs in their career
While these of course vary company to company, the former 2 are interesting in this context in that I wonder how an automated candidate <-> requisition matcher would go about gathering data to make it's decision. Will the stack you work in now cement you into that stack for a longer period of time? Will lack of blog posts or other publicly scrapable details about your knowledge or abilities with certain technology impact your matching score?
Related to the above, if those get gamified, I wonder how that might affect a surge of slightly altered blog posts to make one look more experienced with a technology. And when will the SaaS startup get created that will auto-post technical blogs copied/altered from other existing tech blogs.
It will be interesting to see how this all progresses.
(https://news.ycombinator.com/item?id=45131095)
Uhhh, not really if it's focused on AI-specific talent.
Instead of this TechCrunch reaching for drama just read the announcement post:
https://openai.com/index/expanding-economic-opportunity-with...
I know this is going to get ridiculed since we are in an AI winter as of a month ago, but if (and when) significantly more capable agents hit the market (not AGI, but not dummies like the current ones), the only way OpenAI will have the market to themselves (or convince investors that it does), is to control more of the value chain.