Comment by ldjkfkdsjnv

I worked with the enterprise sales team for awhile when I worked at Udacity and we did some research on job motivations to help answer questions about recruiting & retaining good engineers. We found that salary needs to be “enough” (varies by local market) but that people will literally relocate their families and lives to work on problems that they find interesting and that are a unique opportunity. The salary bump needs to be very large to achieve the same effect as “amazing opportunity”.

Both may be benefiting openAI here. There’s lots of places to work on LLMs but “GPT” is a product/brand that people have heard of, and if OP is to be believed then they certainly seem to be paying “enough”.

Sort of, you will probably attract the best talent by having the hardest problems, fostering a culture of bottom-up innovation, and giving your engineers a lot of freedom rather than micro-managing them. The pay is closely related, but you also get a lot of people trying to game the system and optimize for max pay, without necessarily the skill or creativity you're looking for. Said another way: top pay attracts people looking for top pay, for better or worse. There's probably a strong correlation between "best" people and high pay, but it's also hard to quantify "best".

How do you quantify best? Number of degrees? Publications? Association with prestigious institutions? Past work experience at top companies? Speed of problem solving? All of this is gameable once it starts being _measured_ and enough incentives exist for people to devote their life to winning the "game".

However, if you happen to hire a math olympiad winning PhD with numerous publications from a tier-1 research institution with a known track record in industry, it would be hard to argue they aren't the best. But success breeds success, and top people will keep being poached to other top places. Kinda how money makes more money.

It's both things. Offering more money increases the inbound applicants dramatically. You want this because top talent is disproportionately likely to have better options and may have a high floor. This does, however, create a non-trivial filtering problem, and one that is hard to scale because recruiters can't reliably differentiate real talent from good talkers. The key here is to make sure your best people are active engaged in recruiting and to minimize the type 1 and type 2 errors in hiring. This is hard to sustain at scale, and I think places like Google and Facebook have been losing the battle on this for many years—also due to engineering brand dilution because they don't actually have that much impactful work relative to the headcount—but OpenAI is small and focused enough that they ought to be able to manage pretty effectively.

> but in principle isn't offering a lot of money a good way to attract talent?

offering a lot of money attracts everyone including in-demand talent.

offering less turns away in-demand talent.

You also have to give them something interesting to do but that isn't exactly a problem for recruiting to develop leading/bleeding edge tech

The "real talent" is worth nx of the average salary when the work of the real talent makes the company better.

If one (“talent") is a stellar runner, but when the team goes on a run the "talent" has to wait for their teammates to get to the finish line, most of whom go running once every two months, and the "talent" gets dress shoes instead of spikes etc., the "real talent" is worth, to the team or company, as much as the average employee on that team.

An easy and naïve recommendation would be to remove the constraints that limit the work of talent, but it is much easier for talent to move to organizations that, for a variety of reasons, are more conducive to the expression of their abilities than to fight the inertia of organizations. Those very organizations which, for one reason or another, have existed for a long time and pay salaries to hundreds or thousands or hundreds of thousands of people.

FAANG employees sure are full of themselves considering they've worked at companies that have failed to innovate for two decades. What was the last successful product that Google launched with all their highly paid engineers?