MIG
VanEck Moody's Analytics IG Corporate Bond ETF
stock BATS ETF

Your take is just plain wrong...

Your value add as a DevOps is not writing the yaml files, but writing them in a way that translates best to business requirements, is efficient, incorporates better security practices etc. Can Codex deploy a simple WordPress blog on the cloud, sure. Can it optimize gpucluster usage and make trade-off between MIG och time-slicing ? No (or at least not yet).
It's a little bit the same with quant, it will build (recite) you a zipline backtesting copy in Python. It won't optimize it to your actual use case, data source , frequency etc...
For quant a big issue is going to be that 99% of the public information is "basic". Not necessarily bad, or inaccurate but basic, because it's the 438th paper on portfolio construction study on SP500 or Nasdaq, some random trend-following system by a graduate student etc. Actual new and for a particular problem set useful insights are rare, often not published until years later or ever.
Someone with 10 years of experience in quant will have filtered through a lot of this already, gathered some of this "dark knowledge" and have a heuristic for what to look into next and how to evaluate it.
And for LLMs, they are "normative" and will recite most of the former, rather than the latter. Maybe they will never get there like they do with in mathematics, physics lately, as (quant) investing is not a stationary problem.

Your take is just plain wrong...

Your value add as a DevOps is not writing the yaml files, but writing them in a way that translates best to business requirements, is efficient, incorporates better security practices etc. Can Codex deploy a simple WordPress blog on the cloud, sure. Can it optimize gpucluster usage and make trade-off between MIG och time-slicing ? No (or at least not yet).
It's a little bit the same with quant, it will build (recite) you a zipline backtesting copy in Python. It won't optimize it to your actual use case, data source , frequency etc...
For quant a big issue is going to be that 99% of the public information is "basic". Not necessarily bad, or inaccurate but basic, because it's the 438th paper on portfolio construction study on SP500 or Nasdaq, some random trend-following system by a graduate student etc. Actual new and for a particular problem set useful insights are rare, often not published until years later or ever.
Someone with 10 years of experience in quant will have filtered through a lot of this already, gathered some of this "dark knowledge" and have a heuristic for what to look into next and how to evaluate it.
And for LLMs, they are "normative" and will recite most of the former, rather than the latter. Maybe they will never get there like they do with in mathematics, physics lately, as (quant) investing is not a stationary problem.