Heritable != Molecular / Genetic Mechanism

There is a conflation of these terms in popular discourse that does a disservice to the field of statistical genetics, imo. There are mechanisms of inheritance that operate various length / time scales other than that of biological macromolecules. For example, if you tell me what language your parents natively speak I can tell you your primary language with >90% accuracy.

So before we start getting 3 replies deep into any thead, please remember that retrospective observational data measured with unqualified instruments is notoriously confounded and that we can barely infer causal structure in controlled functional genomics experiments (much less a GWAS of phewas). So let’s all please keep an open mind and not be so certain about our beliefs.

An example from software engineering is that all production code should undergo meticulous human review. Saying “no” to this sounds crazy to an experienced SWE, but might not actually be that crazy.

Think “Smithers, we need to hire some of these kids who know computers!” Only fast forward about 30 years and str.replace(“computers”,”agents”).

0. https://pubmed.ncbi.nlm.nih.gov/35318324/

1. https://www.nature.com/articles/s41586-023-06127-z

I saw something similar in ML when neural nets came around. The whole “stack moar layerz” thing is a meme, but it was a real sentiment about newer entrants into the field not learning anything about ML theory or best practices. As it turns out, neural nets “won” and using them effectively required development and acquisition of some new domain knowledge and best practices. And the kids are ok. The people who scoffed at neural nets and never got up to speed not so much.

Edit: as an aside, I have learned plenty from reviewing coding agent generated implementations of various algorithms or methods.

Put simply, he (and many other tech bros) have galaxy brained themselves into some very stupid stuff.

The irony is that in limiting mobility and competition from the “non elite” out-groups to preserve status, they end up shrinking the overall size of the pie.

OpenAI claims their terms of service for DoD contain the same limitations as Anthropics proposed service agreement. Anthropic claims that this is untrue.

Now given that (a) the DoD terminated their deal with Anthropic, (b) stated that they terminated because Anthropic refused modify their terms of service, and (c) then signed a deal with openAI; I am inclined to believe that there is in fact a substantial difference between the terms of service offered by Anthropic and OpenAI.

That being said, if you have a library of images or some other collection artifacts / assets indexed on their servers that is a different story.

In the land of drug development patient reported outcomes, even when captured with meticulously designed instruments in prospectively designed clinical trails, are notorious for being noisy and confounded by the placebo effect.

There is an abbreviated application for new drug approval (ANDA) pathway meant for generics, but it does not seem like H&H has gone this route. It does require you to open your supply chain up to inspections and to provide evidence that your generic version basically works the same as the brand name.

In my opinion there two things going on here that I strongly feel are true.

1. Something is systemically wrong in the US when we are cutting off people’s access to meds, like GLP-1s, which have profound health benefits.

2. Hims and Hers are also in the wrong. The rules and laws are there for a good reason. It is not just for us to arbitrarily pick and choose when to enforce them.

This type of commentary reminds me of the people during the dot com boom who were adamant that e-commerce was all film flam and would never take off.

Consider that it is possible that both (1) we are in an investment bubble and (2) we are underestimating the long term impact of LLMs and perhaps mispredicting where they will land.