The economic question is whether the average company will have the time or talent to roll their own models instead of eating the cost increases. The firms in question are exactly the same that have already decimated their teams. Can they so quickly pivot to self-hosted models if their AI workloads suddenly cost them 10x more? I bet most will simply start shoveling themselves deeper.
It is really beautiful to see distilled to the core algorithm. Despite AK’s claim that everything else is simply optimization, I am sure there’s a bit more to training a useful frontier model! Still, an excellent teaching tool and nice way to spend an afternoon walking through the code.
While true, it's still a user-hostile move. You kinda have to meet your customers where they are. If people are clicking ads without knowing it, that's a serious design problem. Yes, people should learn to read, but the risk of placing too much burden on users is that all it takes is one ambitious product manager to push an A/B test that generates huge revenue wins while enshittifying the product for everyone else.
> You need to know that your data and conversations are protected and never sold to advertisers.
> we plan to test ads at the bottom of answers in ChatGPT when there’s a relevant sponsored product or service based on your current conversation.
There is a severe disjoint between these two statements: the advertiser now knows what your conversation was about! This gives a lot of leverage to ad campaigns to design the targeting criteria very specifically crafted to identify the exact behavioral and interest segments they want.
If you’re referring to body shop consulting agencies this may be true, but IME as an IC consultant in DS/ML, my rates are well above Staff+ at FAANG, and nobody has ever tried to leetcode interview me. Yes, I have to do a lot of smooth talking, but performance is extremely transparent: if I don’t deliver, I don’t get paid. Honestly I doubt I could pass a leetcode style interview, and I’m glad I don’t have to do that anymore.
I completely side with Gelman on this issue, but you can't point to this one case to dismiss all of social science. One can find examples of p-hacking, irreproducible research, and fabricated data in any field, but the existence of such does not invalidate the entire discipline.
I am merely cautioning against over generalization that just because there are some high profile examples of bad social science, there is still a lot of very good social science happening and it would be a shame to ignore it.
> They are more prone to trends, fads and more personal biases
This is not necessarily true, and I would urge caution when making such generalizations. There is junk social science and there is also plenty of junk "hard" science. Examples of fake data, political and personal biases interfering with the scientific method, occur in all fields. Some "fads" eventually become accepted fact. It took 100 years before plate tectonics was accepted by the majority of the scientific community, there are many such examples.
Don't universally discount social science: it is just as important as physical science. We actually need more social scientists to help understand the reasons behind anti-science movements in the first place, and to help legitimize science as a process, critial thinking, and to debunk illogical arguments.