Both labor and compute have been scaled up over the last several years at big AI companies. My understanding is the scaling in compute was more important for algorithmic progress
That may be the case, but I suppose that in the last several years, compute has been scaled up more than labor. (Labor cost is entirely reoccurring, while compute cost is a one-time cost plus a reoccurring electricity cost, and a progress in compute hardware, from smaller integrated circuits, means that compute cost is decreasing over time.) Then obviously that doesn’t necessarily mean that an AI company with access to FLOP/s compute and AI researchers has an advantage over a company with only FLOP/s compute but researchers.
In fact I think in that sense labor is likely more important than compute for algorithmic progress. And that doesn’t seem so far away from reality, if you model as a US company with cheaper access to compute and as a Chinese company with cheaper access to labor (due to lower wages).
Consider that there are people with high P(doom) who don’t have any depression or anxiety. Emotions are not as much caused by our beliefs as we tend to assume. A therapist might be able to teach more productive thought patterns and behaviors, but they are unlikely to speak with competence on the object level issue of AI doom.
Independently I recommend trying to get a prescription for SSRIs. Most probably won’t help, but some might, and they tend to not have strong side effects in my experience, so trying them doesn’t hurt.
Only problem is that trying different SSRIs can take a very long time: usually you take one for several weeks, nothing happens, the doctor says “up the dosage”, weeks pass, still no effect, and the doctor might increase the dosage again. Only then may they switch you to a different SSRI, and the whole process begins anew. So persistence is required.