There are other studies on this topic with similar results across LLM systems:
Y. Sui, M. Zhou, M. Zhou, S. Han, and D. Zhang, “Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study,” in Proceedings of the 17th ACM International Conference on Web Search and Data Mining, Merida Mexico: ACM, Mar. 2024, pp. 645–654. doi: 10.1145/3616855.3635752.
C. Pang, Y. Cao, C. Yang, and P. Luo, “Uncovering Limitations of Large Language Models in Information Seeking from Tables,” June 06, 2024, arXiv: arXiv:2406.04113. doi: 10.48550/arXiv.2406.04113.
One would expect that if such studies indeed indicate that AI has an effect on early-career workers in AI-exposed occupations, that this would be a global effect. I wonder if there are good comparable non-US studies available.
Mostly from US or at least large companies, though. I live in Kraków, some companies offer 10+k USD monthly (Rippling, Atlassian, maybe Google?), for staff level especially.
More common range would be 7~10k, i think.
Our guys in St.Petersburg 20 years ago were pulling $3-6K. Granted the life there comes with some risks and inconveniences :)
You're probably working in a domestic company which usually pays less than offshored jobs by a large transnational (and domestics say in Russia were paying significantly less than the offshored). I don't think many companies do significant offshoring into Western Europe though.
Comparing salaries between countries with vastly different approaches to taxes, health insurance, living cost, hidden side costs etc. is hard and can easily be hugely misleading.
Not even including any subtle things just what "before and after taxes" means can differ. E.g. where I live "after taxes" does for most people not just deduct taxes but also the base health insurance cost and some other things (but to make it more fun, only most but not all people. This means what after tax means can differ between neighbors ).
And then there are so many hidden cost which can influence taxes, e.g. in some areas you practically have to have a car this means that in effect the cost of a car isn't that different from a fixed sum tax, if you consider social standards it even scales with income up to a certain point income like most taxes (and cost x10+ if you can't drive a car for health reasons). On the other hand if you live in a area with decent public transportation and then a car is a Luxus good, but someone has to pay for the public transportation, and if you area isn't overrun by well paying tourists this means you likely pay more tax (but as public transportation tends to scale better you still likely save money especially if you aren't wealthy).
Anyway so comparing after tax in vastly different countries is IMHO a folly. And even before tax is tricky but you have to choose something. I guess another option is "what to frugal living people with reasonable health insurance and rent have left over at the end of the month" is theoretically the better statistic, but just not practical.
Humans are not allowed to do what AI firms want to do. That was one of the copyright office arguments: a student can't just walk into a library and say "I want a copy of all your books, because I need them for learning".
Justin, I have to admit, I unfollowed you a while ago for this very reason. I'm reading a lot of the same blogs and news sites as you do and was a bit annoyed by the fact that your tweets didn't had any sources ("via ..."). You could provide me with a great service providing pointers to great news sources instead of just copying their headlines. This would've helped me in search for content. The second problem is by spreading out the stories over the day, they became a bit out of date it felt a bit pivoting of information. I'm sorry.
Y. Sui, M. Zhou, M. Zhou, S. Han, and D. Zhang, “Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study,” in Proceedings of the 17th ACM International Conference on Web Search and Data Mining, Merida Mexico: ACM, Mar. 2024, pp. 645–654. doi: 10.1145/3616855.3635752.
C. Pang, Y. Cao, C. Yang, and P. Luo, “Uncovering Limitations of Large Language Models in Information Seeking from Tables,” June 06, 2024, arXiv: arXiv:2406.04113. doi: 10.48550/arXiv.2406.04113.