You could assume the writer has insider info, but that attitude is a bit outdated these days. If Anthropic does have an advantage, it isn't a very significant

You could assume the writer has insider info, but that attitude is a bit outdated these days. If Anthropic does have an advantage, it isn't a very significant one. The vast majority of research is public and most, if not all, big discoveries "circulate" around the economy. If we're looking at how competent "AI" systems will have to be in order to take over all of programming, we'll have to wait for them to topple over almost all other white collar jobs before coming to this one. You could ascribe this to the "First they came for X ... then they came for me and no one was left" viewpoint, but considering the progress we've had since 2021 and the fact that this field is way more popular and hence "stickier" and slower means progress will slow and newer ideas will have to push through waves of mediocre-but-popular and technologically-regressive-but-economically-viable ideas. It seems very unlikely that in 3 years all white-collar jobs will be replaced. Computer hardware is gold in this new boom, and 3 years isn't a significant amount of time for hardware development (considering how consumer and enterprise hardware development goes hand-in-hand in this field). In fact, all the progess visible in 3 years has likely already been decided upon. What most people fail to realize is that LLMs indeed are stochastic parrots, but the internet is so much more vast than they can fathom. It has data on almost EVERYTHING. ALL of that was fed into this recursive architecture that then became half-decent to talk to. To visualize a LLM, imagine a never-ending "net" of information. To reach from one point to another, LLMs can't make a straight line -- they make a sphere with its center at the starting point and when it connects, the volume is the amount of data is has to compute during training. It's a very inefficient algorithm! Filtering and sorting and labelling data is a difficult task and is something that isn't focused on enough. The end result (training on massaged data) is given undue importance because it's easy and accessible. There simply isn't enough time in 3 years to train models or to re-filter/sort/label data that will make this author's predictions come true. As usual, the "hard limit" for tech is human mental capacity. Most people can not learn a new language after 20. Most people are not very good at reading. Changing these stats takes centuries of good education and nutrition. 3 years is little to nothing. After losing your job (primary income stream) the biggest hurdle isn't "saving face" but rather figuring out how one's going to afford to put food on the table and pay off bills. There isn't going to be UBI in 10 years, because efficiency gains lead to major, society-wide human discontentment because of the pyramid's base becoming wider. Hoping for a near-flat structure is utopian. Thinking it'll come across without a war is even more fantastic. Taiwan's chip competence seems to be the bedrock of our modern civilization, and before getting giddy for UBI one should realize that maybe sometimes it's all just too good to be true and might come crashing down any second.