I use LLMs to answer certain questions, but those are often questions that I wouldn't have bothered using a search engine for in the past, rather I would have asked a colleague or just thought through the question on my own. And when I try to ask an LLM questions that search engines are good at, I'm most often disappointed.
In other words, it's not clear to me LLMs are going to eat into the market share of search engines, rather than just providing a tool with largely orthogonal use cases. But we'll see how the tech develops from here.
"When asked to draw circles representing themselves and friends or family, for example, people tend to self-inflate their own circle but they self-inflate more in individualist cultures."
This sort of methodology sounds sketchy to me - how much can we really learn from this? Does it reproduce? If it does, how do we know there isn't some other cause?
The average income of FT readers is £221k [0] and the average income of WSJ readers is $234k [1]. I'm guessing the average reader of most online only newspapers is closer to the US median of $74k [2]. These readers belong to different economic classes with different financial interests, so the agenda divide is not surprising!
The geometric mean (6.9) is all that really matters for investors, not the arithmetic mean (8.4) - the arithmetic mean under-weights the importance of negative years to long term performance.
For example, if the market is down 20% one year and up 20% the next year, the arithmetic mean will be 0%, but you'll be down 4% (0.8*1.2 = 0.96), which is reflected in the geometric mean of (about) -2%.
In other words, it's not clear to me LLMs are going to eat into the market share of search engines, rather than just providing a tool with largely orthogonal use cases. But we'll see how the tech develops from here.