looks like actual numbers don't agree with you
(hint: for TikTok it's Douyin + TikTok)
The claim was very few companies do what Twitter does at that scale, truth is Twitter is not a big fish in that space.
Since you are at it, do GDP of countries where they operate too.
I reckon a $ million in ad revenues in India is harder to come by that the same amount in USA.
That doesn’t mean your AI system performs better than a simpler one. Or that the system is useful in the first place (recommendations.) I’m not saying they were sitting around twiddling their thumbs. I’m saying the vast majority of Twitter staff were not actually improving the Twitter product noticeably to users. They were doing highly complex, cutting-edge engineering that was make-work.
If Twitter tech was so advanced, why were they losing so much money?
Advertising is a hard problem that not many companies have solved at the scale of Twitter, that is what I am trying to get at. There are not too many social media networks out there which have hundreds of millions of users and billions of data points, and it's very misleading to say that work done in such a scenario is "something fairly well-understood and at any rate done by a lot of teams in a lot of places", when literally they're the only ones with Twitter type data outside of a couple of other Chinese social networks.
Yes, this is my point. All this incredible AI engineering did not actually make Twitter a better product. They could have just as well not spent the money. The work was ultimately futile for Twitter, even though it might have advanced our understanding of AI and have incredibly practical applications elsewhere. Conventional measures worked fine.
There are very few teams doing advertising at the scale of Twitter, saying "done by a lot of teams in a lot of places" is accurate just like "programming is done at a lot of places so why is programming hard".