> You see a couple of ads mixed in your feed; behind that there's a big machine selling that space to advertisers and mixing it into the timeline of every user based on whatever profile Twitter has created for you. Then the advertisers want to know how their ads are doing, or they'll stop buying them…and you'll probably need to have salespeople to get them to put money into your ad system in the first place.
This is not crazily complex, bleeding-edge tech. This is something fairly well-understood and at any rate done by a lot of teams in a lot of places. (Twitter's ad profiling also seems awful. Maybe I am hard to pin down.) Probably the most complicated part is coming up with data to make advertisers think their campaign is working. (I am extremely skeptical most ad spend is actually worthwhile.)
> Twitter could be overstaffed. In fact it probably was overstaffed. But it's not overstaffed in the tune of of "it should be 10 people working out of a garage".
I agree 10 is too low for anything but bare-bones keep-the-lights-on-this-month maintenance, but it seems likely you could have a great and functional Twitter run by ~200 employees. I've seen more done with less.
Just as one data point that might tell you why you are misinformed - Twitter's AI team frequently publishes at the biggest venues in AI research and do a wealth of machine learning research on the data and processes they have. Some of that is used in advertising, among other things (recommendations, anti-spam, detecting abuse).
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".
No doubt you can have big teams doing highly complicated work.
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?
The complexity of your product has nothing to do with whether it is profit making or not. If that was the case, you wouldn't have loss making products in the AI space nor would you have profit making products in the garden shovel space.
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.
> The complexity of your product has nothing to do with whether it is profit making or not.
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.
How is revenue the only metric for "scale"? It sounds like you really don't know what you're talking about if when comparing technical complexity, your metric to go to is how much money something makes and not how many user accounts need to be served or the geographical complexity of running a real time view consistent across the globe. By that metric, is Walmart or Saudi Aramco's tech stack more complicated and larger scale than a software company's?
This is not crazily complex, bleeding-edge tech. This is something fairly well-understood and at any rate done by a lot of teams in a lot of places. (Twitter's ad profiling also seems awful. Maybe I am hard to pin down.) Probably the most complicated part is coming up with data to make advertisers think their campaign is working. (I am extremely skeptical most ad spend is actually worthwhile.)
> Twitter could be overstaffed. In fact it probably was overstaffed. But it's not overstaffed in the tune of of "it should be 10 people working out of a garage".
I agree 10 is too low for anything but bare-bones keep-the-lights-on-this-month maintenance, but it seems likely you could have a great and functional Twitter run by ~200 employees. I've seen more done with less.