I think it'll be fine. You don't need 7000 people to run a micro blogging service. It's really that simple.
When you're generating and distributing and moderating multiple TB of tweets in real time every day to billions of people and also feeding other corporations parts of that data... maybe you do.
But I don't see why an company and an application of that size couldn't be run by a company of, say, 1500 people, instead of 7000. 250 engineering staff, 250 doing moderation/support, 800 doing sales & account management and 100 in management and 100 doing sundry tasks.
Let's do some napkin math here:
500 million tweets, let's say 10% are reported, and evenly distributed. That would make for reviewing 200,000 tweets per employee per day. That's 7 tweets that need to be moderated per second for a standard 8 hour day.
190m active users, .1% of which need support daily. On top of 7 tweets per second, that's just shy of three user support tickets per second that they need to manage as well.
And that's without weekends, holidays, sick days, etc.
EDIT: Let's go the other way with napkin math too.
Let's say each content moderator can review 6 tweets per minute.
50m tweets to be moderated at that velocity means you need around 136,000 man hours per day. For an 8 hour day, that's 17,000 employees. Too many.
So you make an ML algorithm that processes the reported tweets, but needs backup and spot checking on 10% of those. 1,700 content moderators (and a team dedicated to building/maintaining the ML report checking algorithm).
Now, about those support requests. Based on my experience from being a CSR for Amazon years ago, you'll probably want 2-3 minutes per support request, and that's if you're sending a form letter back. 1.9m requests at 20 per hour means some 95,000 man-hours per day, or 12k CSRs. Too many. Another ML algorithm (and team to maintain it) and we're down to 1,200 CSRs.
About 3 thousand employees plus their support structure, just to handle tweet reports and CSR requests.
Based on napkin math, 7,000 employees makes a lot of sense to me.
10% reported seems high, possibly by as much as an order of magnitude. The overwhelming majority of tweets are vapid and innocuous.
It seems like an ML algorithm could do better than 90%. It doesn't have to be as perfect as driving a car; if it screws up occasionally it will merely annoy users (and probably the users that are most troublesome).
If twitter becomes a more permissive environment, less censorship is necessary. Agree or disagree with it, it means less work for the censors.
Paid subscriptions give you a significant new trust metric for users.
The rest you can farm out to mechanical turk?
250 seems within the realm of possibility. People will complain about you the same way they complain about Google, but they'll keep using your product.
Also, having worked for a company that did ML sentiment analysis and content analysis against the twitter firehose, the accuracy of ML was closer to 65% than it was 99%. Yes, the company had a huge in-house crew dedicated to checking that 35%, and monitoring twitter manually for anything missed.
Twitters actual numbers (from https://transparency.twitter.com/en/reports/rules-enforcemen...) show that 11.6m reports were generated in the period July to December 2001, which is roughly 65,000 reports per day. ML could easily reduce this number further, but with 100 employees doing moderation, even without ML, that's 650 reports per day. That's getting towards doable.
Not all reviewed-for-moderation tweets would happen because of user reports.
Seeing how poorly ML works for moderation (Too many false positives), I don't think it belongs anywhere near it.
The problem is that you could offer a user a path to request a human review moderation action taken by ML, but bad actors that knowingly break rules will just request human review and at that point, the ML is worthless.
The types of things that require scaling out engineers is supporting video embeds from 100s of different video hosting platforms.
Public companies typically have growth as a goal. Twitter was not making a lot of money. Some of these people were working on building a future Twitter that would grow bigger and richer. I'm not saying they were on the path to success, just that I expect a lot of the activity there could be described that way.
Five years ago you could say of Uber 'You don't need 5000 people to run a freelance taxi app', when they had hundreds (thousands?) of people working on autonomous driving. Amazon didn't seem to need scores of backend engineers to run a web store, but now AWS is a huge business. Google employs vast numbers of people but has a relatively small number of impactful products, only some of which make money.
Again, not claiming they were doing it well, just that they were trying stuff beyond maintaining what we see.
So in this hypothetical, the people building the future of the company have achieved nothing during this time and should absolutely be removed or replaced.
Uber cut their losses. Twitter needed a change of owner to get there.
I don’t like EM at all, but truth is internet companies are making so much goddamn money, they have employee pools absolutely full of turds.
Vastly more innovative and scaled crazy fast without fail whales or anything.
Or look to Telegram today. Delivering a vastly more complex product with a fraction of the company size it seems.
The difference between a messaging app and a microblogging app is basically the difference between an O(n) problem and an O(n^2) problem.
If you don’t think that adds some complexity, you’re kidding yourself.
But have you looked at the scale of what Telegram provides both in width and at scale?
Certainly there are celebrities with more followers on Twitter than the largest Telegram channels, but Telegram scales surprisingly far, and I haven't seen it struggle more than once or twice since the start.
It's clear you are totally clueless about how many features telegram has.
Scaling this is easily done with decomposition and sharding coupled with a suitable key->value mapping of external id to current shard. I first sharded e-mail delivery and storage for millions of users 23 years ago. It was neither hard nor novel to do then, with hardware slower than my current laptop handling hundreds of thousands of users each.
Do you believe that every Twitter user has an inbox stored on disk somewhere that just contains every tweet posted by someone they follow?
You also don't need to store every tweet, you need to store the id's of the tweets (a KV store of the tweet id to full tweet is also easy to shard), and since they're reasonably chronological the id's can be compressed fairly efficiently (quite a few leading digits of tweet id's are chronological).
You also have straightforward options for "hybrid" solutions, such as e.g. dealing with extreme outliers. Have someone followed by more than X% of total userbase? Cache the most recent N tweets from those accounts on that small set of timelinesyour frontends, and do joins over those few with users who follow them.
Most importantly, it's an extensively well tested pattern in a multitude of systems with follower/following graphs whenever consumers/reads dominate over a period of decades at this point, so behaviours and failure modes are well understood with straightforward, well tested solutions for most challenges you'll run into, which matters in the context of whether it'd be possible to build with a small team.
Put another way: I know from first hand experience you can scale this to millions of users per server on modern hardware, so the number of shards you'd need to be able to manage to deal with Twitter-level volume is lower than the number of servers I've had ops teams manage (you'd need more servers total, because your read load means you'd want extensive caching, as well as storage systems for e.g. images and the like - there's lots of other complexity, but scaling the core timeline functionality is not a complex problem)
Look at what they didn't do, which was make any revenue, or support any customers, or moderate any content.
I know I at least was shouting at them to take my money: it was the perfect HN product, reasonably priced, technically superior and with no ads or tracking.
And yes, I paid as well.
Twitter is a 'universe of 100M connected people'.
WhatsApp mostly connected single entities together.
So, for example, 'real time search' and 'relevant updates'.
Imagine taking a firehose of 100M people's random thoughts, putting that into an index, making it instantly searchable. Now pull up the most relevant thoughts from those 100M to each and every other 100M user.
Now moderate all of it in really subtle ways, whereupon most of the 'negative activity' is tantamount to spam or annoying behaviour, and not anything we might normally consider 'abuse'.
That's an incredibly different challenge and that's only two small artifacts of what they are doing.
Twitter is not rocket science, but it's not trivial either.
Also consider that R&D is usually maybe on 20% of overhead - yes - it takes 'all those other jobs and expenses' to run a company.
Some of these statements are a bit glib.
https://blog.twitter.com/engineering/en_us/topics/infrastruc...
(A blog is also a broadcast messaging service, so I think you’re both right).