My understanding is Twitter always had cultural issues but it was not very different from other tech companies of the time, and what most of us would consider "directionally correct." I have it on pretty good authority from a very senior engineer who left before Elon took over (so no grudges other than, you know, "because Elon") that a lot of the things he said publicly about Twitter's technology was highly misleading or downright false. Like, IIRC, something about them not having CI/CD. Total lie.
There's some fraction of that workforce that supported projects intended to make Twitter a viable standalone business, which it probably no longer is. Backoffice / line of business projects intended to support advertisers, that sort of thing. But I don't think you can explain a RIF of Twitter's scale that way.
(I'll try to dig up the Luu post I'm thinking of.)
It's just not viewed as anti-Semitism, probably in the same way that the posts on X aren't viewed as far-right or extremist.
Extremists usually don't experience their views as extreme, but as rational and important.
Firstly, this is a red queen’s race because like security, new types of unwanted content, threats and risks keep arising as the information (and misinformation) landscape and overall zeitgeist keeps shifting. The work is never done and the best that can be done is to build platforms and frameworks to streamline it. There is also a lot of fractal complexity everywhere.
E.g. there’s a ton of technology needed to support the moderators themselves. Infrastructure like review queues to enable them to rapidly handle content classified by type, risk level and priority. Like Jira but not Jira because it can’t scale to the number of queues and issues involved here. So you basically re-implement and maintain a Greenspun’s 10th rule version of Jira.
There is still a huge amount of invisible complexity beyond that. For instance, you need to manage how much of a certain type of content gets exposed to a given moderator because some types (CSAM, gore) lead to burnout and PTSD. You also need to blur these things.
(Also the same type of content often gets reshared, so you need things like reverse image search to auto-filter that, because running the whole pipeline each time is expensive.)
This of course necessitates a ton of machine learning. Because risks keep shifting, and (pre-LLMs) each type requires the entire ML lifecycle and related infra: collecting and cleaning data, building classifiers for them, deploying them, seeing how well they work, and tuning them, and then replacing them when the bad actors eventually adapt to newer means.
ML is also of course needed for bots, spam and scams, which keep evolving. Entirely different techniques here though.
Then there is all the infra needed to handle the fallout of moderation. Counting strikes against users, dealing with their complaints, handling escalations, each case with a long history of interactions that needs to be collated for quick evaluation. Easier said than done because of course the backend is not an RDBMS but a bunch of MongoDB-alikes because webscale.
And all of this is a signal for the ranking used for feed, the main product, which keeps evolving, so a ton of “fire and motion” happening there. You introduce a new feature in the feed? You just introduced a dozen different abuse vectors.
Then there are policy makers and the technology needed to support them. Policy is always shifting as the landscape is shifting. This also includes dealing with regulations, which are also often shifting and require ways to deal with legal requirements and various legal systems like NCMEC. And this varies by jurisdiction. Like not just by countries, sometimes even by states.
(Funny story about NCMEC – it has an API to report CSAM, but I could not find it. So I googled something like “child porn API” and got a blank results page. Pretty sure I’m now on a list somewhere.)
I could go on and on. And I wasn’t even working in this area, just supporting these teams! Admittedly in our case I'd put the relevant headcount in the hundreds and not thousands, but our scale was also very different. For a company that is ENTIRELY about user-generated content at massive scale, up to national-level events like Arab Spring -- even if there was a lot of bloat -- I would not be surprised to learn this function was the majority of the workforce.
And Elon killed pretty much all of this. And, well, we see the results everyday.
To be fair, this could be due to the bias in reporting, as media outlets may have had incentives for over-emphasizing the T&S angle.
I do not deny there was bloat. There was bloat in most tech firms at the time. But I don't think it was 80% bloat. My post was to explain how, even if T&S / moderation seems like a small function, it can require an unexpectedly large headcount -- probably even more for a pure-UGC company like Twitter -- and so could realistically account for the bulk of the cuts.
https://news.ycombinator.com/item?id=45192092
Again, I'll admit Twitter and all other companies had bloat. But based on these industry-wide reports about record levels of burnout, inside knowledge of at least one company that I thought had unjustified layoffs, and a large number of conversations I've been having with connections across the tech industry, I think these layoffs have long gone far beyond the bloat.
The deal with tesla is that there is a relatively small employer pool, so you can be fairly bad employer but still get good outcomes. The same with spaceX. Sure early tesla had some stories about it being fun, but there was/is a darkside.
The issue with xAI is that researchers have a whole bunch of other employers to choose from. Even at meta, where it used to be fairly nice for researchers, the pressure of "delivering" every 6 months lead to bad outcomes. Having someone single you out for what ever reason the boss had a bad day, is not how good research gets done.
We have seen (A few of my friends were at twitter when it was taken over) that Musk has a somewhat unusual approach to managing staff (ie camping at work). Some researcher love that, assuming that they have peace to research, and are listened to. But a lot don't.
Usually just firing 3 to 5% of any company workers have terrible consequences for the company that does it.
It does not speak so well about the workers.
At the time I joked that like Chaos Monkey, we should have an "Elon Monkey" to "fire" arbitrary people by sending them on mandatory vacations with no connectivity to see what falls over.
The ones with stock options in, now, SpaceX?
Aren’t employees also subject to a lock out period where they still can’t sell their stock until $x number of months after an IPO unlike employees of public companies that can sell as soon as they vest?
Honest question, I’ve worked for public $BigTech but haven’t been at a company pre IPO
I'd wager you were saying the same thing about bitcoin until last year.
Is it meant to draw equivalence between crypto and Tesla/SpaceX? That each has roughly similar (i.e., low) value to humanity, or value as businesses?
Is it that the metric of whether a person makes others money is invalid?
The comment seems coy, possibly to avoid making any claim at all, but it must not be that because that wouldn't be very sporting.