> 30-50% of engineers on core teams have been forcefully reassigned to data labeling and RLHF, upsetting folks even more.

This really doesn't sound believable to me, but who knows with all the craziness going on. Software developers in the US are seriously expensive, using them for data labeling would be a waste of resources. And the percentage sounds very high, unless "core teams" is only a small subset of the total developer count.

> Software developers in the US are seriously expensive, using them for data labeling would be a waste of resources.

The frontier work is on labeling and training expert content, by experts. It's unglamorous work and almost certainly doesn't warrant FAANG pay, but neither did most of the work that most FAANG engineers were already doing. But it does require competent talent from the expert domain.

Like their peer companies, Meta is still sitting on a huge pool of vetted-as-competant workers from the hiring boom and expert AI training is the most ripe business opportunity in a fragile economy where pretty much every comparable opportunity has evaporated.

> Software developers in the US are seriously expensive, using them for data labeling would be a waste of resources.

Zuck basically went to a town hall and explained to his employees that their remaining value to him is as training mules for his AI.

Zuck literally said that he wants folks with higher intelligence on the Applied Intelligence team. And the best way to do that was to move folks internally, since they were "intelligent" enough to pass the Meta interviews.

Soooo, yes it is a waste of resources ($$$). But this was the initial intention.

The belief that engineers are not doing anything for x amount of time that could be better spent on other immediately measurable things is as old as the profession itself.

Ironically this vanishes when the tables are turned and we ask for things like better hardware or software. There are plenty of us here with stories of how much effort it took to convince employers that SSDs were worth it when they were new, small, and very expensive.

It's only until Cold Harbor is completed.

From the article it sounds like what they're actually doing is reviewing LLM-generated code, for that you do need good software engineers.

Although it goes without saying that good software engineers won't enjoy doing this very much

>using them for data labeling would be a waste of resources

Would it? It seems like they can spend a few months extracting intelligence and "taste" from their engineers then get years worth of it back from the AI.

Isn't that Scale AI investment in a company that does labeling? what are we missing? Are we all going to be labelers soon too?

I believe it, because it makes a kind of sense. Post-training has a huge impact on how well LLMs perform, and labeled data is what determines the effectiveness of post-training. This is why companies like Anthropic are so worried about distillation.

So if you have access to a large number of highly skilled people, and you really don't absolutely need them to do other things, why wouldn't you force data labeling tasks on them?

Facebook is also planning a 10% layoff, so this also works as encouragement for people to leave voluntarily.

(Before you downvote me, note that I'm not endorsing this or saying it's a good idea. I'm just saying that I believe it's true, because I can see how Facebook's leadership would think it's a good idea.)

Silicon Valley strikes again?

https://www.youtube.com/watch?v=obS-qZO9uCQ