svcrunch

AI is concentrating work. Fewer and fewer people are working like never before ("I've never worked more"), while a massive number of people are not working at all. Exhibit 1: Microsoft's layoff of 25% of its workforce this week.

The capitalist system has no intrinsic solution to this problem---in fact, it's blind to the problem. There is no problem from the capitalist perspective, this is progress! The solution has to come from outside, through public policy.

The promise of "less work" won't happen until society reorganizes around the recognition that most people no longer need to work. Realizing that reorganization will be the defining struggle of our generation.

Times are changing quickly, mainly due to the rapid progress of AI, and those companies which fail to adapt are going to die. Unfortunately, that is going to mean heavier and heavier layoffs.

Workforce displacement due to AI is an issue we'll have to address at a societal level, through public policy. The free market is incapable of fixing this --- at least not without causing immense pain to millions and millions of people.

For me, training data comes from agent runs.

I do fine tuning with pytorch, and RL-forms of distillation with Tinker! I hope that helps. I work with a full stack of models that enable agentic AI: embedders, rerankers, LLMs, decision models.

Curious, why are you asking?

> The blog post is rather confusing because until you understand the entire situation it appears to imply that they fixed vLLM (what the title implies) but really they just needed to configure it correctly.

Just as a point of feedback for the authors, I enjoyed the article, and did not suffer the confusion pointed out above.

Thanks for sharing this writeup.

At my previous company, I led the development of a retrieval embedding model called Boomerang, which focused on minimizing same-language bias. Briefly, the way most embedders are trained, they will prefer worse answers in the language that the question was asked, versus better answers in a different language. I call that same-language bias.

Anyway, an investor at a16z was quizzing me on why this particular model wasn't top-ranked on MTEB. I tried explaining that those benchmarks are gamed and they don't reflect real-world performance well. I could tell he wasn't buying it.

When I saw Jev raise 700m, I really felt that the VC funding model is creating an unserious environment for development of AI/ML. What's rewarded is slick marketing, insider connections, and confidently expounding unrealistic visions of what your team will achieve in the next 3 months.

Under these conditions there isn't much incentive to fix benchmarks.

I remember my manager in Google Research poring through a German-English translation dataset and realizing there was a ton of invalid data inside it. When he filtered out the cruft, models showed big improvements. But the filtering was laborious and not glamorous.

I've been using the Vector APIs for years now, and I'm still waiting for them to GA!

They are useful for neural information retrieval (RAG, memory), which relies heavily on content vectorization and similarity matching using their dot products.