Previously they were a distant fourth. They're not going to single-shot catch up to OpenAI or Anthropic, but they moved up the ladder one rung.
In the short term labs are not profitable, although supposedly Anthropic is close. But Amazon was also famously unprofitable for many many years, and then won huge. Current profits or lack thereof are not necessarily important to investors: what's important is they believe in your future potential profits.
In this case, Elon clearly believes much of the economy will be run by AI in the future, and the economic value of a token will rise faster than the cost of generating the token — including the amortized cost of training the model to produce that token. Thus he is building a lab to train models and charge for inference of those models, and — he believes — it will eventually become profitable even if it isn't now.
You may or may not agree with him (and you may or may not agree he's capable of beating Ant/OAI), but current profits aren't a great indicator of whether he believes future profits are attainable. Tesla and SpaceX were also very unprofitable, until they weren't.
Personally I agree with him that there will be massive profits in the future, although I am not as confident in his ability to beat Ant/OAI, at least given his recent difficulties in retaining researchers.
Interesting: I don't see anything in our error logs but we could be missing something (and personally the chat works for me + my unsubscribed test account). If you email us at [email protected] though we should be able to fix anything you're running into!
I'm biased because I run an inference company, https://synthetic.new. That being said I think we're pretty good at serving at GLM-5.2 — and other models, like Kimi K2.7! — and our privacy policy is quite good: zero data retention for prompts and completions on API requests. Our average streaming TPS for GLM-5.2 (aka, tokens after factoring out time-to-first-token, which varies based on geography) is 97tps over the last 24hrs, although it's slightly lower at peak traffic in the mornings PST where it's 50-70 tps. We're also subscription-based which is nicer for coding than e.g. Fireworks which is per-token billing.
Self-promo but you should try our service synthetic.new. We generally have up-to-date open-source LLMs on the sub, and we have GLM-5.2 :) Perf+stability should be wayyy better than zai.
I wonder if this explains why I hear about this more from Europeans than from the SF tech scene. California is at-will employment, so you can fire an employee as easily as a contractor. Ironically this makes companies more willing to hire and retain employees, since they're not worried about getting stuck with a bad one — and most employees aren't bad, and are better for the company than contractors.
The Wikipedia articles say the majority of scholars believe it's based on Aramaic, while a minority of people (primarily non-linguistic-specialists in India) disagree. I think you're the one drawing from bias.
No, modern Hebrew and ancient Hebrew mapped similarly well to the written script — the primary difference between the two is just consonant drift. Both used the same structure of triconsonant roots with affixed patterns, and modern Hebrew morphology is identical to ancient Hebrew (phonemes changed primarily due to consonant drift, but not its structure). Arabic, for example, is similar and similarly well-mapped to its script, as are other Semitic languages that are closely related to ancient Canaanite.
As per the Wikipedia links, it's generally considered by scholars to be the origin of all alphabets and an early alphabetic script. Abjad is a term invented in 1990 to distinguish early alphabetic scripts without vowels from later scripts with them. Effectively every scholar agrees that Canaanite/Aramaic/Hebrew/Arabic are alphabetic systems (while also acknowledging them as abjads).
Hebrew is not based on Yiddish, lol; only Ashkenazi Hebrew pronunciation was influenced by Yiddish. Modern Israeli Hebrew uses primarily Sephardi pronunciation, and Ashkenazi is mocked (i.e. Shabbat is Sephardi, Shabbos is Ashkenazi; modern Israeli Hebrew uses Shabbat). I grew up around Ashkenazi pronunciation in America, and had to unlearn it when I spent time in Israel. Nonetheless, Yemenite, Sephardi, and Ashkenazi Hebrew — the three major extant pronunciations, only one of which was ever influenced by Yiddish (Ashkenazi) — are all extremely similar and mutually intelligible, and thus all of them are extremely well mapped to the alphabet. Yemenite is most likely closest to the original spoken language, specifically the ע, but there are very few differences. And a modern Hebrew speaker can easily understand Biblical Hebrew — they're closer than even Modern English and Shakespearean.
Also, not all colloquial dialects are mutually intelligible. Different Chinese dialects are still often referred to as "dialects," despite not being mutually intelligible (e.g. Cantonese vs Mandarin). While that's typically mostly the case for Western languages, there's a spectrum even there.
Egyptian heiroglyphs were not an alphabet, even if they had alphabetic elements (in addition to pictographic ones). Scholars generally agree that proto-Sinaitic was the first alphabet, and all subsequent alphabets used today are either direct descendants or directly inspired by it. https://en.wikipedia.org/wiki/History_of_the_alphabet
No: most scholars believe alphabets were only invented once, much like the wheel. All Western alphabets are direct descendants, and the non-Western alphabets were directly inspired by it. [1]
Phonetic alphabets were introduced to most of Asia by various Brahmic scripts; the most widely-used (albeit briefly-used) one being the Mongolian Phags-pa script [2], derived from Tibetan, derived from various Brahmic scripts, derived from Aramaic, derived from Phoenician, derived from — sure enough — proto-Sinaitic. Thai and Khmer are derived from Pallava [3], which is derived from Tamil-Brahmi, derived from other Brahmic scripts, again derived from Aramaic and thus eventually from proto-Sinaitic; etc etc.
It wasn't directly cribbed (unlike Western alphabets), but given that Hangul was invented in the 1400s after exposure to Western alphabets, most scholars still consider alphabets to have only been invented once [1] and then copied, much like the wheel. Although I suppose that's true of Cherokee too!
Fun fact: all (non-Cherokee?) alphabets in use today stem from an ancient Canaanite alphabet called the proto-Sinaitic script [1]. This is why Hebrew's alphabet near-perfectly phonetically represents the spoken language: Hebrew is just a dialect of Canaanite, and all Canaanite dialects are mutually intelligible, and alphabets were invented to represent spoken Canaanite. As the alphabet was cribbed by the Greeks (who were taught a simplified version by seafaring Canaanites — the Phoenicians — and termed it the "Phoenician alphabet" [2] despite the Phoenicians not specifically inventing it), significant alterations had to be made and it's been an imperfect match for most Western languages ever since.
America has somehow managed to hang on to the right to encryption, despite plenty of well-heeled opponents, so it's possible to hang on to the right to open-source models. But it'll take a lot of vocal support, since there's strong incentive for Anthropic to try to cajole the government into banning competition (and they've already crossed that particular Rubicon, whereas OpenAI to my knowledge hasn't and at least still releases some open-source models like gpt-oss-120b).
You don't need to be able to self-host it. It's fine to pay someone else for it. If it's open-source, competition will ensure inference providers support it well enough, and if an open-source provider is dumb enough to nerf their model for (useful) coding tasks, there's plenty of incentive for inference companies to do some lightweight finetuning to restore the capability.
Many, many, many public policy positions; for a clear-cut example, they eventually supported SB 1047 [1] which would have banned open-sourcing any model trained with over 10^26 FLOPS (i.e. what Anthropic reportedly used to train Mythos). Their "Responsible Scaling Policy" [2] — a set of policy proposals that includes recommendations for government regulation — specifically calls out requiring "third-party controls" on model weights to prevent access; for developers to prevent "modification of models" such as fine-tuning (obviously impossible for open-source or open-weight models); prevent usage of model weights in "Automated R&D in key domains" which they specifically call out AI development as a key domain (again, obviously impossible for open-source); etc etc.
They want to ban open-source AI and are not shy about it.
The same way Anthropic is making it difficult to compete with them. They intentionally train the model (via PEFT, as called out in the model card) to be dumber when attempting to do things Anthropic doesn't want — in this case, competing with them, but you could apply the same training process for other domains such as actually-malicious use cases.
"You can't take code produced by our service to make competing services, but we can take code you produced to compete with your service (i.e. software engineering)" is pulling up the ladder IMO. If they can from-scratch train a model without using human-produced code, I think they're within their rights to stop humans from using their model to compete with them. But if they're training on GitHub/Hugging Face/arXiv/Common Crawl/etc, which certainly includes many open-source repos whose licenses they're violating, I don't think they should be legally allowed to prevent people from using their model to produce code that merely competes with them. They themselves have taken other people's code in order to compete with software engineers.
I hope they get nationalized and either the models are open-sourced or the profits are owned by the public.