Not only is he not a shareholder, but based on his opinions, he clearly isn't a user of modern models either. Somehow he still thinks they are party tricks and that "They will never be AGI," with no elaboration on _why_ they _can't_ be AGI, and why that actually matters for these companies (hint: it doesn't really matter much. As it turns out, you could replace a lot of humans with a modern LLM and have a reasonable comparative advantage in doing so, even though the models are ostensibly not "AGI.").

His predictions have also historically just been bad (https://danluu.com/zitron/). Even AI 2027, which is basically semi-fictional, is more accurate and worth paying more attention to than this guy. And the superforecaseters which actually made AI 2027 and actual futurists are generally higher signal.

I know this is somewhat of a meme now but I do think the predominant language will be "typing/speaking to a coding agent in your native language."

I personally think that you _could_ use an LLM to catch these types of boundary case errors without having to port the _entire_ C++ codebase to Rust, but maybe pre-emptively porting to Rust now can catch some of these cases for cheaper than doing a full LLM sweep. Also more cynically, its a good benchmark lol.

I guess if you really believe in curve of LLM capabilities you should just use a language that has the best performance, safety, flexibility, and extensibility, since in the limit few/no people will actually read the code anyway. I think this ends up being Rust.

It was clearly done because some PL guys at google really wanted to make a new cool language and Google was the perfect place to incubate it without it getting axed. Probably got a couple of promos out of it too. This is clearly not the best use of time or money, but I guess if you're google you have so much of both it probably doesn't really make a dent, and you can keep a few very smart people happy with shiny new projects.

Also, LLMs being used for a large portion of coding nowadays sort of remove the need for these types of languages, IMO. They make less "silly" bugs (both logical and structural) that languages like this are meant to catch, and they are much better at languages that are better represented in the training corpus. This somewhat obviates the need for very niche "type/dummy-safe" languages like carbon (and even rust/zig, imo). So even if you did want to use Carbon, you'd likely have to bootstrap a decent amount of your own "good" carbon code to post train an LLM, and even then, it likely won't have that big of a gain vs just having an LLM write C++ or even Rust. If you are a company that still reviews code, you should just have an LLM code in a language most people can understand anyway to make verifiability tractable.

-4% means nothing in context.

"Chief AI officer" does not mean he leads AI research efforts, its just a title. He just takes care of hiring and product direction, both of which I'd say hes done well in. They obviously have actual researchers working on models.

I'd go as far as to say Alexandr Wang can do this better than most other leaders. Young people should be in charge of products, they generally have a better sense of what resonates with consumers, and Meta is a consumer company at the end of the day.

I don't think this will happen, just as Chinese models did not eat Anthropic/OpenAI's lunch on top tier intelligence. The market is way too niche, and the parties already interested in the capabilities behind safeguards are likely already partnered with Anthropic to get around those with Mythos-class models (see project glasswing for cybersec).

Please tell me models are not improving at breakneck speed.

People don't want to believe something as unsatisfying as "Scaling up LLMs" can yield something as profound as AGI/be useful, and just hope that literally anything else can take their mindshare away, and this just happens to be the new rage. Along with clearly-not-frontier-level open source models, non-transformer based architectures, etc.

In reality, none of these really matter. The frontier labs can easily do something like this but likely havent because the size of this market is too small and it is not on the critical path to AGI.

When you have as many resources as OpenAI and Anthropic, theres basically no point in putting compute towards random bets that don't have a predictable return. And at this point, scaling up transformers is almost a surefire way ot putting money in via training and getting money out via increased capabilities AND it speeds up your own business by factors of X. Sidequesting a Jev like product is falling for twitter hype and is likely not going to happen, definitely not by Anthropic, and I'd bet probably not by OpenAI either.

You have to run a legitimate business and make money. After the "takeoff," anything is fair game. It could result in value accruing to capital (lab shareholders) and everyone else is screwed, it could result in the status quo being maintained but people do way more and GDP goes up by a lot, it could result in a post scarcity society etc. Theres a reason why its called the singularity - you can't see past the event horizon.

In any case it is optimal for OpenAI to create good products that generate revenue NOW vs going for some straight shot pie in the sky model that is "AGI." SSI is attempting to do this and I suspect they are about as close as anyone else is, and I'd honestly assume they are likely further away than OpenAI or Anthropic at the moment. But one of these classes of companies has actual revenue that is arguably good for the economy, the other is just a glorified research lab that has produced nothing of value.

Re the point on returning shareholder value, Anthropic is a PBC:

> Public Benefit Purpose: Under Delaware law, a PBC allows directors to legally weigh social and public good alongside maximizing profit for stockholders

Investors surely know this and Anthropic probably has pretty reasonable legal grounds to stand on if they chose to prioritize safety over profits.

Seems pretty clear to me that this is in earnest.

The job of a SWE is to not produce the most sublime readable code with cute language bells and whistles that you can show off to your other programmer friends. Yes its fun to do and interesting, but the main value in writing code is to express your intent so you and other people that are working with you can actually build something of value.

Software has value to the people on your team reading it. The product that you create with your software has a far wider impact than just that. As such, there is for _sure_ optimization pressure on "professional software engineering" to be more productive and actually make the product better to use, faster, with more features users like. The user typically does not care that you used a new C++26 feature, as much as I enjoyed writing it for them.

If I can produce features for users faster and I can iterate on them faster, this is a boon to me. I can program by hand for fun all I want in my spare time, but if I want to produce value for someone else, it is economically infeasible for me to be a luddite, and I will simply be outcompeted. Previously you were outcompeted by smarter teams who out executed you (and who also worked more), and now you can be out executed by basically anyone using an LLM who actually understands that the end goal is making a product that is pleasant to use and actually has utility, not code that is pleasant to read.

You can be an "performative/artisanal programmer" just as you can do "performative/artisanal martial arts." Just know that there will not be a large market for it in the real world.

2 years ago a model could barely solve the AMC, 1 year ago it got IMO gold, and this year models have solved multiple millenium problems.

Even 1 year ago ai code was just unusable (claude code only became available 1.5 years ago!) and now basically everyone I know from independent shops all the way to faang and anthropic/openai themselves exclusively use some AI agent to code.

Why does HN continue to delude itself that "models are not improving?" Maybe for the simple things they care about its "roughly the same," but they are _clearly_ improving.

They have an internal model which could solve a millenium problem end to end with no intervention, whereas current available models can't. They can clearly make better models. Not everything these guys say is some 500iq game theory optimal 4D chess PR or subterfuge strategy.