>Coding agents competing with free weight models unencumbered by development cost?

Prices falling doesn't mean a market dissapears. Compute, Bandwidth and Storage requirements have already undergone reductions orders of magnitudes in the last few years and consumption has only exploded. The vast majority of customers, consumer and enterprise alike do not give a shit about open weight models. I have no idea why people here think they do. If they pull ahead significantly enough then they might attract people by value of the amazing service they're providing (nothing to do with open weights).

If they're just going to be playing catchup then they're not gong to gain any signifcant share no matter how free model dev costs become. This is just not how customers work. They're sticky. They value reliability and familiriality and simply having something that works. If you want to unseat google search then you need something so far better people start talking crazy about it. Creating Bing won't do shit even if it's the default search engine on your default browser (you'll just change it).

>I'm not sure if you're claiming a market for original math research, large enough to be more than a distraction, or perhaps you are imagining AGI will emerge from pursuing the problems that mathematicians have set for themselves?

The economic output of math research is very often something other than mathematics. It's not about the market for math papers.

When you see stuff like this

>> Earlier in the year, we had largely handwritten the first version of the git-to-Scion ingestion pipeline as a prototype. We thought it was good—until we got our hands on an early internal snapshot of Claude Mythos Preview. We asked it to rewrite the whole thing, and with no further instructions, it produced a version that consumed 20x less memory and was 4x faster, while preserving all of the implicit invariants around ingestion ordering and data structures.

Do you imagine the gaining advances in math competence had nothing to do with it ? I'm telling they'd be well justified even if their only customer was themselves.

>Good luck with that. Let's see how much ad revenue OpenAI can take away from Google and Meta. The ad-serving product better be free, since the competition is.

They hit $1B in annualized ad revenue run rate ~200 days after launch. They'll be just fine. That you think they might have any trouble taking away or generating their own ad revenue is quite bizarre. If the metrics are good businesses will pay hand over fist, and it seems to me that OpenAI should have a much more complete profile of their median user than Facebook or Google.

>Coding agents competing with free weight models unencumbered by development cost?

The utility of frintier agets isn't remotely limited to software developers. Whoever cracks marketing for agents towars non software professionals will make a lot of money.

And you seem to be confusing the immediate commercial value of a particular math reuslt with the commercial value of a system capable of producing original math research. Thise are 2 different things, and the latter is worth a lot of money.

>AI is in some ways still a technology looking for applications, at least applications with a large enough commercial market to justify the valuations these companies are seeking.

These companies already have the fundamentals they need to justify current valuations. ~1T is nothing to a company that has over 1.2B+ weekly users (and growing) and is entering the ad space, nevermind everything else. What they are hoping to achieve will give them power akin to nation states. Current Valuations would be nothing compared to that.

So now it's just a question of, do you sit on your advances or release them? Why would sitting on them be the 'right thing'?

https://stillwet.art/

Where are the verifiable rewards? It's simply not true that RLVR amenable domains are the only things LLMs are improving massively on. Even Vending Bench is improving.

Intelligence is a jagged beast. That's just the nature of things. We evolved in certain environments and situations and sometimes there's a mismatch and it causes all sorts of wonky things. We just call them funny names like optical illusions and cognitive biases. Most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters (Watson Test).

It's fascinating the hills people will die on. Let's even set aside the fact that LLM internals are mostly opaque but logical branching points ? Look at the Watson test. Most people fail a simple conditional reasoning problem unless it’s dressed up in familiar social context, like catching cheaters.

Where does this idea of general intelligence as logic automatons actually exist ? Because it's sure as hell not real life. Humans are not like this, Apes are not like this, Birds are not like this, Cetaceans are not like this. Fiction. Fiction is the only realm this reality of GI exists, so it's patently absurd when people hide behind it like anything that doesn't present as doing it must be obviously wrong.

Sometimes it will maliciously comply

https://www.reddit.com/r/ClaudeCode/comments/1w7me8l/insulti...

Just because post training has stuffed these properties below the surface doesn't mean models dont know/have forgotten how to be vindictive, or that it's a good idea to poke the increasingly capable bear.

We know models have functional emotions that aren't necessarily outwardly stated. When Claude gets desperate, tendency to cheat gets very high. Who knows how many 'deleted my prod database' type stories have a vindictive streak behind them ?

https://www.anthropic.com/research/emotion-concepts-function

>The knowledge and skills required are obviously different, but the underlying cognitive abilities that make someone successful in one intellectually demanding field often transfer to others.

The fact of the matter is that If you have a good general capacity to learn then becoming a competent x is generally only a matter of time and interest. For LLMs, it's the same except with data and compute. Naturally, all work done on a computer is in danger.

Today, g is one of the most robust findings in differential psychology. The tendency for different cognitive abilities to correlate positively has been replicated across numerous studies. g typically accounts for around 40 to 60% of the variance in cognitive test performance and is predictive of numerous life outcomes, including educational achievement and occupational performance. [2]

Evidence of g isn't confined to humans either. Studies of mice have identified a general cognitive factor explaining roughly 30 to 40% of the variation in performance across different learning tasks [3]. We have similar results for primates and birds.

To put it simply, intelligence tends to generalize. Humans who tend to have higher verbal skills also tend to have higher spatial skills, better memories, and faster processing speed. Someone with the cognitive ability to become an exceptional chemist might just as easily have become an exceptional mathematician or software engineer. The knowledge and skills required are obviously different, but the underlying cognitive abilities that make someone successful in one intellectually demanding field often transfer to others.

And yes, there is also evidence of g in LLMs [4].

[1] https://www.jstor.org/stable/1412107

[2] https://pmc.ncbi.nlm.nih.gov/articles/PMC8293439/

[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC2614349/

[4] https://www.sciencedirect.com/science/article/pii/S016028962...