I had the same thoughts. I like OpenRouter a lot and still use them, but these numbers make me think more about how much more crowded the space has become. In the last year, you've had startups completely pivot into this niche (Respan and Requesty off the top of my head) who have lots of usage, infra companies like DeepInfra have really leaned in hard to the "serverless interface for open models" positioning, some of the popular harnesses that previously were complimentary to OpenRouter now sell their own gateway/subscription (OpenCode, Nous), and the subscription discount for Claude/Codex is wildly lopsided vs API pricing for solo devs.
One of the big points is that looped architectures don't necessarily remove CoT style reasoning. They definitely change it, but the model can still be prompted for this kind of output. It just spends more computation on individual tokens. I think the hope, in that particular scenario, is that the model spends less tokens on things like backtracking.
But there are lots of approaches to looped transformer architectures. Some have some kind dynamic loop (i.e. they might loop until they satisfy some kind of equilibrium, or they might have some kind of routing function that makes different tokens loop differently, or any number of other approaches). Others are simpler "loop this many times" architectures. It's a really active area of research with some pretty deep roots, if you want to dig in! This paper blew up a while ago, and it's a looped transformer architecture (though not like the ones discussed here) that isn't autoregressive, as an example: https://arxiv.org/html/2510.04871v1
I could imagine that you could loop over and over, but direct the outputs of each layer to two destinations: a) to continue through the layers, looping, and b) a separate CoT text output layer to make those CoT concrete. In fact, the model itself would not need to be aware or have access to those CoT traces at all. I am not an AI guy, so I don't know for sure, but that seems reasonably possible.
If you're genuinely curious, I'd recommend searching around about the history of this persistent urban legend. It's really interesting.
But the short version is that there are lots of people who want to buy any given drug and drug dealers sell them. I don't know exactly what you mean by "WAIT for people to demand the more profitable drugs", but there typically isn't a whole lot of waiting. There's a lot of demand out there. Giving away a bunch of free fentanyl to random people in the hopes that one of them instantly gets addicted would be a pretty wasteful and high risk sales tactic, especially given the number of people that want to buy it already.
...yes? It doesn't literally kill you if use more tokens than usual?
And we're talking about subscription plans from OpenAI/Anthropic here. Switching to a different model and/or harness if the price changes isn't a "the world has run out of opioids and I'm going to need a detox center" level crisis.
The original question was "are you sure an OpenAI subscription isn't heroin?" Different impacts of overuse (death vs slop code) feels like a valid point of difference.
This is a weirdly melodramatic analogy.
If you want to flatten everything to "all things habit forming are definitionally equivalent", you'll get some pretty funky results.
You're equivocating, they were talking about addiction, you're talking about overuse/overdose which is where the analogy breaks down for pretty obvious reasons.
What? (1) Yes.. it's never that [stupid economics, they don't want their purchasers to die.. might as well sell fentanyl], (2) it's always that [hardly any way to propose alternatives.. within the scope of not killing the customer]
I'm not sure exactly what you're implying but if you're curious, there's a lot of writing about the persistence of this particular urban legend. A lot of it starts with 1920s newspapers painting pictures of dastardly drug peddlers luring in children with free drugs to get them hooked.
Turns out that drug dealers by and large prefer the straightforward approach of selling drugs to the huge market of people who want to take them.
How does the crowding out work here? I'm not being pedantic, I'm genuinely curious about the dynamics as you see them.
My experience in the publishing world is that there is already an infinite supply of writing. Lots of things serve as filters, some for better and some for worse, but ultimately the quality of the writing (however we define that) vs. the field is part of that filter. There is a competition for our limited little attention spans and finite waking hours, and writing better suited to our tastes tends to win out.
I don't see how that dynamic changes with AI. If all writers have more or less equal access to AI, some writers will use it in ways that result in writing I enjoy more than others. Some won't use it at all and still write work I really like. The majority of people writing with or without AI, just as today, won't write stuff I really want to read.
I don't know if I've ever read a piece of SEO-driven content marketing and thought it was particularly profound or that it made an impact on me. Or that I really had an inherently meaningful connection to the author. I sort of don't care if people generate 100x more of that sort of thing. It makes Google searches less effective, I guess? Or it may make my For You Page on Instagram less human-rich?
I've read some nice poetry though. I usually go looking for it. There are a handful of authors I tend to like consistently. The overall volume of poetry published (it's quite a lot!) doesn't really have an effect on my preference for their work. If there was 1,000,000 times more poetry published because of AI, I don't think it would prevent me from seeking out new poems from writers/publishers/curators I like, nor would I probably really notice.
I agree, with respect to the status quo where AI writing is associated with lower quality.
In the long term, AI writing - mostly done solo, not directed by humans - will likely have higher quality than human writing, while being better suited to people's tastes since it can be personalized for every reader. If so, it's not hard to imagine it crowding out human writing.
You might ask, though, is that a bad thing? If it's legitimately higher quality, then what's wrong with consuming it? Well, I don't know. It certainly seems dangerous for everyone to be in their own little bubble of content. Social media of course tries to do that today, but there are limits. You can't pick any topic, any set of attitudes you want and get an infinite supply of content tailored exactly for you. Even if you find some super niche community to set up shop in, it's still a community; you're still exposed to the outside world at least a little. What happens when that changes?
Ive anecdotally never found OpenCode to have a real axis where I find it "best". But I havent tried it in a bit, mostly because I don't have a real gap in use case coverage between Codex/CC/Pi. Anything more specific in need probably gets a custom harness.
The Go plan is quite good, possibly the best deal out there. I recommend it for anyone interested in trying open weights ($10/month, very generous quota), and you can use it with any harness or direct curl. I have my Go sub configured through GoModel (no relation, my gateway of choice, also very good).
The history of the term is actually really interesting. There has been debate about whether or not its the correct name basically since the advent of computer science departments in the 60s.
I don't think the author is "wrong" about the gap between theoretical discussions computation and the actual design constraints of building usable computers. I don't know if that's really a general refutation of proving new things mathematically though. The word "computer" doesn't appear once in the paper OpenAI released, nor do they make any claims specifically about "computer science": https://github.com/openai/math/blob/main/preprints/An-explic...
It's a novel bit of math. Whether or not it means anything in the context of writing software realistically is an interesting conversation, but it doesn't have much bearing on the substance of the result itself. OpenAI is pretty explicit that they view these as research results in the domain of mathematics.
I don't really know what it means. It seems to sort of entangle Google Search as a product with a lot of constituent parts and "search" as a specific operation over Google's index of sites. Talking about the product, ML has been a core part of Google's search product for a really long time. AI summaries in Google Search distill information from sites discovered from a "normal" Google search. The Search product, which now incorporates AI, and advertising enabled by it, are still far and away Google's biggest revenue drivers.
If we literally mean AI as in people paying to use/deploy AI via Google, that's still a small part of their revenue, far less than advertising revenue. If we mean AI as in "every Google product that uses ML", then I guess that's been their core product for a long time.
I'd be curious to see stats on overlap among startups by batch historically. My impression is that this has always been a thing. Based on where the market/technology horizon currently is, there are some buckets that are very "startup shaped". Founders who are iterating on ideas using similar criteria will often end up in the same proximity, especially in an accelerator environment. But it would be extremely interesting to see if this has become more prevalent over time, or at least since the explosion in the AI market.
This is a productive line of research with lots to explore, if you're interested. There are different flavors based on what you're describing that you might like. Maybe you already know all of this, but just in case any other reader is curious :)
A classic paper on "expanding" a neural network (the second part of your distil-then-grow loop) is Net2Net. Their goal is basically to get a smaller model's knowledge transferred into a larger one to bootstrap the larger model training: https://arxiv.org/abs/1511.05641 . Bert2Bert is basically the same idea but language models: https://arxiv.org/abs/2110.07143
There's also stuff on "growing" a pretrained LLM that is somewhat similar to what you're suggesting: https://arxiv.org/abs/2303.00980
And then there's a bunch of stuff on "refreshing" weights in the network as well, sort of like pruning but without reducing capacity.
One of the things to explore are assumptions around how information really compresses down in distillation, and what is really happening when we deem a model saturated. There's often an intuition that when you distill the model down, you're finding a sort of "truer", more essential representation of the functions you're modeling. And so if you distill it down and then expand the weights, you've added additional capacity for it to learn more, since the distilled core now contains the important information from the original larger model. But very often that's not actually how it plays out, and the problem is thornier than it seems on its face. Plateaus in training don't necessarily mean the model has run out of representational capacity. And distillation is not guaranteed to preserve the most valuable information. It's entirely possible that the representations the original model has learned for performing on the tasks it is evaluated against are more generalizable than the compressed representations you've distilled.
Initialization is also tricky when you're expanding the weights like this. Non-linearities make it such that splitting the weights directly like you're describing doesn't preserve the function. Not that this means the model can't recover from further training, but it's just not where you want to start training from if you can avoid it. There's also a strong likelihood that the added parameters are redundant relative to the distilled core, which will make learning more challenging. A lot of work in this space as a result focuses on stuff like symmetry breaking. A cool paper you might find interesting that isn't about LLMs, but has a similar "Progressively give the model more degrees of freedom" flavor is ARC-AGI Without Pretraining: https://iliao2345.github.io/blog_posts/arc_agi_without_pretr...
But there's lots of active research around this general space. You should test out your ideas and share them! I can't recall now exactly but I feel like I've seen MoE stuff that touches on some similar ideas.
If you can thoroughly and accurately review 100 * 400 = 40,000 lines of CUDA kernel code per hour, I know roughly 1,000 people who would love to hire you right now.
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