calebkaiser

I'm working on something around this that I'm hopeful I can open source in the next couple of weeks. It's just a research project, so not like a product that can be bought and shipped to prod right away, but I'm excited about it. I think a lot of people are working on this right now, at least in part, with things like memory layers and other approaches to "grounding", or with some of the more recent stuff around certification and governance. But there's sort of a split right now between:

- Groups focused strictly on model capability (i.e. benchmark scores)

- Groups really focused on governance, interpretability, control planes, etc

- A small but excitable minority of weirdos (me included) who think they're the same thing

A lot of the stuff that gets loosely clustered under neurosymbolic methods might be interesting if you're looking to dive in. The sort of pattern you're describing is something that a lot of people have taken stabs at from different directions, or at least have tangentially nipped at while working on related problems.

I think that's a common impression. Interestingly, while there are still a lot of questions marks around what makes someone predisposed to addiction, the few things we do know tend to run counter to the idea that people dealing with addiction are overly sensitive to feelings of reward and pleasure. Or that being less sensitive would make someone more resistant.

One of the most well studied genetic risk factors for addiction vulnerability is the DRD2 gene, which encodes the dopamine receptor D_2. People who are particularly vulnerable to addiction commonly have a deficiency here, meaning getting bored very easily and feeling under stimulated/rewarded by typical things is in part what makes riskier sources of pleasure (i.e. drugs) so appealing.

Disclaimer: Not a medical professional, just like reading about addiction and genetics.

I feel for everyone in this situation. I don't think anyone is wrong to mourn the undeniably massive shift happening to something that has been such a big part of our lives, and I don't think anyone is wrong to be excited. I say this as someone who has worked in ML for coming up on 10 years now and is more towards the latter camp, where I love working with agents, and still spend plenty of time writing code by hand and studying. I don't think it's fair to pretend people are being unreasonable or are just "missing the big picture" for being unhappy.

But I also have found that where you work and what AI usage is like in your company is a pretty big deciding factor. Your comment, which I agree with, made me think about this. I consult a decent amount, and I've worked with companies where the engineering culture has adopted AI in a way I describe as "ugly." I should find a better word. But what I mean is that it's sort of like the attitude is "okay, now we can ship more tickets, so everyone take on 8x the workload". And they do a lot of "best practice" kind of stuff around using coding agents--every new anthropic article gets posted to slack, and there's always a new CI process for using agents--but they're fundamentally just trying to do the same work as before but kinda sorta faster. It's a weirdly miserable experience.

On the other hand, I've worked with companies where the engineering culture almost feels exuberant. I think a consistent difference is that in these companies, it feels like the engineers now have permission to try wildly ambitious things. It's not always optimally productive in the sense that more things get thrown away or end up just not really working out, but overall they move significantly faster and do more interesting work. Commonly, it's things they wanted to do before, but simply weren't realistic with their constraints around capacity. Or it's an idea like "this part of the codebase has always been a performance nightmare, but it's also a beast to untangle. I wonder if I could setup an autoresearch loop and let it run all day, even just to harvest some ideas?"

But yeah, when everyone sort of gives up and/or are forced to move an ungodly number of smaller tickets, it is a miserable time.

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.

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.

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.

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.

...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.

It's weird to me how often this analogy gets used because:

1. In most cases, like this one, the product isn't heroin.

2. Drug dealers don't actually do that.

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.

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.

Maybe the Go plan is changing that for others?

LLMs obviously qualify as an example of the bitter lesson. The point I was replying to was using Stockfish as an example of how the bitter lesson was wrong, and I was pointing out that Stockfish-like systems are the core example used in the actual essay

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.