Over time, I've learned to accept that many people -- even very clever ones -- are incapable of holding a metaphor at arm's length. Once they accept the words of a metaphor as applicable at all, the metaphor collapses entirely into literalism for them. They can no longer see that the metaphor was just a tool with inherent limitatation and boundaries.
Because the field of artificial "intelligence" is constructed around the idea of applying psychological metaphors to computational systems (a very powerful idea!) it's almost a worst case scenario for these people.
Suddenly, they're reversing the metaphors and applying computational schema to psychological processes ("aren't we really just stochastic parrots ourselves?!"); or, like here, they find themselves surprised and confused when they stumble across the natural boundaries of the metaphor experimentally.
It's because they never had sight of the boundaries in the first place and maybe never can quite see them. The words only make sense to them as literal equivalence, and so their surprise when they run into stuff like this is earnest and deep.
I don't think the boundary between "generalization" and "metaphor" is very well defined. When you go from an exemplar of 1 to 2, you're going to find all kinds of edge cases where attributes of the thing being demonstrated that had seemed to be essential turn out to not be necessary.
I think you certainly could look at LLMs as "thinking" metaphorically, but I also don't think it is necessarily only a metaphor.
Well, it's unusual to "generalize" an idea if you only had two exemplars, one so old and so complicated that all your terms are specifically referent to it and often even hard to be precise about; and the other is both extremely novel and plainly distinct in both its mechanisms and behaviors.
While maybe that boundary can be fuzzy, we're unequivocally and deeply in "metaphor" territory here.
The software is "easy", it's the business and operations that are hard: scaling the infrastructure reliably; finding revenue sources with secure margins; deflecting regulatory and risks responsibility; billing and collecting on massive low-margin high-volume transactions; enterprise sales; etc
It's a business line more suited to finance, law, sales, and accounting people than tech people and a pretty laborious one. That's often the case when the tech looks "easy" but the sector only seems to have a few big winners.
In the event that the AI bubble doesn't burst, Stripe needs to have a stake in whatever's coming. They have time to explore and pursue specific strategies, but they just needed to make some big and compatible buy.
In this particular case, OpenRouter represents a M x N bridging and enhancement layer much like Stripe themselves, and so must address a lot of parallel technical, dealmaking, accounting, and legal challenges.
It may not be obvious on the surface because they seem to be working in such different domain, but they have to address a lot of the same problems in comparable ways and that makes it a pretty darn good fit.
> The people in support of these bans generally aren't interested in "good vibe coded" software. They oppose LLMs altogether on various grounds, not just the output quality.
I think you're very wrong.
I think many of the people who "support these bans" do recognize value in LLM's and use them as tools in the kit applied to their workflows but recognize that rampant abuse and misuse of those same tools threatens to ruin (or already has ruined) many community projects that had previously been healthy and thriving.
And so they find themselves insisting upon unambiguous firewalls as an existential necessity for the communities they have invested years or decades of their lives into.
They may have utility in trying to look at the whole landscape of models, but are very misleading when it comes to making 1:1 comparisons or in developing confidence at to how a given model will deliver on your workflow.
Etsy still welcomes small craft artists and makers and provides a convenient e-commerce portal for them, but yes, it's the discovery that's been ruined.
They originally broke through and defined themselves by insisting that vendors only sell original cottage-crafted work, but gave up on that requirement years ago and the sincere low-volume, low-margin crafters can't compete with high-volume, high-margin importers in either advertising spend or in the etsy-profiting metrics that drive algorithmic discovery.
Maybe you were lucky to be raised into financial literacy and only circulate around other people who have, and so can only imagine -- with charity -- what other people must be thinking when they take loans.
But others here have been truly naive themselves or have journeyed with intimacy and openness beside a parent, partner, or friend who really just doesn't get it.
The reality is that there are a lot and lots and lots of real people in the real world who sincerelt just don't understand the terms they're agreeing to, how their own finances work, what's plausible in their own financial future (near or long term), etc -- and the companies the design and promote convenience loans know this and they target these people like sharks hunting prey.
They use any and every trick they can get away with to lure these naive people into agreements for which all outcomes (payment or default) favor the shark.
Without regulation of both loan design and presentation, lending naturally become a vehicle usury, vaccuuming assets and opportunity from the many earnet people who will always be too credulous, too trusting, too naive, or too desperate to resist.
Everyone in the demographics most likely to buy these financial products has seen this stuff play out many times by adulthood. They're not stupid or ignorant, or at least not in those ways. They know how it all plays out or how the math maths. They buy these products anyway for human poverty trap incentive to not make good decisions because it won't actually change anything type reasons.
Regulation won't change the fundamental economic reality of those living paycheck to paycheck. It'll just change the form those hardships take.
So far, AI lowers the quality and increases the quantity of the media we experience.
Blogslam has become cheaper, more voluminous, and worse.
Bot operations on social media sites and forums have become cheaper, more voluminous, and worse.
Advertising content has become cheaper, more voluminous, and worse.
The content of streaming music, online art communities, and video platforms has beckme cheaper, more voluminous, and worse.
The content of public code repositories, and the issue and PR channels associated with those repositories, have become cheaper, more voluminous, and worse.
Customer support services have become cheaper, more voluminous, and worse.
When individuals use AI themselves, towards tasks that they mean to acheive, many of them are individually happy with the results. But pretty much everyone who looks around sees that AI in aggregate is quickly saturating almost everything to which it can be applied to with endless, suffocating slop.
It feels great to pull the handle on the slot machine. It's pretty grim when you stop for a second to look around at the casino and take in the big picture.
I do think this is true but I think it also reflects negatively on the general populace that this new technology is evaluated in terms of the negative effects on their consumption pattens
Transformer models solved the Protein Folding problem in totality but they are categorically "bad" because more of the videos in my feed are low-quality or the advertisement for a cheeseburger looks fake
It's predictable but I do think the entire technology is being collectively processed through the lens of media consumption which is sort of tip of the iceberg of what it does and honestly the least important dimension of it
Did they? From what I heard, transformer models gave scientists better starting points for computational methods, but we're still far from solving the protein folding problem.
What I read is that you're self-conscious about your writing style and that you find the LLM processed version of your own stream of thoughts to be helpful to you yourself.
Which is fine for you, but doesn't change that many people can get more insight out of your original text than they can out of the generated adaptation, and can do so with more interest and less fatigue.
And indeed, you don't have to care about that. But one would think that in publishing or sharing your writing at all, you do in fact have some investment in whether people receive those insights that you have. Otherwise, you could just keep the AI writings to yourself in the first place.
Sure enough, the prompt is infinitely more expressive and interesting.
AI writing is like the voice anonymizer they'd use in TV interviews when they wanted to hide the identity of the person speaking. The output is more adherant to convention but likewise more impersonal and exhausting, and when it's used by too many people it's so indistint that it becomes noise.
It might look good to you in isolation, because you're excited to see that it says what you meant to say without the features you feel self-conscious about, but to others it just reads like yet another sample of the same old AI slop. The message gets entirely lost behind that.
Because the field of artificial "intelligence" is constructed around the idea of applying psychological metaphors to computational systems (a very powerful idea!) it's almost a worst case scenario for these people.
Suddenly, they're reversing the metaphors and applying computational schema to psychological processes ("aren't we really just stochastic parrots ourselves?!"); or, like here, they find themselves surprised and confused when they stumble across the natural boundaries of the metaphor experimentally.
It's because they never had sight of the boundaries in the first place and maybe never can quite see them. The words only make sense to them as literal equivalence, and so their surprise when they run into stuff like this is earnest and deep.