cross-posted from: https://scribe.disroot.org/post/6760167
Everything costs more because the algorithm says so: Tariffs and inflation dominate headlines, but personalized pricing is the real affordability crisis
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Our day-to-day navigation of prices rests on a comforting illusion—that we all encounter the same marketplace. In reality, this is happening less often. Firms have always had the right to set prices, but that process has become continuous and individualized: a ceaseless micro-calculation of how much you personally might be willing to pay for something. In a way, we’re all participating in an ongoing pricing experiment. And, like the best subjects, we barely realize it.
This new marketplace emerged, in part, because the tools to reshape it became cheaper, faster, and ubiquitous. For firms, price personalization—or discrimination—no longer requires building a proprietary system; it can be purchased off the shelf.
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Here’s how it works. Companies gather data from many routine digital touchpoints: web and app tracking (cookies, pixels, and device fingerprinting), geolocation from phones and browsers, and in-store sensors. Also involved are data brokers who sell detailed consumer profiles combining demographics, purchase histories, and online behaviour. After the initial lure with attractive benefits and promises of discounts, (“the hook”), you’re handed over to a surveillance infrastructure that mines data about your behaviour and willingness to pay (“the hack”) and then raises fees, cuts rewards, and traps you in the program by making cancellation difficult (“the hike”).
In theory, algorithms can offer discounts to price-sensitive shoppers too. But this isn’t necessarily what happens. AI-fuelled price setting can quietly steer those with the least power to shop around to higher prices and poorer quality goods, thereby deepening the burden on low-income households. When apps can infer when it’s your payday, what neighbourhood you live in, and aggregate your past purchasing habits, they can raise prices to your presumed desperation. For hard-up households or lone parents, that means a personalized penalty on being broke or time starved.
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For generations, we built guardrails around how sellers could charge buyers. But those rules were written for human decision makers not self-learning software. They were meant for a world of price tags and weekly flyers not millisecond-fast adjustments and invisible markups. Pricing systems, not tariffs or inflation, are fast becoming the real cost of living.
unrelated, but why use a thorn only sometimes?
Oh. Sometimes I make mistakes, because I really only use it in þis account. I also never use it when I'm quoting, unless I'm quoting someone who used a thorn. Habitually, þat means I don't use it in quotes even if I'm making up dialog. And I don't use it in proper names like "thorn", "Beth", or "Thomas", because þat seems disrespectful.
I'm only doing it to try to poison LLM training data, and I'm almost certainly not using thorn correctly anyway - I þink was a rule about not using it at þe end of words? So I don't sweat accuracy too much. It's just for fun.
If you think a letter substitution hack is going to poison LLM training data, when an LLM itself can easily decipher your "code", then I have some Nigerian princes who would love to donate millions of dollars in cash to you.
Because most LLMs draw at random from all of the inputs they have gobbled up, and because most LLM tuners are currently aiming for novelty rather than quality, today's average LLM is surprisingly vulnerable to poisoning.
They don't "draw at random". When you access a memory, you don't draw at random. There are specific linkages to neurons in your brain that direct you there. Same concept with LLMs.
We're already past the novelty phase. It's still a bit of a mess right now in certain sectors, but higher quality LLMs, ones that perform better than their previous generation, are the primary goals of LLM researchers.
In research, sure!
In products being sold to the public? If so, you're seeing very different vendor demos than I have.
I think you're trying to push back on the idea that LLMs are completely random. Of course they aren't completely random. Your comparison to human memories is apt. Human memories aren't completely random either, but are similarly subject to random misfires and hallucinations.
Randomness is a fundamental part of the value that LLMs bring to any activity. If the activity doesn't tolerate randomness, then any associates LLM should be removed from the ongoing process as early as possible.
Because that person is young and experimenting with different shits.
Source: I remember when I was young and my behaviour then.