Certainly! Your point raises a crucial, multifaceted issue that merits deeper examination. It's not just about detection accuracy, it's about the evolving interplay between human intention and algorithmic assistance — a dynamic that resists simple binary classification.

When we delve into the mechanics of modern AI tools, it becomes evident that the boundary between "correction" and "generation" is increasingly porous. Consider the following nuances:

Gradual authorship spectra: Text exists on a continuum — from fully human-crafted prose, to human writing with AI-suggested punctuation, to passages where the AI has restructured entire paragraphs. Detection tools, however, often operate on categorical assumptions that fail to capture this complexity.

The feedback loop phenomenon: Furthermore, as writers internalize AI-suggested patterns — Oxford commas, em dashes, and certain syntactic rhythms — their "human" baseline shifts. This creates a fascinating, almost paradoxical scenario: the more one learns from AI feedback, the more one's unassisted writing may trigger detection algorithms.

Contextual markers vs. stylistic choices: Notably, certain signals — like the increased use of "delve," "testament," or negative parallelism — may reflect genuine stylistic evolution rather than automated generation. It's a testament to how AI influences language culture broadly, not just individual documents.

In conclusion, the challenge you identify is not merely technical but philosophical — forcing us to reconsider what we mean by "authentic" voice in an era of human-AI collaboration. The question, ultimately, is not whether a tool assisted in the writing, but whether the ideas, arguments, and intellectual labor remain genuinely human.

I hope proves helpful! Please let me know if you would like to delve deeper into any specific aspect of these findings, or if you would like me to explore the broader implications for content strategy.

"do a little research" - you do realize that your post would have had just the same educational value but not made you come across as a jerk if you had just left off that first sentence?

I think the obvious counter to an influx in bug reports / pull requests is to have the first layer of review of bug reports / pull requests to be AI as well.

Final remains humans.

I dunno, if that's how people vote, that's how it should be.

Though there is a big question of how they plan to run a vote with multiple options. I hope it's a Condorcet system to avoid the risk of watering down one side with similar competing options...

> Europe isn't socialist. They have some social policies.

So you know that meme, with the Orange County Choppers guys, goes something like this;

"Socialism doesn't work!"
"Norway is Socialist and they're doing great"
"They're not Socialist they're Capitalist with strong social policies"
"Then let's adopt those policies"
"No, that's Socialism!"

That's literally you. You are that asshole. Congratulations.
=Smidge=

> it's never come to fruition before

*gestures vaguely in the direction of Europe*

Y'know it's kinda funny but socialism works fairly well when you don't have capitalist countries actively sabotaging them. You'd think that if it was unworkable you wouldn't have to put in all that effort.

=Smidge=

Oh, but surely they'll win, they indicted [strike]a ham sandwich[/strike] this man! And the Trump administration has such a spectacular record with high-profile prosecutions thusfar...

That is not what they found. What they found is that if you spend 60 minutes doing the homework with AI, you learn approximately as much as someone who spends 60 minutes doing it without AI. If you spend 20 minutes doing it with AI, you learn as much as someone who spends 20 minutes doing it without AI. Etc. The only real difference is that the AI people get way better homework scores, and as a result, tend to stop spending as much time, and thus tend to learn less.

That's what this preprint study found.

100% this. Even a short 5-minute quiz with just a couple questions at the start of each day will do the trick. But you simply can't rely on homework scores to be a proxy of learning or time spent studying anymore. Brief daily quizzes on the previous day's material will force them to.

Also, It's not like students have the excuse of, "I couldn't study, I didn't have anyone to explain X to me" anymore. The very AI that they might otherwise use to cheat on their homework can also explain to them the things they missed in last year's algebra class that are blocking them from learning this year's calculus, or whatnot. Congrats - you now have an infinitely patient personal tutor who will spend hours dumbing things down for you until you get it. Now you have no excuse - learn the material. It becomes all about actually putting the time in.

Yeah. There's been widespread misrepresentation of this (preprint!) in the media. What they actually found: test scores correspond to how much time you spend doing homework, regardless of whether you use AI or not. Which should not be a surprising result! Of course, the AI people, esp. those who had been using AI for more than a few months, stopped spending much time on their homework, and then their test scores fell to match the non-AI users who didn't spend much time in their homework either.

The TL/DR is, in today's age, you cannot use test scores as a proxy of how much time a user is studying or knows the material.

There are of course some caveats, beyond the fact that it's just a preprint - notably that there's nothing to separate why the user started using AI to do their homework, so there's potential confounders in there. For example, if a student is in a poor family, maybe they had to get a job to help pay the bills. But working a job itself can lower test scores - for example, having to work late the night before an exam. So there are some confounder problems with the study. That said, I'm sure the general conclusion is correct: time spent doing the thing = amount of learning the thing (relatively independent of what tools you're using in the process).

Really? You must be in BFE or something.

Here where I am - somewhere in South Florida - FPL has consistently jacked up our rates, for a variety of stated reasons - including feeding the new colos.

Your experience != everyone else's.

My power bill has increased by about 20% DESPITE switching to LED lighting, which reduced my light draw from about 700w to maybe 50w for the whole house minus home cine and bathroom.

My power bill has increased DESPITE setting higher aircon temps.

So yes -- datacenters are absolutely jacking up rates, and have completely made irrelevant the change to LED.

I want my tungsten lamps back. A pox on the next son of a bitch who mandates a switch to a "higher efficiency" light, only see all those gains in efficiency wiped out by a much bigger power user.

I feel like I'm in a constant race to flee enshittification : Used to use Cursor (it's still my only option at work), but then Musk bought it, and they instantly went in on hard-sell tactics. Switched to vscode + copilot + OpenRouter, but was getting concerned about API costs. Just yesterday I set up a Tencent TokenHub account with their Max package, which required that I switch off copilot if I wanted to be able to use some models like Kimi K3 (had a temperature=1.0 requirement, copilot doesn't let you set temperatures and hardcodes them to very low), so now it's vscode + Pi + TokenHub.

Sigh. Please don't make me move again...

(Not that Tencent is in any way "the good guys"... but they're also providing a very generic product)

Haven't you seen all the recent deep cuts to NASA science missions? And science in general. In some cases it will take over a generation to recover.

Ah, the good 'ol "No True Communism" argument :)

"It suggests" if you ignore that their confounder control is terrible. First, sugar wasn't the only restricted food in post-war Britain - there was also rationing of eggs, butter, cheese and meats that ended during the same timeperiod. So you're not actually testing the effects of sugar consumption, now are you? Also, the early 1950s was a period of rapid change in the US, including the maturation and expansion of the NHS (founded in 1948), the introduction of new childhood vaccines, major environmental shifts (for example, the Great Smog of London in 1952 vs. the 1956 Clean Air Act). Saying "all changes = sugar" just based on birthdate seems absurd.

It's also worth mentioning that there's also survivorship bias - anyone who died before the study couldn't be enrolled, and there's also no actual linking to any maternal / childhood dietary logs. Also, the ratios of cancer differences they're describing aren't small, they're massive. Are we to attribute a lung cancer difference on the order of smoking vs. nonsmoking households as being to sugar, or due to, you know, smoking (or pollution) trends not accounted for in their cohort?