u/thirdaccountttt

One of the interesting things regarding the overall opposition to AI is how quickly the people on one side developed their derogatory vocabulary for AI and robots. “Clanker” appears to be mostly a joke, with no one seriously arguing that chatbots are oppressed minorities due to the fact that someone uses a slur. However, even in this case, the psychology behind the desire to come up with such a word for something hated seems quite interesting.

Human beings are extremely good at coming up with terms for those we consider out-groups. As soon as you name a certain group of people, you do not have to discuss every individual of that group anymore; the term carries the argument by itself, marking something as bad, embarrassing, inferior, or something you are expected to despise. Such a process can be seen in nearly every online subculture, where communities develop their own terminology and, in effect, signal membership and what they all despise by using it.

What makes this situation funny and unusual is the fact that people tend to anthropomorphise AI just to further ridicule it. One minute, someone can claim that a certain AI model is not conscious, does not have any feelings and is simply “a machine”, which is a completely normal argument. The next minute, they are treating it like a hated social group and coming up with their own slur for it. This contradiction seems rather amusing to me, as you never come up with derogatory vocabulary for a toaster.

Most likely, the explanation lies in the unique place occupied by modern AI in our minds. It communicates, creates art and writing, and competes with us in intellectual domains, while at the same time we know that it is software. This makes it extremely easy for us to project our social behaviour onto it while still denying it human status in moral terms. And I guess that “clanker” works because it allows people to turn a complicated argument into a simple emotional one. Instead of having to deal with issues of copyright, employment, environmental impact, misinformation, artistic merit, consciousness or anything else, people can use a simple insult and others in their group will immediately understand the attitude they are supposed to have.

None of this means that calling a robot a “clanker” is an act of grave discrimination. In most cases, it is simply a joke related to Star Wars, and taking it too seriously would be ridiculous. However, that may be precisely what makes it interesting. Humans seem to instinctively fall into prejudice-like language and social rituals when they strongly despise something. They categorise it, give it a name, make jokes about how inferior it is, develop stereotypes about its fans and users, and eventually integrate opposition to it into their own group identity.

I’m honestly getting to the point where I’m seriously considering BPC-157 and it’s been stuck in my head constantly. I’ve had an elbow tendon injury for about 2 years now and it still affects loads of exercises. I can’t really do direct bicep work, and on a lot of movements I can’t use full ROM without pain or aggravating it. It’s taken a lot of the fun out of training because I’m constantly working around it.

I’ve been to multiple physios, done rehab exercises, rested it, changed exercises, slowly loaded it again, etc. Nothing has properly fixed it. The problem is I’m also a very anxious person, especially with anything health-related, so the idea of taking BPC-157 scares me. I keep reading about it, then telling myself I’m not going to take it, then thinking about it again because I’m so fed up with this injury.

I know the human research is limited and I’m not pretending it’s some guaranteed cure. I’m just at the point where I’m borderline willing to try it because normal rehab hasn’t got me anywhere. Has anyone here been in a similar situation, especially with chronic elbow/tendon pain? Did BPC actually make a noticeable difference, and did you get any side effects or anxiety from it?

Ross should do a proper Miami gang documentary. He’s covered Jacksonville and parts of the Broward scene, but Miami has its own completely different history with crews like Zoe Pound, Zoe Mafia Family, 3rd World, One Way, Zombie Boys and loads of smaller neighbourhood gangs.

It’d be interesting because Miami isn’t really a simple Bloods vs Crips city like LA. A lot of the gangs are tied to specific neighbourhoods and the Haitian/Caribbean community, so there’s probably a lot of history there that most people outside South Florida know nothing about.

Could easily be one of his better deep dives if he covered how the different crews started, who beefed with who and how the scene changed over time.

One of the most common arguments against AI right now is that it’s going to destroy employment on a massive scale. You’ll see headlines about “X% of jobs exposed to AI” immediately translated into “X% of jobs are going to disappear”. Those are not the same claim.

The International Labour Organisation published an updated global analysis of generative AI and employment in 2025. It looked at almost 30,000 individual job tasks and found that roughly 1 in 4 workers worldwide are in an occupation with some degree of exposure to generative AI. Sounds terrifying until you read the rest. Only 3.3% of global employment fell into the ILO’s highest exposure category. More importantly, their conclusion was that job transformation is considerably more likely than complete job replacement, because most occupations contain a mixture of tasks and still require significant human input. That distinction gets almost completely lost in online discussions.

A lawyer using AI to summarise documents is still a lawyer. A programmer using it to generate boilerplate or debug code is still a programmer. A customer service worker using an AI assistant to retrieve information faster hasn’t been “replaced by AI”. Their workflow has changed. And we’re already getting experimental evidence of what that can look like.

A Microsoft Research study published in 2025 followed around 6,000 knowledge workers for six months in a randomised field experiment. Workers given access to generative AI spent substantially less time dealing with email. Among people who actually used the tool, email time fell by around 3 hours per week, roughly 25%, and there was evidence that documents were completed faster. That is automation, but it’s automation of parts of a job rather than necessarily automation of the person doing it.

Another 2025 field experiment involving 776 professionals at Procter & Gamble tested people working on real product-development problems. Individuals working with AI performed at roughly the same level as traditional two-person teams without AI. That is a pretty significant result. AI in that setting wasn’t simply replacing somebody and producing garbage at lower cost. It was allowing one person to access some of the performance benefits that previously came from collaboration and complementary expertise.

None of this means there will be zero displacement. There are already signs of pressure in highly exposed areas. Stanford’s 2026 AI Index reports that employment among software developers aged 22–25 has fallen by nearly 20% since 2024, and around a third of organisations surveyed expect AI to reduce their workforce over the coming year. That should be taken seriously, especially because entry-level jobs are where people traditionally build the experience needed to become senior workers later. But notice how different that is from the usual claim that “AI is taking everyone’s jobs”. The evidence so far looks much messier.

Some tasks are being automated. Some workers are becoming considerably more productive. Some companies probably will employ fewer people for particular functions. Certain entry-level roles may get hit disproportionately hard. Other jobs will be reorganised around AI rather than eliminated. And the overall labour market has not, so far, experienced anything resembling the mass technological unemployment that the strongest anti-AI predictions imply.

There’s also a strange double standard in how productivity improvements are discussed. For practically every previous technology, reducing the amount of human labour required to produce something was considered productivity growth. Spreadsheets eliminated huge amounts of manual accounting work. Word processors eliminated typing work. Industrial machinery replaced enormous amounts of physical labour. Search engines made certain forms of research dramatically faster. We didn’t conclude from that that making workers more productive was inherently bad. Yet when AI lets somebody perform a task in 20 minutes instead of two hours, the reduction in required labour is often presented as though it is itself evidence that the technology is socially harmful.

The legitimate question is what happens to the gains. If AI makes an employee twice as productive and the entire benefit goes to the employer while the employee gets laid off, that creates an obvious distribution problem. If AI makes goods and services cheaper, raises output, removes tedious work and lets workers accomplish more, the effect looks very different. Those are economic and political questions about how a technology is deployed. They aren’t arguments that the underlying productivity improvement shouldn’t exist.

There are genuine reasons to worry about AI’s effect on employment, particularly for younger workers trying to enter certain white-collar industries. But “some occupations contain automatable tasks” is not equivalent to “those occupations will disappear”, and “AI increases worker productivity” is definitely not the same thing as “AI makes human workers obsolete”. So far, the actual evidence supports a much less dramatic conclusion: AI is beginning to restructure work, sometimes substantially. We do not yet have evidence of the economy-wide job apocalypse that people have been predicting.

Sources: International Labour Organisation, Generative AI and Jobs: A 2025 Update; Stanford HAI, 2026 AI Index Report; Microsoft Research, Shifting Work Patterns with Generative AI; Dell’Acqua et al., The Cybernetic Teammate.

Has the Read Aloud option disappeared for anyone else? I used it pretty regularly and now I can’t seem to find it at all.

Was it intentionally removed, moved somewhere else, or is this just a bug/update affecting some users? Really annoying if they’ve actually removed it because it was one of the most useful features for me.

The way people are treating Ross over this trial is getting ridiculous. You don’t even have to like the guy or like his old documentaries to admit he’s actually been one of the few people covering this case properly. He’s physically sitting in the federal courtroom listening to the testimony and reporting what is being said. Every time something comes out that looks bad for Durk, people immediately call Ross a liar or say he’s pushing an agenda, then independent reporters covering the exact same trial end up reporting the same basic thing.

The best example is the Vonni situation. Ross said Durk’s defence was putting the blame on Vonni, and people acted like he completely invented some snitching narrative. But that literally is the defence theory being presented. Durk’s lawyers have argued that Vonni organised the alleged plot and acted independently of Durk. You can argue Ross worded the tweet dramatically, but acting like he fabricated the substance of it is just false.

And the idea he’s just sitting there trying to make Durk look guilty makes even less sense when you actually follow his updates. When Brian Steel started catching OTF Jam on changing answers and inconsistencies, Ross was immediately posting about it and saying Steel caught Jam in multiple lies. That’s obviously GOOD for Durk’s defence. He didn’t hide it or try to spin it because it didn’t fit some supposed anti-Durk agenda.

Reporting what a prosecution witness says also doesn’t mean Ross personally believes every word of it. That’s what reporting a trial is. If Jam gets on the stand and makes a crazy allegation, Ross is supposed to tell people that happened. If Steel cooks Jam on cross the next day, Ross reports that too.

Meanwhile, people are confronting him outside court, getting in his face, demanding he delete tweets, accusing him of doing “police work” just for covering a public trial, and apparently even trying to interfere with his videos. That’s lame as hell. Disagree with his interpretation all you want, but trying to intimidate a reporter because the testimony coming out of court makes your favourite rapper look bad is insane.

If Ross actually lies about something, then post what he said, post what was actually said in court, and prove him wrong. But screaming “biased”, “fed”, and “liar” every time he reports something you don’t like isn’t proving anything.

Durk is entitled to the presumption of innocence, and his lawyers are clearly fighting the case hard. That doesn’t require everybody covering the trial to become part of the Free Durk campaign. Ross has been reporting the good and the bad. People need to stop shooting the messenger.

One of the worst arguments for the use of AI in anti-AI spaces is “proving” AI usage through vibes.

First, there is the “AI detector” approach. These tools are presented as forensic evidence despite being extremely unreliable. Human writing can be identified as generated by AI, AI writing can be recognised as human, and small modifications can totally change the result. An AI detector assigning a piece of writing 80% or 90% score does not imply 80% or 90% probability of AI involvement. It just says that certain patterns were detected by a machine and associated with AI-generated text.

And then comes the even worse argument: “it sounds like AI”. What does that even mean? Proper grammar? Logical structure? Certain words? Long sentences? Bullet points? Over-formality? AI models learn from huge amounts of human writing. Of course, the text generated by AI can be similar to human writing - that’s exactly its purpose. If a person uses a certain style that AI was taught to emulate, “this text sounds like AI” is not proof of the human use of AI because that’s how AI works: we recognise it because it sounds like human writing.

People also mention such details as em dashes, “it’s important to note” expressions, good structure, or “GPT-like vocabulary”. All of those belong to humans, not AI. Those writing styles existed way before ChatGPT became popular.

There may be a case when there is actual proof of someone using AI, for example, accidentally leaving “Here’s a revised version”, or prompt-response artefacts in the writing. But most of the cases are not nearly that strong.

If all you’ve got is an AI detection score and “bro this sounds like ChatGPT”, you’ve failed proving anything. You’ve just made a guess.

Alibaba just announced plans to raise around HK$80 billion, roughly $10.2 billion, with a major chunk of that money going towards expanding its AI capabilities.

This is one of the biggest fresh AI investment pushes we’ve seen from a Chinese tech giant and another sign that the US-China AI race is only getting more aggressive.

Alibaba already has its Qwen model family, cloud infrastructure, and a huge domestic ecosystem behind it. Giving that operation another $10B+ in capital could seriously strengthen China’s position in the global AI race.

For anyone still acting like the current AI boom is about to fizzle out, companies are quite literally raising tens of billions to scale it further.

Nvidia is making one of the biggest bets on open AI we’ve seen yet.

The company is reportedly paying around $6 billion for a non-exclusive licence to Poolside’s AI models while hiring more than 100 of its employees, alongside another roughly $1 billion investment into the company.

What makes this especially interesting isn’t just the
money. Nvidia appears to be positioning Poolside as a serious US-based competitor in open-weight AI, at a time when frontier development has increasingly been dominated by closed systems from a handful of labs.

For all the constant talk about AI becoming more centralised, this is basically the opposite direction: one of the most powerful companies in the industry putting billions behind models that can be distributed and built upon more openly.
If this strategy works, the future of frontier AI could end up a lot less closed than people assumed

People talk like AI suddenly invented low-quality content, which is ridiculous

The internet was already drowning in spam, clickbait, lazy reaction videos, ugly thumbnails, stolen memes, bad fan art, boring essays, fake outrage, recycled jokes, scam ads, content farms, and soulless corporate garbage long before AI became mainstream

AI can make slop faster. Sure. But pretending slop itself is new is just dishonest

The actual issue is incentives. Platforms reward volume, rage, cheap engagement, and constant posting. That problem existed before AI and it will still exist even if every AI tool vanished tomorrow

Blaming AI for every bad post online is just too easy

A pretty important part of the EU AI Act came into force on August 2 and I think the distinction between transparency and stigma is going to matter a lot over the next few years.

Article 50 now requires providers of generative AI systems to make generated or manipulated audio, images, video and text detectable through machine-readable marking. Chatbots and similar systems also have to make it clear that you’re interacting with AI unless that’s already obvious. (Digital Strategy)

There are separate rules for people and organisations actually deploying the technology. Deepfakes have to be disclosed, and AI-generated text informing the public about matters of public interest generally has to be labelled when it hasn’t undergone genuine human review or editorial control. (Digital Strategy)

That part makes sense to me.

If someone produces a convincing fake video of a politician saying something they never said, people should have a reliable way of knowing it’s synthetic. The same applies to scams, fake evidence, impersonation and deliberately deceptive media. Provenance is useful.

But there’s a massive difference between “this content has a synthetic origin” and “this content is inferior or suspicious because AI touched it.”

That second attitude is already becoming normal online.

People will dismiss an image, essay, song, bit of code or even somebody’s grammar because they think AI was involved. The label stops being information about how something was made and turns into a scarlet letter.

And once that happens, disclosure creates a pretty obvious incentive: people stop disclosing.

We already accept countless forms of technological assistance without demanding a warning underneath the finished work. Photographers edit photos. Musicians use digital instruments, pitch correction and samples. Writers use spellcheckers and search engines. Programmers use libraries, autocomplete and increasingly coding assistants.

The meaningful question should be what the finished product actually is and whether someone is being deceived about something important, not whether a machine participated somewhere in the workflow.

Interestingly, the EU rules themselves are considerably narrower than the internet discourse around them. Personal, non-professional use is generally outside the definition of a deployer, standard editing can be exempt from the machine-readable marking requirement, and AI-generated public-interest text does not require the same disclosure where there has been substantive human review and editorial responsibility. (Digital Strategy)

So I don’t think “AI transparency” is inherently anti-AI.

A good system could actually help AI adoption. Invisible provenance metadata could make it easier to verify origins without plastering every creative work with a giant MADE WITH AI badge. Clear disclosure makes sense where authenticity itself matters, especially deepfakes and news-like content.

What I don’t want is a world where “AI-generated” becomes legally or socially synonymous with “untrustworthy”, while equally manipulated human-made media gets treated as authentic by default.

AI should be judged by what it produces and how it is used.

Transparency should tell us something factual about provenance. It shouldn’t become a moral judgement about the person who used the tool.

Curious where everyone here draws the line. Would you support mandatory provenance metadata for generated content? What about visible labels? And at what point does AI assistance become substantial enough that you think disclosure is actually necessary?

everyone keeps arguing over whether death or lwop is the harsher punishment but i keep thinking a death sentence could actually benefit him in some ways

it would drag the case out for years, guarantee automatic appeals and keep his name in the news far longer. every hearing and appeal gives his defence another opportunity to challenge evidence and delay finality

lwop would be much quieter. one conviction, no realistic chance of release and decades of being forgotten inside prison while everyone else moves on

for someone who built his whole life around fame and attention, being erased from public relevance might be a worse punishment than becoming another notorious death row case

d4vd is already eligible for the death penalty. california allows it for first degree murder with special circumstances, and prosecutors have alleged several here including lying in wait, financial gain and killing a witness

my theory is that the DA will eventually announce they're seeking death. the allegations go far beyond a normal murder case; an adult allegedly sexually abusing a child, luring her to his house, killing her to protect his career, mutilating her body and hiding her for months

the only reason i could maybeee see them choosing life without parole instead is california's current moratorium on executions and how much longer a death penalty case can take. either way, there doesn't need to be a new law or petition specifically for him. the law already allows it

do you think prosecutors will actually seek death or settle for life without parole?