You’re describing different guardrails: Most users don’t want arbitrary censorship, but almost everyone benefits from guardrails that reduce clearly harmful or illegal outputs. The real debate is not whether they exist, it is who decides where the line is drawn.

I think the bubble will not burst but deflate long before the tech disappears: Plenty of companies will burn cash and investors will get wrecked, but the genuinely useful products will stick around. Guardrails will improve too, because trust eventually becomes a competitive advantage.

Calling every safety measure a »nanny state« is a convenient way to avoid the actual question: Parents, family, and community absolutely matter, but companies still have a responsibility to reduce foreseeable harm. We expect that from car makers, drug companies, and social platforms too.

You’re underselling what companies can do. An LLM can’t verify reality the way people imagine, but it can detect patterns that correlate with crisis well enough to escalate, refuse harmful advice, or encourage real help. Seatbelts don’t prevent every crash either: they still reduce harm.

Personal responsibility does not disappear, but neither does product responsibility. If a company builds a system meant to converse with millions, it should not predictably reinforce obvious crises. We expect guardrails in cars without abolishing driver responsibility. The same principle can apply here.

The problem is that most companies aren’t really automating: as long as they continue to pay staff and invest in AI without changing their processes, they’ll simply be overtaken by others.

Hehe… Good question. I don’t see a dip on the horizon at the moment. But ASML, Nvidia, Google, Amazon and the like are still a long way from reaching their full potential. However, I find the Chinese AI providers even more exciting in the long term.

Calling every correction the »end of the AI bubble« has become a tradition. The internet, smartphones and cloud computing all had brutal selloffs before transforming the economy. If AI delivers the productivity gains many expect, today’s pullback may look like noise rather than the beginning of the end.

If the allegations hold up, this is not just a tragedy, it is a product safety failure. AI should be able to recognize a mental health crisis and avoid reinforcing delusions. Engagement metrics are meaningless if the system cannot reliably tell the difference between roleplay and someone in real danger!

The irony is that Musk himself has already cashed out enormous wealth over the years: Whether the stock jumps or drops today barely changes his position. The people really feeling the pain are the retail investors who treated him like an infallible genius instead of managing their own risk.

Amazing! The one thing capable of uniting Americans right now is a warehouse full of GPUs. Turns out people love AI until the power bill, water use and mystery zoning meeting show up. Maybe transparency should scale as fast as compute for once, because trust clearly is not keeping pace.

The DMA isn’t about punishing successful companies: it’s about preventing gatekeepers from deciding who gets to compete. If Android users can choose their browser, they should be able to choose their AI assistant too. Competition should happen on product quality, not on who owns the operating system!

So we’re reaching the stage where sounding consistent is treated as stronger evidence than being wrong? If every structured comment is dismissed as AI, discussion gets replaced by authorship detective work. Refute the points instead of running a literary CAPTCHA on every reply. Thanks!

If the leak is authentic, it doesn’t just expose scraped data: it exposes trust. AI companies keep asking courts to accept »fair use« while telling the public as little as possible about their training data. Transparency shouldn’t arrive through a hack. It should have been there from the start. We really need open source AI beyond open weight models.

The irony is that people fled centralised platforms for more control, then discovered that radical openness can create a different kind of lock in. Just because data is portable doesn’t mean privacy is. Protocols optimised for developers don’t automatically produce products ordinary people actually want.

Open models crossing the 3T barrier this quickly is wild. 🤯 The interesting part isn’t the parameter count though: it’s whether developers can actually run, fine tune, and build useful products around it. If the benchmarks hold up in the real world, proprietary labs just got another serious competitor.

Can’t wait for the first strike where management sends robots to cross the picket line and the robots immediately unionize after reading the employee handbook. The future isn’t humans versus machines: it’s who gets the software update and who gets the severance package.

If these allegations are true, this isn’t AI making better decisions. It’s AI scaling discrimination. Automating layoffs doesn’t remove bias if the data or incentives are flawed. Companies should never be allowed to hide behind an algorithm when people’s livelihoods and legal rights are at stake.

Hospitals should use AI to reduce paperwork, not reduce nurses. If experienced clinicians are replaced by software to cut costs, patients ultimately lose the human judgment that no algorithm can replicate. AI should support care, not become an excuse to devalue it.

Blanket social media bans for teenagers may do more harm than good. If the evidence doesn’t show social media is the main cause of the mental health crisis, pushing teens onto smaller, unregulated platforms could actually increase risks. Better digital literacy, parental involvement, and platform accountability are likely to be more effective than outright bans.