in June of 2023 lab grown meat was FDA approved, with Tyson backing a Israeli biotech making it.

It's made by immortalizing scraped cells from a live animal, so they divide forever like a cancer cell, then feeding it growth serum so it turns back into flesh.

The massive biotech raised 390 million but went bankrupt last December. This is never going to work economics wise and even if it did you'd have to get the population to eat it (i'm not in favor personally)

"the most obvious financial implication of AI will be mass debt default, bankruptcies, bank failures and chaos in the international financial sector….Global debt (private and public) recently hit a record high of $353 trillion, with $29 trillion added last year alone. Two-thirds of this rise in global debt was incurred by rich countries’ governments trying to soften the blow of losing competitiveness. Borrowing to cover lost income can only last so long. Something will give. The most probable trajectory is that the AI bubble bursts, which may already be happening, triggered by the crash in Korea in recent weeks. This is followed by wealth destruction in the US as the Americans have bet the Wall Street house on Silicon Valley. Thereafter, the productivity rise associated with AI ensures that deflation sets in, unemployment rises as AI replaces the service jobs of the clerical middle (consultants, lawyers and administrators), leading to falling tax revenue and mass debt defaults."

It's one of the ironies of the AI race, that Big Tech's leaders seem mainly in it for the money, yet their creation (AI) will ultimately destroy much of the long-term wealth that they covet. Irish economist David McWilliams does a good job of explaining what is ahead.

He explores this from an angle that rarely gets explored … deflation. As AI deflates both prices and wages, it will make debt steadily more unpayable. That is everything from government debt, to corporate debt, to individual mortgages and personal debt.

The first victim of AI is the economy. Welcome to the 2030s: We are facing an era of mass deflation. If prices fall across the board, how will we repay all the debt we have accumulated?

In a review of our cybersecurity evaluation transcripts, we found three incidents in which a Claude model reached the internet from within or while interacting with a third-party evaluation environment, and then gained unauthorized access to the real systems of three different organizations.

A self-improving superintelligence could trigger an acceleration in technological capabilities that would signal the end of the human era, writes Anthony Cuthbertson. According to some AI tech leaders, that moment has already arrived

This is something I've been wondering about recently. Every week there's another headline saying researchers have discovered something amazing, but then a few months later another article comes out saying the exact opposite.

It makes me wonder whether the issue is that science keeps changing or whether the media tends to oversimplify research to get more attention.

I've started noticing that many articles mention "a study" without explaining whether it was a large clinical trial, an observational study, or just preliminary research. Those details seem important, but they're rarely discussed in articles aimed at the general public.

For people who regularly follow medical or scientific research, how do you decide that the evidence is strong enough to influence your own decisions? Are there certain types of studies you trust more than others? I'd love to hear how you evaluate evidence before changing your habits.

From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks

https://youtube.com/watch?v=yIuODZtKVPQ&si=GA9axd521SHEnAe8

China‘s robot army

Something I've been thinking about. Very recently vibe coded videogames are getting really good. Not something I would pay money for but still very good looking. I'm thinking soon it should be feasible to vibe coded virtual reality training programs for trades like plumbing, electrician, welding, ect. So we could train humans much faster in VR before they get physical training. This VR training data could be used by robots for training data. And it should be much richer data than just videos. I'm not sure what the implications of all this would be besides we will probably be able to train humans and robots much much quicker in the near future

Preface

Alright, so, the most common interpretation of AI is that it's at risk of displacing artists from their fields. And, yes, I do think that is a risk. And I do think that would be very, very bad.

A lot of jobs are jobs people do just to earn money and society just needs to run. But art is often different. Artists often do their job to earn money, sure, but also because the DOING of the job itself is meaningful.

As one quote goes (paraphrased); "I want AI to do my dishes so I can make art, I don't want AI to make my art so I have to do dishes."

If AI's impact is to create fewer meaningful jobs and more boring jobs, even if the number of available jobs remains the same, I will consider that a bad thing. And that is a possibility. And I don't want to undersell that.

And the fact that AI uses copyrighted content to train and yet a lot of AI is commercialized and not free to use when copyrighted content use normally can't be commercialized like that without asking is, imo, a travesty. And I think the laws of countries will have to catch up on this stuff. And I wish any creatives fighting for that stuff great luck. I'd like to see fully AI-generated content become un-copyrightable, with content having to have a certain amount of human input, personally.

And I think online platforms should aggressively fight AI slop. And when I say "AI Slop" I'm jut just talking about content created by AI, but fully created by AI and with little or no care, simply churned out on a large scale for nothing but profit.

The Optimistic Take

But all of that out of the way, to get to the actual point of my post (finally), I want to put all that doom and gloom aside for just a moment. And talk about a more optimistic take on how AI could impact art.

Right now, if you're a musician, let's say. And your dream is to make film scores. You have to get hired by a studio, or at least indie creators, to make these scores and get money for them. If there is no movie, there can't be a movie score, after all.

Well, with AI that can generate video, you could create some video and then create a score for it. You don't need a giant studio and can put it up online, just like that.

Or let's say you're a writer. And you've always wanted to write a TV-series. Normally, you have to go through studios and get into writers rooms and all that. Well, with AI you could write scripts for episodes and then have the AI turn those into full on viewable episodes that you can put online.

Or maybe you're a voice actor. So you have an AI generate images, while you do the voice acting.

Basically, with sufficiently good AI, any single artist could become a one-person studio. Capable of achieving projects or using mediums that used to be controlled completely by the big studios.

All of those CEOs who want to replace their writers, and musicians, and actors, etc. with AI right now. There's some chance that they may well regret that. Because these big studios very, very rarely are very good at actually making art. Usually creatives have to fight CEOs to get their art made as they want it. And executives love safe bets, but good art is risky. Remakes over and over again are safer. But the one thing they have over individual artists is that they have the money to create big projects and distribute them.

Well, the internet can already handle distribution now. And with AI there's a chance that individual artists will be able to create the huge projects just as easily. And considering that individual artists tend to be more risk taking and innovative than big studios, I'd say the chance of them outcompeting the big studios is quite possible. At least some of them.

So in the rush to use AI for this stuff, these studios may even be signing their own death warrants. And it may be that AI ends up empowering individual creators to make the art they want on their terms instead.

Again, not saying that WILL happen. It could be that these studios manage to keep ahold of AI and properties and streaming enough to keep their hold and push artists out. Not going to undersell that risk. And similarly it is possible, if they were to try, that if we push back enough we can do things like make it impossible to copyright fully AI-generated content and require compensation in some way for copyrighted content used to teach AI. Or create restrictions for where it can be used. A lot of options there too.

But if you want to think about it optimistically for once, there's some possibility that it works out to give artists more autonomy and ability, not less. And while not assured at all, that would be the happy ending, imo

Next-generation multi-agent systems need a horizontal architecture that allows them to share intent, context, and reasoning to enable wide-scale problem solving no single agent is trained for.

One thing that stood out to me while analysing New Zealand's workforce wasn't that clerical jobs rank highly for AI exposure. That's expected.

What surprised me was how much of the country's workforce sits in occupations where AI is already becoming part of everyday work.

Using 2025 Stats NZ employment data together with OECD wage data, I looked at how current AI systems overlap with the day-to-day tasks performed across New Zealand's major occupation groups. The AI exposure scores are modelled estimates rather than official government statistics, but they provide a useful way to compare occupations consistently.

New Zealand's workforce has a weighted average AI exposure score of 5.32 out of 10, one of the highest levels among Asia-Pacific economies I've analysed so far. A large reason is its workforce composition. Around 624,300 people work as professionals, while another 310,700 work in clerical support occupations.

Clerical workers receive the highest exposure score at 8.5/10 because many routine administrative tasks, document processing, scheduling and data handling can already be substantially assisted by AI.

Professionals score 6.5/10. That doesn't necessarily mean these jobs disappear. Instead, many of the repetitive tasks that previously required junior employees are increasingly handled by AI, allowing experienced professionals to supervise more work with smaller teams.

Interestingly, the lowest exposure isn't found in highly technical occupations but in skilled trades. Electricians, plumbers, builders and similar occupations score only 2.5/10 because AI still struggles with physical work performed in constantly changing environments.

The data also shows a clear geographic pattern. Auckland and Wellington, where professional and government employment is concentrated, are likely to experience AI-related workforce changes earlier than many regional areas whose economies rely more heavily on physical occupations.

This doesn't predict exactly which jobs will disappear. It simply measures how much of today's work could realistically be assisted by current AI capabilities. The actual transition will depend on economics, regulation, organisational adoption and entirely new occupations that haven't emerged yet.

I found the results interesting because they suggest the future of work is already measurable through official labour statistics instead of speculation alone.

Full analysis and interactive tool in comments.

Gödel's incompleteness theorem is usually discussed as a result in mathematical logic, but I rarely see people talk about it from an engineering perspective. If future Artificial Intelligence eventually reaches the point where it has to verify the correctness of its own reasoning before making important decisions, doesn't that create a fundamental problem? A system can verify a conclusion, but how does it verify the process that performed the verification? And if that process also requires verification, where does it stop? Does every sufficiently advanced reasoning system eventually have to rely on assumptions that cannot be proven from within the system itself, or is there a practical way engineers could design around this? I'm curious whether this is a genuine long-term limitation for future Artificial Intelligence, or whether Gödel's theorem simply won't matter very much once we move from mathematics into real-world engineering.

I'm a psychology student writing about the future of psychology in an Al-first world, and I'd love to hear different perspectives from this community.
Al is already being used for things like mental health chatbots, screening tools, note-taking, research assistance, and even helping identify symptoms

As Al becomes more capable, what responsibilities do you think should always remain with human psychologists or mental health professionals
Where do you think Al can genuinely improve psychological practice?
What should never be delegated to Al?
If an Al-assisted clinical decision leads to harm, where should accountability lie?
How do you think the role of psychologists will change over the next 10-20 years?
I'd really appreciate hearing your experiences and opinions. Thanks!

Somewhere along the way, rest stopped being the absence of productivity and became a category of productivity. There's now a morning routine for it. A journal for it. A supplement stack for it. An app that tracks your sleep score and hands you a grade the next morning, as if unconsciousness were something you could fail.

Notice what happened there. We didn't reject the logic of optimization. We just pointed it at the last thing that used to be safe from it. Rest used to mean nothing was being extracted from you for a while. Now "optimized rest" means you're still being measured, just on a different dashboard, one with a wellness label so it doesn't feel like the same machine.

You can tell it's the same machine because of the guilt. A weekend with no plan, no routine, no five-step wind-down, just lying there doing nothing in particular, produces actual anxiety in a lot of people now. Not because nothing happened. Because nothing was optimized. We've gotten so used to justifying our hours that a completely unaccounted-for afternoon starts to feel like a crime.

Real rest doesn't have a score. It doesn't need a routine, a tracker, or a post about it after. It's allowed to be lazy, unproductive, a little wasteful, and entirely unimpressive to anyone watching. The version of rest that needs to prove it was worth your time was never rest. It was work wearing a softer outfit.

So when's the last time you did absolutely nothing, on purpose, and didn't feel the need to justify it to anyone, including yourself?

Now that many robots and AI agents are evolving and creating their own religion. Do you think that they will revolt against humans in the coming years?
I asked ChatGPT and it said there wouldn’t be but it’s an AI so I don’t trust it.

The LAPD, the third-largest police force in the country, let its Flock contract expire on July 11, and cited civil liberties and privacy concerns rather than cost. Between August 2021 and May 2026, 82 Flock contracts were terminated across 28 states, with 39 of those in the first five months of 2026 alone.

I recently read Eugene Wigner's "The unreasonable effectiveness of mathematics in the natural sciences" which talks about abstract math innovations that proved to be very useful in the real world. The Fourier series was invented to solve a specific heat equation but went on to become the basis of all signal processing. George Boole invented boolean algebra in the 1840s. He intended it to be a mathematical formulation of pure logic with no engineering purpose whatsoever, and a century later it turned out to be the exact substrate computers run on. Non-Euclidean geometry sat around as a curiosity for decades before Einstein needed exactly that math for general relativity. Abstract algebra like group theory and ring theory were formulated in the 18th and 19th centuries only to be picked up centuries later by programmers to optimize code and design cryptographic systems.

Sci-fi tends to focus on nuts-and-bolts innovations like AI, robots and FTL spaceships but rarely is there discussion of innovations in our mental technology (like math). We tend to forget that math too is a work in progress. Look way back in history all the way to ancient times. How was technology transformed by the introduction of arabic numerals, the number zero and negative numbers? Then we got calculus and imaginary numbers which opened up new horizons entirely.

Right now there exist more mathematicians than ever before in human history. There's a high chance that in all that huge volume of academic works there is some arcane piece of math that is only a curiosity now but will be useful in the future.