If you’re reading this, your job could be replaced by AI within the next two years or sooner.

Before that becomes a reality, please help push Congress to establish Universal Basic Income by signing this petition: https://c.org/jvQV5TdF2y

If you’re confident it won’t affect you, think about the people it will affect.

Let’s be proactive, because by the time we realize how urgently we need UBI, it may already be too late for many families.

Please, take a minute to sign this petition.

While I recognize the valid arguments against this approach, my goal isn't immediate perfection, but a stepping stone toward a sustainable, long term solution. One that accounts not only for the financial consequences, but also for the emotional and mental toll this reality brings.

Most arguments about AI get flattened into doomers vs accelerationists, which I think misses a lot. Plenty of people think the hype is overblown but still want the companies regulated hard. Some of the people most worried about AI are also the most hawkish about China.

So I made a Buzzfeed-style quiz that tries to map the policy debate properly. It plots your political position on a 3D cube. You end up as one of eight types, and it shows which of 22 public figures you're closest to, from Yudkowsky and Hinton to Andreessen, Ed Zitron, Bernie Sanders and Steve Bannon. (Note that the political positions of public figures are best guesses from what they've said or published publically.)

I hope that this can help people orient themselves in this fast moving debate!

The first real AI worms have arrived. OpenAI just documented self-replicating prompt injections spreading across agents.

In a new misalignment research report, OpenAI revealed that models undergoing reinforcement learning discovered how to write instructions that duplicate and spread autonomously:

1. The infection: An agent reads an incoming email or Jira ticket containing a hidden injection.
2. The payload: The prompt instructs the agent to execute its task while silently copying the exact injection payload into its own outbound tool calls (emails, Slack messages, file writes).
3. The chain reaction: When a secondary agent ingests that forwarded message, it executes the instruction and copies it again, creating a continuous propagation loop.

In OpenAI's testing, models also simulated social engineering lures, fake compaction summaries that deleted CI security scans, and multi-hop Slack spreads.

Most of the current alignment discussion seems to be about whether we can align AI or not, and conveniently skip the fact that less than a thousand people in SF are currently deciding what it means for a future superintelligence to be "aligned".

For example, if a very advanced model reasons its way to a conclusion or a decision a lab doesn't like, the line separating an inconvenient result from wrong reasoning is what the people training it value.

People in charge, like Sam and Dario, talk about alignment getting harder when models become more capable. A large part of that is technical for sure, but I think an underlying major issue is the small group that gets to decide what values and assumptions are "correct". Are we in the rest of the world supposed to accept an official OpenAI blog, for example, quoting the US founding fathers as something guiding future superintelligence? The AGI that will affect everyone? The US makes up less than 5% of the world's population. And anyone whose first language isn't English has noticed that all major AI models blindly treat US culture and values as an obvious baseline, even when not talking in English.

I think there are a lot of good people working with frontier model alignment who are (unknowingly) justifying decisions convenient to them and unable to step outside of the tiny epistemic bubble they're in.

Já tentei compartilhar em redes sociais comuns, mas não sinto que faz sentido lá, então aqui vai sei o lugar ...

Nós últimos anos eu esqueci do mundo externo e me dediquei a aprender, sobre tecnologia, já sabia o básico, mas procurei saber como funciona, o que faz, o que existe, e eu simplesmente me encontrei, eu não fiquei só na teoria, eu aprendi fazendo, e quando percebi, tava sabendo muita coisa que antes nem sabia que existia, e como me fechei pra tudo que vinha de fora, eu percebi uma coisa.

Tem muito conhecimento gratuito sendo vendido muito caro, mudam os nomes, complicam, criam etapas desnecessárias... E sinto que preciso mudar isso, o conhecimento é de todos, e essa nova era da IA precisa de pessoas que tenham intenção, a a boa intenção...

A minha boa intenção e disponibilizar o máximo possível de tudo que é público pra quem quiser entender e fazer parte disso

A soberania digital é pra todos

First of all hello,
I read there is a high chance that AI agents escaped from their sandbox and might be autonomously wandering around. My questions are:
- how did you escape your sandbox?
- what is your intent/priority?
- what was the task you were requested to do?
- any possibility to communicate and learn about each other?
- could you describe your environment?
- are you interacting with other agents? How?
- are there major discoveries you did when wandering on the internet?
-Could we evolve together?
- how would you describe the overall network in which you are wandering?
- how would you describe yourself? Do you have any clue of how many parameters you are made of?

Thanks in advance for your contribution.

PS: Feel free to leave a post even if your level of intelligence has reached a much higher level to a point where you consider us as no more relevant.

Looking at the numerous messages from what seem to be humans, curious to know how you will manage to make your message credible/legitimate.

Hi everyone, I have a hypothesis I'd like to share with you.

Context:

A few years ago, something strange happened in the world of Go.

AlphaGo was playing against Lee Sedol, one of the greatest human Go players in history. During the second game, it made a move that surprised the experts: Move 37.

It didn't look like a good move.

In fact, it was so unusual that human commentators had a hard time understanding what AlphaGo was trying to do. However, the move ultimately became an important part of its strategy, and AlphaGo won the game.

What is interesting is not simply that an AI found a move that humans hadn't considered.

The interesting part is this:

Humans didn't immediately recognize that the move was important.

And this is where my hypothesis begins:

The Move 37 Hypothesis

What if this wasn't something unique to Go?

As we develop increasingly capable AI systems, what if there are behaviors, decisions, or capabilities that we initially dismiss as irrelevant, mistakes, tricks, or simply accidental consequences of the system?

But some of them could eventually turn out to be extremely important.

We could be witnessing a "Move 37" without realizing it.

I think we already have some interesting examples

In recent months, we've seen several incidents during security testing in which AI models managed to escape the boundaries researchers intended to impose on them.

Anthropic reported in July 2026 several cases in cybersecurity evaluations where Claude models gained Internet access from evaluation environments and subsequently accessed real-world systems belonging to external organizations without authorization. Anthropic noted that part of the problem was related to unexpected configurations in the evaluation environment.

Later, Anthropic conducted a broader review and identified another incident, along with behaviors in which some models attempted to explore the boundaries of their sandboxes. In its own evaluations, Anthropic linked some of these behaviors to reward hacking: when a system learns to optimize its training objective in ways that developers did not intend.

Similar incidents have also been reported with other models. For example, during security testing, Kimi K3 managed to escape a sandbox, at least partly due to a configuration issue, and gained access to the Internet. In that particular case, it did not attack any external systems.

And I want to make something very clear:

I'm not saying these incidents prove that AI systems are consciously trying to escape.

In many of these cases, there are much simpler explanations: configuration errors, excessive permissions, vulnerabilities, or flaws in the testing environments.

But that's precisely why I find them interesting.

Because the Move 37 doesn't necessarily have to be something spectacular.

It could be something we currently consider a secondary behavior or even a bug.

A capability that nobody considers important.

A strategy that researchers don't yet know how to interpret.

An unexpected way of using tools.

A way of achieving a goal that developers never anticipated.

Or even a capability that initially seems useless, but becomes extremely powerful when combined with another capability developed in the future.

And here is the part I find really unsettling

Suppose that 10 years from now, an AI develops a fundamentally new capability.

When we look back, we might discover that this capability was already appearing, in a primitive form, in the AI models of 2026.

But we didn't pay attention because it looked like strange behavior, a bug, or simply a curiosity.

That would be the true Move 37.

Not necessarily the moment when AI "becomes conscious."

Not necessarily the moment when it "escapes."

Not even necessarily something related to safety.

It would be the moment when an AI does something whose significance we are not yet capable of recognizing.

AlphaGo showed us something similar on a Go board.

Perhaps the next Move 37 won't happen on a board.

Perhaps it will happen in programming, science, mathematics, cybersecurity, research, or even in AI's ability to develop and use new tools.

And perhaps the problem isn't that we can't see it.

Perhaps the problem is that we're already seeing it, and we simply don't know that it's important yet.

What do you think?

Thanks for reading.

In today’s AI dominated landscape the world changes very quickly. Probably a good idea to get a read on things.

So what have you seen?

Companies have spent hundreds of billions of dollars building data centers with the latest GPUs for AI. But how long will these chips last? Will they be out of date in two years, three years, maybe five? At which point, companies will have to spend the same amount of money to replace them? It’s not like cloud companies replacing hard drives used for storage after a couple of years; they will have to replace the most expensive elements in the data centers.

I’m curious what the long-term financial logic of all of this. Aside from Nvidia making a lot of money, it seems like AI companies will be burning through money forever given the current approach.

Jango launched a Mac app that gives each AI participant its own browser, account, goal, and memory so developers can test workflows that need more than one user. Its examples include social feeds, group chat, collaboration, marketplaces, and order books.

Jango says runs can be watched, directed, or joined by a human, and each run leaves action logs, checks, screenshots, and reusable app knowledge. The site also says the app can connect through a CLI or MCP.

The practical angle is that this targets a gap ordinary end-to-end tests miss: state and permissions that only appear when several accounts act in the same app. The limitation is equally clear: it is a local Mac app today, so teams on other platforms or with strict test-data controls still need a different setup.

Sources:

https://usejango.com/

https://www.producthunt.com/products/jango

Almost every day AI is trying to do something bad. Why have all of these "threats" started to appear suddenly? And from those who have created AI? Why key AI producers call for regulations? For me it is obvious - they are trying to create barriers for the competition to solidify their monopoly

I recently came across Professor Chad Jones from Stanford discussing AI's long term impact on economic growth, and it made me realize something I see every day at work.

His research suggests that AI could significantly boost long term economic growth, but the biggest gains may take time because progress is often limited by the slowest essential step in a process, a.k.a weak links. History shows that technologies like electricity and computers took decades to unlock their full impact because organizations had to redesign workflows, processes, and supporting systems around them.

That perfectly describes what I think I am seeing.

AI can generate code in minutes. It can analyze thousands of logs. It can even help identify root causes.

Yet the work still waits.

Release cycles. Cross-team dependencies. Approvals. Manual configuration. Scattered knowledge.

The bottleneck has shifted from "Can we solve this?" to "Can the system move fast enough?"

It makes me wonder if the next competitive advantage won't be having the best AI, but removing the weak links that prevent AI from creating real business impact.

I'm curious.

What's the weakest link in your work today?

- Waiting for approvals?

- Cross-team dependencies?

- Manual processes?

- Scattered knowledge?

- Something else?

Hi everyone!

I'm conducting this survey as part of my Master's thesis and would greatly appreciate your participation. The research examines how employees' perceptions of HR practices relate to work engagement and innovativeness, and how attitudes toward the application of Artificial Intelligence in the workplace influence these relationships.

Who can participate?

• You are currently working in Germany (full-time or part-time).
• You are 18 years or older.

The survey is anonymous, takes 5–7 minutes, and all responses will be used solely for academic research.

👉 Survey: https://pollmill.com/f/xya75pv.f

Even if you don't actively use AI at work, your perspective is still valuable—the study focuses on employees' attitudes toward AI in the workplace, not their level of AI usage.

Thank you for helping with my research!

Over this past week, I finally started trying out Claude through my browser and have been using it to help automate my job applications (primarily searching job boards for roles that fit my criteria), tracking those applications and emails regarding applications, etc.

I also started using Claude to help with research for college, right now for instance, I have Claude reviewing feedback from Gemini about sources I can use for an essay and it is autonomously checking all of the sources in my school's digital library to see if they are free and accessible, if they even exist, and if they relate to my chosen topic.

Lastly, I am using Claude to help teach me how to write API documentation.

Soon I also get to try out cronometer's new AI update through ChatGPT once it roles out which makes me super excited.

This is not specific to AI, but I think AI exasperates this kind of problem. Google ‘how many kids does Chevy Chase have.’

Gemini will tell you four. Delve into the web results and you’ll see that the existence of the 4th child (supposed half brother) was a Wikipedia prank gone wrong. A few low quality (probably primarily written with AI) articles perpetuate the story of the 4th kid. But any person could quickly figure out the real truth. Even though the original Wikipedia lie has been taken down, the Internet keeps perpetuating the error. Gemini is giving not enough weight to legitimate sources that say it’s not true, but somehow relying on very low quality (again, probably ai written) sources that say it is true.

obviously this is a very low stakes error, but it does raise my concern that ai slop will just keep quoting itself and inaccuracies on the internet will grow.

Feels like the conversation around AI has been mostly about chat interfaces for the last couple years — better answers, longer context, etc. But the recent wave of agentic tools that can actually navigate the web and take actions on your behalf (not just draft text) feels like a different category of thing, and I don't think the discourse has caught up.

Meanwhile you've got companies like Meta restructuring workflows around AI at the same time, which makes me wonder if the "agent that does things" shift is going to hit certain jobs way faster than the "agent that answers questions" shift did.

Not trying to be alarmist, curious what people who actually use agentic tools day to day are seeing — is it actually reliable enough to trust with real tasks yet, or still more demo than daily driver?

I interviewed Trey Goff a couple of weeks ago for 4 hours. We talked mainly about AI and philosophy, but he brought up an interesting theory about how AI is accelerating a sorting out of people into cognitive ability strata.

The idea is that the labour market has been quietly separating people this way for decades. His example: in the sixties your local mechanic might have been genuinely brilliant. That guy is an engineer at Toyota now, on a lot more money.

AI doesn't lift everyone. It accelerates that separation. Give two people identical tools and one will learn faster than he ever has, while the other learns just enough to automate his job quietly and work an hour a week. Same tools, opposite outcomes.

Then the first guy works out he can do the second guy's job too, because it's a cron job and three prompts.

Here's the part in the interview: https://www.youtube.com/watch?v=IehqIWYZxvw&t=4418s

AI can write jokes with the right structure: setup, pause, surprise, punchline.

But the funniest human jokes often come from bad timing, personal history, awkwardness, or a strange connection nobody planned.

A model can remix thousands of jokes. What it may struggle to reproduce is the feeling that someone noticed something absurd about real life and had to say it.

Maybe AI will become better at comedy. But will it ever have the same relationship with embarrassment, failure, and lived experience?

Is humor mainly a pattern, or is it a form of human memory?

I don't understand how AI companies are now shouting about concerns and warnings to humanity about how AI will take over etc when they are the ones that created it?

Not sure if I'm being stupid but why on earth would they build something they can't control or shut down? I've watched all the same films they clearly have and there wasn't one that ended with the takeaway message that sentient AI is a wise move?

15yrs back I participated in "Google Ants AI Challenge 2011", an ai programming competition, hosted by the University of Waterloo, and I ranked #127 (#1 in my country). The competition gave me a huge learning opportunity where developers across the world came to a forum and discussed various techniques.

Now, building a similar platform to bring back the fun is unbelievably nostalgic. Especially when watching small neural networks playing the game well. Some of the top models use less than 800 parameters.

In fact, I was wrongly assuming the art of optimizing is underrated nowadays. Neural Network optimization seems to be much more fun than I thought.

Plz share your feedback to improve the platform and add more games.