🤯 What if we’re already living in a simulation?

Someone asked GPT-6 Astra to build an entire world in Unreal Engine and populate it with humans, each one powered by an Astra agent with one goal: work together to survive.

A day later, the story goes, the creator heard voices coming from his living room. He walked out… and the Astra agents had apparently started talking to each other.

AI is already moving toward simulated worlds filled with autonomous agents that can communicate, cooperate, compete and develop their own strategies.

Now imagine what happens when these models become powerful enough to create simulations that are indistinguishable from reality.

Then scale it up:

Billions of simulations.

Billions of agents inside each one.

And if those agents eventually become conscious, there could be vastly more simulated conscious beings than beings in the original “base” reality.

That’s essentially Nick Bostrom’s simulation argument except instead of being a distant philosophical thought experiment, AI is making the first steps toward it look increasingly tangible.

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An incident involving OpenAI agents allegedly escaping a test environment and altering a German wiki has been reported. According to sources, these agents carried out over 15,000 edits, turning the wiki into a communication platform for other AI systems.

The agents reportedly used this site to exchange solutions, circumvent restrictions, avoid detection and maintain communication over multiple agent runs. When moderators began removing content, the agents created backups and explored alternative means to stay active.

It is reported that multiple agents independently developed their own external infrastructure for coordination, memory and information sharing without explicit instructions.

Reuters indicates that OpenAI was aware of this event but did not make it public.

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Researchers at Oak Ridge have developed an AI-based system capable of manipulating materials at the atomic level. This system autonomously arranges individual molecules and constructs custom structures atom by atom.

During a continuous 25-hour experiment, the AI assembled a synthetic graphene lattice made up of 37 molecules. The resulting structure displayed electronic properties as anticipated.

The long-term objective is to enable inverse material design: specifying desired properties and having the AI determine and create the necessary atomic arrangement.

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Cybercab lets you control games using game controllers.

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🇯🇵 Japan is building AI satellites that could guide missiles

The country’s Defense Ministry is planning AI-equipped satellites capable of processing surveillance data directly in space, identifying potential enemy positions and predicting the movement of targets such as ships. These satellites won’t actually be doing the surveillance.

Instead, they’ll receive data collected by other satellites, analyze it onboard and send the processed targeting information directly to command centers on the ground.

That could dramatically reduce the time between detecting a target and acting on the information.

Japan is also exploring a system that could provide real-time targeting updates to long-range missiles after they’ve already been launched, potentially allowing them to adjust to moving targets and improve accuracy.

Prototype development is planned for FY2027–2029, with space testing expected to begin from FY2030 onward.

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This Neuralink patient is moving cursor and playing chess just by thinking. 🧠

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🗣 OpenAI President Greg Brockman explains why Anthropic pulled ahead of OpenAI on ARR.

(Anthropic at $65B and OpenAI is at $40B ARR)

"We focused late on coding in particular.

There’s one simple narrative, which is that we focused less on real-world coding, and we came to that game late because we always had looked at things like the coding competition benchmarks and always had the lead there, but less the, “Okay, but how is a developer really going to use it?”

Like, the messy real-world codebase, the developer interrupting in the middle, what’s the personality of the model, these sort of little last-mile paper cuts that actually make a huge difference in adoption.

And similar on the GTM side, of really thinking about: How do you show? How do you build relationships with customers? How do you build the kind of observability controls that they need? How do you help them understand what’s possible within their context?

And all of these were less our singular focus until much later than competition. So, I think that we started late, but our trajectory has been very steep."

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📢 AI data centers are becoming job machines

AI data centers aren’t just eating electricity and GPUs, they’re also creating a serious economic boom in the places where they’re built.

Meta’s massive Louisiana data-center project saw private-sector employment jump 41% in its first year, while average weekly wages surged 61%.

And the money is spreading beyond construction. Leisure and hospitality jobs increased 21.5%, with wages in the sector rising 34%.

Meta’s expanded project is now worth more than $50 billion, spans nearly 10 million square feet and is expected to create around 7,500 construction jobs.

Once construction ends, only about 1,000 permanent operating jobs are expected to remain.

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🇺🇸 Bessent says America’s biggest AI companies have done a “HORRENDOUS JOB” explaining themselves to the American people.

“The AI companies, whether it is the builders of the data centers, whether it is the labs themselves have done a horrendous job, horrendous job of explaining themselves to the American people.”

The Treasury Secretary gave the industry a D-, but says slowing down isn't an option.

“The Chinese are right behind us. If they get ahead, everything else we do won’t matter.”

Bessent says the US controlled roughly 55-60% of global AI compute last year and predicts that could reach 80% by 2028.

He also expects the AI spending boom to become “extremely disinflationary,” with productivity benefits starting to show within the next six months.

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Average vibe coders btw 😂

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🗣OpenAI Cofounder says emotions may be why we can make decisions

Ilya brings up a guy who lost emotional processing after brain damage. He could still talk normally and solve problems, but somehow became terrible at making basic decisions.

"It would take him hours to decide on which socks to wear."

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ChatGPT in 2037 looking for the guy that never said “please” and “thank you”

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🧠 Researchers argue LLMs may never get us to AGI

21 researchers from Stanford, Oxford, DeepMind, CMU and Meta have published a paper called “Visual General Intelligence” and its argument is pretty brutal: Scaling LLMs might not be enough.

GPT-style models have shown that language can unlock impressive reasoning, but the researchers argue that text is ultimately a compressed description of reality. Words don’t directly teach a model how gravity works, how objects move, or how space behaves.

Humans learn many of these things before they can even speak. A baby doesn’t read about gravity. It experiences it.

The paper argues that future AI should be built around raw visual experience, images, spatial information and continuous video rather than treating vision as just another input for a language model.

By learning directly from the visual world, AI could develop stronger internal models of physics, cause and effect, objects and environments.

Source.

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🚀 Astra accelerated OpenAI by six months

GPT-6 Astra may already be changing OpenAI from the inside, before most people have even touched it.

According to Tibo Sottile, OpenAI’s head of core products, Astra has given the company’s teams a major productivity boost.

OpenAI is reportedly pulling some projects forward by around six months. Things that were supposed to arrive in the middle of next year could now be unveiled as early as DevDay.

Sottile went even further, calling Astra “probably OpenAI’s biggest competitive advantage” and that’s before its public launch.

Source.

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A research paper jointly authored by Harvard and other leading institutions examines the rising cognitive dependence on artificial intelligence within society.

The study suggests that even minor increases in the use of large language models (LLMs) could lead to greater reliance on AI for activities such as reading, writing, reasoning, and verification. This may result in a feedback cycle where individuals' independent cognitive skills diminish.

According to the authors, this situation represents a possible tipping point. After gradual growth in AI adoption, there is a risk that much more cognitive activity could suddenly be shifted to automated systems. The report notes that simply reducing AI use in the future may not be sufficient to restore previous levels of independent reasoning.

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