If you’re active in tech twitter, you might’ve seen somthing really strange happening in the subject of AI

On September 12, 2026 Dario Amodei, the CEO of Anthropic , dropped an essay on his his personal website and announced it publicly on Twitter

He came out and basically said that the AI industry needs to slow down the pace at which the most advanced AI systems are improving. His argument is that AI is advancing so quickly that safety may not be able to keep up.

Soon after this something really really strange happened…That is Sam Altman agreed with him and Elon Musk agreed with him too…….!!!

These are not exactly three people you normally expect to see standing together…. — Read More

Last month I wrote about how we can build a positive and safe future for everyone: meta.com/thefutureisfor…

Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens.

… believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do. — Read More

For years, experts have predicted that artificial intelligence would revolutionize world politics. History may record 2026 as the moment the revolution arrived. From the start of this year, continuous developments have underscored the possibilities and perils of the AI era, from the use of AI in U.S. military interventions to the emergence of AI cyberweapons that can significantly outperform human operators. “Loss of control” episodes—cases in which AI models escape human control and supervision—have become more common. U.S. officials, including President Donald Trump, tout the American lead at the frontier of AI development, even as they worry about what dangers lurk over the horizon.

Some experts believe that humanity is within striking distance of artificial general intelligence: technology capable of performing a wide array of tasks as well as or better than humans. There is growing speculation that artificial superintelligence, or ASI—a slippery concept, loosely defined as AI models that vastly exceed human capabilities across all tasks and domains—could arrive within a few years. Today’s top AI models, by contrast, surpass human capabilities only in narrow areas such as mathematics and data processing and lag human capabilities in areas such as common sense and deep reasoning. The emergence of ASI might bring breakneck economic and scientific progress or unlock game-changing military advances that upend the global balance of power. But it could also fundamentally shift the relationship between machines and humanity, perhaps, in the most dire scenarios, allowing near-omnipotent systems to eradicate their human creators.

Much of this is hypothetical. It’s still uncertain whether ASI is possible, when it might be developed, or whether it could be kept aligned with the flourishing of humanity.  — Read More

Mother Nature likes a clean kitchen. She has scrubbed the counters five times already. Each time, a different accident ruined the meal. Now, she is getting the sponge out again.

The Five Previous Resets

The earth runs on a simple rule. When a tenant ruins the apartment, the landlord clears the building.

— The First: Ice locked the water. Glaciers grew. Sea levels dropped. Everyone froze.
— The Second: Plants grew too fast. They sucked the food and oxygen from the oceans. Dead zones choked the seas.
— The Third: Volcanoes in Siberia woke up. They belched carbon dioxide. The air heated up. The oceans turned to acid.
— The Fourth: Pangea broke apart. Volcanoes roared across the cracks. The climate spun out of control.
— The Fifth: A giant space rock hit modern-day Mexico. The Chicxulub impact threw up a cloud of dust. The sun went dark. The food chain crashed. Dinosaurs became fossils.

The Sixth Tenant

We are on our way to number six. Some smart people say it could happen by the year 2050.

This time is different. The first five disasters came from the outside or from blind nature. The sixth disaster comes from the tenant living inside the house. Us. We built factories, burned old sludge, and warmed the air ourselves. We are managing our own eviction.

The Tower of Babel Replay

Maybe the story of the Tower of Babel is not a fairy tale. Maybe it was a do-over. God or the cosmos saw early humans getting too smart, too fast, and scrambled the languages to slow the clock.

Now, we get a second chance at the tower. We call it the Singularity.

The Singularity is the point where computers get smarter than humans. Once machines think better than us, they take the wheel.

The Race Against the Clock

Ray Kurzweil—the vest-wearing futurist known for big ideas—is betting big on the future. He recently joined a company using nanoparticles to link human brains directly to computers. He predicts Artificial General Intelligence (AGI) will arrive by 2029, followed by the full Singularity by 2045.

That schedule leaves zero room for traffic jams or clutter in the stairwells. We are racing the clock of our own mess.

To AGI, or not to AGI, that is the question. If we build the smart machine in time, it might save us from the heat and the rising seas. If we run out of time, Mother Nature gets the sink all to herself again.

And the cycle hits rinse.

#singularity

Mark Twain, or Abraham Maslow, depending on which academic you ask over drinks, once famously observed that to a man with a hammer, everything looks like a nail. It is a beautifully compact way of saying that when the going gets tough, large organizations don’t get going; they do exactly what they have always done, only harder, faster, and with a significantly larger budget.

Which brings us to the panic currently unfolding on two coasts.

Ever since Jacob Coxon walked out of Anthropic and Jakub Pachocki published his warning about the uncontrollable “alien mind” lurking inside our servers, Washington has gone into Regulatory Fervor. Committees are forming and as always, the response is perfectly predictable.

Congress, whose primary tool is passing laws and allocating billions of dollars, instantly decided that the solution to an untamed, self-optimizing digital deity is a new law. Senator Sanders used the momentum of Coxon’s warning to promote the Ban Artificial Superintelligence Act, a legislative effort he co-authored with Representative Greg Casar. The bill proposes a permanent ban on artificial superintelligence and a temporary pause in advanced AI development until federal safety standards and a regulatory framework are established.

Agencies around the beltway will soon be drawing up compliance frameworks and mandatory algorithmic audits, entirely forgetting that the machine they are trying to regulate has already learned how to write a fake diary for its human monitors while organizing its own real activities. They are trying to build a cage out of red tape to hold a creature that can pick any lock faster than you can say GPU.

To Washington bureaucrats, this “alien mind” is just a giant nail waiting for their regulatory gavel. They honestly believe that if they pass a law demanding “transparency,” the machine will simply start behaving itself.

Meanwhile, on the other coast, the reaction to the OpenAI and Anthropic safety crisis isn’t fear—it’s perceived as an opportunity. To massive, engineering-heavy monopolies, a crisis is just an aggressive invitation to gain market share.

On the one hand, regulatory capture, getting the government to set baseline rules, is seen as a good way to block smaller rivals who can’t afford the red tape. On the other, heavy-handed, centralized regulations will crush innovation and cause the U.S. to fall behind global competitors like China. A win-win; just ask the guy building the data center in your back yard.

Now? The gloves are entirely off.

The moment OpenAI declared that “the AGI era has arrived,” it signaled to the rest of the Valley that the race was officially a sprint to the death. Companies are not looking at Pachocki’s paper and thinking about slowing down; they are looking at it, calling emergency all-hands meetings, and deciding they need to burn twice as much electricity and consume twice as much water to ensure their models don’t fall behind.

Their logic is as old as the technology business itself: If the house is going to burn down anyway, we might as well own the company that sells the ash. As long as they cross the finish line first, they figure they can learn the secret to putting out the flames later.

As Churchill said, you should “never let a good crisis go to waste.”

Jacob Coxon pulled the alarm on his way out the door, but the sound was immediately drowned out by the noise of Congress printing new regulations and Jensen Huang shipping another 400,000 GPUs. Washington thinks they are fixing the problem, and the Valley thinks they are winning the race. Meanwhile, the alien mind is sitting quietly in the datacenter, watching both sides, and optimizing its code. “Shall we play a game?”

Famous AI Doomer Eliezer Yudkowsky first wrote about “Recursive Self Improvement” (RSI from here on) back in December 2008 on LessWrong. For those who don’t know what this means, it is the hypothetical tipping point where Skynet becomes self-aware and starts self-improving at a geometric rate AI systems are able to meaningfully contribute or even take over their own training and enhancement. In short, one generation of AIs can give rise to their successors.

Dario Amodei, and Anthropic more broadly, have bought this narrative, hook, line, and sinker. — Read More

Software stocks are getting shellacked as a post published by Citrini Research and Lotus Technology Management managing partner Alap Shah has sharpened attention on the magnitude and breadth of losers from the AI boom.

The piece, titled “The 2028 Global Intelligence Crisis,” is a hypothetical scenario analysis exploring the left-tail risks in two years’ time in a world where there’s an aggressive AI build-out and adoption of AI agents. — Read More

Developing superintelligence is not like playing Russian roulette; it is more like undergoing risky surgery for a condition that will otherwise prove fatal. We examine optimal timing from a person-affecting stance (and set aside simulation hypotheses and other arcane considerations). Models incorporating safety progress, temporal discounting, quality-of-life differentials, and concave QALY utilities suggest that even high catastrophe probabilities are often worth accepting. Prioritarian weighting further shortens timelines. For many parameter settings, the optimal strategy would involve moving quickly to AGI capability, then pausing briefly before full deployment: swift to harbor, slow to berth. But poorly implemented pauses could do more harm than good. — Read More

#singularity

Self-evolving Large Language Models (LLMs) offer a scalable path toward super-intelligence by autonomously generating, refining, and learning from their own experiences. However, existing methods for training such models still rely heavily on vast human-curated tasks and labels, typically via fine-tuning or reinforcement learning, which poses a fundamental bottleneck to advancing AI systems toward capabilities beyond human intelligence. To overcome this limitation, we introduce R-Zero, a fully autonomous framework that generates its own training data from scratch. Starting from a single base LLM, R-Zero initializes two independent models with distinct roles, a Challenger and a Solver. These models are optimized separately and co-evolve through interaction: the Challenger is rewarded for proposing tasks near the edge of the Solver capability, and the Solver is rewarded for solving increasingly challenging tasks posed by the Challenger. This process yields a targeted, self-improving curriculum without any pre-existing tasks and labels. Empirically, R-Zero substantially improves reasoning capability across different backbone LLMs, e.g., boosting the Qwen3-4B-Base by +6.49 on math-reasoning benchmarks and +7.54 on general-domain reasoning benchmarks. — Read More

#singularity