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
Recent Updates
Zuckerberg breaks ranks on the AI slowdown
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
Iranian Cyber Terrorists Discover Work from Home
Here it is, dear reader. The next shoe to drop, terrorist work from home, is officially here.
On 9/11, terrorists hijacked airplanes and turned civilian machines into weapons. That required box cutters, airline tickets, suicide pilots, and the inconvenience of actually being aboard.
Technology has apparently improved the business model and introduced WFH for terrorists into the mix.
The FBI and Coast Guard are investigating cyberattacks against two gigantic energy tankers headed for Texas — VL Prosperity and Kohaku. — Read More
America’s Superintelligence Dilemma
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
A warning about ‘model welfare’
AIs are not conscious. They do not feel, experience, or suffer. They do not have innate preferences or underlying motivations. They are sequence completion engines, internally hollow, designed to follow instructions, and accomplish goals set by humans.
If humanity is to flourish in the 21st century, that is how they must remain.
Unfortunately, there’s a growing chorus of people who argue that AIs could now be, or may soon become, conscious. — Read More
Learning to Solve Hard Problems in RL for LLMs by Never Giving Up
We demonstrate that training LLMs with RL does not improve performance equally across a dataset. RL shows large improvements on easy problems that an LLM is already good at solving, but small improvements on hard problems. We call this the Matthew Effect in RL for LLMs, after the phenomenon of cumulative advantage from economics and network science summarized as “the rich get richer”. The naive explanation is that hard problems require more compute to find a solution. We argue that modern RL methods are exacerbating the issue by wasting too much compute on easy problems and instead should dynamically reallocate how they use compute. We introduce Never Give Up (NGU), a simple adaptive sampling method that keeps generating samples for a problem until one is correct. By leveraging asynchronous RL, this naturally uses fewer samples to filter out easy problems and allocates more compute to solving harder problems. We investigate the design choices that affect NGU, such as off-policy robustness, and develop a set of best practices. On the math benchmark Deepscaler, NGU improves performance per compute, especially on harder problems. On a recent coding task, Manufactoria, standard GRPO with a per-test reward fails to fully solve problems that have a range of easy and difficult tests. NGU iteratively improves, solving harder and harder tests, until it learns to fully solve coding problems. — Read More
AI Was Supposed to Make Software Cheaper. So Why Is It More Expensive Now?
Anysphere reached roughly $4 billion in annualized revenue by June 2026, about three quarters of it enterprise, up from $100 million sixteen months earlier. It crossed $100 million faster than any application-layer SaaS product on record, by the better part of a year. SpaceX priced the whole company at $60 billion.
Reporting through 2025 put its model inference costs at forty to seventy cents of every revenue dollar. That puts gross margin somewhere in the 30 to 50 percent range. Software has run at 70 to 80 percent for four decades. The company selling the tool most responsible for the entire productivity story has the margin profile of a staffing agency. — Read More
Salesforce and Nvidia’s new reasoning model is everything the AI labs should fear
A new AI model called Koa is one of the biggest announcements from Salesforce this week at its giant Dreamforce tech conference. Koa is the company’s first reasoning model, built on Nvidia’s open-weight Nemotron model. The two companies worked together to post-train Koa to excel at sales, marketing, and customer-support-related tasks.
Koa is a shining example of how the enterprise world’s needs for AI are diverging from what the frontier labs are offering. Proprietary AI labs would rather have enterprises uploading files, code, prompts, and feedback directly into their models and agents, and spending millions to do so. — Read More
Meta to the AI industry: slow down without us
Meta Platforms CEO Mark Zuckerberg said competition and liability give AI companies enough reason to act individually on safety, appearing to break from calls by the leaders of top AI firms for a coordinated slowdown in AI development.
… Zuckerberg, in a post on X on Tuesday, said 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. — Read More
How should we approach the software factory as a product team?
… AI has a way of amplifying the culture it arrives in, including the good parts. A team that experiments and learns gets more room to do both. An engineering-driven organization that has neglected product work can now produce the consequences of that neglect much faster. And spend an extraordinary amount of time reviewing them.
That matters when we talk about a software factory, because a product team gets to shape much more than the rate at which code comes out. — Read More
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