🗣Anthropic co-founder Jack Clark tells the IMF the world is about to go from 10 million scientists to 10 billion (almost all virtual):

“According to UNESCO estimates, there are approximately 10 million scientists in the world. And even with only 10 million scientists, we have too few humans to oversee the work of today, too few laboratories to do material science analysis, too few biological labs to develop and test new drugs, and too few large pieces of scientific equipment in space or on the Earth for the experiments we want to run.

"Tell me what happens when instead of 10 million scientists, we have a billion virtual scientists directed by our own 10 million. Or 10 billion [virtual scientists]. That's the world predictably coming right for us. And we have to get ready. If we do get ready, we are going to be in a better position to translate the intelligence of AI systems into gains for us all. You know, longer, healthier lives, more energy, more food, more wealth.”

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Anthropics co-founder Jack Clark: "I believe that this year, we've gained convincing evidence that it [The Singularity] has now begun".

Even if you don’t believe that we have entered the singularity, Anthropic assumes that we have.

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⚠️AI is making fake medical research harder to detect

AI is giving scientific fraudsters a powerful new tool: the ability to manufacture convincing medical studies at scale.

Researchers are warning that paper mills are using generative AI to produce fabricated patient data, misleading statistical analyses and fake references. What once required weeks of work can now be generated in minutes, making fraudulent research harder for journals to identify.

The danger goes beyond academic dishonesty. If fabricated studies enter medical databases, they could influence systematic reviews, distort clinical guidelines and ultimately affect decisions about patient treatment. AI-generated citations add another layer of confusion, making it harder to distinguish reliable evidence from scientific fiction.

The problem is that peer review was never designed to handle an industrial-scale flood of synthetic research. Experts are calling for stronger editorial checks, automated fraud detection and better verification of underlying data.

AI could accelerate medical breakthroughs, but it could also pollute the scientific evidence doctors depend on. The challenge is no longer just producing reliable research, it’s making sure the research being published is real.

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🤖 Anthropic is putting $48 billion toward the public good

Anthropic is committing 5% of its shares to a new fund designed to support the public good, a stake worth roughly $48 billion based on the company’s reported $965 billion valuation.

And the fund could grow even larger. Anthropic’s board can add another 2% of the company’s shares for every 50% increase in its value above an undisclosed baseline.

Unlike a traditional charitable foundation, the fund will operate as a wholly owned LLC, giving it flexibility to make large donations to nonprofits or distribute money directly to specific groups of people. Its equity cannot be used to fund lobbying or political donations.

This builds on Anthropic’s existing commitments. Its co-founders have pledged to give away 80% of their own shares, while roughly half of employees donate some of their shares through a matching program. Together, these commitments could put around one-fifth of the company’s equity toward public causes.

Anthropic is reportedly preparing for a potential IPO, with speculation about a valuation approaching $2 trillion. Meanwhile, the AI industry faces growing criticism over job displacement, data-center expansion and safety risks.

Source.

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Elon says that instead of putting shares into a Public Benefit Fund he will be giving shares directly to the public. Back in April, OpenAI also proposed a Public Wealth Fund that would distribute returns directly to US citizens through a dividend.

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❗️At 17, a Chinese AI researcher reportedly earning ¥70 million a year is leaving Kimi for Silicon Valley.

Chen Guangyu joined Kimi’s AI research team in November 2025, when he was just 16 and still in high school. He reportedly earns around 70 million yuan annually, roughly $10 million despite having no university degree or PhD.

His research has already attracted international attention. Chen became the first co-author of a paper recognized by specialists worldwide, with findings used in a model containing 2.8 trillion parameters.

On October 15, Chen announced his departure from Kimi and revealed plans to move to the San Francisco Bay Area, home to many of the world’s leading AI labs.

Source.

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🤖 OpenAI bugging case takes a Saudi twist

An engineer accused of planting a listening device in a room used by OpenAI executives was reportedly exchanging WhatsApp messages with a Saudi royal just four days before his arrest.

According to prosecutors, the royal repeatedly tried to recruit him for a Saudi AI project, even telling him, “Saudi needs you more than the U.S!” The engineer allegedly responded with a short but telling reply: “100.”

Investigators say the listening device was hidden behind a TV in a room where OpenAI executives were meeting with government officials.

The case became even more intriguing after the arrest. Prosecutors say the engineer’s wife and their three-month-old son flew out of the United States in business class, with the trip reportedly paid for by a member of the Saudi military.

However, prosecutors have not claimed that the royal ordered the engineer to plant the device. They are pushing to keep him in custody without bail, arguing that his international connections could help him flee.

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😐 Jensen Huang drops a number most people haven’t heard:

The cost of an AI token is falling 30 to 40x every year. Cheaper tokens mean cheaper intelligence. And he says the benefits to customers are going up exponentially.

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🤖 Anthropic may be spending 75% of its computing power building future models

Despite reportedly achieving an impressive 85% gross margin in Q3, Anthropic is directing only around 25% of its computing resources toward serving existing customers.

The remaining 75% is going toward training future models, post-training improvements and technical experiments. In other words, most of its computing capacity is being used to build what comes next rather than serve today’s demand.

Based on the figures cited, Anthropic generates roughly $100 million in annualized revenue per gigawatt of computing capacity across 5 GW. That would imply only 1.25 GW is currently dedicated to inference — the computing power needed to run models for users.

The company reportedly plans to add another 10.5 GW in 2027, a massive expansion of its computing infrastructure.

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