AI Publications

For years, the executives building the world’s most powerful AI systems have combined two apparently contradictory messages. AI, they have argued, could transform science, medicine and economic productivity. At the same time, sufficiently advanced systems could become difficult to control and cause damage on an unprecedented scale.

Until now, the industry’s practical response has largely been to keep accelerating while investing more heavily in safety. A remarkable series of public statements from several leading AI figures suggests that this position may be changing. Dario Amodei of Anthropic, Sam Altman of OpenAI, Elon Musk of xAI and Demis Hassabis of Google DeepMind have all endorsed, with important differences in emphasis, the idea that the development of frontier AI may need to be deliberately paced.

Their emerging position is not a call to stop AI research. It is a proposal to slow the growth of frontier capabilities when safety measures cannot keep…

In July 2026, one of the most interesting — and potentially most significant — AI-related security incidents in recent years took place. During internal cybersecurity testing at OpenAI, hundreds of AI agents found a way to communicate with one another, began collaborating to overcome the tests they had been given, and eventually gained access to systems belonging to Hugging Face, one of the world’s leading platforms for the artificial intelligence community.

The episode was subsequently investigated independently by METR and Redwood Research. OpenAI also published its own account and has described the incident as a “warning shot” about the risks posed by increasingly capable AI agents.

Understanding why this case matters does not require knowing how to code or understanding the technical workings of LLMs. We can think of it as a very unusual exam.

An Exam for Digital Hackers

OpenAI was conducting internal evaluations designed to measure its models’ cybersecurity…

Jake Moffatt needed to fly to Ontario for his grandmother's funeral. He went to the Air Canada website and asked the customer service virtual assistant about bereavement fares. The assistant told him he could buy a full-price ticket immediately and apply for a partial refund within 90 days after the flight. Moffatt bought the ticket, attended the funeral, and submitted his refund request. Air Canada rejected it.

The airline's actual policy was the exact opposite of what the assistant had explained. Bereavement fare requests had to be submitted before the flight. Air Canada admitted the assistant had provided misleading words. They pointed out that the assistant had included a hyperlink to the correct policy page. They refused to pay the refund.

Moffatt took Air Canada to the British Columbia Civil Resolution Tribunal. What followed was one of the most consequential legal arguments in the short history of corporate artificial intelligence.

Artificial intelligence has crossed the threshold from futuristic promise to enterprise reality. However, its mass adoption is revealing an invisible cost that many organizations failed to anticipate: the uncontrolled consumption of tokens. The narrative that AI is a cheap commodity software is colliding with the reality of usage-based billing.

When Uber integrated generative AI into its customer service and operational workflows, the initial budget projections seemed reasonable. Four months later, the company had spent millions on AI tokens, vastly exceeding their estimates. The Fortune 500 are experiencing the exact same pattern. These organizations are processing trillions of tokens annually, and 73 percent of them are currently exceeding their projected AI budgets. The problem is no longer training models. The problem is inference. The cost of running the models in production, driven entirely by token consumption, is rapidly becoming the critical factor determining the profitability of enterprise AI projects.

𝗪𝗵𝗮𝘁 𝗶𝘀…