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AI Advances Spark Anxiety, While Guinea Worm Eradication Nears
The rapid advancement of artificial intelligence (AI) is causing anxiety among some in the tech world, including OpenAI CEO Sam Altman, even as global health efforts make significant strides in eradicating Guinea worm disease. Altman admitted to feeling "a little useless" after using his own company's AI tools, experiencing a sense of obsolescence as the technology surpassed his own abilities, according to Fortune. This sentiment reflects a growing concern among professionals who fear their skills are becoming outdated due to increasingly sophisticated AI.
Altman described his initial enjoyment in building an app with Codex, OpenAI's AI coding agent, but his mood shifted when the system generated feature ideas that were superior to his own. "I felt...at least a couple of them were better than I was thinking of," Altman stated in a post on X, Fortune reported. This experience highlights a new form of workplace anxiety, where individuals feel threatened not by a lack of skills, but by the superior capabilities of the AI tools they use.
Meanwhile, in global health news, efforts to eradicate Guinea worm are nearing success. In 2025, only 10 human cases of the debilitating parasitic infection were reported worldwide, marking an all-time low, according to Hacker News, citing the Carter Center. If health workers succeed in fully eliminating the worm, it will become only the second human disease to be eradicated, following smallpox. Guinea worm (Dracunculus medinensis) is transmitted through water contaminated with small crustacean copepods that harbor the worm's larvae.
In related tech news, AI's growing computational demands are driving interest in next-generation nuclear power plants. These plants are viewed as a potential source of electricity for massive data centers that support AI development, according to MIT Technology Review. These next-generation nuclear facilities could be cheaper to construct and safer to operate than older models. MIT Technology Review held a subscriber-exclusive roundtable discussion on hyperscale AI data centers and next-gen nuclear power, technologies featured on its 10 Breakthrough Technologies of 2026 list.
Furthermore, researchers are exploring ways to improve the efficiency of AI models. A paper submitted to arXiv in January 2026, and highlighted by Hacker News, proposes a new method for self-attention in Transformer models. Franz A. Heinsen and Leo Kozachkov, the authors of "Self-Attention at Constant Cost per Token via Symmetry-Aware Taylor Approximation," argue that their approach can reduce the computational costs associated with self-attention, which currently increase with context length. This could help address the growing demand for storage, compute, and energy required by AI models.
In response to growing concerns about AI security, experts are advocating for stricter governance of agentic systems. An article in MIT Technology Review suggests treating AI agents like powerful, semi-autonomous users and enforcing rules at the boundaries where they interact with identity, tools, data, and outputs. The article outlines an eight-step plan for governing agentic systems, emphasizing the importance of controls at the boundary.
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