We investigate the salience of extinction risk as a source of impatience. Our framework distinguishes between human extinction risk and individual mortality risk while allowing for various degrees of intergenerational altruism. Additionally, we consider the evolutionarily motivated "selfish gene" perspective. We find that the risk of human extinction is an indispensable component of the discount rate, whereas individual mortality risk can be hedged against - partially or fully, depending on the setup - through human reproduction. Overall, we show that in the face of extinction risk, people become more impatient rather than more farsighted. Thus, the greater the threat of extinction, the less incentive there is to invest in avoiding it. Our framework can help explain why humanity consistently underinvests in mitigation of catastrophic risks, ranging from climate change mitigation, via pandemic prevention, to addressing the emerging risks of transformative artificial intelligence.

Recent advances in artificial intelligence (AI) have led to a diverse set of predictions about its long-term impact on humanity. A central focus is the potential emergence of transformative AI (TAI), eventually capable of outperforming humans in all economically valuable tasks and fully automating labor. Discussed scenarios range from human extinction after a misaligned TAI takes over ("AI doom") to unprecedented economic growth and abundance ("post-scarcity"). However, the probabilities and implications of these scenarios remain highly uncertain. Here, we organize the various scenarios and evaluate their associated existential risks and economic outcomes in terms of aggregate welfare. Our analysis shows that even low-probability catastrophic outcomes justify large investments in AI safety and alignment research. We find that the optimizing representative individual would rationally allocate substantial resources to mitigate extinction risk; in some cases, she would prefer not to develop TAI at all. This result highlights that current global efforts in AI safety and alignment research are vastly insufficient relative to the scale and urgency of existential risks posed by TAI. Our findings therefore underscore the need for stronger safeguards to balance the potential economic benefits of TAI with the prevention of irreversible harm. Addressing these risks is crucial for steering technological progress toward sustainable human prosperity.

The post-2020 global biodiversity framework needs ambitious, research-based targets. Estimating the accelerated extinction risk due to climate change is critical. The International Union for Conservation of Nature (IUCN) measures the extinction risk of species. Automatic methods have been developed to provide information on the IUCN status of under-assessed taxa. However, these compensatory methods are based on current species characteristics, mainly geographical, which precludes their use in future projections. Here, we evaluate a novel method for classifying the IUCN status of species benefiting from the generalisation power of species distribution models based on deep learning. Our method matches state-of-the-art classification performance while relying on flexible SDM-based features that capture species' environmental preferences. Cross-validation yields average accuracies of 0.61 for status classification and 0.78 for binary classification. Climate change will reshape future species distributions. Under the species-environment equilibrium hypothesis, SDM projections approximate plausible future outcomes. Two extremes of species dispersal capacity are considered: unlimited or null. The projected species distributions are translated into features feeding our IUCN classification method. Finally, trends in threatened species are analysed over time and i) by continent and as a function of average ii) latitude or iii) altitude. The proportion of threatened species is increasing globally, with critical rates in Africa, Asia and South America. Furthermore, the proportion of threatened species is predicted to peak around the two Tropics, at the Equator, in the lowlands and at altitudes of 800-1,500 m.

Leading figures in the development of artificial intelligence systems, including OpenAI CEO Sam Altman and Google DeepMind CEO Demis Hassabis, have signed a statement warning that the technology they are building may someday pose an existential threat to humanity comparable to that of nuclear war and pandemics. "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks, such as pandemics and nuclear war," reads a one-sentence statement, released today by the Center for AI Safety, a nonprofit. The idea that AI might become difficult to control, and either accidentally or deliberately destroy humanity, has long been debated by philosophers. But in the past six months, following some surprising and unnerving leaps in the performance of AI algorithms, the issue has become a lot more widely and seriously discussed. In addition to Altman and Hassabis, the statement was signed by Dario Amodei, CEO of Anthropic, a startup dedicated to developing AI with a focus on safety.

A desert smoke tree is illuminated by half-moon light in the Trilobite Wilderness region of Mojave Trails National Monument near Essex, California. If you know the animals in your neighborhood but not the plants, you're not alone. Scientists have documented nearly 400,000 plant species and expect to identify many more. But unlike well-known endangered animals, such as elephants, tigers, and parrots, we don't currently understand the conservation status of more than 90 percent of the world's plant species. Plant growth and communities drive the ecosystems, food chains, and agriculture on every continent, yet we don't know the conditions that cause them to thrive or disappear.