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Current energy projections often envision an expansion of nuclear capacities to decarbonize future energy systems. However, this contrasts with the historic and current status of the nuclear industry, marked by techno-economic challenges for both light-water and non-light-water reactor technologies. Regardless, projections of strong nuclear growth have persisted since the 1970s. This paper investigates the “nuclear energy paradox” which shows the recurring divergence between historical projections and actual developments. A data compilation of long-term energy projections from international organizations such as the IAEA and the IEA as well as energy system models like GCAM and MESSAGE, as used in the IPCC, reveal a recurring pattern of high-growth projections for nuclear power. Such projections often rest on techno-economic assumptions such as substantial cost reductions. We propose the concept of nuclear imaginaries to show that these assumptions are embedded into techno-economic visions of nuclear power de
Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed. Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed.
We investigate the potential implications of large language models (LLMs), such as Generative Pretrained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15%
