AI industry says Trump plans to tax chips in the “single dumbest way imaginable”

The story

arstechnica.com: Donald Trump may be preparing to announce sweeping new semiconductor tariffs at the absolute worst time, the tech industry fears. On Thursday, Politico reported that a wide range of new semiconductor tariffs, which the tech industry expects will “doom” artificial intelligence innovation in the US, could be imposed in the coming “weeks or months.” About eight people familiar with the Trump administration’s plans were granted anonymity to discuss how the tariffs might work. They suggested that the framework for tariffs may change as it becomes finalized, but one approach under consideration could “dramatically expand the number of tech products subject to the duties, hitting not just chips but potentially many of the goods made with them, such as gaming consoles or the servers that fill data centers.” Read full article Comments

arxiv.org: arXiv:2608.23875v2 Announce Type: new Abstract: Artificial Intelligence (AI) algorithms frequently learn creative and unexpected solutions, surprising even expert researchers who develop and study them. They often astonish practitioners by discovering unanticipated behavior, exploiting loopholes in reward signals, or spontaneously uncovering previously unknown scientific phenomena. However, accounts of such unconventional behavior across machine learning are seldom formally documented. This work presents 26 curated firsthand anecdotes from various machine learning subfields representing the work of over 100 researchers. These anecdotes showcase the capability of modern AI systems to circumvent human-imposed design limitations and discover unexpected solutions to the tasks we train them on. Furthermore, these accounts are particularly important for the safety of future AI systems. They illustrate the fundamental challenge of aligning models with human values without diminishing their creativity, so they can make surprising discoveries without producing surprising, potentially harmful outcomes. The paper first details AI achieving superhuman success through reinforcement learning across many challenging domains. However, reward-driven optimization can fail when the model learns to hack an underspecified reward or unarticulated constraint. We then present case studies suggesting that harnessing internet-scale foundation models (FMs) has not resolved these fundamental challenges and, in fact, can supercharge them. Nevertheless, we argue that these same learning dynamics can be harnessed to accelerate scientific discovery. Finally, we hope this work provides a consolidated resource to inform future research and demonstrates that the tendency toward unexpected behaviors is commonplace in modern AI, highlighting the need to anticipate and manage AI's capacity for innovative, yet unpredictable, solutions. (abstract abridged)