Core Viewpoint - The article discusses the shift of investment towards "hard assets" in the AI era, emphasizing the appeal of capital-intensive entities such as utilities, infrastructure, and industrial capacities due to their high physical barriers and low obsolescence risk [1][7]. Group 1: Investment Trends - Since late February, a basket of HALO stocks has been on the rise, indicating a growing investor interest in these assets [2]. - Goldman Sachs predicts that changes in U.S. tariff policies could lead to a decrease in actual tariff rates by approximately 100 basis points, providing additional support for transactions involving physical assets [3]. - The easing of costs for infrastructure and industrial companies reliant on physical imports will further strengthen the market's inclination towards tangible assets in the short term [4]. Group 2: Valuation Shifts - Investors are assigning higher valuations to traditional capital-intensive companies and extending their focus to supply chain and broader economic sectors [5]. - The current technological growth is increasingly dependent on physical assets like data centers and energy supplies, marking a significant shift from the software and platform models that dominated the market for the past 25 years [6][7]. Group 3: Characteristics of Preferred Assets - Companies defined as optimal "HALO" targets possess two key characteristics: high reconstruction costs and regulatory barriers, along with long-term economic value [8][9]. - Specific sectors of focus include electric grids, pipelines, utilities, transportation infrastructure, critical machinery, and long-cycle industrial capacities [9][13]. Group 4: Challenges for Soft Assets - The rise of hard assets comes alongside ongoing pressure on soft assets, with software, media, consulting, and certain financial sectors facing market reevaluation [10]. - Concerns about AI potentially disrupting these sectors and enabling low-cost competitors to undermine traditional business models are prevalent [11]. - However, not all software companies are equally affected, and those demonstrating historical expertise in delivering quality AI outcomes may still present stable or improving fundamentals [12].