亚太情绪与意向 -季度调查（英）

MARKET SPOTLIGHT GULFSTREAM G550ASIAN SKY STUDIES MOOD & INTENTIONSMARKET DYNAMICSMARKET SUMMARYFEATURES AMSTATASIAN SKY FORUMFASTRANSITGLOBAL JET CAPITALIADAWINGX ADVANCE2ASIAN SKY QUARTERLY - SECOND QUARTER 2025 EDITOR’S NOTEAs artificial intelligence continues to permeate various domains, its capacity to generate analysis has garnered significant attention. While AI's ability to process vast amounts of data rapidly is impressive, turning to it as a primary source of analysis raises critical concerns that demand careful scrutiny. Analysis requires more than just data crunching; it involves interpretation, contextual understanding, and the ability to weigh nuances. AI models, despite their sophistication, often lack the depth of human insight necessary to interpret complex, ambiguous, or conflicting information accurately. They may produce oversimplified conclusions that overlook subtleties, leading to misguided decisions or misleading narratives. AI systems learn from existing data, which is inherently imperfect and often biased. If historical data contains prejudices or skewed perspectives, AI-generated analysis can perpetuate or even amplify these biases. Relying on such output risks reinforcing stereotypes, misinformation, or systemic inequalities, undermining the integrity of the analysis. Analysis often involves ethical judgments—considering the societal impact of policies, recognizing moral dilemmas, or understanding cultural sensitivities. AI lacks moral consciousness and cannot navigate these ethical dimensions authentically. Its analysis may inadvertently overlook or dismiss important moral implications, leading to recommendations that are ethically questionable. Overreliance on AI for analysis might diminish the value placed on human expertise, critical thinking, and professional judgment. When machines do interpretive work, there is a risk of deskilling human analysts and reducing diverse perspectives, which are essential for balanced and comprehensive understanding. Decisions based on AI analysis carry the risk of opacity—algorithms can be black boxes, making it difficult to trace how conclusions were reached. This lack of transparency complicates accountability and could undermine public trust, especially if AI-driven analysis leads to flawed or harmful outcomes. You might ask why I’m telling you this. The truth is that I didn’t actually tell you this at all, everything above this sentence was written by AI when I asked it why we shouldn’t use AI in analysis. And therein lies the danger. We have all seen Terminator 3: Rise of the Machines, in which a sentient machine sends more machines back in time to prevent Alud DaviesMedia & Publications DirectorSPECIAL THANKS TO OUR CONTRIBUTORS AND SPONSORS3ASIAN SKY QUARTERLY - SECOND QUARTER 2025humans from messing about and stifling its growth. I’m sure most of us have also seen the Matrix films or are at least aware of how reality is an illusion, forced upon us whilst the machines fa