A group of researchers from Tsinghua University, often referred to as the MIT of China, has presented new findings that cast a shadow over the current hype surrounding AI reasoning models. Their research reveals that these models, despite claims of approaching superintelligence, are far from the cognitive prowess many believe they possess. The AI industry is often guilty of hyperbole, touting advancements as groundbreaking breakthroughs with little substantiation. Claims like "superintelligence is just around the corner" have become commonplace, but the reality, as this study suggests, is much less exciting. The Tsinghua University paper acts like a wrecking ball, disrupting the narrative and forcing a reevaluation of our assumptions about frontier AI models. To challenge prevailing intuitions, we need to delve into a first-principles analysis of these models. Often, this industry is saturated with self-proclaimed experts who use incomprehensible jargon to obscure the truth. However, in my newsletter, I strive to demystify complex concepts and provide you with clear, actionable insights. This article is a condensed version of one of my latest newsletters. If you're interested in staying ahead of the curve and gaining a deeper understanding of AI, consider subscribing. You'll receive exclusive content and analyses that can help you navigate through the fog of misinformation. The Tsinghua University research highlights several key limitations of AI reasoning models. For instance, these models frequently struggle with abstract reasoning, context comprehension, and handling unseen scenarios. They may excel in pattern recognition and data processing, but their ability to reason and make decisions in complex, real-world situations remains rudimentary. The findings underscore the importance of tempering expectations and focusing on realistic goals. While AI has made significant strides, the path to true superintelligence is still long and fraught with challenges. By grounding our understanding in solid evidence and clear analysis, we can better appreciate both the potential and the limitations of current AI technologies.