Core Viewpoint - The article emphasizes that the current obsession with Large Language Models (LLMs) is misguided, equating LLMs to a mere "slice of bread" while neglecting the broader and more complex landscape of artificial intelligence (AI) [1][2][4]. Group 1: AI History and Development - The essence of AI is to enable machines to think and act like humans, and it has never been dominated by a single technology like LLMs [5]. - Since the inception of AI in 1956, various technologies have contributed to its evolution, including perceptrons, expert systems, and advancements in machine learning and computer vision [6][8]. - LLMs are a recent development in the long history of AI, and their prominence should not overshadow other significant advancements in the field [8][9]. Group 2: Innovation and Market Trends - True innovation often occurs in overlooked areas rather than in the spotlight, as evidenced by historical technological breakthroughs [10][11]. - The current trend in AI focuses excessively on the scale of LLMs, leading to a competitive environment where companies prioritize parameter counts over meaningful advancements [14][15]. - Future opportunities in AI may lie in areas such as Agentic AI, model compression, and neuro-symbolic AI, which address practical challenges rather than merely expanding LLM capabilities [15][16]. Group 3: Concerns in China's AI Landscape - The rapid establishment of AI colleges in China has led to a narrow focus on LLMs, sidelining other critical areas like machine vision and reinforcement learning [17][18]. - This one-size-fits-all educational approach risks creating a talent shortage in essential AI fields, as the industry increasingly demands diverse skill sets [18][19]. - The article warns that an overemphasis on LLMs could stifle innovation and limit the development of alternative AI pathways, which are crucial for future advancements [19][20]. Group 4: Conclusion and Future Directions - While LLMs represent a significant milestone in AI, they are not the endpoint; a comprehensive approach involving various AI technologies is necessary for true progress [23][24]. - Companies should focus on their specific needs rather than blindly following LLM trends, as practical applications like machine vision in manufacturing may yield better results [24]. - The future of AI will belong to those willing to explore uncharted territories and challenge the prevailing notion that LLMs are synonymous with AI [25][26].