Core Insights - The article emphasizes that the current state of AI is primarily focused on financial returns, with a significant shift towards understanding its practical applications in business settings [5][6] - It highlights a collective realization that AI's role is often limited to enhancing existing processes rather than creating revolutionary new solutions [12][21] Group 1: AI in Consumer and Business Sectors - In the consumer sector, while AI tools like ByteDance's Douyin and DeepSeek have seen high user engagement, the willingness to pay for advanced services remains low, with a subscription rate of only 25% to 30% in AI education [5][8] - The business sector, however, is more pragmatic, with traditional industries actively seeking to integrate AI to solve specific cost-related challenges, such as reducing bad debt losses in finance or shortening drug development cycles in pharmaceuticals [8][9] Group 2: Challenges in AI Implementation - Many AI startups struggle to demonstrate effective delivery capabilities, as businesses demand integration with existing systems and cost efficiency that outperforms hiring interns [10][11] - The article points out a "productivity paradox," where AI's current applications often lead to increased production of low-value content rather than meaningful improvements [11][18] Group 3: Data and Automation Debt - A significant barrier to effective AI deployment is the "data debt," where many companies lack proper data governance and training, leading to fragmented and unreliable data systems [22][23] - The article also discusses "automation debt," particularly in traditional manufacturing, where outdated software and lack of integration hinder AI's potential [24][25] Group 4: Future of AI - By 2026, the article predicts a major transformation in AI applications, driven by a significant reduction in inference costs, potentially down to 1% of human labor costs, which would fundamentally change the business logic of AI [28] - The emergence of "agent" AI, capable of autonomously completing tasks, is anticipated, with companies needing to encapsulate industry-specific knowledge into software to maintain competitive advantages [30][32] - The article concludes that successful AI applications will seamlessly integrate into existing business processes, focusing on tangible problem-solving rather than abstract concepts [36]