Core Insights - The article discusses the ongoing competition in the AI landscape, drawing parallels between the past rivalry between Google and Microsoft and the current dynamics involving OpenAI and Google [3][5][74] - It introduces the concept of "Intelligence Scale Effect," which emphasizes that merely having a smarter model is insufficient; understanding real-world data is crucial for success [5][7][24][74] Group 1: Intelligence Scale Effect - The "Intelligence Scale Effect" can be summarized by the formula: AI effectiveness = Model intelligence level × Depth of real-world understanding [5][74] - The first component, "model intelligence level," refers to the AI's foundational capabilities, determined by architecture, training data, parameters, and computational resources [13][14] - The second component, "depth of real-world understanding," is likened to the AI's ability to process and comprehend specific, real-time, and proprietary data [23][24] Group 2: Data Competition - Companies in the AI sector are entering a fierce competition to expand their data boundaries, which is essential for maximizing effectiveness [9][10][25] - The article highlights a shift from static to real-time data processing, exemplified by Perplexity AI, which combines real-time web information retrieval with large language models [34][36][38] - Microsoft 365 Copilot is presented as a solution to data silos within enterprises, leveraging Microsoft Graph to integrate private data for enhanced productivity [40][45][46] Group 3: Future Trends - The ultimate goal of AI applications is to transition from digital to physical realms, utilizing wearable devices and IoT to enhance the "Intelligence Scale Effect" [47][49] - The competition in the AI space is expected to be more intense than in previous internet eras, with a focus on context and real-world understanding as the new battleground [52][55][59] - The article warns of the potential privacy and trust issues arising from AI's need to access extensive personal and proprietary data [70][72][73]

Core Insights - The article discusses the ongoing competition in the AI landscape, highlighting the shift from traditional tech giants to new players like OpenAI, which is now positioned similarly to Google in the past [1][3] - It introduces the concept of "Intelligence Scale Effect," emphasizing that the effectiveness of AI applications will depend on both the intelligence level of large models and their depth of understanding of real-world contexts [3][12] Group 1: Intelligence Scale Effect - The formula for AI effectiveness is defined as: AI effectiveness = Large model intelligence level × Depth of real-world understanding [3][12] - The competition will increasingly focus on the second factor, "depth of understanding," as companies strive to expand their data boundaries [4][12] Group 2: Key Components of AI Effectiveness - The "intelligence level" of large models is determined by architecture, training data volume, parameter scale, and computational resources [7] - The "depth of understanding" refers to the model's ability to access and comprehend specific, real-time, private, or proprietary data [10][11] Group 3: Data Acquisition Strategies - Companies are entering a "data land grab" to maximize their AI effectiveness, with OpenAI's ChatGPT Atlas seen as a significant move against Google [13] - The shift from cloud-based solutions to desktop applications aims to enhance user experience and data acquisition [13][14] Group 4: Real-time and Private Data Utilization - Examples like Perplexity AI demonstrate the importance of real-time data retrieval to enhance AI responses, contrasting with traditional models that rely on outdated information [16][21] - Microsoft's Copilot integrates deeply with enterprise data, addressing the issue of data silos and improving operational efficiency [17][21] Group 5: Future Trends and Challenges - The ultimate goal is to bridge the digital and physical worlds through IoT and wearable devices, enhancing the "Intelligence Scale Effect" [23][24] - The competition is expected to be more intense than previous tech eras, with a focus on context and understanding rather than mere attention [26][29] Group 6: Trust and Privacy Concerns - The expansion of data boundaries raises significant privacy and trust issues, as users must decide how much personal data they are willing to share for improved AI performance [35][37] - The future competition will not only be about data acquisition but also about handling data in a trustworthy and secure manner [37][38]