Core Insights - The article discusses the transformation of the intelligent language service industry, highlighting the shift from simple language conversion to knowledge understanding and application [2][3][4] - The competitive landscape of large models is evolving, with new players emerging and the industry still in its early stages, akin to the electrical era of the 1920s [2][12] - The paradox of increasing order volume but stagnant profitability in the intelligent language service sector is emphasized, indicating a need for companies to adapt their strategies [5][6] Industry Trends - The intelligent language service industry is experiencing a significant transformation, where machine translation is becoming more prevalent, yet human translators are still needed for quality assurance [4][5] - The demand for intelligent language services is growing, with a reported 30% increase in order volume for a leading company, while revenue only grew by 10% [5] - The industry is moving towards a model where companies provide foundational technologies and tools to partners rather than directly serving end customers [6][7] Data Quality vs. Quantity - The focus is shifting from "big data" to the quality of data, as high-quality data is essential for effective machine learning and AI applications [7][8] - Companies are encouraged to separate data from reasoning to enhance AI systems' adaptability and efficiency [8][9] - The value of data is increasingly recognized as being tied to its knowledge density rather than sheer volume [9][10] Future of Large Models - The current state of large models is not yet mature, and the market dynamics are still evolving, with many applications yet to be discovered [10][12] - The competition in AI is expected to focus more on foundational technology frameworks rather than just parameter size [11][12] - The future of AI services in the enterprise market is unlikely to be free, as solving business problems incurs costs [12]