美国调研纪要

Financial Data and Key Metrics Changes - The overall impression from the North American AI industry research indicates that the progress of large models has plateaued compared to the previous year's excitement surrounding ChatGPT, while the demand for computing power remains strong due to a clear supply-demand tightness [9][11]. Business Line Data and Key Metrics Changes - The application of AI in products is facing challenges, with a notable gap in user expectations versus the capabilities of current large models, particularly in creative processes like PPT and Excel, which are complex and prone to cumulative errors [21][22]. Market Data and Key Metrics Changes - The competitive landscape shows that traditional tech giants like Google and Meta are still leading in talent acquisition and investment in R&D, although the gap with OpenAI is narrowing as the industry matures [20][15]. Company Strategy and Development Direction - The focus is shifting towards B-end applications that address current needs and improve efficiency, rather than creating new demands. Innovations in system-level terminals like AI PCs and AI phones are seen as crucial for C-end applications [22][19]. Management Comments on Operating Environment and Future Outlook - Experts express cautious optimism regarding the continuous iteration of large models over the next 2-3 years, although the pace of progress is not as rapid as before. The sustainability of scaling laws and the effectiveness of current model architectures remain under scrutiny [11][12][13]. Other Important Information - The research highlights that the current bottleneck in AI applications is primarily in the C-end native products, with a stronger focus on B-end demands. There is a need for adjustments from hardware manufacturers to unlock innovation [23][22]. Q&A Session Summary Question: Is the capability of large models still progressing? - Experts maintain a cautiously optimistic view on the iterative progress of large models over the next 2-3 years, although this optimism is based on specific advancements rather than a broad expectation of revolutionary changes [11]. Question: What are the current challenges in AI applications? - The main challenges include high user expectations and the complexity of creative processes, which lead to significant cumulative errors in applications like PPT and Excel [21][22].