作者 | 允毅、木子 2025 年的硅谷 AI 圈，最激烈的战场已不止于模型参数和榜单上，另一场残酷的战争也在暗中同步升级。 当大模型一路卷到极限，算力、参数规模、基准测试分数开始出现明显的边际递减，真正被重新定价的，是"人"。 过去几年，硅谷 AI 的主叙事是"谁能训练出更大的模型、刷出更高的分数"。 但进入 2025 年，模型能力仍然重要，却不再是唯一的决定因素；大家的关注重心逐渐从"模型参数与评测分数"，转向"谁能够将模型纳入产品与系统核 心，并持续推动其在真实业务场景中发挥作用"。 这一变化，非常直观地体现在一连串人员流动中： 一边是科技巨头高调宣布重金抢人、疯狂扩招 Agent、系统、基础设施方向的研究与工程负责人；另一边，他们又在内部对原有 AI 研究体系进行重组， 让多位中高层研究负责人选择离开舞台中央。 在一系列重大人事变动中，Meta 今年的变化尤为瞩目：比如前两天豪掷 20 亿美元买下智能体公司 Manus，顺手也把 Manus 创始人肖弘"纳入囊中"。另外 据《华尔街日报》7 月报道，Meta 采用"爆炸式 offer"战术：签约金最高达 1 亿美元，决策窗口短至几小时。 而作为 Met ...

Core Insights - The AI landscape in Silicon Valley is shifting from a focus on model parameters and benchmark scores to the ability to integrate models into products and systems that create real business value [4][6] - The talent market is experiencing simultaneous layoffs and aggressive hiring, reflecting a transition from general artificial intelligence (AGI) aspirations to a consensus on application-specific artificial superintelligence (ASI) [8][10] - The operational focus is moving from technical breakthroughs to engineering execution, with companies prioritizing the conversion of existing model capabilities into stable systems and deployable products [12][16] Talent Dynamics - Major tech companies are aggressively recruiting talent in areas such as agent systems, multimodal capabilities, and AI infrastructure, indicating a shift in the types of AI skills that are in demand [25][30] - High-profile personnel changes, particularly at Meta, illustrate a strategic pivot towards product-centric development, leading to the departure of key research figures [15][19] - The influx of Chinese engineers into critical roles highlights the competitive nature of the talent market, with companies offering substantial signing bonuses to attract top talent [24][28] Market Trends - The operational costs associated with maintaining AI models are rising, leading to a reevaluation of investment strategies and a focus on commercial viability [10][11] - The decline in the marginal returns of increasing model size and complexity is prompting companies to seek more practical applications of AI technology [10][11] - The emergence of new startups and research labs, such as Advanced Machine Intelligence Labs and Thinking Machines Lab, reflects a diversification of approaches to AI development [20][23] Strategic Shifts - The decline of foundational research initiatives, such as Meta's FAIR lab, signifies a broader trend where research must directly contribute to product development to retain strategic importance [17][18] - The focus on practical applications of AI is reshaping the landscape, with companies prioritizing the ability to deploy AI systems effectively over theoretical advancements [12][16] - The competitive landscape is increasingly defined by the ability to optimize AI systems for real-world applications, moving beyond traditional metrics of success [35][36]