喝点VC｜YC 内部内部复盘：AI 正在进入稳定期，并逐渐形成一套可复用的AI原生公司构建路径

Core Insights - The AI economy is stabilizing, with clear differentiation between model, application, and infrastructure layers, leading to a more mature path for building AI-native companies [32][20][17] - Anthropic has surpassed OpenAI as the most preferred API among YC founders, with a usage rate exceeding 52% in the latest Winter26 batch, marking a significant shift in the competitive landscape [7][5][6] - The emergence of various models, including Gemini, is reshaping preferences, with Gemini gaining traction and accounting for approximately 23% of usage in the Winter26 batch [8][10] Group 1: AI Model Preferences - Anthropic's rapid growth is attributed to its performance in coding tools and the emergence of vibe coding, which has created significant value [7][6] - The competitive landscape is shifting from model capabilities to productization, as models become commoditized and computational power becomes cheaper [7][8] - Founders are increasingly using multiple models for specific tasks, indicating a trend towards model orchestration in AI applications [15][16] Group 2: AI Bubble Discussion - Concerns about an AI bubble are likened to the telecom bubble of the 1990s, where excess infrastructure investment ultimately led to the emergence of successful applications like YouTube [17][18] - The current phase is seen as an installation stage, with heavy capital investment in infrastructure, which will eventually lead to a deployment phase where applications flourish [20][21] - The competitive dynamics among AI labs and model companies are expected to benefit startups entering the application layer, similar to the opportunities seen during the internet boom [19][18] Group 3: Trends in AI Startups - There is a growing interest in establishing smaller models and niche applications, reminiscent of the early days of SaaS startups [26][27] - The ability to fine-tune models for specific domains, such as healthcare, is becoming more prevalent, with some startups outperforming larger models like OpenAI in specific benchmarks [28][29] - The expectation is that as more models become available, there will be an increase in AI applications tailored for various tasks, driven by advancements in open-source models and reinforcement learning [28][27] Group 4: Workforce and Efficiency - AI has improved efficiency for startups, but the expectation for higher performance has led to continued hiring rather than a reduction in workforce [36][35] - The trend indicates that while AI can enhance productivity, the demand for skilled personnel remains high to meet growing customer expectations [39][36] - The narrative around AI's impact on employment is evolving, with some believing it will lead to fewer employees needed, while others argue it will necessitate more hiring to maintain service quality [39][36]