为什么说大多数LLM初创企业注定都将失败？

Group 1 - The AI startup ecosystem is facing a harsh reality, with many companies mistakenly believing they are building on a stable platform provided by large language models (LLMs), when in fact they are nesting within predators [2][4] - The core illusion of modularity in the LLM startup boom is flawed, as model suppliers are not neutral layers but vertically integrated companies that control user interfaces and distribution channels [3][4] - The influx of venture capital into LLM-based startups has led to a strategic miscalculation, conflating the ease of prototype development with the sustainability of business models [4][5] Group 2 - Some startups may survive the collapse by possessing irreplaceable competitive advantages, such as distribution barriers, proprietary data, or control over inference [5][6] - The allure of the LLM shell model is rooted in its perceived advantages in a capital-driven environment, but it obscures the fundamental strategic flaw of lacking control over value engines [7][8] - The behavior of model suppliers reflects rational choices typical of monopolistic enterprises, as they seek to expand upstream and capture profits rather than serve as passive infrastructure [6][8] Group 3 - Founders must critically assess their reliance on others' LLMs and consider their business positioning, asking key questions about their unique advantages and potential vulnerabilities [8][9] - The new decision-making criteria for startups include rapid prototyping, quick iterations, and minimal cash burn, emphasizing the need for a solid foundation beyond mere API usage [8][10] - The era of LLM shell products has ended, and the new landscape favors those who control data, distribution, and infrastructure as the true competitive barriers [12]