A joint working paper by Harvard Business School and INSEAD reveals that AI-native startups are fundamentally restructuring workforce composition, favoring senior technical talent over entry-level positions. Analyzing data from Y Combinator cohorts and a broader sample of United States venture-backed companies funded between 2020 and 2024, the study introduces a new category of AI-native firms distinguished by dual productivity channels: internal process optimization and AI-embedded customer products. The research demonstrates that these companies operate with significantly leaner, flatter organizational structures. On average, AI-native startups maintain workforces twenty-five percent smaller than traditional peers, while increasing their engineering headcount by thirteen percent. Concurrently, the proportion of entry-level staff and middle managers has declined by approximately fifteen percent. Senior-level representation, by contrast, has risen by twenty percent, indicating a pronounced shift toward expert talent acquisition. The findings challenge prevailing assumptions that artificial intelligence will broadly democratize technical careers by enabling junior employees to rapidly assume complex responsibilities or by lowering barriers to entry through generative coding tools. Instead, the data suggests that AI adoption is reinforcing existing elite networks. Firms tagged as AI-focused predominantly recruit highly credentialed professionals from top-tier academic institutions, with hiring patterns heavily concentrated in Silicon Valley and skewing male. Researchers caution that this concentration of opportunity may exacerbate broader socioeconomic and demographic divides. By accelerating skill development and performance for workers who already possess advanced technical backgrounds, AI tools risk widening the competency gap between seasoned professionals and those new to the field. The authors note that differential adoption rates could further entrench advantages for established entrepreneurs and technical experts, effectively centralizing market access rather than expanding it. As the technology sector continues to integrate generative models into both internal workflows and commercial offerings, the study underscores a critical labor market transition. The emerging AI-native enterprise model prioritizes depth over breadth in human capital, fundamentally altering traditional career progression pathways and demanding a strategic realignment of workforce development initiatives. Stakeholders across venture capital, corporate hiring, and policy-making must now address the structural implications of a workforce increasingly optimized for senior expertise rather than entry-level scalability.