Advocating for a decolonial approach in both AI practice and education to ensure AI technologies do not reinforce old patterns under new guises.
America's leadership in artificial intelligence (AI) depends on two foundational inputs: international scientific talent and federal research funding. Using hand-collected data on researcher origins and startup founders, supplemented with large-scale datasets linking patents, publications, and funding sources, we quantify the contributions of each input. International researchers account for 66% of new computer science Ph.D.s and constitute the majority of award-winning authors at premier AI conferences, averaging 61% of recipients at U.S.-affiliated institutions across five leading venues from 2013 to 2024. Among authors at U.S. institutions in the 50 most-cited AI papers, 59% are international. International cofounders are present in firms representing over 95% of aggregate valuation among leading private AI startups. Federal research investment supports 75–80% of CS Ph.D. training, doubles patent commercial value when combined with industry funding, and accounts for 91% of elite startup valuations through federally trained founders. Startups combining international founders and federally funded doctoral training account for over $1.6 trillion in valuation, far exceeding all other combinations. Immigration restrictions and federal budget cuts directly threaten the competitive advantages they claim to protect. We present evidence-based policy recommendations to preserve both inputs within current institutional frameworks.
AI programming agents have profoundly disrupted computing education. Rather than treating this as a threat, we argue that it is an opportunity to refocus early curricula. We introduce the “flaky compiler” metaphor to frame what AI programming enables and what it demands of its users. We argue that introductory computing courses can and must become early software-engineering courses, with the objective of teaching students to take responsibility for the work they produce with AI. This induces the need for—indeed, demands—research into how to rethink teaching program specification.
Shaoshan Liu views AI proliferation as depending on human agency, but a consequential transition could create a dependency inversion.
