Andrew J. Peterson

Andrew J. Peterson

Governments are increasingly interested in using AI to make administrative decisions cheaper, more scalable, and more consistent. But for probabilistic AI to be incorporated into public administration it must be embedded in a compliance layer that makes decisions reviewable, repeatable, and legally defensible. That layer can improve oversight by making departures from law easier to detect. But it can also create a stable approval boundary that political successors learn to navigate while preserving the appearance of lawful administration. We develop a formal model in which institutions choose the scale of automation, the degree of codification, and safeguards on iterative use. The model shows when these systems become vulnerable to strategic use from within government, why reforms that initially improve oversight can later increase that vulnerability, and why expansions in AI use may be difficult to unwind. Making AI usable can thus make procedures easier for future governments to learn and exploit.

Andrew J. Peterson

While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large language models are trained on vast amounts of diverse data, they naturally generate output towards the 'center' of the distribution. This is generally useful, but widespread reliance on recursive AI systems could lead to a process we define as "knowledge collapse", and argue this could harm innovation and the richness of human understanding and culture. However, unlike AI models that cannot choose what data they are trained on, humans may strategically seek out diverse forms of knowledge if they perceive them to be worthwhile. To investigate this, we provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process and identify conditions under which knowledge collapse occurs. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount. An empirical approach to measuring the distribution of LLM outputs is provided in theoretical terms and illustrated through a specific example comparing the diversity of outputs across different models and prompting styles. Finally, based on the results, we consider further research directions to counteract such outcomes.

Andrew J. Peterson

Artificial intelligence simultaneously transforms human capital production in schools and its demand in labor markets. Analyzing these effects in isolation can lead to a significant misallocation of educational resources. We model an educational planner whose decision to adopt AI is driven by its teaching productivity, failing to internalize AI's future wage-suppressing effect on those same skills. Our core assumption, motivated by a pilot survey, is that there is a positive correlation between these two effects. This drives our central proposition: this information failure creates a skill mismatch that monotonically increases with AI prevalence. Extensions show the mismatch is exacerbated by the neglect of unpriced non-cognitive skills and by a school's endogenous over-investment in AI. Our findings caution that policies promoting AI in education, if not paired with forward-looking labor market signals, may paradoxically undermine students' long-term human capital, especially if reliance on AI crowds out the development of unpriced non-cognitive skills, such as persistence, that are forged through intellectual struggle.