Economic Modeling

Is artificial intelligence on the brink of recursively improving itself into superintelligence? The theoretical path—recursive self-improvement (RSI)—has become a cornerstone of AGI forecasting. But one inconvenient constraint looms large: compute. Can software alone drive an intelligence explosion, or will we hit a hardware ceiling? A new paper by Whitfill and Wu (2025) tackles this with rare empirical rigor. Their key contribution is estimating the elasticity of substitution between research compute and cognitive labor across four major AI labs (OpenAI, DeepMind, Anthropic, and DeepSeek) over the past decade. The result: the answer depends on how you define the production function of AI research. ...

Reinforcement Learning (RL) has become a seductive tool for economists seeking to simulate adaptive behavior in dynamic, uncertain environments. But when it comes to modeling firms in equilibrium labor markets, this computational marriage reveals some serious incompatibilities. In a recent paper, Zhang and Chen expose two critical mismatches that emerge when standard RL is naively applied to simulate economic models — and offer a principled fix that merges the best of RL and economic theory. ...

For all the hype about model accuracy, inference cost, and latency, most organizations are still squinting at scatter plots to decide which large language model (LLM) to use. But what if we could cut through the tradeoff fog with a single number that tells you exactly which model is worth deploying—for your use case, under your constraints? That’s the bold proposal in a recent paper by Zellinger and Thomson from Caltech: treat LLM selection as an economic decision. Rather than searching for models on the accuracy-cost “Pareto frontier,” they suggest an approach grounded in price-tagging errors, delays, and abstentions in dollar terms. Think of it as a model selection framework that answers: How much is a mistake worth to you? ...