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Restrepo (2025) develops a framework for economic growth in which Artificial General Intelligence (AGI) can perform any human task given sufficient computational resources. In his model, all economically essential "bottleneck" work is eventually automated, wages converge to the computational cost of replicating human work, and labor's share of GDP approaches zero as computational resources expand. This note relaxes one of his assumptions: that all task types have uniform automation costs. Drawing on Moravec's Paradox [1]--the observation that tasks humans find effortless (perception, mobility, manipulation) often require enormous computational resources, while tasks humans find difficult (mathematics, logic) require relatively modest computation--we extend his model to allow for differential automation costs across cognitive and physical tasks.

TL;DR: At SmashLab, we're creating an intelligent assistant that uses the sensors in a smartwatch to support physical tasks such as cooking and DIY. This blog post explores how we use less intrusive scene understanding--compared to cameras--to enable helpful, context-aware interactions for task execution in their daily lives. Every day, we perform many tasks, including cooking, crafting, and medical self-care (like the COVID-19 self-test kit), which involve a series of discrete steps. Accurately executing all the steps can be difficult; when we try a new recipe, for example, we might have questions at any step and might make mistakes by skipping important steps or doing them in the wrong order. This project, Procedural Interaction from Sensing Module (PrISM), aims to support users in executing these kinds of tasks through dialogue-based interactions.

The first wave of artificial intelligence (AI) has already replaced humans for repetitive physical tasks like inspecting equipment, manufacturing goods, repairing things, and crunching numbers. That shift started way back with the Industrial Revolution. This gave rise to our current Thinking Economy, where employment and wages are more tied to workers' abilities to process, analyze and interpret information to make decisions and solve problems … Just like the industrial revolution automated physical tasks by decreasing the value of human strength and increasing the value of human cognition, AI is now reshaping the landscape and ushering in a Feeling Economy. What characterizes this emerging economy? Consider, for example, the role of a financial analyst, which seems pretty quantitative and thinking-oriented.

As a world, we are caught up in an exciting period of rapid technological advances and innovations. And healthcare is similarly in a period of rapid innovation and technological advances. So how do these two trends merge or connect? Does every technological advance or tool have application in healthcare? How do we decide what technologies will add value to our healthcare organization and to the patients we serve?