Nathaniel Whittemore discusses reported limits and costs for personal AI agentsAI inference economics examines the revenue, cost, utilization, pricing, and margins involved in serving trained models. alongside arguments about pricing, subsidies and adoption. He contrasts skeptical market interpretations with the possibility that efficient models and business uses could change the economics.
Nathaniel Whittemore reviews reported research in which a larger model edited a smaller model’s weightsWeight space learning treats neural network parameters as data for another learning system. It can learn representations of trained models, predict their properties or generate new parameter values., including criticism that the improvement involved fixing a reasoning loop and caveats about model scale. He considers how research acceleration might relate to recursive self-improvementRecursive self-improvement is the proposed process in which an AI system helps improve its own capabilities, then uses those improvements to support further advances. and why better model capability does not automatically translate into adoption or revenue.
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