The efficient frontier of AI models (luizsol.com)

A recent analysis has introduced a portfolio theory-inspired approach to evaluating AI models based on their "intelligence per dollar" efficiency. This framework identifies which models deliver the most cognitive capability for the least expense, creating a Pareto frontier of optimal choices. The analysis uncovered that many models are not worth considering; only those that are either smarter or cheaper than others make the list of contenders. Among these, GPT-6 Luna was identified as the most efficient overall, outperforming multiple alternative models across various scenarios, including specific cases where time and human resources are factors. This discussion is significant for the AI/ML community as it provides a structured methodology for determining model efficiency, akin to investment strategies in finance. The concept of "points per dollar," while not a direct Sharpe ratio, offers a useful metric to evaluate AI models, especially as operational costs and intelligence outputs become more quantifiable. However, the methodology does face limitations—namely the inability to fractionally allocate tasks between models or leverage them for greater output—emphasizing the complexity in selecting the optimal model for a given task. Overall, this analysis is a pioneering step in rationalizing model selection based on empirical cost and performance assessments.