I am reading a paper (details not very relevant) which assumes that the market cost $C$ of a trade is related to $N$ predictors $X_1,\dots,X_N$ (page 25) through a linear relationship
$$C = \beta_0 + \beta^TX_{1:N}$$
where the number of predictors $N=30$. The paper makes the following claim (page 29)
We estimate the linear regression model using ordinary least squares. We apply the Akaike criterion to delete any redundant explanatory variables from the regression model.
I am familiar with AIC as a method for model selection, but here there are 30 different variables and $2^{30}$ possible models. How did the authors use the AIC to determine which explanatory variables were 'redundant'?