Is high AIC a bad feature of the model? I have a model with AIC equal to 78809. Does this mean this is a very bad model or the intepretation should be different? There are 15 variables, 2-level response variable and 40000 rows.
step() function from R statistical package returns almost the same AIC: 78600. Is there even a sense of applying step() function in that case?
 A: This is from the description of AIC: 

The Akaike information criterion (AIC) is a measure of the relative
  quality of a statistical model for a given set of data. As such, AIC
  provides a means for model selection.

I don't pay attention to the absolute value of AIC. I only use it to compare in-sample fit of the candidate models. Note, that if you're building the forecasting models, it is important to also consider out-of-sample fit.
A: As others said, there is not much point in evaluating a single model according to the absolute value of its AIC.
The point is to compare the AIC values of different models and the model which has lower AIC value than the other is better than the other in the sense that it is less complex but still a good fit for the data.
In no way I mean that ONLY less complex model = lower AIC. I am saying "less complex but still a good fit for the data". Obviously, a more complex problem may be preferable if your model is underfitting so obviously it is not necessary that a less complex model is better or has a lower AIC but in general a less complex problem which is not underfitting is better than a more complex one.
