IMO, it does make sense, to some extent. Although it depends on what you mean by "multicollinearity" and "simple linear regression". Many people have distinct definitions for the above terms, and I might not be on the same page with you or others.
Case 1: Simple Linear Regression is a model with one explanatory variable and an intercept.
In this case you actually have two explanatory variables, it's just that one of them is constant (the intercept). If your other explanatory variable is also constant - you'll have the same problems as multivariable linear model having multicollinear variables. In particular - your coefficients could not be interpretable or make any sense.
Case 2: Simple Linear Regression is a model with one explanatory variable and no intercept.
In this case multicollinearity means your training data or data matrix is a vector of zeroes. It might not fit with some definitions for "multicollinearity", but eventually you're gonna have the same problems - in a nutshell, your coefficient(s) will be uninterpretable.