Why can't we interpret the meaning of intercept in the sample regression function by putting x=0?

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In the book Woolridge, this is written, "The intercept, $\beta_o$, is the predicted value of y when x = 0, although in some cases it will not make sense to set x = 0. In those situations, $\beta_o$ is not, in itself, very interesting."

This is in the context of SRF, where $\beta_o$ "hat" is the estimated intercept.

What are those situations which the author is talking about?