There is a sense in which it is 'bad' for covariates to be highly correlated in a regression model, namely, that it can lead to multicollinearity.  However, I don't think it's very meaningful to claim that correlation between the slope and the intercept to be correlated.  

That said, your question is really about how there can be a correlation between the slope and the intercept, when these are always just $2$ points.  This confusion is perfectly sensible.  The problem is that the fact has been stated in an imprecise way.  (I'm not being critical of whomever wrote that—I speak like that all the time.)  

A more precise way to state the underlying fact is that the *sampling distributions* of the slope and intercept are correlated.  An easy way to see this is through a simple simulation:  Generate (pseudo)random samples of $X$ and $Y$ data from a single data generating process, fit a simple regression model in the same way to each sample, and store the estimates.  Then you can compute the correlation, or plot them as you like.  

    set.seed(6781)  # this makes the example exactly reproducible
    
    B         = 100  # the number of simulations we'll do
    N         =  20  # the number of data in each sample
    estimates = matrix(NA, nrow=B, ncol=4)  # this will hold the results
    colnames(estimates) = c("i0", "s0", "i1", "s1")
    for(i in 1:B){
      x0 = rnorm(N, mean=0, sd=1)  # generating X data w/ mean 0
      x1 = rnorm(N, mean=1, sd=1)  # generating X data w/ mean 1
      e  = rnorm(N, mean=0, sd=1)  # error data
      y0 = 5 + 1*x0 + e            # the true data generating process
      y1 = 5 + 1*x1 + e
      m0 = lm(y0~x0)               # fitting the models
      m1 = lm(y1~x1)
      estimates[i,1:2] = coef(m0)  # storing the estimates
      estimates[i,3:4] = coef(m1)
    }
    cor(estimates[,"i0"], estimates[,"s0"])  # [1] -0.06876971  # uncorrelated
    cor(estimates[,"i1"], estimates[,"s1"])  # [1] -0.7426974   # highly correlated
    windows(height=4, width=7)
      layout(matrix(1:2, nrow=1))
      plot(i0~s0, estimates)
      abline(h=5, col="gray")  # these are the population parameters
      abline(v=1, col="gray")
      plot(i1~s1, estimates)
      abline(h=5, col="gray")
      abline(v=1, col="gray")

[![enter image description here][1]][1]

For some related information, it may help to read some of my other answers:  

1. [How to interpret coefficient standard errors in linear regression?][2]
2. [Are all slope coefficients correlated with the intercept in multiple linear regression?][3]
3. [Why does the standard error of the intercept increase the further x¯
is from 0?][4]


  [1]: https://i.sstatic.net/72JF7.png
  [2]: https://stats.stackexchange.com/a/18213/7290
  [3]: https://stats.stackexchange.com/a/438262/7290
  [4]: https://stats.stackexchange.com/a/89794/7290