In trying to explain generalized linear models, I often say something like: GLMs are very similar to linear models but with different domains for the target y, e.g. positive numbers, outcomes in {0,1}, non-negative integers, etc. This explanation bypasses the more interesting point though, that the optimization problem for fitting the coefficients is totally different, after applying the link function.
This can be seen by comparing the coefficients from a linear regression of log counts to those from a Poisson regression. For some cases, the fitted lines are quite similar, however they diverge if you introduce outliers. A casual explanation here would be that the Poisson likelihood is thrown off more by high counts than by low counts; the high count pulls up the expected value for x=2 in the second plot, but the low count does not substantially pull down the expected value for x=3 in the third plot.
n <- 20
x <- rep(c(2,3),each=n/2)
y <- rpois(n,lambda=exp(x))
lmfit <- lm(log(y) ~ x)
glmfit <- glm(y ~ x, family="poisson")
par(mfrow=c(1,3))
xlim <- c(1.5,3.5)
plot(x,log(y),xlim=xlim)
abline(coef(lmfit),col="red")
abline(coef(glmfit),col="blue")
legend("topleft",c("lm","glm"),col=c("red","blue"),lty=1)
y[1] <- 50
lmfit <- lm(log(y) ~ x)
glmfit <- glm(y ~ x, family="poisson")
plot(x,log(y),xlim=xlim)
abline(coef(lmfit),col="red")
abline(coef(glmfit),col="blue")
y <- rpois(n,lambda=exp(x))
y[n] <- 2
lmfit <- lm(log(y) ~ x)
glmfit <- glm(y ~ x, family="poisson")
plot(x,log(y),xlim=xlim)
abline(coef(lmfit),col="red")
abline(coef(glmfit),col="blue")
