Why does the value of a conversion rate change the number of observations required when calculating statistical power?

This is probably basic, but I haven’t come across it before:

With a minimum detectable effect of 10% and a baseline conversion rate of 0.44% it takes 265,857 observations per cohort to reach 80% power (1-sided test with 5% alpha).

Keeping the same parameters, but changing the baseline rate to, say, 44%, we see only 1,573 observations are needed per cohort.

Why does the value of the conversion rate change the number of observations required?

Code (Using R power.prop.test)

# Choose baseline (control) conversion rate
      #BaselineConversion <- 0.0044 # <- This is the real conversion rate
      BaselineConversion <-  0.44   # <- This is the adjusted rate for comparision
    
  # We want to be able to detect a minumium of 10% drop in conversion rates (i.e. if the reduction in conversion rates is <10% we don't care)
    minDetectedDrop_10pct <- 0.1
    
  # Power calculation: -10%
    minDetectedConversion_10pct <- BaselineConversion*(1-minDetectedDrop_10pct)
    testResult_10pct <- power.prop.test(
      p1= BaselineConversion,
      p2 = minDetectedConversion_10pct,
      sig.level = 0.05,
      power = 0.8,
      alternative = 'one.sided')
    paste0('Number observations needed with baseline conversion of: ',BaselineConversion,' is: ', round(testResult_10pct$n))

N = 1000
x_ = 0:N
plot(x_/N, dbinom(x_, size=N, prob=0.05) * N, type='l', xlab='Mean success number', ylab='Density')
lines(x_/N, dbinom(x_, size=N, prob=0.05*0.9) * N, lty=2)

lines(x_/N, dbinom(x_, size=N, prob=0.5) * N, col='red')
lines(x_/N, dbinom(x_, size=N, prob=0.5*0.9) * N, lty=2, col='red')

lines(x_/N, dbinom(x_, size=N, prob=0.95) * N, col='blue')
lines(x_/N, dbinom(x_, size=N, prob=0.95*0.9) * N, lty=2, col='blue')