It's not true for any random sample of numbers. Spin up a Jupyter notebook and paste the following:

    %matplotlib inline
    import matplotlib.pyplot as plt
    import numpy as np
    import seaborn as sns
    sns.set_context('poster', font_scale=1.3)

    n_values = 10000
    lower_bound = 1
    upper_bound = 100000
    nums = np.array(
        [int(str(x)[0]) for x in np.random.randint(lower_bound, upper_bound, size=n_values)]
    )

    fig, ax = plt.subplots(figsize=(12, 8))
    sns.distplot(nums, kde=False, bins=list(range(1, 11)), ax=ax)
    ax.set_xticks(list(range(1, 11)))
    fig.tight_layout()

And you'll see it's essentially a uniform distribution of leading digits.