Biology is immeasurably harder than physics, the systems so much more complex and opaque and non amenable to fundamental explanation. Rote memorisation is a must.
Statements of this form are essentially always false: basically everything has the same difficulty because the standards for what constitutes progress in a field are set according to what the people working in it can do on average.
It's easy to write down what you thought of Infinite Jest, but in order to achieve any special success as a critic you're going to have to beat out everybody else that's doing the same thing. That makes it very hard. Likewise in an alternate universe where physics was easy, the standards for how much you had to say in a paper would rise until the average paper was about as substantial as it is now.
For example, in physics they require p < 3e-7 for a discovery. While in biology it is p < 0.05 and sometimes even p < .1 is allowed.
If there is a lot of data they would have too many "discoveries" (it would seem too easy) so they need a more stringent threshold. If the data is very expensive they would have too few "discoveries" so the threshold gets relaxed.
A successful critic isn't a critic, they're an author that writes for whoever is making them successful. So the critic at the top of academia would be the best at writing criticims that people understood: no garuntee of understanding the most, but that's still a skill that, say, Feynman would find it hard to replicate. The heirarchal nature of almost everything garuntees that there will always be "the best," and that they will be hard to beat, regardless of how much it means in the grand scheme of things. Finally, the scarcity of grant money means that participating in any field means beating the best: a regulatory effect that's at the center of the discussion.
Memorizing large numbers of facts give way to understanding and intuition in my experience. It’s quite similar to how a chess player’s experience regarding strategy starts out by exhaustively enumerating potential futures and eventually turns into an intuitive understanding of future plays and how training a neural network leads to a high dimensional manifold representation of a concept.
I will say that I'm skeptical of a lot of the work done in biology in relation to networks and complexity. It's often either done by those without sufficient mathematical background (and, typically, wholly statistically invalid) or a bunch of hand-wavy, feel-good nonsense for a sexy tagline by those who should know better.
Better work is done by researchers in complexity who analyze biological systems.