There are typical LLM voices and styles, just like human writers have differentiated voices and styles. And some common elements of the typical LLM style are distinct from humans I've previously read.
I had the same reaction, but the article is not AI-generated according to pangram, which I've generally found reliable. I wonder if LLM turns of phrase and even thought patterns are creeping into normal human thought.
This isn't my project, but I shared it here because it has a few important ideas I've been thinking about in my own work. Effect type systems in particular are a really good fit for LLMs because they allow you to reason very precisely about a program's capabilities before runtime (basically, using the type system for capability proofs). This helps you trust agent-created code (for example, you know it can't do IO), or, if the code does require certain capabilities, run it in a sandbox (e.g., mock network or filesystem). This kind of language design also provides a safer foundation for complex meta-systems of agents-that-create-agents, depending on how the runtime is implemented, though Vera may be somewhat limited in that particular respect.
The major design decision I'm a little skeptical about is removing variable names; it would be interesting to see empirical data on that as it seems a bit unintuitive. I would expect almost the opposite, that variable names give LLMs some useful local semantics.
I agree, the more likely psychology of the Greg character is that he doesn't understand the way he presents himself in the pictures damns his surface level framing. You can really go quite far with more sophisticated versions of this technique in fiction -- Ishiguro's Remains of the Day is my favorite example!
These days it's almost trivial to design a binder against a target of interest with computation alone (tools like boltzgen, many others). While that's not the main bottleneck to drug development (imo you are correct about the main bottlenecks), it's still a huge change from the state of technology even 1 or 2 years ago, where finding that same binder could take months or years, and generally with a lot more resources thrown at the problem. These kinds of computational tools only started working really well quite recently (e.g., high enough hit rates for small scale screening where you just order a few designs, good Kd, target specificity out of the box).
So both things can be true: the more important bottlenecks remain, but progress on discovery work has been very exciting.