I think that Dr. Frank is right that there is something missing from the reductionist approach, and that life is a good starting point for understanding where reductionism falls short. I'm intrigued by the work of Michael Levin at Tufts, who has been looking at the behavior of systems and trying to understand what guides them from first principles. He's positing the existence of some 'platonic space' that contains patterns that help dictate the behavior of all kinds of systems, allegedly even silicon-based algorithms.

He has organized a multidisciplinary virtual symposium on this, and it's fascinating to watch/read.

https://thoughtforms.life/symposium-on-the-platonic-space/

Articles like this are what Pauli invented the term 'not even wrong' for.

For a start it horribly misrepresents what physicists actually do. Yes there people working on string theory and atomic physics. But there are thousands and thousands of physicists working, for instance, on studying the climate. And, trust me, those people are not building their models from the ground up, because doing that would be insane. They're looking at the Earth system the models they make are called Earth system models. They are, in fact, thinking about things at the system level.

This goes for many other places where physicists work. Physicists do in fact know about emergent phenomena. They understand that complexity is a thing, and that complex systems have complex emergent behaviour which can be studied independently of the fine details that underly those systems. I mean, really, they do.

The writer says

Give me a simple cell from the early days of Earth’s history, and I could never predict that some 4 billion years later it would evolve into a giant rabbit that can punch you in the face.

That's just false: given a good enough simulation and a fat enough computer you could absolutely predict the vast array of possible futures from that time, and among them would be many in which there were giant rabbits that could punch you in the face, some of which would be kangaroos, not actual giant rabbits. Of course, running such a model is inconcievable in terms of resources and it would be entirely[*] uninteresting in any case. But you could run it in principle.

I get the feeling that the author has never written a computer program of any complexity. Because if you have, you'll know that such programs have complicated, emergent behaviour which is not usefully understood by thinking of the billions of little switches which are at the bottom of the thing. Yet nobody is saying 'oh, those programmers are all focussed on how the teeny tiny switches behave, they need to spend time understanding the emergent behaviour of the huge collections of them they are building'. They're not saying that because that's what programmers do. I mean, it's pretty much the job description. The people who focus on the teeny tiny switches are a small subgroup of chip designers: not programmers (I mean they may also be programmers, of course).

Then there's the whole thing the article wants to imply but can't actually say, because it's so silly: that somewhere underlying life is some magic which is not explained by physics. It can't say that, because there just is no evidence, at all, for that, but it sure wants you to think that, doesn't it?

[*] Actually there might be interesting things you would find. And example would be to understand what proportion of the possible futures have, for instance giant rabbits, or in what proportion does life continue at all. Those are things that would be interesting to know. This is very similar to the way ensemble forecasts work for weather and climate models: you run a bunch of copies of the model with slightly perturbed initial conditions (since the models are usually deterministic) and use the results to try and estimate how likely some outcome is.