Also your dunning-kruger is showing.
Simple answer to complex problems, outside science and engineering, are almost always wrong. The world is complex and naive answers are not sufficient, even if the subjectively make sense to you.
The guy is part of the establishment you're talking about: reason dominated the humankind for a few centuries and we are just now managing to converge to something better, while still being immersed in a reason-based society that is crumbling, leaving big voids filled by any kind of ideological scum because the post-reason is not here yet. But anyway, your conspiracy of philosophers against engineers has no root in reality and it's just fear of the complexity of the world.
True, but it seems that the universe is simple at its foundation and complex in some of the things that emerge from it.
The standard model fits on a sheet of paper and it commonly makes predictions that are accurate to the ~20th decimal place. Neural networks can be expressed in fraction of that complexity and evolution is even simpler. Computations that emerge from these things are often complex beyond our comprehension, and hence it is a good heuristic to distrust simple explanations in the sciences about emergent phenomena such as psychology, sociology, economy and biology.
> Neural networks can be expressed in fraction of that complexity
That's not an explanation. That's a rule to construct them. Same for evolution: one thing is to define a basic rule that underlies a phenomena, another thing is to explain why we like peanut butter and jelly using evolutionary psychology. The basic rule doesn't capture much. And sometimes, maybe, there's not even a basic rule to begin with, because we conflate a lot of stuff in concepts that have no root or relationships in the physical world.
The universe is fractally structured regarding simplicity: It is simple at its foundation upon which we find layers of evidently chaotic processes (e.g. brownian motion, thermodynamics, fluid dynamics), which in turn converge to metastable rule sets that are seemingly simple again (e.g. evolution, neural networks). Unpredictable fluctuation from the chaotic layers below are either exploited as a computational mechanism (e.g. for probabilistic modeling or adaptations of unpredictability in behavior) or averaged out by regulation (homeostasis), so simple rules remain plausible despite the underlying chaos. Those simple rule sets in turn produce complex processes (e.g. psychology, science), which in turn produce simple processes (e.g. game theory, economics). Of course the higher up in this hierarchy, the more unstable rule sets become, e.g. most economic theories make poor predictions, but evolution is an extremely reliable theory.