That's an unfair take. Yes, people have always been using the most interesting or complex phenomena they knew as metaphors to explain the world, starting with animals and ending with computers. But it's not about being impressed, but rather finding similarities. And most importantly, bodily fluids != clockworks != systems of pipes != computers.
There's a qualitative jump we've made in the last ~150 years. The tight feedback loop between math, natural sciences and engineering, that was earnestly established some centuries earlier, finally picked up speed. We're no longer "impressed" by a deer or a clockwork and saying the world must work like it. We're not imagining the universe to be like something we know - we're mapping alike concepts using precise, formal, well-tested reasoning. We're applying models to the world, and we know exactly how much fidelity they have. We chose those models to be useful, not evocative.
In short, back then we were doing artistic impressions of a landscape. Nowadays, we're drawing proper maps[0].
There's a popular meme that ~150 years ago, physicists thought they had it all neatly figured out, and all that's left to do is to make numbers accurate in far decimal places - and then they stumbled on relativity, nuclear physics and quantum physics, turning everything upside down. The meme is inaccurate, and its implication - that we still don't know shit - isn't particularly convincing to me. Those new fields didn't replace our understanding of the world - they enriched it, solidified it, filled in holes. We have a more complete picture now, especially of the fundamentals. We may not have solved quantum gravity, we may not know if and what dark matter is, etc. - but we know enough to put bounds on the possible consequences and possible surprises[1].
The point I'm making is that, when we now say that e.g. the brain is a computer, it's not the same thing as people 500 years ago saying the brain is like a clockwork. We're not vaguely hinting at similarities - we're applying a specific, precise model. A model that makes concrete testable predictions. A model that can be studied to yield more understanding. A model that's tied to what we now recognize as fundamental - computation isn't some gears and belts trick, it's one of the most basic and impactful ideas in mathematics.
It's similar the case of "simulation argument". Do we live in a simulation? Who knows? We're not sure if we could tell (unless it's a really hacky one) or if it would matter much. Is it possible for our universe to be a computer simulation? Probably. We know enough about physics, biology and information theory to have a justified belief it can be done, especially if it's designed around players. Decades of videogame development experience tells us how to do it; mathematics and natural sciences give it green light and say it's an engineering problem.
And as a final point to this little rant: when I say that "we know" something today, it's not the same kind of knowing as we had 500 years ago, or even 200 years ago. Mathematics and natural sciences are thoroughly interwoven. The parts that we are sure of are all mutually reinforcing - if we're wrong about any one of it, it would mean we're wrong about most of the rest, across many fields and disciplines. 200 years ago, that might have been possible. Today? We've built so many technologies and processes based on our scientific models that everything we do today, every second of every single person's life on this planet, is its own experiment confirming that our models are good fit.
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[0] - Comparing maps from 500 years ago to maps today is a good exercise. The difference isn't just in accuracy - it's qualitative, as we now have a deep theoretical understanding of what maps are and how to make them, capacity to make them arbitrarily good for desired purposes, and experience in putting them to actual use.
[1] - This is, sadly, what makes me very pessimistic about faster-than-light travel and certain other sci-fi dreams. We may not know enough to rule those out directly just yet, but what we do know surrounds the problem space tightly and lets us rule them out via indirect proof.