I'm not sure if that's entirely true. My analogy here would be how we observe classical mechanics or even just regular objects. We can predict the path a ball will take through the air in painstaking and incredibly accurate detail (more detail than we have tools to properly measure), but that doesn't require that we simulate all of the component pieces that make up the ball. We compress that by just calculating what the whole lot of atoms will do on average and substitute that in when we have millions of them and the error is negligible. That's essentially the basis of statistical mechanics. Why could we not make a simpler simulation of a life form (that its 'direct' real implementation) with similar processes of compressing information on component pieces and making approximations where the magnitude of the error is smaller than the accuracy of the measurement?

It seems to me that there is also a similar analogy to be made in the methods that we can use to compress information. Is there a functional difference between a compression algorithm that is lossless and one that would maybe corrupt 1 rgb value a tiny nudge in an entire image with millions of pixels if our only tool for examining it for corruption were our eyes? What if we then used that compression algorithm only in situations where we know the tools used to examine the results wouldn't be able to identify the losses, and used a lossless compression only when such tools were available?

All this is to say that I believe you could feasibly create a perfect simulation of something more complex than the thing performing the simulation with proper optimizations, but it would require certain stipulations like knowing when to use various optimizations to avoid contaminating fidelity of the simulation. Simple example would be simulating human history before a microscope was invented would allow us to constrain all approximations to have less error than would be visible to the most precise observer's senses of measurement.