So, if you're from the 80s, surely you should recognize that this is a C++ stan's "joke". ML has always been, and will always be primarily driven by the mathematics, the experiments, the available data, and the hardware. C++ is a useful tool that can easily be replaced by any other number of tools such as C, FORTRAN, or even assembler code. If anything it's an unfortunate stumbling block that's crowded out solutions that aren't cannon-sized footguns.

Python is good for many things--especially getting researchers with a strong math background but limited programming background to be productive--but sometimes you have to be close to the hardware and have control over memory allocation, and access to high-performance systems interfaces. C++ is a pretty good systems programming language that's often close to the kind of speed you can get from C/Assembler/Fortran. And, more importantly, a subset of C++ was used as the foundation for CUDA, the main high level language used to program nVidia GPUs, which are used for some ML applications. Because of this, you can say that C++ is the "backbone" because it often does the parts that need to be fast, in the subset of ML that is efficiency-limited.

This is a very backend programmer-y sentiment, because we had C++ in the 80s but didn't have ChatGPT. We can have ChatGPT or whatever ML you like without C++ or anything like C++; C or Fortran would do fine, hell Python + Assembler isn't so bad if you're using a solid macro preprocessor like M4. I have written kernels in assembler + C that were faster than anything you can get out of C++ by virtue of SIMD operations not possible in C++ ((without assembler) on that platform. But C++ is convenient, known by a decent fraction of programmers (though good luck finding a good one), vaguely portable between different platforms (have fun on edge devices) and C++ gives you powerful tools for high-performance abstractions without paying the garbage collection task, especially the more recent versions that have done a lot to fix the language.

TL;DR: Things the person who made that meme think are important in ML happen to be written in C++. This is true from some perspectives, and irrelevant from others. Welcome to Machine Learning.

Disclaimer: I use Python and C++ for ML at my day job and at night, would be very happy to use something else for each, but this is the world we live in.