A recent arXiv paper reveals that diverse large language models independently develop strikingly similar internal representations for numbers, a phenomenon researchers liken to convergent evolution. This finding indicates that despite varying architectures and training methodologies, different AI systems naturally converge on comparable mathematical concepts during their learning process.