In today's digital world, verifying who actually wrote a piece of text is more complicated than ever, thanks to the rise of powerful AI writing tools. Large Language Models (LLMs) like ChatGPT can mimic human writing styles so convincingly that it's becoming increasingly difficult to tell human writing from AI-generated text. This raises critical questions about authenticity, plagiarism, and even the future of writing itself. New research explores this complex landscape of authorship attribution in the LLM era, breaking down the challenge into four key problems: figuring out who wrote a human-written text, detecting if a text was AI-generated, determining *which* AI model created a given text, and finally, the trickiest problem of all, untangling authorship when humans and AIs collaborate on a piece of writing. Traditional methods like stylometry, which analyzes an author's unique writing style, are being put to the test. Think of it like a linguistic fingerprint—each person has their own way of using words, sentence structures, and even punctuation. However, LLMs are now learning to mimic these fingerprints, making it harder for these traditional methods to work. Researchers are developing new AI-powered tools that can learn the subtle differences between human and LLM-generated text. These tools are getting better at spotting patterns and inconsistencies that humans might miss. But, as these detectors improve, so do the techniques to bypass them. It's a constant cat-and-mouse game! There are significant hurdles to overcome. For one, AI models often struggle to generalize their knowledge to new domains or writing styles. What works for detecting AI-generated news articles might not work for scientific papers or poetry. Also, many of these detection tools are opaque—