AI-powered QA is revolutionizing software testing with Large Language Models (LLMs), but they also come with limitations and challenges that need to be addressed. LLMs can generate test scenarios that sound right on the surface but miss critical system dependencies, comprehend complex architectural relationships poorly, and spew convincing-sounding gibberish. They introduce random variables such as hallucinations, non-deterministic results, and inconsistent logic, which can defeat the purpose of reproducible testing. Additionally, LLMs require significant computational overhead, higher infrastructure costs, energy inefficiency, scaling problems for large software systems, and larger context windows that are technically limited. The training data limitations also pose a challenge as they encode historical decision-making patterns, perpetuate existing organizational blind spots, and create test scenarios that favor specific user demographics. Furthermore, the lack of transparency in AI testing tools introduces significant risks such as reduced confidence in testing methodologies, potential regulatory compliance challenges, increased legal and professional liability, erosion of trust in quality assurance processes, and a decrease in human understanding of the technologies. The future of QA lies in striking a balance between human judgment and AI-powered automation, and AI-native solutions like LambdaTest's Kane AI can bridge the gap between efficiency and practical real-world QA needs.