Imagine a future where your car's software isn't painstakingly coded by human engineers, but generated by artificial intelligence. This isn't science fiction, but the subject of cutting-edge research exploring how Large Language Models (LLMs), the brains behind tools like ChatGPT, can write the complex, safety-critical code that powers self-driving cars. Researchers are developing new frameworks that combine the creative code-generation abilities of LLMs with the rigorous checking of formal verification tools. These tools act as meticulous critics, ensuring the AI-generated code adheres to strict safety standards. One such framework, called "spec2code," takes detailed specifications, both in human language and formal logic, and uses them to guide an LLM in producing code. This code is then subjected to intense scrutiny by verification tools like Frama-C, which use mathematical proofs to guarantee the code behaves exactly as intended. In a feasibility study, researchers used spec2code to generate code for real-world automotive modules like oil level detection and brake light activation. The results are promising, showing that LLMs can indeed produce functionally correct code, even without extensive fine-tuning. However, significant challenges remain. One hurdle is the inherent ambiguity of natural language. LLMs can sometimes misinterpret specifications, leading to code that doesn't quite meet the mark. Additionally, achieving perfect equivalence between AI-generated code and human-written code remains elusive due to differences in coding styles and the complexities of real-world systems. While a fully autonomous AI software engineer is still a way off, this research offers a tantalizing glimpse into a future where AI accelerates the development of safe and reliable software for critical systems like self-driving cars.