AsyncIO in Python: What It Actually Is and Why Your ‘Async’ Code Might Not Be Async

Author(s): Rizwanhoda Originally published on Towards AI. First: What Problem Does AsyncIO Solve? Adding async and await to your code doesn't make it asynchronous. It makes it eligible to be asynchronous. There's a big difference and it bites almost everyone the first time. Photo by Árpád Czapp on UnsplashThe article explains that AsyncIO is designed to improve performance for I/O-bound workloads by using cooperative multitasking: while tasks are waiting, the event loop can run other pending work rather than blocking a single thread. It walks through how the event loop schedules coroutines and why yielding only happens at proper await points. It also clarifies common failure modes—using sequential awaits when concurrency is needed, accidentally blocking the event loop with synchronous libraries or CPU-heavy work, forgetting to actually run the event loop, and mixing sync/async incorrectly. Through a real FastAPI “before vs after” example and a mental model, the piece shows that async/await are signaling mechanisms, not speed buttons, and real parallelism requires launching multiple coroutines concurrently (e.g., with asyncio.gather or create_task). Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI