Every few years, the industry latches onto an interaction paradigm and tries to make it the answer to everything. A decade ago it was “make it an app.” Now it’s “just make it a chat.” The chatbot-as-default impulse is strong right now, and it’s leading teams to ship worse experiences than what they’re replacing.
Katya Korovkina, writing for UX Collective, calls this “chatbot-first thinking” and lays out a convincing case for why it’s a trap:
Many of the tasks we deal with in our personal life and at work require rich, multi-modal interaction patterns that conversational interfaces simply cannot support.
She walks through a series of validating questions product teams should ask before defaulting to a conversational UI, and the one that stuck with me is about discoverability. The food ordering example is a good one—if you don’t know what you want, listening to a menu read aloud is objectively worse than scanning one visually. But the real issue is who chat-first interfaces actually serve:
Prompt-based products work best for the users who already know how to ask the right question.
Jakob Nielsen has written about this as the “articulation barrier,” and Korovkina cites the stat that nearly half the population in wealthy countries struggles with complex texts. We’re building interfaces that require clear, precise written communication from people who don’t have that skill. And we’re acting like that’s fine because the technology is impressive.
Korovkina also makes a practical point that gets overlooked. She describes using a ChatGPT agent to get a YouTube transcript — a task that takes four clicks with a dedicated tool — and watching the agent spend minutes crawling the web, hitting paywalls, and retrying failures:
When an LLM agent spends five minutes crawling the web, calling tools, retrying failures, reasoning through intermediate steps, it is running on energy-intensive infrastructure, contributing to real data-center load, energy usage, and CO₂ emissions. For a task that could be solved with less energy by a specialised service, this is computational overkill.
The question she lands on—“was AI the right tool for this task at all?”—is the one product teams keep skipping. Sometimes a button, a dropdown, and a confirmation screen is the better answer.
