ChatGPT put a weird idea into our heads about how AI should look and act

Welcome to AI Decoded, Fast Company ’s weekly newsletter that breaks down the most important news in the world of AI. I’m Mark Sullivan, a senior writer at Fast Company, covering emerging tech, AI, and tech policy. This week, I’m focusing on how and why AI will grow from something that chats to something that works in 2026. I also focus on a new privacy-focused AI platform from the maker of Signal, and on Google’s work on e-commerce agents. Sign up to receive this newsletter every week via email here . And if you have comments on this issue and/or ideas for future ones, drop me a line at sullivan@fastcompany.com, and follow me on X (formerly Twitter) @thesullivan . Our relationship with AI is changing rapidly Anthropic kicked off 2026 with a bang. It announced Coworker , a new version of its powerful Claude Code coding assistant that’s built for non-developers. As I wrote on January 14, Coworker lets users put AI agents, or teams of agents, to work on complex tasks. It offers all the agentic power of Claude Code while being far more approachable for regular workers (it runs within the Claude chatbot desktop app, not in the terminal as Claude Code does). It also runs at the file system level on the user’s computer, and can access email and third-party work apps such as Teams. Coworker is very likely just the first product of its kind that we’ll see this year. Some have expressed surprise that OpenAI hasn’t already offered such an agentic tool to consumers and enterprises—it probably will, as may Google and Microsoft, in some form. I think we’ll look back at Coworker a year from now and recognize it as a real shift in the way we think about and use AI for our work tasks. AI companies have been talking for a long time about viewing AI as a coworker or “copilot,” but Cowork may make that concept a reality for many nontechnical workers. OpenAI’s ChatGPT, which debuted in late 2022, gave us a mental picture of how consumer AI would look and act. It was just a little dialog box, mainly nonvisual and text-based. This shouldn’t have been too surprising. After all, the chatbot interface was built by a bunch of researchers who spent their careers teaching machines how to understand words and text. Functionally, early chatbots could act like a search engine. They could write or summarize text, or listen to problems and give supportive feedback. But their outputs were driven almost entirely by their pretraining, in which they ingested and processed a compressed version of the entire internet. Using ChatGPT was something like text messaging with a smart and informed friend. Large language models do way, way more than that today. They understand imagery, they reason, they search the web, and call external tools. But the AI labs continue to try to push much of their new functionality through that same chatbot-style interface. It’s time to graduate from that mindset...

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