Ethan Mollick — the Wharton professor who wrote Co-Intelligence, one of the most widely read books on working alongside AI — is now saying that the cooperative model he popularized is a waypoint, not the destination. The AI companies' long-term goal, he argues, is not a better chatbot you collaborate with. It is highly autonomous systems that outperform humans at most economically valuable work.

The distinction matters, and it's worth unpacking. The model most people use today is cooperative: you ask a question, the AI answers; you draft an email, it polishes; you stay in the loop for every step. Mollick called this co-intelligence — human and machine thinking together, with the human firmly in charge. An autonomous agent is different in kind, not degree. You give it a goal — research this market, reconcile these accounts, plan this project — and it works on its own for minutes, hours, or longer, making intermediate decisions without checking in. The human moves from collaborator to supervisor, and eventually, perhaps, out of the loop entirely for some kinds of work.

Mollick isn't describing a research paper or a speculative roadmap. He frames this as a shift already underway — the stated ambition of the companies building these systems, and increasingly visible in products that can take actions, use tools, and complete multi-step tasks rather than just producing text.

Who should care: anyone who currently uses AI for work or daily tasks and wants to understand where the technology is heading. That's most readers here, and this isn't a developer-only concern. The transition from assistant to agent changes the practical question you ask when you sit down with an AI system. Today's question is how do I prompt this well enough to help me? The emerging question is what am I comfortable delegating, and how do I check what it did? That's a management judgment, not a programming skill — deciding what to hand off, reviewing output you didn't produce, catching errors before they compound.

It also changes the stakes of trusting these systems. A chatbot that gives you a bad answer wastes a few minutes. An agent acting autonomously on a bad judgment could send the wrong message, make the wrong purchase, or file the wrong version — on your behalf, at scale.

Some honesty about the state of things: this is a direction, not a finished product. Mollick describes a trajectory the industry is pursuing, and parts of it are shipping now — agents exist and do real multi-step work — but the full claim, systems outperforming humans at most economically valuable work, is a goal, not a measurement. Nobody has demonstrated that. It's also worth noting what he doesn't settle: how quickly autonomy improves, which tasks resist it, and who bears the cost when an agent gets something wrong. The gap between a system that can act independently and one you'd trust to act independently is the central unresolved problem.

The practical takeaway isn't to adopt anything. It's that the mental model of AI as a smarter autocomplete — something that waits for you to type — has a shelf life. If you build work habits around tools that only assist, those habits will age poorly. The skills that carry over are the supervisory ones: stating goals clearly, defining what done looks like, and reviewing work you didn't personally produce.