Ethan Mollick's latest argument is that the center of gravity in AI use is moving. For the past few years, the default model has been what he calls co-intelligence: a chatbot you work with in real time, prompting, correcting, and iterating in a back-and-forth conversation. That model is giving way to something different — autonomous agents that you hand a task to, and that then run for a long stretch with far less input from you.

The distinction matters more than it might sound. In the chatbot model, you are a collaborator. You sit with the tool, shape each response, and the quality of the output depends heavily on how well you steer it turn by turn. In the agent model, you are closer to a manager. You define the work, hand it off, and then review what comes back. The skill shifts from having a good conversation to writing a good assignment and judging the result.

Mollick's point is that this second mode needs different equipment. Agents that run for hours rather than seconds need what he describes as harnesses — the scaffolding that lets an AI system keep track of a long task, recover from mistakes, use tools, and know when it is done — along with specialized apps built around handing off work rather than chatting. The plain chat window was designed for the old model, and it shows.

Who is this for? Mollick frames it as relevant to anyone using AI for work or personal projects, and that framing is fair — but with a caveat worth stating plainly. Right now, the people actually living in agent-mode are mostly developers. Coding agents were the first category where long autonomous runs proved useful, and the harnesses and specialized apps Mollick points to are concentrated there. If you are not a developer, this piece is less a set of instructions than a weather report: the tools you use are likely to be rebuilt around delegation rather than conversation, and it helps to know that is coming before the interface changes under you.

On whether this is real today or still an idea: Mollick describes the shift as already shipping, not speculative. Agent systems that execute extended tasks exist and are in use. What remains uneven is the experience outside software work. For non-technical tasks, the apps are thinner, and the management burden — checking whether the agent did the right thing over a long run — is genuinely new work, not a free lunch. Delegating a task you cannot evaluate is just hoping.

There is also a trade-off the framing makes easy to miss. Co-intelligence put a human in every loop, which was slow but meant constant judgment. Agent systems remove much of that friction, which is the point — but it also means errors can compound over a long run before anyone looks. The managerial skill Mollick's shift demands is not optional overhead; it is the cost of the autonomy.

None of this requires you to change anything this week. But the mental model is worth updating now: the question is drifting from how do I talk to this thing well to what work can I hand it, and how will I check what it returns.