The shift Ethan Mollick describes is a change in what an AI session is for. The first wave of mainstream AI tools worked like a conversation: you asked a question, got an answer, asked a follow-up, and the human did all the actual work in between. What has emerged since is something different in kind, not just degree — systems that take a goal, break it into steps, and carry out those steps themselves over a long stretch, sometimes the equivalent of many hours of human effort, before handing back a finished result.

The mechanism is worth understanding in plain terms. The AI model — the part that does the reasoning — is the same kind of technology as before. What changed is what it is connected to. These newer systems pair the model with tools and computer access: the ability to browse, read and write files, run programs, use apps, and check its own output. The loop of "think, act, look at what happened, think again" is what lets a single instruction turn into an extended piece of work rather than a single reply. People in the field call this an agentic system, meaning the AI has some agency — it decides on next steps instead of waiting for you at each one.

Who this is for is broader than you might expect. Mollick is a business school professor who writes about AI for general audiences, and his point is aimed at regular knowledge workers, not programmers. If your job involves drafting documents, researching topics, pulling together analyses, preparing presentations, or working through multi-step projects, the claim is that you can now hand an AI a substantial chunk of that work — the kind you might previously have spent an afternoon on — and get a first pass back in one go. The practical difference from chat is delegation rather than consultation: instead of asking the AI questions while you do the work, you describe the outcome you want and review what it produces.

That said, honest limits apply. The observation that these systems can do hours of equivalent work is an argument about capability, not a guarantee of quality on your particular task. An agent that runs for a long time unsupervised can also run wrong for a long time — pursuing a bad interpretation of your instruction, or producing output that looks polished but contains errors you still have to catch. The work shifts from doing the task to specifying it clearly and reviewing the result carefully, which is real skill and real time, just less of it. Mollick's framing does not pin down exactly which tools deliver this best or what they cost; the claim is about the category, not a product recommendation.

On whether this is real today: yes. This is shipping technology, not a research proposal or a prediction about next year. Agentic AI systems are available now and are already in use. The open questions are more about fit than existence — how much supervision a given task needs, where errors tend to hide, and which kinds of work delegate well. Tasks with clear success criteria and output you can verify tend to work better than tasks where quality is a matter of taste.

The useful mental model is the one the observation implies: treat these systems less like a search box and more like a capable but literal-minded colleague you can brief and send off. The better you can describe what done looks like, the more of the hours this actually saves.