Co-Intelligence: The Definitive, Bestselling Guide to Living and Working with AI

by Ethan Mollick

we will get even better quality if we remember the principle of telling AI who it is: You are an expert at marketing.

Another key aspect of idea generation is to embrace variance. Research shows that, to find good novel ideas,11 we likely have to come up with many bad novel ideas because most new ideas are pretty bad.

The AI will not naturally deliver novelty (as we discussed above, it tends to give the crowd-pleasing “average” answer that is most likely from its training data), but we can make it do so with a little work. We need to push the AI from an average answer to a high-variance, weird one. We can do this again by telling the AI who it is. Force it to give you less likely answers, and you will find more original combinations.

The results can be interesting as sources of inspiration (I like the idea of the virtual coffee workshops!) but still require a human in the loop to filter and select the best ideas. Yet it allows us to outsource some of the most difficult aspects of creativity.

AI can even turn nonprogrammers into coders, of a sort. I can’t write code in any modern language, but I have had AI write a dozen programs for me. The idea of programming by intent, by asking the AI to do something and having it create the code, is likely to have significant impacts in an industry whose workers earn a total of $464 billion a year.

AI is trained on vast swaths of humanity’s cultural heritage, so it can often best be wielded by people who have a knowledge of that heritage. To get the AI to do unique things, you need to understand parts of the culture more deeply than everyone else using the same AI systems.

we need people who have deep or broad knowledge of unusual fields to use AI in ways that others cannot, developing unexpected and valuable prompts and testing the limits of how they work. AI could catalyze interest in the humanities as a sought-after field of study, since the knowledge of the humanities makes AI users uniquely qualified to work with the AI.

every major office application and email client will include a button to help you create a draft of your work. It deserves capital letters: The Button. When faced with the tyranny of the blank page, people are going to push The Button.20 It is so much easier to start with something than nothing.

The implications of having AI write our first drafts (even if we do the work ourselves, which is not a given) are huge. One consequence is that we could lose our creativity and originality. When we use AI to generate our first drafts, we tend to anchor on the first idea that the machine produces, which influences our future work.

better solutions and insights. Another consequence is that we could reduce the quality and depth of our thinking and reasoning. When we use AI to generate our first drafts, we don’t have to think as hard or as deeply about what we write.

We also miss the opportunity to learn from our mistakes

We are going to need to reconstruct meaning, in art and in the rituals of creative work. This is not an easy process, but we have done it before, many times.

almost all of our jobs will overlap with the capabilities of AI.

Research by economists Ed Felten, Manav Raj, and Rob Seamans concluded that AI overlaps most1 with the most highly compensated, highly creative, and highly educated work. College professors make up most of the top 20 jobs that overlap with AI

So, regardless of its nature, your job is likely to overlap with AI in the near future. That doesn’t mean your job will be replaced. To understand why, we need to consider jobs more carefully, viewing them from multiple levels. Jobs are composed of bundles of tasks. Jobs fit into larger systems. Without considering systems and tasks, we can’t really understand the impact of AI on jobs.

Can AI take over some of these tasks? The answer is yes, and frankly, there are tasks that I wouldn’t mind offloading to AI, like administrative paperwork. But does that mean my job will vanish? Not really. Getting rid of some tasks doesn’t mean the job disappears.

AI has the potential to automate mundane tasks, freeing us for work that requires uniquely human traits such as creativity and critical thinking—or, possibly, managing and curating the AI’s creative output, as we discussed in the last chapter.

So let’s put AI into context and talk about what it can do at the level of tasks and systems. Tasks and the Jagged Frontier

It is a problem I see repeatedly when people first use AI: they just paste in the exact question they are asked and let the AI answer it. There is danger in working with AIs—danger that we make ourselves redundant, of course, but also danger that we trust AIs for work too much.

Dell’Acqua developed a mathematical model to explain the trade-off between AI quality and human effort. When the AI is very good, humans have no reason to work hard and pay attention. They let the AI take over instead of using it as a tool, which can hurt human learning, skill development, and productivity. He called this “falling asleep at the wheel.”

The future of understanding how AI impacts work involves understanding how human interaction with AI changes, depending on where tasks are placed on this frontier and how the frontier will change. That takes time and experience, which is why it is important to stick with the principle of inviting AI to everything, letting us learn the shape of the Jagged Frontier and how it maps onto the unique complex of tasks that comprise our individual jobs. With that knowledge, we need to be conscious about the tasks we are giving AI, so as to take advantage of its strengths and our weaknesses. We want to be more efficient while doing less boring work, and to remain the human in the loop while also addressing the value of AI. To do this well, we need a framework, where we divide our tasks into categories that are more or less suitable for AI disruption.

At the level of tasks, we need to think about what AI does well and what it does badly. But we also need to consider what we do well and what tasks we need to remain human.

Delegating the task to an AI, no matter how sophisticated, could risk losing that personal touch, and the process of writing helps us think. Or whatever the AI said. The third reason I won’t delegate my writing to an AI is the delicate issue of copyrights and the law. Right now, it is unclear whether the AI’s output is protected by copyright.

The key is to recognize the tasks that are meaningful and fulfilling for you as a human being and that you would rather not delegate or share with an AI system.

The next category of tasks is Delegated Tasks. These are tasks that you assign the AI and may carefully check (remember, the AI makes stuff up all the time), but ultimately do not want to spend a lot of time on. This is usually stuff you really don’t want to do and is of low importance, or time-consuming. The perfect Delegated Task is tedious, repetitive, or boring for humans but easy and efficient for AI.

The future of delegation will require further reductions in hallucination rates, and better transparency of AI decision-making, so that we can trust it more. The whole goal of delegation is to save us time and allow us to focus on tasks where we can be, or want to be, of value.

Then there are Automated Tasks, ones you leave completely to the AI and don’t even check on.

Until AIs become very good at a range of Automated Tasks, the most valuable way to use AI at work is to become a Centaur or Cyborg. Fortunately, this does not involve getting cursed to turn into the half human–half horse of Greek myth or grafting electronic gizmos to your body. They are rather two approaches to co-intelligence that integrate the work of person and machine. Centaur work has a clear line between person and machine, like the clear line between the human torso and horse body of the mythical centaur. It depends on a strategic division of labor, switching between AI and human tasks, allocating responsibilities based on the strengths and capabilities of each entity.

On the other hand, Cyborgs blend machine and person, integrating the two deeply. Cyborgs don’t just delegate tasks; they intertwine their efforts with AI, moving back and forth over the Jagged Frontier. Bits of tasks get handed to the AI, such as initiating a sentence for the AI to complete, so that Cyborgs find themselves working in tandem with the AI.

Using AI as a co-intelligence, as I did while writing, is where AI is the most valuable. Figure out a way to do this yourself if you can. As a starting point, follow the first principle (invite AI to everything) until you start to learn the shape of the Jagged Frontier in your work. This will let you know what the AI can do and what it can’t. Then start working like a Centaur. Give the tasks that you hate but can easily check (like writing meaningless reports or low-priority emails) to the AI and see whether it improves your life. You will likely start to transition naturally into Cyborg usage, as you find the AI indispensable in overcoming small barriers and helping with tricky tasks. At that point, you have found a co-intelligence. You also have to remember that AI is changing, and the boundaries between these task types are permeable and will likely shift as AI capabilities improve over time.

We know from research that when people learn they are receiving AI-created content, they judge it differently than if they assume it comes from a human.

At least for now, the best way for an organization to benefit from AI is to get the help of their most advanced users while encouraging more workers to use AI.

Second, leaders need to figure out a way to decrease the fear associated with revealing AI use.

Third, organizations should highly incentivize AI users to come forward, and expand the number of people using AI overall. That means not just permitting AI use but also offering substantial rewards to people finding substantial opportunities for AI to help.

Finally, companies need to start thinking about the other component of effectively using AI: systems. The pressure for organizations to take a stand on a technology that affects their most highly paid workers will be immense, as will the value of these workers becoming more productive. Without a fundamental restructuring of how organizations work, the benefits of AI will never be recognized.

People who use AI to do tasks enjoy work more and feel they are better able to use their talents12 and abilities. The ability to outsource crappy, meaningless tasks to the AI can be freeing. The worst parts of your job go to AI so that you get to focus on the good stuff.

As we have seen, it seems very likely that AI will take over human tasks. If we take advantage of all that AI has to offer, this could be a good thing. Boring tasks, or tasks that we are not good at, can be outsourced to AI, leaving good and high-value tasks to us, or at least to AI-human Cyborg teams. This fits into historical patterns of automation, where the bundles of tasks that make up jobs change as new technologies are developed.

Those who had the weakest skills benefited the most from AI, but even the highest performers gained. This suggests the potential for a more radical reconfiguration of work, where AI acts as a great leveler, turning everyone into an excellent worker.

In this case, the nature of jobs will change a lot, as education and skill become less valuable.

This suggests that there is something unique and powerful about the interaction between a tutor and a student that cannot be easily replicated by other means. So it is not surprising that a powerful, adaptable, and cheap personalized tutor is the holy grail of education. This is where AI comes in.

AI, however, is very good at summarizing and applying information. And it can now read PDFs. Or even entire books. This means that students will be tempted to ask the AI for help summarizing written content. Sure, the results can contain errors and simplifications, but even if they are correct, these summaries will shape a student’s thinking. Further, taking this shortcut may lower the degree to which the student cares about their interpretation of a reading, making in-class discussions less intellectually useful because the stakes are lower.

And, of course, AI has come for the king of assignments, the essay. Essays are ubiquitous in education, where they serve many purposes, from demonstrating how students think to providing an opportunity for reflection. But they are also really easy for any LLM to generate, and AI-based essays are getting better and better. At first, AI style was conspicuous, but newer models write in a less awkward and circular way and can easily be prompted to write in a style appropriate for a student. Plus, the problem of hallucinated references and obvious errors is now much less common and easy to catch. Mistakes are subtle, rather than obvious. References are real. Additionally, and most important: there is no way to detect5 whether or not a piece of text is AI-generated.

Every school or instructor will need to think hard about what AI use is acceptable:

So students will cheat with AI. But as we saw with user innovation earlier, they also will begin to integrate AI into everything they do, raising new questions for educators. Students will want to understand why they are doing assignments that seem obsolete thanks to AI. They will want to use AI as a learning companion, a coauthor, or a teammate.

But the moment isn’t just about preserving old types of assignments. AI provides the chance to generate new approaches to pedagogy that push students in ambitious ways.

Of course, all this raises the even bigger question: What should we actually teach?

the default output of many of these models can sound very generic, since they tend to follow similar patterns that are common in the written documents the AI was trained on. By breaking the pattern, you can get much more useful and interesting outputs. The easiest way to do that is to provide context and constraints,

it can be worth thinking about how you can provide a very clear and logical prompt to the AI.

it can help to give the AI explicit instructions that go step by step through what you want. One approach, called chain-of-thought prompting,11 gives the AI an example of how you want it to reason, before you make your request. Even more usefully, you can also provide step-by-step instructions that build on each other, making it easier to check the output of each step (letting you refine the prompt later), and which will tend to make the output of your prompts more accurate.

an AI tutor guides students through the academic terrain, offering real-time feedback, adjusting based on the student’s progress, and providing alternative learning paths when necessary. This analogy also emphasizes the idea that while the tool provides guidance, it’s up to the user (or student) to drive and make the journey, reinforcing the collaborative nature of learning with AI. Much improved, due to a little prompt engineering.