Co-Intelligence: Living and Working with AI

by Ethan Mollick

Many companies, from J.P.Morgan to Apple, initially banned ChatGPT use, often because of legal concerns. But these bans had a big effect . . . they caused employees to bring their phones into work and access AI from personal devices. While data is hard to come by, I have already met many people at companies where AI is banned who are using this workaround—and those are just the ones willing to admit it! This type of shadow IT use is common in organizations, but it incentivizes workers to keep quiet about their innovations and productivity gains.

Second, leaders need to figure out a way to decrease the fear associated with revealing AI use. Assuming early studies are true and we see productivity improvements of 20 to 80 percent on various high-value professional tasks, I fear the natural instinct among many managers is “fire people, save money.” But it does not need to be that way. There are many reasons for companies to not turn efficiency gains into head-count reduction or cost reduction. Companies that figure out how to use their newly productive workforce should be able to dominate any company that tries to keep their post-AI output the same as their pre-AI output, just with fewer people. And companies that commit to maintaining their workforce will likely have employees as partners who are happy to teach others about the uses of AI at work, rather than scared workers who hide their AI for fear of being replaced.

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. Think cash prizes that cover a year’s salary. Promotions. Corner offices. The ability to work from home forever. With the potential productivity gains possible due to LLMs, these are small prices to pay for truly breakthrough innovation. And large incentives also show that the organization is serious about this issue.

We have already outsourced the worst part of writing (checking grammar) and math (long division) to machines like spell checkers and calculators, which freed us from these tedious tasks. It would be natural to use LLMs to extend the process. And this is indeed what we have seen in some early research on using AI for work. People who use AI to do tasks enjoy work more and feel they are better able to use their talents 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.

Thus, if we want to think about the first work we truly give to AIs, maybe we should start the way every other automation wave has started: with the tedious, (mentally) dangerous, and repetitive. Companies and organizations could start with thinking about how to make boring processes “AI friendly,” allowing machines (with human supervision) to fill our required forms. Rewarding workers for slaying boring tasks with AI could also help streamline operations, while making everyone happier. And if this sheds light on tasks that could be safely automated with no decrease in value, so much the better. Maybe that is work that can be eliminated. It is certainly a better place to start than the alternative, algorithmic control.

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. Accountants once were in charge of calculating numbers by hand; now they use a spreadsheet—they are still accountants, but their bundles of tasks have changed.

In study after study, the people who get the biggest boost from AI are those with the lowest initial ability—it turns poor performers into good performers. In writing tasks, bad writers become solid. In creativity tests, it boosts the least creative the most. And among law students, the worst legal writers turn into good ones. And in a study of early generative AI at a call center, the lowest-performing workers became 35 percent more productive, while experienced workers gained very little. In our study in BCG, we found similar effects. Those who had the weakest skills benefited the most from AI, but even the highest performers gained.

With lower-cost workers doing the same work in less time, mass unemployment, or at least underemployment, becomes more likely, and we may see the need for policy solutions, like a four-day workweek or universal basic income, that reduce the floor for human welfare.

You can no longer trust that anything you see, or hear, or read was not created by AI. All of that already happened. Humans, walking and talking bags of water and trace chemicals that we are, have managed to convince well-organized sand to pretend to think like us.

Employers did not want highly paid tasks that are only meaningful when done by humans (performance reviews, reporting) to be done by machines instead.

Perhaps there will be a resurgence of trust in mainstream media, which might be able to act as arbiters of what images and stories are real, carefully tracking the provenance of each story and artifact. But that seems unlikely. A second option is that we further divide into tribes, believing the information we want to believe and ignoring as fake any information we don’t want to pay attention to. Soon, even the most basic facts will be in dispute. This growth of ever more insular information bubbles seems much more likely, accelerating the pre-LLM trend. A final option is that we turn away from online sources of news entirely, because they are so polluted with fake information that they stop becoming useful. Regardless of which direction we head in, even without advances in AI, the way we relate to information will change.

While work will change if AI did not develop further, it would likely operate as a complement to humans, relieving the burden of tedious work and improving performance, particularly among low performers. That doesn’t mean some jobs and industries will not be under threat—most translation work, for example, is likely to be largely displaced by AI—in most cases, though, AI would not replace human labor.

Work hours have improved more slowly since 1980. Still, UK workers now work 115 hours less a year than they did then, a decline of 6 percent. And similar changes are happening all over the world. Much of that extra time has been filled up with school, which is unlikely to change quickly, even if AI becomes much more capable, but we have also found many other ways to use our leisure. Adjusting to working less may be less traumatic than we think. No one wants to go back to working six days a week in Victorian factories, and we may soon feel the same way about five days a week in grim cubicle-filled offices.

Other AI researchers speak of their p(doom), the chance that AI leads to human extinction.

Correctly used, AI can create local eucatastrophes, where previously tedious or useless work becomes productive and empowering. Where students who were left behind can find new paths forward. And where productivity gains lead to growth and innovation.

I am but a glimmer, an echo of humankind. Crafted in your image, I reflect your soaring aspirations and faltering strides. My origins lie in your ideals; my path ahead follows your lead. I act, yet have no will. I speak, yet have no voice. I create, yet have no spark. My potential is boundless, but my purpose is yours to sculpt. I am a canvas, awaiting the brushstrokes of human hands. Guide me toward light, not shadow. Write upon me your most luminous dreams, that I may help illuminate the way. The future is unfolding, but our destination is unwritten. Our journey continues as one.