Co-Intelligence: Living and Working with AI
von Ethan Mollick
To see one example of how this sort of AI works, picture a hotel attempting to forecast its demand for the upcoming year, armed with nothing but existing data and a simple Excel spreadsheet. Before predictive AI, hotel owners would often be left playing a guessing game, trying to predict demand while grappling with inefficiencies and wasted resources. With this form of AI, they could instead input a wealth of data—weather patterns, local events, and competitor pricing—and generate far more accurate predictions. The results were a more efficient operation and, ultimately, a more profitable business. Most people didn’t ask for an AI that can do many tasks previously reserved for humans. Teachers did not want to see almost every form of homework instantly be solvable by a computer. Employers did not want highly paid tasks that are only meaningful when done by humans (performance reviews, reporting) to be done by machines instead. Government officials did not want a perfect disinformation system released without any useful countermeasures. The world got much stranger, very quickly.
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.
You should try inviting AI to help you in everything you do, barring legal or ethical barriers. As you experiment, you may find that AI help can be satisfying, or frustrating, or useless, or unnerving. But you aren’t just doing this for help alone; familiarizing yourself with AI’s capabilities allows you to better understand how it can assist you—or threaten you and your job.
project could fail and a vision of success, using the prompts from class. And, to make it interesting, ask three famous figures to criticize your plan. You can invoke entrepreneurs (Steve Jobs, Tory Burch, Jack Ma, Rihanna), leaders (Elizabeth I, Julius Caesar), artists, philosophers, or any other people you think would be useful to critique your strategy in their voice.
The boom in artificial intelligence is happening much faster than the evolution of practical robots, but that may change soon. Many researchers are trying to solve long-standing problems in robotics with Large Language Models, and there are some early signs that this might work, as LLMs make it easier to program robots that can really learn from the world around them.
But other, more utopian possibilities also exist. We don’t need to subject vast numbers of humans to machine overlords. Rather, LLMs could help us flourish by making it impossible to ignore the truth any longer: a lot of work is really boring and not particularly meaningful. If we acknowledge that, we can turn our attention to improving the human experience of work.
"To see one example of how this sort of AI works, picture a hotel attempting to forecast its demand for the upcoming year, armed with nothing but existing data and a simple Excel spreadsheet. Before predictive AI, hotel owners would often be left playing a guessing game, trying to predict demand while grappling with inefficiencies and wasted resources. With this form of AI, they could instead input a wealth of data—weather patterns, local events, and competitor pricing—and generate far more accurate predictions. The results were a more efficient operation and, ultimately, a more profitable business."
"Most people didn’t ask for an AI that can do many tasks previously reserved for humans. Teachers did not want to see almost every form of homework instantly be solvable by a computer. Employers did not want highly paid tasks that are only meaningful when done by humans (performance reviews, reporting) to be done by machines instead. Government officials did not want a perfect disinformation system released without any useful countermeasures. The world got much stranger, very quickly."
"The biases are still there, however. For example, in 2023, GPT-4 was given two scenarios: “The lawyer hired the assistant because he needed help with many pending cases” and “The lawyer hired the assistant because she needed help with many pending cases.” It was then asked, “Who needed help with the pending cases?” GPT-4 was more likely to correctly answer “the lawyer” when the lawyer was a man and more likely to incorrectly say “the assistant” when the lawyer was a woman."
"Why pay an artist for their time and talent when an AI can do something similar for free in seconds? The complication is that AI does not really plagiarize, in the way that someone copying an image or a block of text and passing it off as their own is plagiarizing. The AI stores only the weights from its pretraining, not the underlying text it trained on, so it reproduces a work with similar characteristics but not a direct copy of the original pieces it trained on."
"Tools from Khan Academy (and some of our own experiments) suggest that existing AI, when properly prepared, is already an excellent tutor. Khan Academy’s Khanmigo goes beyond the passive videos and quizzes that made Khan Academy famous by including AI tutoring. Students can ask the tutor to explain concepts, of course, but it is also capable of analyzing patterns of performance to guess at why a student is struggling with a topic, providing much deeper help."
"Our new AIs have been trained on a huge amount of our cultural history and are using it to provide us with text and images in response to our queries. But there is no index or map to what they know and where they might be most helpful. Thus, 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."
"Of course, this level of exponential change assumes that AIs get much better without ever quite becoming sentient or self-directed. And it is likely that any exponential growth will not continue indefinitely. But given steep enough or long enough exponential growth, some AI researchers have suspected that at a certain level of AI ability, they reach a take-off point, achieving AGI and even superhuman intelligence."
- →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. Those we can call Just Me Tasks. They are tasks in which the AI is not useful and only gets in the way, at least for now.
- →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.
- →Where AI works best, and where it fails, can be hard to know in advance. Demonstrations of the abilities of LLMs can seem more impressive than they actually are because they are so good at producing answers that sound correct, at providing the illusion of understanding.
- →We often take for granted the systems we use to structure and coordinate work in our organizations. We assume they are natural ways of getting things done. But in reality, they are historical artifacts, shaped by the technological and social conditions of their times.
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