Ethan Mollick

Consumer AI has arrived. And with it, inescapable upheaval as we grapple with what it means for our jobs, lives and the future of humanity.

Cutting through the noise of AI evangelists and AI doom-mongers, Wharton professor Ethan Mollick has become one of the most prominent and provocative explainers of AI, focusing on the practical aspects of how these new tools for thought can transform our world. In Co-Intelligence, he urges us to engage with AI as co-worker, co-teacher and coach. Wide ranging, hugely thought-provoking and optimistic, Co-Intelligence reveals the promise and power of this new era.

Living and Working with AI

Co-Intelligence

Bastien Vaucher

leaders need to figure out a way to decrease the fear associated with revealing Al 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-Al output the same as their pre-Al 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 Al at work, rather than scared workers who hide their Al for fear of being replaced.

So human biases also work their way into the training data. First, much of the training comes from the open web, which is nobody's idea of a nontoxic, friendly place to learn from. But those biases are compounded by the fact that the data itself is limited to what primarily American and generally English-speaking Al firms decided to gather. And those firms tend to be dominated by male computer scientists, who bring their own biases to decisions about what data is important to collect. The result gives Als a skewed picture of the world, as its training data is far from representing the diversity of the population of the internet, let alone the planet.

Companies have been experimenting with forms of computerized control over workers since long before this generation of AI. Time clocks, cameras, and other forms of monitoring have been common for over a century, but these approaches kicked into high gear with the rise of pre-LLM Al, and especially the use of algorithms to control work and workers.

We could imagine how LLMs might supercharge this process, creating an even more comprehensive panopticon: in this system, every aspect of work is monitored and controlled by AI. Al tracks the activities, behaviors, outputs, and outcomes of workers and managers. Al sets goals and targets for them, assigns tasks and roles to them, evaluates their performance, and rewards them accordingly

This doesn't mean we shouldn't teach about Al in schools. It is critical to give students an understanding of the downsides of Al, and the ways it can be biased or wrong or can be used unethically. However, rather than distorting our education system around learning to work with Al via prompt engineer-ing, we need to focus on teaching students to be the humans in the loop, bringing their own expertise to bear on problems.

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