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

by Ethan Mollick

AI is what those of us who study technology call a General Purpose Technology (ironically, also abbreviated GPT). These advances are once-in-a-generation technologies, like steam power or the internet, that touch every industry and every aspect of life.

The search for high-quality content for training material has become a major topic in AI development, since information-hungry AI companies are running out of good, free sources.

That feedback is then used to do additional training, fine-tuning the AI’s performance to fit the preferences of the human, providing additional learning that reinforces good answers and reduces bad answers, which is why the process is called Reinforcement Learning from Human Feedback (RLHF).

Workers who figure out how to make AI useful for their jobs will have a large impact.

A common misconception tends to hinder our understanding of AI: the belief that AI, being made of software, should behave like other software. It is a little bit like saying humans, made of biochemical systems, should behave like other biochemical systems.

You are a cyborg because you use machines to enhance your abilities and extend your senses. You use computers to store and process information, phones to communicate and connect, cameras to capture and share images, and so on. You rely on machines to perform tasks and solve problems that you cannot do by yourself.

The issue is that we often mistake novelty for originality. New ideas do not come from the ether; they are based on existing concepts. Innovation scholars have long pointed to the importance of recombination in generating ideas. Breakthroughs often happen when people connect distant, seemingly unrelated ideas.

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.

Boredom is not just boring; it is dangerous in its own way.

Amara’s Law, named after futurist Roy Amara, says: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”

In field after field, we are finding that a human working with an AI co-intelligence outperforms all but the best humans working without an AI.

Think of how televisions get a bit better every year. You don’t need to throw out your old TV, but new ones are likely quite a bit better and cheaper than the one you bought a few years ago.

The burden of knowledge is increasing, in that there is too much to know before a new scientist has enough expertise to start doing research themselves.