This week we are leaning into multiple metaphors: AI as a mirror, UX design as a window or room, and life as information. Plus: read about Michael Levin's upcoming presentation at the Imagining Summit, Helen's Book of the Week, and our upcoming events.
It’s easy to fall prey to the design illusion that because LLMs look sleek, they must be well-designed. But aesthetics alone do not equal design. As Steve Jobs once said, “Design is not just what it looks and feels like. Design is how it works.”
It’s curious that these two papers, tackling such similar ideas, came out at the same time. Is this coincidence, or does it tell us something about where the study of life and intelligence is heading?
In this issue, we dig into the question of how AI affects expertise and skills: how AI affects mastering a complex field like coding, preserving human expertise in the age of AI, and a conversation with Matt Beane about his book, The Skill Code.
The way we develop expertise is changing. Traditionally, mastering a complex field involved years of rigorous learning, collaboration, and problem-solving with others. This process was often difficult and frustrating, yet it was essential. Now, AI offers a shortcut and this ease may come at a cost, not only in terms of individual skill development but also in the loss of collective intelligence and collaborative problem-solving.
New research highlights that mental effort is inherently aversive—it's hard and often unpleasant. However, this struggle is what pushes people to engage deeply with their work, develop critical thinking skills, and learn to work effectively with others. When AI takes over, it risks isolating individuals from the collaborative experiences that have historically been crucial for building expertise. The collective brainstorming, troubleshooting, and shared victories that come from joint problem-solving are irreplaceable parts of the learning process.
For instance, consider coding. Coders often work together, debugging complex issues, sharing insights, and learning from one another's mistakes. Collaborative effort builds not just individual skills but also a shared understanding and a collective intelligence that is greater than the sum of its parts. When AI takes over debugging or code writing, it removes the need for this teamwork, leading to a more isolated learning experience where individuals might produce code but fail to develop a deep understanding of programming logic or the ability to anticipate issues.
This meme captures the shift in the developer's role brought about by AI tools like ChatGPT. Instead of engaging in the creative problem-solving process of coding, developers are editors and fixers of AI-generated code, which means spending more time debugging than actually writing original code. The challenge has shifted from creation to correction, making it clear that while AI can speed up the initial coding process, it may also lead to more complex and time-consuming debugging tasks.
Similarly, in law, junior lawyers traditionally learn by collaborating with peers and mentors, analyzing legal documents, debating interpretations, and collectively developing strategies. This joint problem-solving is crucial for building the analytical rigor and deep expertise necessary for successful legal practice. When AI handles the bulk of the research or document analysis, it may lead to quicker results but at the cost of losing the collaborative learning and the personal growth and joint understanding that comes from working through problems together.
As we integrate AI into complex fields, we have to design new ways of preserving the friction that makes learning hard and the collaboration that makes it meaningful. The struggle and the teamwork are not just necessary evils: they are the foundation of true mastery. Without them, we risk losing the essence of what it means to be genuinely skilled and capable of working effectively in a community of practice.
Join Barbara Tversky, author of Mind in Motion and a pioneer in understanding the link between spatial reasoning and abstract thought, for a unique exploration of AI’s challenges. This session will explore what AI might struggle with in the physical world, starting with something as everyday as gesturing. Barbara will reveal how gestures are integral to our thinking process—how we gesture even before we think, and how sitting on our hands hampers our ability to explain. This session highlights how our understanding of human cognition informs what we need to design AI that truly aids our thinking. It offers a fresh perspective on cognitive processes beyond language.
Check out the agenda for The Imagining Summit and send us a message if you would like to join us. We're excited to meet our Artificiality community in person!
The Skill Code: How to Save Human Ability in an Age of Intelligent Machines, by Matt Beane
AI is changing the traditional apprenticeship mode, altering how we learn and develop skills across industries. This is creating a tension between short-term performance gains and long-term skill development.
Dr. Matt Beane, an assistant professor at UC Santa Barbara and author of "The Skill Code," has studied this change. His research shows that while AI can significantly boost productivity it may be undermining critical aspects of skill development. Much of Beane’s work has been observing the relationship between junior and senior surgeons in the operating theater. "In robotic surgery, I was seeing that the way technology was being handled in the operating room was assassinating this relationship," he told us.
"In robotic surgery, the junior surgeon now sets up a robot, attaches it to a patient then heads to a control console to sit there for four hours and watch the senior surgeon operate." This scenario, repeated in hospitals worldwide, epitomizes a broader trend where AI and advanced technologies are reshaping how we transfer skills from experts to novices. See one, do one, teach one, is becoming See one, and if-you're-lucky do one, and not-on-your-life teach one, Beane writes.
Beane argues that three key elements are essential for developing expertise: challenge, complexity, and connection. "Everyone intuitively knows when you really learned something in your life. It was not exactly a pleasant experience, right?" Struggle matters in the learning process. Struggle doesn’t just build skills, it builds trust and respect with others, which is a critical aspect of how the entire system of human expertise works.
Select upcoming events where we'll be—join us!
The Artificiality Weekend Briefing: About AI, Not Written by AI
