Matt Beane is an assistant professor in the technology management department at UC Santa Barbara and a digital fellow with Stanford’s Digital Economy Lab. His research focuses on building skills in a world filled with intelligent technologies, often necessitating field work investigating robots and AI in the workplace. He has been published in Administrative Science Quarterly and Harvard Business Review and has spoken on the TED stage. Matt also helped found and fund Humatics, an MIT-connected, full-stack IoT startup.
Below, Beane shares five key insights from his new book, The Skill Code: How to Save Human Ability in an Age of Intelligent Machines. Listen to the audio version—read by Beane himself—in the Next Big Idea App.
1. The 160,000-year-old school hidden in plain sight
Consider Athens, 507 BC. Twelve-year-old Menelaos begins his second year as apprentice to Stephanos, the master sculptor. Today, he walks to the carpenter’s workshop for lumber. Then to the brass smith for pins and braces. He brings it all back and keeps it organized as the senior boys finish the scaffolding for a new piece. All day, he hauls blocks of marble around the workshop, directed by the senior boys, who take their cues from Stephanos. As the sun goes down, he’s cleaning up after everyone.
Throughout, he’s been watching. Noticing the marble scraps and bent tools. Listening as they told stories and talked technique. Asking a question or two while he did his work. Next year, if he works hard, he’ll be splitting the marble, keeping tools organized and sharp, and learning about the next tasks up the apprenticeship chain—roughing out blocks, negotiating for supplies, talking to customers. Six years later, he’ll be carving his first solo work on the city’s outskirts, with apprentices looking up to him. And six years later, he will be carving his first solo work in his own studio on the outskirts of the city, with new apprentices looking up to him. This is all likely true, by the way: We have one of his masterworks, a marble statue of Orestes and Electra, signed “Menelaos, the pupil of Stephanos.”
When it comes to skill—ability we can rely on under pressure—this expert-novice bond has been the foundation of skill development for millennia. In fact, the archaeological evidence is pretty clear that it’s been in place just about as long as we’ve had language: about 160,000 years.
2. The skill code
Over the last 12 years of my research, I’ve found the hidden code that makes the expert-novice relationship so powerful. When I say “code,” I’m talking about something like the DNA of how we learn our most valuable skills. That working relationship between experts and novices is a bundle of three Cs humans need to develop mastery: challenge, complexity, and connection. Work near your limits, engage with the bigger picture, and build bonds of trust and respect. Like the four amino acids are to genetics, the three Cs are the basic building blocks of learning valuable skills. Look back, and you will find them embedded in Menelaos’s story. You’ll find them in your own journey to mastery and in how you’ve helped others build mastery.
Just as knowing the building blocks was only the beginning in genetics, so it is just the beginning with skill. Challenge, complexity, and connection must occur in certain healthy, sometimes counterintuitive, ways to produce reliable skill. Sometimes, these follow specific sequences that we’re used to—that map with our beliefs of how skill development happens. But our world is changing. New sequences are emerging, others are dying off, and one size doesn’t fit every person, occupation, or organization. Knowing this skill code empowers us not just to re-create the 160,000-year-old school, but to identify and preserve healthy skill building in any form it might take in this dizzying, modern world.
3. We’re breaking the best school we’ve got
If we don’t put knowledge of the skill code to use right now, our species is in deep trouble; we’re handling intelligent technologies in ways that subtly degrade human ability.
In millions of workplaces, we’re blocking the ability to master new skills because we are separating junior workers from senior workers (novices from experts) by inserting technology between them. In a grail-like quest to optimize productivity, we are disrupting the components of the skill code, taking for granted the necessary bundling of challenge, complexity, and connection that could help us build the skill we need to work with intelligent machines.
Let’s visit Kristen in the OR to see how this is playing out. Six months after her open surgical rotation, she wheels a prostate patient into the operating room where a four-armed, thousand-pound robot is waiting. The attending surgeon attaches the robot to the patient. Then they both rip off their scrubs and head to control consoles 15 feet away to do the whole operation “remotely.”
Kirsten just watches as her attending manipulates the robot’s arms, retracting and dissecting tissue. Just like many intelligent technologies, the robot allows him to do the work himself, so he basically does. He knows Kristen needs practice; he wants to give her control. But he also knows she would be slower and make more mistakes. So, she barely gets to try. No chance she’s a better surgeon after this procedure. I have top-quality data on this problem from all sectors of the global economy, and the same goes for hundreds of millions of us around the world. This is a multitrillion-dollar problem.
4. Learning from the shadows
The Skill Code is not a “sky is falling” book. I bring good news from the front lines. I’ve gone to great lengths to find people who are defying the odds and getting good results. Faced with the erosion of the expert-novice bond, some people are finding a new way that the rest of us and our organizations can learn from.
Take Beth, another surgical resident who—on paper—was in the same spot Kristen was in. They were in the same top hospital. She came from a similar medical school with similar courses. She had the same attending surgeon, similar patients, and the same formal training in robotic surgery. But right away, I could tell Beth didn’t feel frustrated, bored, or shut out of the learning process like Kristen did. That’s because she wasn’t; every time she came into the OR, the attending let her operate between 10 and 50 times as long as Kristen. Where Kristen looked like a baby foal learning to walk, Beth was good.
Beth was a bit of a rebel, but one with a cause: skill. The approved way to learn robotic surgery didn’t work. People like Beth intuitively find rule-bending ways to build skill anyway. In her case, this means watching tons of surgery on YouTube and operating on patients with limited or no supervision. . . . Cringing yet? I called this “shadow learning” for a reason.
But at a high level, shadow learning gets results for workers who could be shamed or fired for it. It has to work to be worth the risk. Their tactics—and the skill code that underwrites them—are crucial for the road ahead.
5. Reworking the skill code
We need to go much further than simply retrenching to protect the skill code. Intelligent technologies can—and in many places, must—be part of the skill solution. You may be tempted to conclude we’re living in a “John Henry” moment, where it’s techno-productivity versus human ability. However, my findings show that we can transcend this dilemma: in many cases, we can get new, breathtaking results by augmenting the skill code with intelligent technologies.
Technology itself is not causing any of the problems we’re experiencing with skill. We’re the ones striking a deal with our increasingly intelligent tools: offer us techno-enhanced productivity, and we’ll sacrifice the expert-novice bond. We must realize that such a deal is optional.
To supercharge skill development to levels yet unseen in the history of our species, we need a new, always-on, accessible, globe-scale infrastructure for ensuring healthy challenge, complexity, and connection. Humans alone aren’t capable of something so complex. We need the best of human and nonhuman intelligences to make that vision manifest.
Right now, we could have systems that coach us toward more productive outcomes. For instance, turning to ChatGPT for help with a persuasive email. We might write the first prompt, and the system would reply, “I’ve got to ask you a couple of questions to make sure this gets you the sale. And by the way, do you want to get better at this by the time the email’s done or get an intro to an expert on this? That would only take a few extra minutes.” No human in the loop. Just an AI-enabled engine with two objectives: get the user the desired results and nudge them toward more skill.
Thoreau wrote, “Love must be a light, as much as it is a flame.” I know we both love humanity, and I hope these insights shed light on how to save human ability in the age of intelligent machines and also light a flame for you to get out there and do something about it.
This article originally appeared in Next Big Idea Club magazine and is reprinted with permission.
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