AI Won’t Take Your Job if You Know About IA

Love it or hate it, artificial intelligence (AI) is here, and it’s not going away. As the technology evolves, AI will only become more prominent in our everyday interactions, shaping everything from how students learn to the work employees do at the office.

The fear of artificial intelligence replacing human intelligence in white-collar jobs comes, in part, from the attention-grabbing headlines of skilled employees being at risk of unemployment as more companies implement AI into their work processes.

The less exciting but more likely reality is that the changes AI brings to the workplace will entail upskilling — when workers learn new skills — not reskilling or complete replacement. And upskilling is not about surrendering to AI but instead about mastering intelligence augmentation, or IA, which is what happens when humans and AI work together to accomplish more as a team than either could flying solo. That collaboration is based on the distinction between two concepts: reckoning and judgment.

Reckoning vs. Judgment

A critical difference between AI and humans is our primary mode of operation. AI operates through what is often referred to as “reckoning,” such as calculative prediction. By that, we mean AI’s true capabilities are grounded in facts and historical knowledge — data that can be calculated, memorized, and repeated. It makes predictions based on what it knows.

By contrast, humans operate through judgment or practical wisdom. We understand things AI can’t possibly know. That’s because we humans have lived experiences that continually inform how we see the world. It’s the intangible factors, like ethical considerations and empathetic responses, that make humans better equipped than AI to make complex decisions about human affairs.

This practical wisdom is one reason humans must keep creating new things for IA to advance. Imagine what would happen if humans stopped writing original content. The current model of AI could never pass a certain point because it would never encounter new, innovative ideas. Eventually, AI would run out of new ideas to share, and our collective knowledge would stagnate. AI is like moonlight; its ideas come from the reflected sunlight of human insights.

Now, it’s essential to recognize that this contrast between AI and humans is nothing to fear. Instead, it shows us why “human + AI” is an ideal work partnership. AI can perform massive calculations in the blink of an eye at a pace far superior to even the most intelligent human on the planet. When paired with a human capable of making quick decisions based on lived experience, judgment, and practical wisdom, it’s an immensely more powerful tool than it is on its own.

Upskilling and Unlearning

As AI becomes more prevalent in the workforce, upskilling will be a primary pathway to career development and advancement. AI can enhance human judgment and decision-making, but it is important to recognize its limitations and not overestimate its capabilities.

A contemporary example of humans and AI collaboration comes from our favorite genre, science fiction. We’re talking, of course, about Captain Picard from Star Trek: The Next Generation and Data, the android AI who looks like a human.

It’s evident that Data can do things Picard cannot in terms of scanning enormous amounts of data to make elaborate predictions and forecasts. But Data’s toolkit has considerable holes. His weaknesses (namely, his lack of ability to think, feel, and judge like a human) are why Data remains in his role as a subordinate to Picard, the captain.

While a product of science fiction, this example illustrates an idealized version of how humans and AI can work together to achieve IA.

The upskilling work humans need to undertake lies in the area of judgment: anyone looking for their first or next job should double down on their skills and abilities in the areas that AI cannot touch. They need to develop their durable skills — skills that will last a lifetime, such as creativity and teamwork — and embody the role of Captain Picard. Employers will seek out employees who have the insightful skills their AI tools lack so they can effectively augment AI’s calculative abilities.

However, humans must “unlearn” current practices and turn over reckoning to AI. It might feel uncomfortable to pass some tasks off to AI, especially initially, and you might not trust it. But you will need to unlearn the idea that you, as a human, are the only one capable of doing your job. Instead, you’ll need to take on AI as your trusted partner.

Learning and Demonstrating IA

No matter their career path, today’s students and employees will undoubtedly work with AI in the future. A question that may determine their success or failure is “How will educators and employers prepare them?”

Effective upskilling includes three stages: direct instruction, simulations, and internships/shadowing. Direct instruction is simply adding content knowledge through classic instruction or training. Simulations help you understand how the content knowledge is applied within a context, and interning or shadowing exposes you to the detailed aspects of work that you can't experience in a classroom or a simulation. 

An example that underlines the importance of each stage is an airplane pilot learning to fly a new type of plane. The first thing you do is you learn about the new plane: the automatic pilot now has this kind of control mechanism, the engines have this much more power, it has a longer range but it's less maneuverable. 

Once you know the facts and figures, you’re ready for the flight simulator. This is where you get used to the change in the controls, how the plane handles, and emergency procedures that work differently in the new plane than in the old plane.

Finally, you fly the new plane with an instructor pilot — which is the equivalent of an internship or job shadowing. Upskilling for pilots requires all three stages. No one would certify any pilot training that left out any of the three, and yet in a lot of our current job preparation, the simulation is left out, which is like asking people to jump right from learning the horsepower to flying solo.

Simulations also allow individuals to hone their durable “soft” skills, apply what they’ve learned, and prepare for exercising judgment in various fields, such as negotiations. In real life, just as you can’t re-land the plane after a crash, you can’t go back in time to renegotiate when you’re unhappy with the outcome. With a digital simulation, though, you can repeat the negotiation process repeatedly, trying different methods to figure out what does and doesn’t work — and, more importantly, why.

Through repetition, students can improve their decision-making skills without any of the dangers of real life. These skills are as essential for an eager entry-level office worker getting their first job out of college as they are for a football quarterback. The difference between a good quarterback and a great one isn’t athletic ability — it’s rapid decision-making, which is learned through repetition and application.

Educators and employers can empower their students and workforce by providing simulated experiences to practice durable skills and apply what they learned to real-world situations. 

Learning and showcasing durable skills, such as critical thinking and problem-solving, has become even more critical in a world where AI plays a significant role and IA is the gold standard. As the job market continues to evolve, students going into the marketplace who possess a combination of durable and hard skills and the ability to collaborate effectively with AI will be well-positioned for career success.