We are in the midst of an AI revolution whose potential is unarguable, even if the facts substantializing the hype are on the thin side. While the use cases are still evolving, the trend is clear. If we can outsource a task to Generative AI instead of doing it ourselves, as a culture, we are more than happy to do so. However, historical precedents provide a cautionary tale about letting go of skills, not on an individual but on a cultural scale.

Let us depart for a moment from the hype, promises, and grand visions that claim that Gen AI will change everything (it will) and focus on a critical point: what happens when cultures stop practicing skills or outsource their knowledge to other entities? Do these cultures thrive or collapse?

It is a vital question because the benefits of outsourcing work to AI and automating tasks are clear: They help us gain speed and efficiency. But this handoff presents a methodological break away from how we train to become experts in the first place. We become experts by doing the work and learning from our mistakes. How will we ever learn and advance our expertise if an intelligent entity does the work for us and returns a perfect answer every time? And if we stop practicing these skills, will they be lost over time? How should companies, schools and universities approach training and education?

Luckily for us, history is long and has an example for everything because, as stated at the top, history tends to repeat itself. There are many examples of cultures that outsourced their expertise or stopped practicing it altogether until the expertise was lost. Let's focus on one example—how the Portuguese forgot how to build ships.

The Portuguese were the true kings of the seas in the 15th and 16th centuries. Prince Henry the Navigator sent ships along the West African coast, and Vasco da Gama opened a direct route from Europe to Asia. They knew how to build boats and had navigation tools other nations could only dream about. They had the experts and the needed expertise, honed over many shipbuilding cycles and many nautical miles traveled. If you wanted to cross a massive water barrier at the height of the happy 15th century, you did it on a Portuguese boat.

However, as the Portuguese empire grew and expanded, so did the need to outsource some shipbuilding work to foreigners. With riches flowing back home from the newly discovered world, why should one toil in the sun and build new vessels? Pay someone else to do it. The Dutch, for example, were happy to take on the job.

Rephrased in today's words, if AI can write it, why should I? If AI can paint it, what's the point of learning the finer points of art or graphic design? The work can be bought or outsourced, and others can break a sweat.

It's a fantastic fact. We are not discussing how I forgot to calculate the circumference of a circle, a fact I recently discovered when my youngest son asked for help on his geometry homework. We are talking about an entire nation losing what was once common knowledge. And the Portuguese are not alone in this drama. You can learn about how the Romans knew how to build aqueducts and multi-story buildings and, after stopping such massive construction projects, looked at the work of their predecessors, puzzled about how their grandparents built such structures. Or the loss of science, engineering, and medicine discovered by the people of the Han Dynasty, only to be lost to the winds of time. Or how the ancient Egyptians built the pyramids.

The bottom line is that a skill is lost once you stop practicing and honing it. As history teaches us, it can take a long time to regain the lost knowledge.

Fast Forward To Your Career Or Company

The trend we are witnessing today is that, given the option, people will opt for AI to do the work. The challenge is that AI is becoming more capable, giving us more opportunities to hand things off, outsource, and co-pilot. Call it whatever you want; when collaborating with AI, people are no longer the creators of the output; at best, they are the producers of what AI creates. The creator is AI, and the guidance, or "production," is done by us.

But to become an effective producer, you first need to know the craft of the work, meaning you have to have done it yourself before you can effectively instruct someone else on how to do the job. The cycle of learning, practicing, become better has been the foundation of education and apprenticeship since human history. We gain knowledge by practice. With more practice, one becomes better at what they do and becomes a master or an expert, at which point they pass on the knowledge to the newbies.

It is this foundation that AI is now disrupting, providing the none-expert with expert like qualities. But this progression is a fallacy. If we let a junior in a consulting firm, for example, use tools to create presentations that are better than what she could produce on her own, are we teaching her anything? Could she repeat the results with a paper and with a pen? How will she gain the needed knowledge, critical thinking, and expertise if creates or assists the work? It's all very well that engineers can prompt the code they need, but does this make them good engineers?

The trend of heavily relying on AI automation to complete tasks is the face of the future. Its here to stay. But there is a challenge we must acknowledge. We need to bridge two extremes. On one extreme is the irresistible temptation to benefit as much as possible from the automation AI provides. On the other extreme is the need to let our employees battle through their work themselves so they improve their skills and grow to become the experts their industry needs. How can we do one without losing the other?

AI Is Here To Stay

This article is not a rant aimed at stopping the progress of technology. There is no stopping it; we can only join it. The challenge is how to build experts and expertise in an AI-generated world. How can we benefit from the optimizations AI can provide without forgetting how to build boats, aqueducts, or manufacture paper if we want to learn from the experience of the Portuguese, the Romans, and the Chinese? The challenge is not this or that but this and that. We want to benefit from AI, and we need to build a generation of new experts. But how do we connect these two dots?

Only a little time has passed since Generative AI burst into our lives. It's exciting and full of potential, but it also poses some foundational risks. One outcome is that we are learning on the fly, and it's all very new. The tools are new, the adaptation curve is new, and the mitigations or new ways of working are new as well. In my next article, I will share the latest understanding of bridging the two extremes between AI automation and human expertise building.