The Hill Is the Point

Recently I was riding my bike up a long hill. I am not ashamed to admit I was struggling. My legs were burning and I was sucking wind pretty hard when an older man on an e-bike passed me. He moved by steadily and comfortably, without even breaking a sweat. For a moment, I felt jealous, he was moving faster, but I was working harder.

But as I have thought about it more, I realized this was not really a story about one bike being better than another. There is nothing wrong with e-bikes. In fact, it was probably exactly the right tool for him. It allowed him to ride, climb, travel farther, and enjoy the road in a way that would have been more difficult on a traditional bike.

But my bike was serving a different purpose. I am preparing for a multi-day bike trip through the mountains, so the hill was not just an obstacle between me and the top. It was part of the training. The resistance was the point. It was building the strength, stamina, and endurance I will need later. 

If my only goal had been to get up the hill as easily and quickly as possible, an e-bike would have been the better tool. It would have reduced the strain and made the ride more comfortable. But because my goal is to prepare my body for the mountains, removing the strain also removes part of the preparation.

I think this is the distinction we often miss when we talk about technology, AI, and digital tools. A tool can make something easier, and sometimes that is exactly what we need. It can extend access, reduce unnecessary barriers, support us when we are overwhelmed, and make tasks more efficient. But easier is not always the same as better. Sometimes the effort is not a problem to solve. Sometimes the effort is the point. 

A tool can get us to the top of the hill while bypassing the muscles we need to build.

We Have Confused Ease with Progress

This is not to say that digital tools and AI are inherently bad. Many of them are useful. The problem is that when the goal is learning, removing productive struggle can undermine the very development we are trying to support.. 

For most learning tasks, the primary goal is not just to complete the task, it is to develop new skills and abilities.  Some struggle or friction is necessary. For example, struggling to find the right words while explaining something helps clarify our understanding. Making a first attempt at a difficult task builds agency and confidence. Working through confusion help use discover wat we know and what we need to practice next. 

When every point of difficulty is removed, we may complete the task but fail to develop that capacity that task was meant to build..

Outsourcing Is Not the Problem; Unconscious Outsourcing Is

Humans are natural economizers. We tend to conserve mental and physical resources when we can, and much of human progress has depended on doing this wisely. We outsource memory to writing, navigation to maps, calculations to calculators, and physical labour to machines.

The problem is not outsourcing itself. The problem is outsourcing without asking what role the task plays in our development. Sometimes a tool removes an unnecessary burden. Other times, it removes necessary practice.

A calculator, for example, can be useful after a child has developed number sense. A summary tool can support a reader after they have first tried to understand the material. A GPS can make navigation easier, but total reliance on it may weaken spatial awareness. In each case, the issue is not whether the tool works. The issue is what happens when we no longer practice the underlying capacity.

The key question is: “What happens to me if I never learn to do this myself?”

If learning is treated as something to avoid or simply overcome, we miss the messy, uncertain, unfinished stage between not knowing and knowing. This is where many important capacities develop. Digital tools often shine at producing polished outputs, but human development usually happens before the output is polished. AI can produce a finished paragraph, but it cannot give the learner the experience of persisting through confusion, organizing ideas, and gradually learning how to make meaning.

Children Do Not Just Need Answers; They Need Practice

A student who receives an answer may successfully complete an assignment. A student who has to work their way through a problem has an opportunity to develop attention, strategy, self-awareness, and resilience.

Effective learning involves encountering and working through confusion and partial understanding. It requires revision, feedback, reflection and the ability to challenge one’ own thought processes.

This does not mean students should suffer through poorly designed tasks. It means that parents, schools and teachers need to protect the developmental value of effort. The goal of education is not merely to produce correct answers. It is to form people who can think, evaluate, adapt, and continue learning when answers are not immediately available.

The same capacity is at stake outside the classroom, because outsourcing rarely stays confined to homework. If a child cannot tolerate the discomfort of an unanswered question, they likely  will struggle to tolerate the discomfort of an empty afternoon, an awkward conversation, or a difficult emotion.  It is the same muscle, applied to a different kind of difficulty. 

This means the daily decisions parents face are not separate from the larger questions of technology. Should I entertain a bored child or let them sit in boredom for a while? Should I solve a social conflict or help the child work through it? Should I remove this discomfort, or is this a capacity building moment? 

Children do not become capable by having every obstacle removed. They become capable through supported encounters with manageable difficulty.

The New Literacy: Discernment, Not Refusal 

The new literacy is the ability to ask better questions before using a tool. The literate person of the future will not be the one who uses every tool available. It will be the one who knows which parts of being human should not be automated.

Some possible questions to ask about digital tool or AI use:

• Is this task meant to produce an output or build a capacity?

• Am I using the tool to support my thinking or replace it?

• Have I tried first?

• What part of this task is worth struggling through?

• What am I avoiding?

• What skill might weaken if I always delegate this?

• Would I still understand the result if the tool disappeared?

A Better Model: Use Tools After the First Encounter

Instead of banning tools, we can begin with a different principle:first encounter the task yourself, then use the tool.

Try to summarize the article before asking AI for a summary. Sit with an emotion before turning to a digital device for distraction. Attempt a difficult conversation before having AI script it entirely. Make the first attempt, even if it is messy.

The first attempt matters because it reveals what we understand, what we avoid, and where growth is needed.

Conclusion

The future will not belong simply to those who use AI fluently. It will belong to those who know when not to use it.

That may be the new literacy: not resistance to technology, but discernment within it. Not rejecting help, but understanding the difference between support and substitution. Not asking only what a tool can do, but asking what kind of person we become when we let it do too much.

Because some tasks are not just tasks. They are training grounds. When we outsource the training ground, we may still get the product  but we lose the growth that develops through the process.