In high school, I had a habit of skipping steps in my geometry homework. I’d recognize the pattern, jump straight to the answer, and move on. My teachers weren’t impressed. More than once, I lost half the points on a test—not because my answer was wrong, but because I didn’t show my work.
At the time, I found it frustrating. If I could see the solution in my head, why go through the motions? But the lesson stuck with me: being right isn’t just about the answer—it’s about how you got there.
That lesson has resurfaced in an unexpected way as I’ve worked with AI. People assume AI is thinking because it produces answers quickly and methodically, much like I did in math class. But AI isn’t thinking—it’s just predicting, pattern-matching, and computing results faster than we ever could. The real danger? Mistaking that speed for intelligence.
AI Computes—Humans Think
If you’ve ever solved a right triangle problem using the Pythagorean theorem, you know the process:
- Plug the numbers into the formula: A² + B² = C²
- Square the known sides.
- Take the square root to find the missing value.
A calculator can do this instantly, but it doesn’t understand what a triangle is. It doesn’t stop to consider if the problem itself makes sense. It simply follows the steps, like a well-trained student meticulously showing their work.
AI operates similarly. Given enough data, it can predict text, generate images, and even write code. But all it’s doing is executing learned patterns. It doesn’t question, rethink, or understand anything the way a person does.
That said, AI isn’t just “showing its work” in the way a student does. It’s making probabilistic predictions based on vast amounts of learned data. Unlike traditional rule-based programs, AI models don’t follow a strict set of steps but rather infer the most likely response based on past training.
The Illusion of Intelligence
People often mistake AI’s rapid response for true intelligence. The problem is that we tend to equate speed with understanding—but those are two very different things.
Take chess engines, for example. AI can now beat the best human players, not because it strategizes like a human, but because it evaluates millions of moves per second. It doesn’t grasp concepts like risk, intuition, or psychology the way a grandmaster does—it simply calculates probabilities at an inhuman scale.
Similarly, in business, AI can generate insights from financial models, but it doesn’t grasp the human dynamics of leadership, negotiation, or innovation. A hiring algorithm might rank candidates based on historical success patterns, but it doesn’t perceive soft skills, team dynamics, or cultural fit the way a good manager does.
Why Human Thinking Is Different
Back in school, skipping steps in my work wasn’t a sign of laziness—it was an intuitive leap. I had processed the problem in a way that didn’t require every step to be explicitly written out. That’s something AI simply doesn’t do.
1. Context and Adaptation
AI learns from past data, but humans can adapt to new, unprecedented situations in real-time. We weigh factors that don’t fit neatly into an algorithm—social norms, emotions, and gut instincts. AI can process new data, but it doesn’t contextualize information the way humans do.
2. Creativity and Problem-Solving
In geometry, I sometimes found shortcuts my teacher hadn’t shown us. That’s the kind of flexible, creative problem-solving humans bring to the table—something AI can’t truly replicate. While AI can generate novel outputs, it does so by remixing learned patterns rather than by experiencing insight. AI follows formulas; humans rewrite them.
3. Ethical and Moral Reasoning
AI can rank options by probability, but it doesn’t weigh morality. A self-driving car’s algorithm can decide the safest route, but it doesn’t wrestle with ethical dilemmas the way a human does in a split-second emergency.
The Real Danger: Letting AI Think for Us
The real risk isn’t that AI will surpass human intelligence—it’s that we might become over-reliant on it, assuming it has intelligence when it doesn’t.
- AI can assist in diagnosing diseases, but doctors still need to interpret results holistically.
- AI can write financial reports, but executives must make the strategic decisions.
- AI can generate legal documents, but lawyers must navigate intent, nuance, and consequences.
If we let AI dictate decisions without human oversight, we risk outsourcing judgment to a machine that doesn’t actually think—it just processes data.
AI as a Tool, Not a Replacement
What I wish I had realized sooner in school was that showing my work wasn’t about proving I knew the steps—it was about helping others see how I arrived at my answer. AI can do that too, but it lacks the intuition to skip steps, rethink the question, or challenge its own conclusions.
That’s why the best way forward isn’t replacing human decision-making with AI but integrating it as a tool. Let AI handle the heavy lifting—the calculations, the data crunching, the repetitive tasks—while humans do what we do best: think, innovate, and decide with wisdom.
Because at the end of the day, intelligence isn’t just about getting the right answer. It’s about knowing which questions are worth asking in the first place.
So as AI becomes more integrated into our decision-making, we need to ask ourselves:
Are we using AI as a tool, or are we letting it think for us?
