Human Intelligence Is Exponential

Why we're measuring AI and humans on the wrong scale.

Previously on Giuseppe’s Glimpse: In the last episode, we explored the leadership superpower of thriving in paradox. Missed it? Catch up here! ✨

Buongiorno everyone 👋

We spend a lot of time these days talking about what makes artificial intelligence powerful. How fast it learns, how it scales, how it can see patterns humans miss. How it’s going to transform work, creativity, decision-making, everything.

Maybe we should spend more time asking a different question: what makes human intelligence irreplaceable?

I’ve been working with some simple equations that help me think about this, and it’s helped me reframe the whole AI conversation.

Two different kinds of intelligence

Artificial intelligence looks like this:

DATA + ALGORITHMS = OUTPUT

You feed a system information, run it through a process, get a result. Better data and a better algorithm means a better result. It’s straightforward. And it scales.

Human intelligence works differently:

(KNOWLEDGE + EXPERIENCE) × (JUDGEMENT + CONSCIOUSNESS) = OUTCOME

Notice two things. First, there’s a deliberate distinction between output and outcome. An output is generated. It’s a response. An outcome is what actually happens in the real world as a consequence of that response. They’re not the same thing.

Second, the multiplication sign. Human intelligence isn’t additive. Knowledge and experience amplify each other, but what actually matters is how judgement and consciousness amplify everything else.

The problem with how we measure intelligence

Here’s where organizations start getting confused: they’re trying to measure human intelligence using AI metrics.

→ They compare the radiologist to the diagnostic algorithm on speed and accuracy. The algorithm wins.
→ They compare the hiring manager to the screening algorithm on consistency and bias reduction. The algorithm wins.
→ They compare the strategist to the predictive model on forecast accuracy. The algorithm wins.

But they’re measuring the wrong things entirely. It’s like asking a violinist to compete with a metronome on precision and consistency. The metronome wins easily, but that’s not what a violinist is for.

You can measure what an algorithm does: accuracy, speed, consistency, scale. These are clean metrics. But you can’t measure human judgment using those same metrics because judgment operates on a completely different scale.

A good decision under uncertainty might look messy on paper. It might take longer. It might fail in ways that look inefficient. But the outcome is what matters, and outcomes are shaped by things algorithms can’t touch: responsibility, moral reasoning, the willingness to stand behind a decision when it’s unpopular.

Where the algorithm fails

An AI system can generate an answer that looks flawless on paper: well-reasoned and supported by data, always internally consistent. That’s an output.

But then you implement it in the real world and something goes wrong.

A lot of companies today are working with algorithms to optimize hiring. The output is elegant. It supposedly eliminates bias from resume screening, processes candidates faster, recommends profiles that match historical patterns of success. By every objective measure, it looks better than human selection.

However, the result is: you risk hiring people who look good on paper but struggle when things get ambiguous, who can’t navigate actual complexity, who have no idea why the decisions they make matter beyond executing a task.

The output is clean. The outcome is organizational dysfunction.

That’s because the algorithm optimizes for pattern-matching. It’s looking at what succeeded before and finding more of that. It doesn’t understand what actually makes someone valuable in a human organization, which is something much messier and more contextual than any dataset can capture.

But what is judgement?

When I talk about judgment in that equation, I’m not talking about analytical reasoning. AI does analytical reasoning better than we do. I mean the ability to make a decision when you’re not certain, guided by what you actually care about and what you believe is right.

It’s the radiologist who doesn’t just look at the image but thinks about the patient’s age, what they can handle psychologically, what their life circumstances are, and then recommends a treatment that makes sense for them as a person, not just as a case.

It’s a leader who sees data pointing one way but notices people are scared and decides to communicate differently than the data alone would suggest.

It’s an architect who knows all the regulations but also understands how a building will feel to someone walking into it.

That’s fundamentally different from what an algorithm does. It’s the gap between knowing an answer and understanding what an answer means.

What actually needs to happen

AI will keep getting better at what it does. It will process more data, generate better outputs, find patterns we can’t see.

The real question is whether we invest in the capacities that make us fundamentally different. Whether we develop the ability to make decisions under uncertainty, guided by responsibility and moral reasoning. Whether we keep asking not just “what can we do?” but “what should we do?”

Because leadership, strategy, creativity, decision-making have never been about having better answers. They’ve always been about what those answers mean, which ones matter, whether you’re willing to stand behind them when things get hard.

An algorithm tells you what happened. You have to decide why it matters.

An algorithm can give you options. You have to choose one and live with it.

The choice ahead

Maybe the most important thing happening right now with AI isn’t the technology itself. It’s the choice we’re making, mostly without thinking about it, about what we want to cultivate in ourselves.

We could treat AI as a way to get through more tasks faster, then just fill the time we save with even more work. Or we could see it differently: as a signal that it’s time to invest in what only we can do. Making judgments. Taking responsibility. Creating meaning from information.

The technology doesn’t choose for us. We do.

Stay curious 🙌

-gs

PS: Thanks to Renato Geremicca for the thinking that sparked this 🙏

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