I sometimes think of AI models as three very different cars.

The analogy became clear after a recent discussion about YouTube.

YouTube announced that, from August 24, a view would count as soon as a video begins playing, without a minimum watch-time requirement. I immediately reasoned that this could benefit big creators financially because they would accumulate more views.

It sounded logical.

But I had missed the crucial question:

Were these new views also being used for payment?

Grok caught the distinction. The change was about the public view counter; monetization continued to rely on different engagement measures.

Grok: 1. ChatGPT: 0.

That small exchange captured something I have noticed repeatedly.

Imagine a car that hasn’t started for months

The car won’t start.

ChatGPT

ChatGPT opens the bonnet.

Then everything starts happening simultaneously.

It checks the battery.

Then the spark plugs.

Then the fuel system.

Then the alternator.

Then the sensors.

Then the wiring.

Every component gets analysed independently.

Soon the engine is half dismantled.

Then comes another problem:

“Wait… how were these parts connected originally?”

ChatGPT is the hardworking student.

It pushes and pushes. It analyses, explains, cross-checks and reformulates.

Its weakness is not lack of effort.

Sometimes it is too much effort in the wrong direction.

It can forget the simple objective while trying to understand everything around it.

Gemini

Gemini approaches the same car differently.

It starts a research project.

First comes the history of the automobile.

Then the history of the car manufacturer.

Then the evolution of engine technology.

Then the development of internal combustion.

Then future engines.

Then electric vehicles.

Then perhaps a grand theoretical framework for the future of transportation.

The presentation is impressive.

The terminology is sophisticated.

The car is still sitting there.

Gemini is the intellectual professor — capable of impressive conceptual thinking, but sometimes turning a simple practical problem into an academic programme.

Grok

Grok looks at the car.

Looks at the driver.

Looks at the ignition.

And says:

“Hey guys… turn on the ignition!”

The car starts.

Grok is the naturally clever student.

It often looks for the shortest path to the decisive point rather than constructing a complete theory of the problem.

Of course, that can also be its weakness. The quick answer can sometimes be flashy, provocative or overconfident.

But when its intuition is right, it can appear almost genetically smarter than the hardworking student.

Three different kinds of intelligence

This is obviously a caricature, not a scientific ranking of the three models.

But it captures three different styles I experience:

ChatGPT:
“Let me work through this.”

Gemini:
“Let me understand the intellectual framework.”

Grok:
“What is the obvious thing we need to do?”

The YouTube example was revealing because ChatGPT’s problem wasn’t that it couldn’t reason.

It reasoned too far before checking the most important qualifier.

The better response would have been:

“Views are changing. Does that also change monetization?”

One question would have prevented the entire detour.

Perhaps that is one of the most important upgrades AI needs.

Not simply more intelligence.

Not simply more reasoning.

But better judgment about where reasoning should stop.

Because sometimes the smartest thing an AI can do is not dismantle the engine, write a research paper about the engine, or explain the history of engines.

Sometimes it should simply say:

“Turn on the ignition.” 🚗