Not All AI Thinks the Same: Why You Need the Right AI for the Right Job

I was explaining to Véro that different AI platforms are suited to different tasks, that Grok handles the repetitive essential work while Claude does the research and exploration, and she said “that’s Wall-E.” Véro is my ex-wife and the creative partner behind Boowa and Kwala (the children’s cartoon we co-created in the late 90s) and she still has the ability to find the perfect reference that makes a complex idea instantly clear.

She is right. On the Axiom in Wall-E, every robot has a job matched to its capabilities. WALL-E compacts trash (the janitor). M-O scrubs floors with single-minded efficiency (the maintenance crew). AUTO runs ship operations with methodical reliability (the operations manager). EVE scans, analyses, and explores (the scientist). Nobody asks EVE to compact trash. Nobody sends WALL-E on a research mission.

We are at the same point with AI. Grok is WALL-E: efficient, repetitive, essential. Gemini is AUTO: systematic and reliable. ChatGPT is the service bots: competent generalists handling varied tasks. Claude is EVE: the one you send when the job requires genuine exploration. And just like the Axiom, the system works because each robot does what it was built for.

The difference is why, and that is worth understanding because it has significant implications for how businesses use AI.

Reward Mechanisms Shape How Each AI Thinks

When engineers build an AI system, they do not just feed it data. They create a feedback loop. The model generates output, humans rate that output, and the model adjusts. Over thousands of iterations, this reward mechanism shapes the AI’s personality as surely as upbringing shapes a person’s.

Google’s Gemini is rewarded for being reliable, for getting facts right, for being consistent. That makes it exceptional at analysing pages, categorising content, and processing structured information. It is methodical and thorough in a way the others simply are not. But that same reliability training means it rarely takes creative leaps. It confirms rather than invents.

OpenAI’s ChatGPT sits in the middle. Its reward mechanism emphasises helpfulness and broad competence. It handles analysis well, generates decent content, and adapts to a wide range of tasks. It is the generalist in the team.

Anthropic’s Claude is trained with a different philosophy entirely. Its reward mechanism prioritises nuance, honesty about uncertainty, and careful reasoning. That makes it less suited to mechanical tasks like bulk page categorisation, but exceptionally good at research, gap analysis, and tasks that require genuine thinking rather than pattern matching.

Grok, by contrast, is built for efficiency. It handles straightforward tasks competently and at a fraction of the cost of the more sophisticated models, which makes it the smart choice when the task does not require sophistication.

For me, the key insight is this: these differences are not bugs. They are features. And they are getting more pronounced, not less.

Each AI Platform Excels at a Different Level of Work

I think about it like a company with different roles.

Grok is the janitor. And I mean that with respect, because a good janitor keeps the entire building running, handling the essential, repetitive work that nobody notices until it stops getting done. Bulk data cleaning, basic formatting, straightforward classification where the criteria are unambiguous. These are tasks where consistency matters more than creativity, so you assign them to the model that delivers reliability at the lowest cost.

Gemini is the operations manager: systematic, reliable, thorough. Give it a stack of pages to analyse, content to categorise, or structured data to verify, and it will outperform the others consistently because that is exactly what its training optimised it for.

ChatGPT is the project manager: competent across domains, good at synthesising information from multiple sources, and capable of handling varied tasks without deep specialisation in any single one. When you need versatility and a solid all-round performance, that is where it delivers.

Claude is the scientist. The one you give the work that requires genuine exploration, careful analysis of ambiguity, and an honest assessment of what is and is not known. We use it for research and strategic analysis where getting the nuance right matters more than getting the answer fast.

Using One AI for Everything Is Like Hiring a Scientist to Sweep Floors

Most businesses pick one AI and use it for everything. That is like hiring a scientist to sweep the floors and wondering why your cleaning bill is so high.

The practical approach is to match the task to the tool. At Kalicube®, we use this principle systematically: when we need to analyse thousands of pages and categorise them against our UCD framework, Gemini handles it because reliability at scale is exactly what it was trained for. When we need to extract nuanced brand positioning from messy, contradictory sources, Claude gets the job because it thrives on ambiguity. And when we need efficient processing of straightforward data, there is no reason to use a premium model for a task that does not require one.

This is not about which AI is “best.” That question makes as much sense as asking whether a surgeon is better than an accountant. They are different tools for different jobs, and the differences are growing.

AI Platforms Are Diverging, Not Converging, and That Changes Everything

Here is what most people are missing: these AI platforms are going to diverge more, not less. Each training cycle reinforces the reward mechanisms already in place, Gemini gets more reliable, Claude gets more thoughtful, and the gap between their respective strengths widens with every iteration.

For businesses using AI seriously, this means keeping an eye on which specific tasks each AI is becoming better at. The future is not going to settle into a single winner. It is going to look more like a specialist workforce where you pick the right employee for the right job.

My bet is that within a year or two, serious AI users will routinely work with four or five different models, choosing between them the way you would choose between team members for a specific project. Not a hundred models, but enough that “which AI should we use for this?” becomes a standard operational question rather than a strategic one.

WALL-E Developed Curiosity and Imagination. AI Will Not.

The emotional heart of Pixar’s film is that WALL-E, the janitor, turns out to be the most curious and creative robot on the ship. He collects treasures, watches musicals, falls in love. The janitor transcends his programming.

The part of me that co-created Boowa and Kwala with Véro, the imaginative, dreaming, non-analytical part, would love that to be true for AI. The idea that Grok, grinding through bulk data day after day, might one day develop a spark of genuine curiosity is a wonderful thought. But it is not how reward mechanisms work. Each training cycle reinforces what the model was already rewarded for. Grok gets more efficient at being Grok. Gemini gets more reliable at being Gemini. Claude gets more thoughtful at being Claude. Unlike WALL-E, these systems do not transcend their training. They deepen it.

There is a quote attributed to Confucius: “head in the clouds, feet on the ground.” The ability to dream and analyse at the same time, to imagine what could be true while seeing clearly what is true, that combination is rare and precious. It is also distinctly human. No AI has it. The dreamer in me wishes WALL-E’s curiosity were possible. The analyst in me knows it is not, and builds systems accordingly.

That is precisely why matching the right AI to the right task matters. You are not going to get a pleasant surprise where your janitor AI suddenly starts doing brilliant research. What you will get is a team of specialists that, when properly managed, outperforms any single model trying to do everything. The businesses that start building this multi-model workflow now will have a significant head start when the rest of the market catches up.