Understanding AI isn't just about knowing tools — it's about developing a thinking discipline.This article demonstrates it.
Read my book 📘 AI for the Rest of Us to build this discipline systematically, or reach out to me for group training sessions where we explore these nuances together in real time.
Whether through the book or in-group conversations, learn how to distinguish AI's reasoning capabilities from its perceptual limitations — the difference that separates intuition from insight.
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What 2025 Quietly Taught Me About AI (And Why It Matters in 2026)
Some lessons you only learn by actually using AI — not watching demos.
As 2026 begins, I found myself reflecting on something unexpected. It wasn't the speed of AI, the hype, or even the sheer number of tools released. Instead, it was the small, practical lessons that only surface when you work hands-on with the technology — especially when you try to do something very simple and very real.
In my case, it started with a New Year visual.
A Simple Task That Revealed a Bigger Truth
All I wanted was a simple visual: a pie chart showing how people used Chat GPT in 2025, inspired by recent articles and reports on real‑world usage. I had just four clear data points with neat percentages – nothing complicated at all.
Yet, no matter which advanced image model I used — and I tried several — something strange kept happening. The pie chart kept coming out wrong. Sometimes it had five slices instead of four; sometimes the values repeated or the labels drifted. Most often, the proportions simply didn’t make mathematical sense.
AI image tools are not as intelligent as LLMs — they create visuals, not understanding
🧠Technical Insight: Why Visual AI Struggles with Math
While Large Language Models (LLMs) are built to predict the next logical token in a sequence—often involving "reasoning" steps—Image Generation engines use a process called Diffusion.
They work by denoising a cloud of pixels to match a visual pattern. When you ask for a chart, the AI isn't doing math; it's simply trying to recreate the aesthetic of a chart it saw in its training data. To a visual AI, a "pie chart" is just a shape, not a mathematical ratio.
The Important Realization
AI image models don’t actually understand charts. They don’t understand numbers, proportions, scales, or the relationships between values. They generate images based on visual patterns they’ve seen before — not on logic or data integrity.
When you ask an image model to “draw a pie chart,” it doesn’t calculate anything. It simply produces something that looks like a pie chart. This is why:
Four slices become five.
Values repeat or contradict each other.
Proportions look aesthetically pleasing but remain inaccurate.
Text appears in nonsensical places.
Where I Finally Got It Right
The breakthrough came when I stopped asking image models to do data work. Instead, I used Perplexity AI, which leverages Python-based computation for charts. That made all the difference.
Because when numbers matter, a code-based approach:
Calculates accurately.
Respects scale.
Place labels correctly.
Doesn’t “guess” visually.
The chart was perfect in a single attempt. That’s when the lesson really landed for me.
The Real Lesson: Use the Right Tool for the Job
AI today is incredibly powerful — but only when used correctly. Here’s what I learned the hard way:
Image models are excellent for design, mood, and presentation, but they are not meant for numerical accuracy. Language models are great for brainstorming and structure, while data tools (Python, Excel, BI tools) remain essential for truth and precision.
When we mix these wisely, AI becomes a superpower. When we expect one tool to do everything, frustration follows.
A Thought for 2026
As we step into the new year, this is the mindset I’m carrying forward: AI is not here to replace thinking; it’s here to support it.
While working through these experiments, one thought stayed with me.
We spent centuries warming the planet — often without realizing the long-term impact.
Today, AI seems to be warming the internet much faster.
Not because it’s harmful, but because it’s being used without enough thought.
When speed replaces understanding, noise grows faster than insight.
Perhaps the real skill in 2026 is not using more AI —
but using it more deliberately.
The real skill in 2026 won’t be just learning new tools. It will be knowing which tool to trust — and when not to. Sometimes, the smartest thing we can do with AI is to let it assist, and then apply human judgment to the result.
Let’s use AI wisely — not blindly.
Wishing you a thoughtful, grounded, and meaningful 2026.
⚡ A Paradox Worth Noting: The Unreliability of Accuracy
This cover image itself was generated by visual AI—and it rendered mathematically accurate proportions (equal 25% quarters). Yet this accuracy was accidental, not deliberate. Why? Because visual AI models don't understand percentages or mathematics. They simply recreate visual patterns from their training data. Equal quarters appear often in charts and graphics, so the model successfully matched this common pattern.
When prompted again with the same request, the image model produced different errors—proving that visual AI has no consistent logic. It succeeded once by pattern-matching luck, not by reasoning. This is precisely the paradox: visual AI can appear accurate while being fundamentally unreliable.
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