Expectation vs. Reality: Why We Get AI Wrong
Are we projecting too much humanity onto tools that are fundamentally not human?
We’re asking AI to do a lot: draft content, summarize legislation, offer legal advice, even help govern. And in some ways, it delivers.
But in others?
We’re failing ourselves—not because AI is flawed, but because we’re expecting something it was never designed to do.
AI isn’t thinking. We are. And we’re projecting that thinking onto a machine.
The expectation trap
Large Language Models (LLMs) like ChatGPT, Claude, and Gemini have changed how we interact with technology. They sound fluent. They generate convincing answers. They feel responsive.
But what’s happening behind the scenes is not cognition.
It’s computation.
These models are trained to predict the next word in a sentence based on patterns from massive datasets. They don’t know facts. They don’t understand meaning. They aren’t aware they’re answering anything at all.
And yet—we keep treating them like they do.
When AI fails, it’s often our fault
We talk about AI “hallucinating,” as if the system has an imagination.
We expect it to be logical, insightful, ethical.
We ask for opinions, delegate decisions, and follow its advice.
But these models aren’t built for accuracy or accountability. They’re built for plausibility.
“We’re not too early in the technology.
We’re too early in how we’re choosing to use it.”
This misalignment has real-world consequences
Let’s be clear: expectation isn’t bad. In fact, it fuels innovation.
But when expectation gets ahead of understanding, we get problems:
In education, students trust outputs that cite non-existent sources.
In journalism, summaries contain subtle distortions and fabricated quotes.
In medicine, AI-generated drafts risk missing nuance and context.
In governments, automated tools are being piloted for public service delivery—often with little transparency or ethical review.
The issue isn’t the tech.
It’s how we frame it.
How to expect more—without getting it wrong
We need a new approach to expectation: one rooted in the actual capabilities of the systems, not the hype surrounding them.
Here’s what that looks like:
✅ Expect acceleration, not autonomy
Use AI to assist—but never as the final decision-maker.
✅ Expect fluency, not truth
LLMs write well, but that doesn’t make them right.
✅ Expect progress, not arrival
Today’s models are impressive—but they are not the destination. They’re a stage.
✅ Expect responsibility—especially from ourselves
Understanding what AI can’t do is just as important as knowing what it can.
A cultural course correction
We’re at a pivotal moment. The tools are evolving fast—but our expectations are evolving faster.
“The biggest risk isn’t that AI is too powerful.
It’s that we forget it isn’t.”
If we treat today’s models like they’re already intelligent, we’ll design systems, policies, and products on a false foundation.
We need clarity. We need transparency. And we need to reset how we think about thinking machines.
The bottom line
This is not a call for less ambition.
It’s a call for smarter ambition.
We should keep building, keep expecting more, and keep exploring what AI can become. But we have to do it with clear eyes and grounded frameworks.
Because when we get AI wrong, it’s not because the machine failed.
It’s because we forgot what it is.