The Clothes Have No Emperor: AI is Ready, but Humanity is Not

Author(s): Sophia Banton

Originally published on Towards AI.

“It hallucinates too much.”

“I just used it to write an essay.”

“It can’t reason.”

“I just made my Studio Ghibli image.”

These varied reactions underscore a fundamental truth: very few topics divide people as much as AI does. For some it evokes wonder, others fear, and among some disdain.

The Entitlement of Control

Human beings conveniently have short-term memory when it comes to technological innovation. Has anyone seen a VCR lately? Yet, if Netflix goes down even for a few minutes, the internet is flooded with commentary from “dissatisfied customers”.

This sense of entitlement to own and rule technology isn’t limited to streaming platforms. ChatGPT is amazing as long as it “obeys”, but if it asserts an idea, it’s suddenly hallucinating. I would argue that the obsession with hallucinating AI models has very little to do with technical benchmarks in percentage points and much to do with control. The “emperor” could be our perceived need for control over technology, and the “clothes” are the justifications we use (like “hallucinations”) to maintain that control.

The Hypocrisy of Language

But what exactly is an AI hallucination? A hallucination is defined as “a perception of something that is not present or a false sensory experience.” In clinical terms, it’s when the brain creates sensory input that doesn’t exist in reality. Ironically, many critics of AI warn us not to humanize AI; yet their most common pejorative for its ‘errors’ is a deeply human, clinical term. A hallucination is a human quality.

Mathematically, AI models don’t hallucinate. They arrive at predictive responses based on pattern matching and sometimes the patterns revealed are uncanny. But in that uncanniness, there can be brilliance, humor, or error. But don’t humans do the same? We don’t call it hallucinations when a human fumbles around trying to answer a question; rather we show intra-species level grace, perhaps saying “Oh, you misspoke,” or “That’s not quite right.”

Humans learn by making mistakes. AI learns by identifying patterns and refining predictions. Both make “errors” or “unintended outputs.” But when the AI can’t be 100% accurate all the time, it is deemed a failure simply because it cannot be controlled.

Shifting the Goal Post

“Is it AGI (artificial general intelligence)?”

“No, it’s not. AGI has to do this, this, and this.”

“Well Google’s AI just made text to video outputs with integrated sound effects, voice and music.”

“Oh well, to be ‘real AGI’, the AI has to do this, this, and now this.”

It’s not about AGI vs not AGI, AI has long surpassed our cognitive abilities. The calculator beat us in math more than a century ago.

What Intelligence Actually Looks Like

AI has nothing left to prove. It can already do more than we ever imagined it would… and the real question now is “what’s left for us to do?”

I recently read a quote where someone said,

“AI is creating art and music while I’m doing my laundry, but I want AI to do my laundry while I create art and music.”

Fundamental to that train of thought is the misalignment between what intelligence is and what humans expect machines to be. Art and music creation are expressions of intelligence, as are writing and of course solving mathematical problems. While the public may have expected automation and industry may have hoped for digital laborers, researchers never set out to build artificial brute force workers; they set out to build artificially intelligent systems… and that’s what we did.

So yes, AI is creating art, music and so much more. The challenge for us now is to use AI tools to create new ideas and find novel ways to express ourselves as photographers did with cameras and cinematographers did with video and film. These tools didn’t just change existing work; they created entirely new industries, from commercial photography to Hollywood studios, spawning countless job roles that didn’t exist before. The possibilities are endless.

As for the laundry folding robot, it’s coming too. But remember humans, every machine has to be maintained and serviced. Not everything can and should be automated. Brooms and mops are still stocked in stores, Swiffers too, despite the advanced robotic vacuum cleaners and mops available for purchase. Why? Because sometimes automation isn’t necessary. Or to put it another way, sometimes you will need to sweep the corners of your house and sometimes you will need to fold your jeans.

A Tale of Two Generations

If we step back as a species we have two options. We can mourn our displacement as the “top of the food chain” or welcome company at the top.

We weren’t ready. We weren’t ready to see ChatGPT write an essay in milliseconds. We weren’t ready for image generators to create art in the styles of the masters, or for AI music generators to compose entire songs in seconds.

But the “we” here doesn’t apply to “all of us”, because our children were ready. Children of this generation don’t fear AI or mourn its arrival the way we do. The same way our thumbs adapted to typing, their cognition will adapt to having AI collaborators. They won’t debate about whether prompt engineering is an essential skill set, they will just talk to AI. They won’t feel unsure about using AI-generated music, they will just choose the songs they like. I have seen this in my own life. The children are ready. Us adults are not.

You know zoos were always a source of contention. They’re great for educational purposes some argued … while others said the animals didn’t deserve to be held captive. Well today a child can put on a VR headset and see a lion in its natural habitat, use AI image generators to draw them, use AI video generators to create short films of them in the wild, while we look on and question our entire existence.

Because unlike the child we consider the job displacement behind those activities: no artist, no videographer, no sound engineer. But instead, what we have to realize is that there are new jobs behind those images, videos and experiences: AI prompt engineers, AI ethicists, data curators, AI-powered content strategists, and roles we haven’t even named yet. It’s just that most of us aren’t trained to do them.

Getting Ready

So rather than continue to say AI isn’t ready using weak arguments that vanish like morning dew, we do as children do, and we get ready. We get ready by realizing that control isn’t necessary to thrive. The best parts of us were never the consequence of being “cognitively dominant” on our planet, but what we built as part of our universe.

Now go prompt, speak, or simply engage with AI.

About the Author

Sophia Banton is an Associate Director and AI Solution Lead in biopharma, specializing in Responsible AI governance, workplace AI adoption, and building and scaling AI solutions across IT and business functions.

With a background in bioinformatics, public health, and data science, she brings an interdisciplinary lens to AI implementation — balancing technical execution, ethical design, and business alignment in highly regulated environments. Her writing explores the real-world impact of AI beyond theory, helping organizations adopt AI responsibly and sustainably.

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Published via Towards AI