What AI Can Do — And What It Can’t

Today's AI tools are, in a way, our Electric Monks. Your email client suggests it can capture your tone and intent. Your coding assistant claims it can architect your solutions. Your writing tool offers to generate your reports. Each promise pushes us to question what we can truly delegate to machines.

As a writer, this fascinates me: why can AI now code at the level of top programmers, yet fail to produce meaningful literature? The answer lies in patterns.

The Pattern Paradox

OpenAI recently announced their O3 model, claiming coding that would place it among the world's top 200 programmers. While we await real-world demonstration of these, the contrast is telling: the same AI that could potentially match elite programmers still struggles to understand why a joke is funny.

This paradox explains why people tend to both overestimate and underestimate AI's abilities. The key to understanding AI is not about how it measures up to human intelligence, but how well it handles patterns. Anything that can be reduced to patterns, no matter how complex, is potentially within AI's grasp. (Though sadly, despite my wishful thinking, I haven't found an AI that could handle my family patterns over Christmas.)

As a former yoga teacher, I've spent years helping people break free from limiting patterns—emotional reactions, thought loops, habitual behaviours. Yet here's AI, excelling precisely because it masters patterns rather than breaking them.

This isn't a coincidence.

While patterns are incredibly valuable—they help us learn, grow, and function efficiently—many of humanity's most precious achievements come from transcending them. Our greatest breakthroughs, our deepest connections, our most meaningful experiences often emerge when we step beyond established structures.

Patterns at Work

Your workplace is full of patterns. The data analysis you run each month. The code you write to automate tasks. The research you compile for presentations. AI handles these beautifully because they follow recognisable structures.

Googles new deep research model (Gemini 1.5 Deep Research) demonstrated this so effectively to me that I renewed my subscription, despite already using Claude for most of my AI needs.

When writing articles requiring extensive research, the new model replaced my need of opening twenty browser tabs and spending hours collating disparate pieces of information. It created grounded, factual foundations for original writing. No hallucinations. No made-up sources. Just solid research synthesis.

For a copywriter this is awesome. It’s way beyond having a sketchpad full of ideas (although I still need that too); it’s a deep background document on my chosen topic that I can have in my hands in a matter of minutes.

So of course it works great for this. Analysing vast amounts of information is probably the one of AI’s most important use cases at the moment.

But then there's everything else.

The email where tone really matters. The presentation that needs to inspire. The conversation that builds trust between teams. All of these things rely on patterns, but only become truly meaningful if they also somehow transcend the structural elements that enable them to be constituted in the first place.

Why We Don't Have AI Literature (Yet)

Yes, AI can write novels—particularly when they follow familiar formulas. But we're still waiting for those original pieces of world-class literature that reshape how we see ourselves. Why?

Because the greatest human achievements aren't about following patterns—they're about breaking them. Great literature emerges from that uniquely human space where consciousness meets intention, where the deep desire to communicate transforms into something beyond mere words.

Great literature breaks through established forms to forge new connections between minds.

You see, AI writing is built on averages—statistical patterns drawn from millions of texts, optimised for what works most often, for most people. Like factory-produced food, it's engineered to satisfy the average palate.

But here's the thing about averages: they capture everything and nothing at the same time. They see the pattern but miss the person. When you read something that truly moves you, it's never because it was crafted for everyone—it's because it breaks from the average in exactly the right ways.

This mirrors why self-driving cars remain such a challenge. Statistical models can predict how often a child might chase a ball into the street, but they can't know if this specific moment will be that moment.

The concrete situation often eludes statistical analysis.

Your Pattern Recognition Audit

Take a moment to examine your work. Which tasks follow clear patterns? Here's a list of examples:

• Data analysis and reporting

• Standard code development

• Research compilation

• Regular communications

Now, which tasks require breaking patterns? Some more examples:

• Strategic planning

• Creative problem-solving

• Inspirational leadership

• Deep human connection

AI amplifies your capabilities in pattern-based tasks while your human intention remains irreplaceable in others.

The Future Isn't Either/Or

AI will discover patterns we can't yet see. It might even create literature that feels unique through patterns too complex for us to recognise. And as technology advances, I am quite sure that it will.

The resulting artwork won't be unique in the human sense, but it will follow such vast patterns that we can only see fragments, unable to grasp their full complexity—and thus we will deem them unique.

The future belongs to those who can work with AI's amazing powers of pattern prediction while applying human intention where it matters most.

Like Douglas Adams's Electric Monk, which believed so humans wouldn't have to, our AI tools predict patterns so we can focus on what matters most: why these patterns matter, how we can use them with intention, how they connect to human experience, and where they lead us next.