I've spent a lot of time working with Large Language Models (LLMs), and I've can tell you why they both amaze and frustrate us. It's not because they're flawed or incomplete. It's because we're fundamentally misaligned on how to best make them work for us.

The AI you have access to today isn't "intelligent" in the sci-fi sense. It's the most sophisticated pattern matching machines ever created. And that realization changes everything about how you should use them.

When I first started using these models, I quickly noticed a recurring pattern. They could write elegant prose, generate complex code, and even mimic different writing styles with amazing accuracy.

Yet they would sometimes fail at seemingly simple tasks. They struggled with catching obvious logical contradictions. They couldn't explain why a joke was funny.

This might have bugged you until you realized something important: these models aren't thinking - they're pattern matching at a scale we've never seen before.

This isn't a limitation – it's the source of their power, but also the key to understanding their boundaries.

The Pattern Matching Perspective Changes Everything

Think about how a jazz musician improvises. They're not calculating each note mathematically; they're recognizing patterns they've internalized through years of practice and recombining them in creative ways. AI models do something similar, but with language and concepts.

They've been trained on vast amounts of text, learning to recognize and reproduce patterns in how humans communicate. This explains both their incredible capabilities and their sometimes frustrating limitations:

• They excel at tasks that are fundamentally about pattern recognition: writing in specific styles, generating code that follows established patterns, or identifying themes in text.

• They struggle with tasks that require true causal reasoning or real-world understanding: explaining why a logical argument is flawed, or predicting the real-world consequences of a hypothetical scenario.

For example, you could ask an LLM to help write a technical article, and it will generate something that looks remarkably professional. That's because it's recognizing and reproducing patterns from thousands of technical articles it's seen.

But if you ask it to verify whether the technical concepts it's describing actually work in practice, it falters. It can match the pattern of what technical explanations look like, but it can't actually test or validate the concepts.

A New Framework for Working with AI

While I use the Three As framework to help businesses identify their best AI opportunities, fundamentally understanding AI as pattern matching machines gives you a practical framework for using them effectively:

1. Pattern Replication Tasks: Use AI for tasks where recognizing and reproducing patterns is the core challenge. This includes:

   • Initial drafts and outlines

   • Code generation following established patterns

   • Style matching and tone adaptation

   • Language translation

   • Data summarization

2. Human Judgment Tasks: Rely on human capabilities for:

   • Validating logical consistency

   • Testing real-world applicability

   • Making novel connections

   • Understanding causal relationships

   • Evaluating ethical implications

The power comes from combining these approaches. For instance, when I'm writing, I might use AI to generate multiple versions of a paragraph, each with a different stylistic approach. But I rely on my judgment to evaluate which version actually serves my purpose and makes logical sense in the broader context.

The Future of Work

This realization about pattern matching suggests something important about the future of work. The most powerful workflows won't try to make AI do everything, nor will they reject it entirely. Instead, they'll create feedback loops between AI's pattern-matching capabilities and human judgment.

When starting to build mobile apps back in 2007, we learned that successful mobile apps weren't about cramming in every possible feature, but about understanding what mobile did best and building around those strengths. The same principle applies here. Once you understand that you're working with a pattern matching machine rather than a general intelligence, you can design workflows that leverage this strength while accounting for its limitations.

The people who will thrive in this new era won't be those who try to push AI beyond its pattern-matching nature, nor those who dismiss it because it's "just" pattern matching. They'll be the ones who master the art of combining AI's pattern recognition capabilities with human judgment and creativity.

Understanding AI as pattern matching machines doesn't make them less magical – it makes them more useful. It helps us stop trying to force them to be something they're not and start leveraging what they actually are: unprecedented tools for recognizing, generating, and recombining patterns in ways that can amplify human creativity and capability.

The real revolution isn't in building machines that think like humans. It's in understanding how to combine human thinking with machine pattern matching in ways we're only beginning to explore.