Code Is the Highest-Resolution Expression of Intelligence

My 14-year-old asked me recently about the future of coding, and whether learning to code is a good idea when AI can already do it for us. It’s a fair question, and one I hear from adults as often as from teenagers.

I’ve spent my career building products with engineers. Early on I thought of coding as a skill and a tool, not a philosophy. But the longer I worked with engineers and watched how they think, the more I realized that coding was never just about writing instructions for computers. Code is a language for understanding, and the place where thought meets consequence.

Code has unique fidelity. Every assumption must be expressed precisely. Code forces thought into logic, and captures reasoning at the level of instruction. Writing can persuade. Design can inspire. Math can prove. But only code can show you, in real time, whether your reasoning holds.

Every developer knows the moment when a misplaced bracket or broken loop derails a system; it’s a reminder that thought and consequence are inseparable. Coding rewards precision, but it also rewards imagination, because even the most logical systems begin with a leap of thought.

In that way, coding is more than a craft. It’s a mirror for intelligence itself. It forces you to translate thought into structure, to predict cause and effect, to test and to learn. Code is the highest-resolution expression of intelligence humans have built. It takes how we reason, imagine, and create and turns it into something that works. It is thought that runs.

You can see this play out across the industry. When I watch developers use Copilot on GitHub, the productivity gains are now standard occurrence. What's striking is the quality of thinking that happens in the process. A developer types an idea, sees a suggestion, and refines it. The system responds. Together, they converge on clarity. It’s reasoning in dialogue, not mere automation.

That same pattern, reasoning in dialogue, is becoming visible in how machines learn too. Researchers call this the coding and reasoning hypothesis. Recent studies show that adding code during pre-training modestly improved reasoning accuracy across tasks, but notably it allowed a 2.6 billion-parameter model to outperform a 13 billion-parameter one trained only on natural language. When models were fine-tuned on code instructions, they gained roughly 6–12 points (about 13–35 percent) on benchmarks such as GSM8K and LogiQA. This suggests that structured syntax strengthens reasoning far beyond programming. Code’s logical structure and low ambiguity make models better at solving complex tasks, even outside programming domains.

Whether in training AI models or building software, code remains the medium where thought and feedback meet directly. This is why so much of the world’s compute is moving toward code. Code is not new, but it is the purest environment for intelligence to improve. Every execution, every failure, every correction becomes a lesson.

It’s the same feedback loop you see when a test fails or a build breaks, and each correction refines both the system and the reasoning behind it. Whether it’s a person or a model, the process is the same: you think, you build, you test, you learn.

Coding also teaches humility. The compiler doesn’t negotiate; it simply tells the truth. That relationship between an idea and its result makes code a training ground for intelligence in every form. If intelligence is the ability to reason and adapt, then code is its clearest expression; it compresses what we know, exposes what we don’t, and shows us the difference.

So, when my kids ask if they should learn to code, I say yes. Not for jobs or syntax (which I think are important), but for agency. Coding teaches you to think in systems, to understand cause and effect, to test your beliefs in the world. That’s the part that feels bigger than technology to me.

This is the moment to become literate in systems. Coding remains the fastest path to that literacy. You don’t need to become a software engineer, but to exercise the think-build-test-learn sequence that will teach you to reason in systems and build meaningfully with AI.