K-Shaped AI Adoption

AI is transforming how we work, the way we communicate, and even the way we make decisions. I use it daily for many different things, both personally and professionally. If you're reading this, I imagine you do too.

I'm lucky to be in a role where I get to speak with a huge number of people. From CxOs and founders to fellow board members to practitioners from a very wide variety of industries, companies, and disciplines. This enables me to spot patterns. One thing I hear consistently: everyone thinks they are behind when it comes to AI.

But digging in one level deeper, I'm seeing something more interesting inside many organizations: AI adoption is becoming K-shaped

AI isn't just increasing productivity. It's increasing the variance in productivity between engineers.

Some engineers are enthusiastic adopters. Even if they haven't gone full gas town, they have mature harnesses set up, have adopted AI into most facets of their work, and are seeing real productivity gains. They're starting to trust the output, and as a result are fundamentally changing how they work.

Some engineers on the other hand have been very slow to adopt, or are not adopting at all.

What’s striking is that I see this pattern across a wide variety of organizations. Startups, large tech companies, traditional enterprises... almost everywhere outside AI-native companies. The divide often appears within the same team.

Some of the hesitation is understandable. I often hear valid concerns around topics like environmental impact. Sometimes it's fear, uncertainty, doubt. Many are skeptical of the output itself.

But I don’t think those factors fully explain what’s happening. I've talked to people who share those concerns but are still huge adopters, and others who have none of those concerns but still avoid using AI in their work. Company culture and seniority do not appear to be consistent factors.

Part of it comes down to mental plasticity and curiosity, sure. Change can be uncomfortable. But I think there's something deeper going on.

For many engineers, it comes down to how they think about coding itself and what impact that has on their identity.

For some people, and I am very much in this camp, coding has always been about outcomes. It's about solving a problem and building something useful. Code is a tool. We're builders.

For builders, coding agents have been an amazing accelerant. They allow us to move faster, try more ideas, and get more things into the world. As I said in a previous post, I'm having more fun than I've had in a long time.

But for others, code is a craft.

Writing clean, elegant code or honing an algorithm is part of who they are. Thinking deeply about implementation details is where they find joy. These engineers are artisans.

For this group, AI agents aren't just a productivity tool. In some ways they are literally destroying the part of coding artisans find joy in.

A builder looks at a project coded mostly (completely?) by an AI agent and says: "Look how fast I was able to solve this problem"

An artisan looks at the same project and says: "Sure, but look at the logic in this part of the code, and what is happening on line 181??"

I'm not sure what the future holds. The current range of plausible outcomes seems very wide. But the coding agent is not going back in the box. Software development has been changed forever, and it's very likely we're only getting started.

Do artisans adapt, finding joy in the architecture and systems they design for agents to implement? Will they prefer to use their skills in other industries or occupations?

Is AI creating a Builder-Artisan divide in software engineering? I'm genuinely not sure.

But the growing divide in how engineers are approaching AI feels like a conversation worth having. AI may not replace engineers, but it may change which engineers thrive.

What I do know is that we're in this together.

--jeremy