Navigating New Horizons

If you’ve spent any time reading about technology adoption, you’ve probably heard the story of steam and electricity. Edison built the first commercial generating station in 1882. Electric motors could drive factory machinery. But decades later, most factories still looked the same. The punchline usually lands on the timeline: it took 30-40 years for electricity to produce meaningful productivity gains. The lesson, as typically told, is about patience. Transformative technology takes time. Don’t expect instant results.

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A lot of organizations have been relying on slow work to stay coordinated.

Not intentionally. Nobody designed it this way. But when work takes weeks or months to complete, something useful happens in the background. Questions surface gradually. Managers course-correct in small increments. Adjacent teams notice conflicts before they become catastrophes. Priorities have time to clarify themselves.

The weeks it takes someone to finish a project aren’t just production time. They’re also coordination time. Slow execution creates a natural buffer for alignment.

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Dan Shapiro recently proposed a five-level model for AI-assisted programming that’s been rattling around in my head since I read it (hat tip to Simon Willison for amplifying it). It’s useful partly because it’s a good model, and partly because I think it applies to far more than just writing code.

If you’ve spent any time in analytics or data strategy, you’ve seen maturity models before. Gartner loves them. Every consulting firm has their version. They usually describe a journey from chaotic, ad-hoc practices toward something systematic and optimized. Shapiro’s model does the same thing for AI collaboration, but with a twist: the endpoint isn’t just “optimized.” It’s autonomous.

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Anthropic just published a randomized controlled trial studying how AI assistance affects skill development in software engineers. The headline finding: participants using AI scored 17% lower on a mastery quiz than those who coded by hand—nearly two letter grades.

The reactions will write themselves. Some will read this as vindication: “See? AI makes us dumber.” Others will dismiss it: “We’ll adapt.” Both miss what the study actually found.

The study’s most interesting finding isn’t that AI hurts learning. It’s that how you use AI determines whether you learn at all.

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Last September, I wrote about betting on people over pure AI automation while Salesforce celebrated cutting thousands of support staff. The thesis was simple: human expertise isn’t a cost center to optimize away. It’s a competitive advantage.

Three months later, Salesforce executives admitted they’d been “too confident” in AI’s ability to replace humans. Service quality dropped. Complaint volumes rose. Remaining employees spent their time correcting AI outputs instead of helping customers. The savings evaporated into secondary costs.

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I was having a conversation with a friend after a week packed with meetings, presentations, and more conversations than I could count. Something he said stuck with me: “A lot of people forget they have agency.”

He was being kind, talking about how I’d helped push through some changes by not waiting around for permission. The observation hit harder than the compliment.

You don’t have to wait.

This isn’t universally true. There are real constraints, real hierarchies, real reasons to proceed carefully. Really, this is about choice—and you probably have more than you believe. There are obstacles, sure. Maybe it’s a micromanaging boss, a broken process, or lack of resources. But you usually have control over something, even if it’s just the framing you bring to the table. Most people have more than that. They can take on a side project. Tinker and learn something new. Make connections across their networks. Shape the work they’re already doing. You might not be able to do it all, but you can almost always do something that matters.

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AI appears human, or at least like something that bears the hallmarks that we take as an individual personality we see in other humans and many animals. It’s not though. It is not only a distorted reflection of us, but it absorbs and reflects the things it consumes, often in ways we do not fully understand and often in ways we cannot predict.

That is not to say it’s inherently dangerous, I do not subscribe to the camp that says that if anybody builds it, everyone dies - though Hank Green has a great interview with Nate Soares if you are interested understanding that view point. My point is that it’s something to be careful with. Something to approach with care and caution. I think we can turn to a fictional story for a better illustration, but not in the traditional AI Sci-fi space, but in the fantasy world created by Hayao Miyazaki.

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