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AI has a multiplying effect on existing technical skills

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Why This Matters

This article highlights how AI amplifies the value of existing technical skills rather than replacing human developers. For the tech industry and consumers, it underscores the importance of deep expertise, as AI tools serve to enhance productivity and innovation for skilled professionals. Embracing AI as a complementary asset can lead to more efficient development and advanced technological breakthroughs.

Key Takeaways

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Hi there!

I want to talk a bit about AI and the related shifts in the tech industry. I know this is top-of-mind for lots of y’all, and you might be wondering if it even makes sense to learn new programming skills in this environment.

Let’s start with an uncomfortable truth: AI models have become shockingly good at completing a wide variety of programming tasks. They’re certainly not perfect, but in many cases, they’re good enough. I’m not happy about this, for a wide variety of ethical/environmental/safety reasons, but it is what it is.

In this email, I want to zoom into one specific thing: I think people are jumping to the wrong conclusion about what AI means for their careers.

Alright, so the biggest concern I’ve seen from my fellow developers is that human developers won’t be necessary in the near future, since LLMs? Large Language Models will be able to fully design and build projects of all sizes and scales. And, well, I just haven’t seen any evidence of that. 😅

In fact, it’s kind of the opposite. The biggest AI success stories I’ve seen have been from people who are highly technical, folks with deep subject matter expertise.

For example, Matt Perry recently shared in his newsletter that he was leaning into AI in 2026. Matt is the author of several animation libraries including Popmotion, Motion One, and Motion (formerly Framer Motion). There aren’t many people on this planet who know as much about animations on a technical level. The layout projection engine he created for Motion is one of the most sophisticated pieces of engineering I’ve ever seen.

In his email, Matt explains that he set a goal of closing 60 issues in Q1, and wound up closing 160. He wanted to do a major refactor of Motion in Q2, and got it done in a single January afternoon! AI has significantly boosted his productivity.

This is remarkable, and you might think that this is evidence that LLMs are even better than the best human developers… but that implies that everyone is having the same success with AI tooling as Matt. And that’s just not true.

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