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Average is all you need

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Why This Matters

This article highlights how large language models (LLMs) are democratizing the creation of average content, data, and software, making it easier for individuals and organizations to produce functional, if not exceptional, results. While this shift may seem mundane, it fundamentally transforms productivity and accessibility across industries, emphasizing the value of leveraging AI for routine tasks. For consumers and the tech industry, this means a future where average work is more efficient, enabling focus on innovation and higher-level problem-solving.

Key Takeaways

April 13, 2026 · 6 min read · Editorial

Average Is All You Need

LLMs will make more of your average stuff. And that's OK.

This is not going to be much of a hot take but whether we like it or not, whether we want to admit it or not: LLMs have eaten the world.

They first had a go at creative fields where they essentially made everyone capable of publishing an average text with some average ideas for an average audience, but incredibly fast and easy. Whereas before, average was expensive in terms of both time and effort, average became cheap.

Software is now getting the same treatment. Very likely other fields are going to experience the same average treatment; and bets are high on everything text and based on descriptive textual semantics (IP, lawyers, translators, Marvel movies...)

Now there is nothing inherently bad about average stuff, it sits, by definition, in the middle of the normal distribution of stuff. It is in fact amazing that anyone can now create average things whereas before they had to fight hard for sub-par; they now have to settle for average or do better and try to think about it.

I can't draw. But now, I still can't draw, but better.

Data is the same problem, but better.

Here is the thing about data: your intuitive knowledge of what you want from it is much higher than average. You know what is in your organisation's data and you can likely "feel" what is hidden in it. You just do not necessarily know how to get the information out of it effectively; most people do not write SQL that well, do not understand syncing strategies that intuitively, do not know how to generate charts that nicely.

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