Can Kimi K3 Write?

Three frontier models wrote the same essay. Four AI judges picked a winner.

This post is going out early as a thank-you to paid subscribers. The full piece opens up to free subscribers next Tuesday. I’m grateful you’re here, and grateful for your commitment to rethinking what education can be. Your support is what keeps this work going.

On July 16, Moonshot AI released Kimi K3, which the company describes as the first open-weight model in the three-trillion-parameter class. As I am writing this, the weights themselves are not yet out. Moonshot has said all 2.8 trillion of them will be published on July 27 under a modified MIT license.

I should note that “open weights” does not mean “local execution” in this case. In its native format, the model requires roughly a terabyte and a half of video memory. That is before you count anything else that has to sit in memory alongside the weights. Nobody will be able to run this at home.

But what open weights buy is open competition: once the files are public, third-party hosts will be able to serve K3 without asking anyone’s permission, at a launch price low enough that the proprietary labs will have to answer it. For the first time, the open-weight ecosystem is breathing down the necks of Anthropic’s Claude Fable 5 and OpenAI’s ChatGPT 5.6 Sol.

That industry story is interesting in itself. But the question I want to address in this newsletter is more personal. Can the thing write? I mean, not benchmark-write. Write under constraints, with a voice a human editor would want to work with.

So in today’s post, I want to walk through an experiment I ran to find out exactly that: one research brief, one style guide, three models drafting, four judging blind, and a closing round of guess-who-wrote-what that went almost too well.

One brief, no scaffolding

Regular readers know the drafting workflow I usually follow. I have described it in an earlier post.

I start by sending an essay idea into Gemini 3.5 Deep Research to develop a sourced brief. The brief then goes to a language model, usually the latest Claude model, along with The Augmented Educator style guide. The model returns a rough draft. Only then does the actual writing start. I use an iterative process and rewrite until it sounds like me.

The essay idea I used for this experiment came from a YouTube video in which the creator made the following deceptively logical claim: “In art, the effort does not matter. The art itself matters.” His thesis was that talented artists will thrive with AI tools, whereas untalented artists will fall behind regardless of AI use.

I felt there might be some historical context to unpack here, since this is likely not the first time this claim was made. It therefore seemed like a good seed for an essay on what happens to art education when machines absorb the effort. Perfect for The Augmented Educator.