The Parrot and the Photograph

Image prompting cuts AI costs for developers and gives students a new way to talk to machines

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.

If you have spent any time in developer corners of social media over the last few weeks, you will have run into pxpipe. It is a small open-source proxy, published at pxpipe.dev, that sits between AI coding tools like Claude Code and the models behind them. Before a request leaves your machine, pxpipe takes the bulkiest parts of the prompt, the standing instructions, the tool documentation, or the older conversation history, and renders them as PNG images. Instead of sending the model your text, it sends the model a picture of your text.

The point of this is money. On real production workloads, pxpipe reports cutting the total bill by 59 to 70 percent. Its demo shows the same coding session costing $42.21 with plain text and $6.06 with images. Same task, same output. The repository collected thousands of GitHub stars within days of going viral, and developers have spent the past weeks arguing about whether this is a clever hack or an accident waiting to happen.

For anyone who has not followed the image capabilities of current AI models, this should sound backward. A picture of a page is surely more data than the page. How can it be cheaper to show a machine a photograph of your words than to hand it the words themselves? And, stranger still, why does the machine read the photograph just as well?

So in today’s post, I want to unpack image prompting as a cost optimization technique, and then follow the trail somewhere more interesting than a billing statement. Most readers of this blog will never run a proxy or worry about API pricing. Nevertheless, the very fact that this trick is effective shows something significant about the way these systems process information when they read.

It is, I will argue, one more crack in the “stochastic parrot” picture of AI, the idea that a language model is nothing more than a very fluent autocomplete. And it opens a door for educators that has little to do with saving money.

Why a picture of words can cost less than the words

Language models do not read letters or words. They read tokens, small fragments of text, typically a few characters or a short word each, and commercial AI providers bill by the token. A thousand-word document costs you roughly 1,300 tokens every time you send it. If your prompt includes long instructions or an entire stack of reference material, you pay for all of it on every single request.

Images are billed differently. An image costs a fixed number of tokens determined by its pixel dimensions, not by what is in it. A page-sized image holding 150 characters and a page-sized image holding 15,000 characters cost exactly the same. Think of the difference between a telegram and a photograph of the telegram. The telegram bills by the word. The photograph costs the same, however many words are on it.

That gap is the whole trick. According to pxpipe’s own documentation, about 48,000 characters of standing instructions cost roughly 25,000 tokens as text and roughly 2,700 tokens when rendered as images. Dense material like code, logs, and structured data packs about three characters into each image token, against about one character per text token.

To be clear, nothing shady is happening here. Providers price images by area because that is how their vision systems slice them up into a grid of patches. The pricing simply never expected that anyone would send text through the picture channel in disguise.

The strange part is not the price

The strange part is that the model reads the disguised text fluently. In October 2025, Yanhong Li of the Allen Institute for AI, Zixuan Lan of the University of Chicago, and Jiawei Zhou of Stony Brook University published a paper with the pleasingly blunt title “Text or Pixels? It Takes Half.” They rendered long text inputs as single images, fed them to off-the-shelf multimodal models, and measured what happened. Token counts dropped by roughly half. Accuracy did not drop at all.