Why Your Child’s Storybook Takes an Hour (and What It’s Competing With)

The claim first, because it explains everything else: your child’s storybook is generated on the same finite pool of computing power as every other AI request on Earth, and right now there is not enough of that power to go around.

I know the wait this causes is real because parents message me about it. Someone starts a book on Enchantably, checks back forty-five minutes later, and it’s still going. The messages are polite, but they carry the unmistakable energy of is this thing broken?

It’s not broken. It’s waiting in line.

The line it’s waiting in is one of the strangest queues in the history of technology. I want to walk you through it, partly so you understand why your book takes a minute, and partly because once you see what’s happening behind that loading screen, you won’t think about AI the same way again.

Your book is sharing a brain with the entire world

Nobody really explains this about AI, so let me: it all runs on the same infrastructure.

The servers that generate your child’s personalized storybook, the illustrations and the poem and the layout, draw from the same global pool of computing power that is simultaneously being used to detect cancer in medical imaging, accelerate drug discovery, write code, generate memes, translate languages, power chatbots, compose music, and run the backend of nearly every major tech product you use daily.

It’s not that your book is on one computer and cancer research is on another. They’re all pulling from one shared, finite resource, AI compute, and in 2026 demand for it has outrun supply.

This isn’t a hypothetical. AI companies are running out of capacity in real time. OpenAI scaled back its video-generation tool earlier this year just to keep its core services running. Anthropic’s users have been hitting usage caps faster than expected. Google’s CEO warned that compute limits were already constraining growth. The GPU chips that power all of this have wait times of 36 to 52 weeks, which is nearly a year just to get the hardware.

Your child’s bedtime story is standing in a line that stretches around the planet.

What’s actually in this line

I think a lot about the absurd democracy of this queue. If you could see the requests lined up alongside your child’s book, it would look something like this:

A radiologist’s AI assistant analyzing a mammogram for early-stage breast cancer. A pharmaceutical company running molecular simulations to find a drug candidate that could save thousands of lives. A teenager making a meme about a cat wearing a top hat. A developer asking an AI to write a sorting algorithm. Three separate startups generating marketing copy that nobody will read. A climate scientist modeling ocean temperature patterns. A college student asking an AI to explain the French Revolution. Someone generating 47 variations of a logo. A researcher training a model to predict protein structures.

And your kid’s book, right there in the mix, waiting its turn alongside the lifesaving and the time-wasting alike.

There’s no priority lane for importance. The meme and the mammogram wait in the same queue.

The token-maxing bros

And then there are the token-maxing bros. If you’re not in tech, a quick translation: in AI, a “token” is a unit of text or data that the model processes. Every word, every image prompt, every request eats tokens. And right now, in Silicon Valley, there is a competitive subculture of developers and AI enthusiasts who are essentially gobbling as many tokens as they can: running massive automated queries, stress-testing models, building elaborate AI agent chains that call AI to call AI to call AI, burning through compute at industrial scale not because they’re solving a problem, but because they can. Because it’s a flex. Because the leaderboard is measured in throughput.

I’m not here to judge what anyone does with their compute. But I am here to tell you that when your child’s book takes an hour instead of ten minutes, part of the reason is that the same pool of resources is being consumed by someone running recursive AI loops to see how many tokens they can burn in a day.

The demand for AI compute grew from about 6 million tokens per minute in October 2025 to roughly 15 billion tokens per minute by March 2026. That’s not a typo. That’s a 2,500x increase in six months. And the infrastructure, the physical data centers and chips and electricity and cooling systems, simply cannot keep up.

What your book actually requires

So what actually happens when you hit “create” on Enchantably?

Your book isn’t one AI call. It’s many. The system generates a personalized story based on the arc you selected: your child’s name, their companion, the setting, the emotional journey. That’s one call. Then it generates a poem for each page, tailored to the story. Multiple calls. Then it generates an illustration for each page. Each illustration is its own compute-intensive request, because image generation requires significantly more processing power than text. Then there’s the layout, the dedication page, the cover.

A single book can involve dozens of individual AI requests, each one waiting its turn in that global queue. When the servers are busy (and in 2026, the servers are always busy), each of those requests takes a little longer. The delays compound. And what should take fifteen minutes stretches to forty-five, or an hour.

I wish it were faster. I’m constantly working on optimization: batching requests more efficiently, reducing redundant calls, making the pipeline leaner. But the bottleneck isn’t my code. The world wants more AI than it has built the machines to deliver.

The part that amazes me anyway

What steadies me, even on the days when the generation times make me want to throw my laptop into the yard, is embarrassingly simple.

It works at all.

The fact that a parent in Virginia can open their phone, answer a few questions about their child, and receive, in under an hour, a fully illustrated, personalized, original storybook with their kid’s name on every page, a unique poem on every spread, and illustrations that look like they belong on a bookshop shelf?

Three years ago this was not difficult or expensive. It was impossible.

The same AI infrastructure that’s detecting cancer and discovering drugs is also generating a story about your child’s stuffed elephant navigating Tooth Town. And it’s doing it for the cost of an avocado toast. The sheer improbability of that, world-changing tools available at this scale and this price to a solo founder building children’s books out of her kitchen, still knocks the wind out of me sometimes.

The wait time is real. I feel it. You feel it. But what’s happening during that wait is extraordinary: a global network of the most powerful computers ever built is painting a picture of your child riding a dragon, and the reason it takes a minute is because it’s simultaneously helping someone else fight cancer. I can live with that. I think you can too.

What I’m doing about it

I don’t want to leave this at “the wait is worth it” and shrug. I’m actively working on making the experience faster:

I’m optimizing the generation pipeline to reduce the number of AI calls per book. I’m implementing smarter caching so that repeated elements don’t need to be regenerated from scratch. I’m monitoring the compute landscape and adjusting when I see capacity windows (yes, there are times of day when the queue is shorter, and I’m working to leverage that). And as AI infrastructure catches up to demand, with more data centers coming online, more efficient chips, and better memory, the generation times will come down. This is a when, not an if.

There’s one more thing I want to be transparent about. The image models I use for illustrations are top of the line; they’re the reason the books look as good as they do. But they’re also pre-GA, which is tech-speak for “not yet officially released to the general public.” I made a deliberate choice to use these models because the quality difference is night and day. The tradeoff is that pre-GA models come with lower service-level guarantees. They’re not running on the same battle-hardened infrastructure as a fully launched product. They’re faster some days, slower others. Occasionally they hiccup. It’s the price of the cutting edge over the safe middle, and for the quality of illustrations your child gets, I’ll take that tradeoff every time. But I want you to know it’s a factor.

In the meantime, I’d rather be honest about why it takes the time it takes than pretend the loading bar doesn’t exist.

A strange and wonderful moment in time

We’re living in a window, probably a short one, where the demand for AI has dramatically outstripped the supply. It’s a little like the early days of the internet, when you’d wait three minutes for a single image to load over a dial-up modem and your mom would pick up the phone and kill your connection. It was slow and it was annoying, and it was also the beginning of everything.

Your child’s book is being generated in the middle of a global compute crunch, alongside cancer research and meme generation and climate modeling and protein folding. It’s sharing a brain with the best and silliest of what humanity is building right now. That’s not a bug. That’s the moment we’re in.

And when your book arrives, with your kid’s name on the cover and a story that was made just for them, it is worth the wait.