Welcome to the Acceleration

Things are going faster than I expected. It’s not even three months since the prevailing view was that AI-generated code was “slop,” and we’re now seeing the narrative shift completely. It turns out, the researchers who were warning us and were laughed at simply had access to models we did not.

— (@)

But now we have access to them, and they were indeed not exaggerating. In the past week, I’ve built two entirely new apps and made insane progress on a third one I’ve been working on for quite some time, with literally zero lines of code written by me. All AI.

Building software is now more about knowing what you want and how to convey it than about knowing how to do it, to the point seasoned developers are proclaiming “most productive day ever” one day after the previous.

The era of declarative software that I’ve been promoting for more than two years is now upon us. I can’t hide my excitement, but we need to be realistic too.

This article covers what AI is today and what it isn’t, my personal guide to vibe-coding apps, and where all this takes us.

Let’s dive in.

Why The Acceleration is Here

Ever since OpenAI’s GPT-5.2 Codex and especially Anthropic’s Claude Opus 4.5 dropped in the last innings of last year, the speed at which you can build software has skyrocketed.

It Just Works.

It’s not that models are faster; it’s that they simply work. If you write the correct prompt, it’s very likely they'll get you something that is at least 70% of the way there. In some cases, if the project is simple enough, you can get the models to one-shot it.

The key seems to be that AI Labs have finally cracked the code of long-horizon execution; the models can now work for several minutes, sometimes hours (see diagram below), and still execute decisively.

If you demand 80% success rate, the longest task falls to ~1 hour, still impressive though.

This diagram shows the longest task LLMs can execute a given percentage of the time, measured in human hours (how long it would take an expert human to do the task).

Importantly, prompting is no longer about holding the model’s hand all the way; it’s about getting the one prompt you are going to send them right. You are no longer defining constraints, methodologies, or best practices; you are defining goals.

Prompts are now works of art in a way; they take hours to write because they force you to really “see” what you want to build in advance. They are iterative, and funnily enough, done well, it is a part of the process that takes longer than the code writing itself.

In fact, one way to know you’re doing your job is that, from start to finish, the prompt-building part took probably more than 75% of the total building time, with the remaining 25% being the AI actually writing code, and your UAT testing.

To prove…

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