Every big leap in software has been about letting go of a layer we used to worry about. Operating systems freed us from the hardware. Virtualization freed us from the physical machine. Containers freed us from the environment, and orchestration freed us from the machines entirely.

Hardware → Operating System → Virtualization → Containerization → Orchestration → ?

And yet, at the very top of that ladder, we still hand-craft applications the same way we did decades ago. Controllers, repositories, mappers, wiring, plumbing. The business outcome we're actually trying to achieve is buried in there somewhere, spread across a hundred files. Ask anyone on the team what the system does, and they'll point you to a wiki page that stopped being true two sprints ago.

The thing is, we're now entering a world where AI writes more and more of our code. That changes the question. It's no longer "how fast can we type", it's "how precisely can we describe what we want". What we're missing is a target to compile to. An assembly language for information systems.

That's what Screenplay is. A new abstraction level, the Application Model, that sits on top of the ladder we've been climbing for fifty years. Screenplay is built on Event Modeling and describes systems as vertical slices: commands, events, read models, and the flows that connect them. You describe the business outcomes. An engine turns that description into a running, event-sourced solution. No infrastructure decisions, no plumbing, no drift between the design and what's actually running.

In this talk I'll make the case for why this abstraction is inevitable, show you what Screenplay looks like, and go from a business-level description to a running system. Live, no slides pretending it works. Just the thing itself, working.

You'll see:

- Why the next rung on the abstraction ladder is the application itself, not more infrastructure
- How Event Modeling's building blocks (commands, events, read models) become an executable language
- What it means to describe outcomes instead of implementations, and let an engine do the rest
- Why AI makes a precise, verifiable application model more valuable than the code it generates