When the model is the software
For 60 years, software was prerecorded: write code, compile, ship, run. The model is a different artifact. It generates the program at the moment of use, contextually, in response to intent rather than instruction. That changes what software is, how it is built, and what business models work on top.
Prerecorded vs. generated software
As established in the von Neumann vs. neural paradigm shift, we are transitioning from the stored-program era of frozen binaries to generated software.
For 60 years, software was a prerecorded, frozen artifact: a developer wrote code, compiled it into fixed binary instructions, and shipped it to the user. A modern frontier model is different. It does not run static code; it is a learned function that generates the program contextually at the moment of use in response to user intent.
This shift changes the basic unit of software. Software is no longer a static bill of materials to be authored. It is the output of a learned network running on a vast accelerator, completely transforming the economics, release cadences, and architecture of digital applications.
What changes when the model is the product
The unit of release changes. You no longer ship a binary; you ship a model weight, a system prompt, a tool harness, an eval suite. Each can change independently. The cadence of release flips from quarterly to continuous.
The cost structure changes. A binary has near-zero marginal cost to run. A model has real marginal cost: every request consumes tokens, electricity, and inference capacity. Pricing models that worked for software (perpetual licences, seat fees) bend under workloads that are usage-priced at the chip level.
The competitive surface changes. The frozen-binary era rewarded distribution and lock-in. The generated-software era rewards model quality, eval coverage, latency, and the integration of the model into a working product. The winning stack is the one where the model, the harness, and the product evolve together.
How to read every product decision through this lens
Any AI product roadmap is implicitly a bet on which side of the prerecorded/generated boundary the product sits. A copilot that just generates text is on the generated side. A workflow that uses a model to fill in a fixed template is mostly prerecorded with a sprinkle of generation. The buyer's economics depend on which side dominates.
The same lens applies to companies. A "software business that uses AI" is closer to Microsoft. An "AI-native business" is closer to OpenAI. Their gross margins, distribution challenges, and platform risks differ in ways the lens makes obvious.