The rise of models like GPT, Claude, and Copilot has transformed how we write software. Today, much of our code is no longer written from scratch but co-written with generative models that assist, suggest, and complete entire fragments.
Amid this shift, a common but mistaken idea has emerged: that AI-generated code is “probabilistic.”
Many developers distrust code produced by AI because they believe it is probabilistic. In my view, this stems from confusing how the model predicts tokens with the nature of the resulting code. One thing is the generation process, and another, very different, is the final product.
How models generate code
Large language models (LLMs) such as GPT or Claude generate text token by token, choosing the next word based on a probability distribution.
This variability has been empirically documented: even with identical initial conditions, outputs can differ in structure and content.
That part of the process is indeed stochastic.
The resulting code is deterministic
However, once the model produces code, probability disappears.
The file that remains in your editor is no longer a prediction but a deterministic and knowable structure—you can read it, test it, and debug it.
It is not a black box. It is plain, open, legible text.
The code may contain errors or inconsistencies, but its behavior is verifiable: it either compiles or it doesn’t, it passes tests or it doesn’t.
In that sense, it ceases to belong to the domain of the probabilistic and enters that of logical and verifiable reasoning.
Research confirms it
Several researchers have pointed out the need to distinguish between the stochastic nature of the model and the deterministic character of the environment in which it operates.
Ensigno (2024) notes that models “operate probabilistically in domains that demand exact prediction,” and that the solution is to design deterministic workflows that control and validate model outputs.
This principle also underpins proposals such as Blueprint First, Model Second, which advocate architectures where the main system logic remains deterministic, delegating only flexible generative tasks to the LLM.
Your responsibility: read critically
A developer’s real responsibility is not to expect perfect accuracy from AI, but to read its output critically.
As Miguel Grinberg (2024) reminds us, reviewing AI-generated code is an essential skill, not a waste of time: “understanding it is part of the job.”
Models do not come to prevent us from reading code, but to force us to read better.
If you can’t process the repetitive or imperfect code an AI produces, the problem is not the model but a lack of technical literacy.
Because code, though born from a probabilistic process, is not probabilistic in itself.
It is knowable.
And once you understand it, it’s yours.
References
- An Empirical Study of the Non-determinism of ChatGPT in Code Generation (2023).
- Ensigno (2024). The probabilistic paradox: Why LLMs fail in deterministic domains and how to fix it. Medium.
- Blueprint First, Model Second: A Framework for Deterministic LLM Workflow (2025).
- Grinberg, M. (2024). Why Generative AI Coding Tools and Agents Do Not Work for Me.