The Future of Software: Delivered as Prompts, Not Compiled Code

How It Started

In this week’s Prompt Review, I will share the results of a fascinating experiment I conducted and the eureka moment that followed. I tested four AI models—ChatGPT, Claude, Gemini, and DeepSeek — on the task of writing a Python Snake Game. The results were striking: ChatGPT and Claude succeeded on the first attempt, producing complete, functional code. DeepSeek required one additional try, while Gemini failed entirely, unable to generate a working solution even after multiple refinements. This simple experiment revealed some interesting insights. The eureka moment came with the realization that, in the near future, software could be delivered as AI-generated prompts, customized to meet the unique needs of both individuals and businesses.

Experiment

Requirements (Prompt)

Write a Python program for a classic Snake Game named "snake_game.py". The game should have the following features:

1. The screen dimensions are 800x600 pixels, and the grid cell size is 20 pixels.

2. The snake starts at a fixed position and grows when it eats food. The initial snake body is three blocks long, and the snake moves in four directions (up, down, left, right) controlled by arrow keys.

3. If the snake eats food, the score increases by 10 points, and the food respawns at a random location within the screen's bounds.

4. The snake should wrap around the screen edges when crossing boundaries.

5. The game should end if the snake collides with itself.

6.?The snake is drawn as multi-colored blocks cycling through green, blue, yellow, orange, and purple.

7.??Food is displayed as a red block, and the score is shown at the top left of the screen.

8.?Add instructions to quit the game using the 'Q' key at the bottom-left corner of the screen.

9.?Use Pygame for the graphical interface and control the game speed at 15 frames per second.

10.?Ensure the game includes clean and modular code, structured into functions where appropriate.

Provide the complete Python code for this game.

Results

This experiment revealed more than just varying AI capabilities — As I ponder the experiment, it occurred to me that a fundamental shift in software development and distribution could be unfolding. That is, instead of distributing pre-built applications, we could be moving toward a future where software is generated on-demand through carefully crafted prompts, with AI serving as the “compiler” of natural language or structured prompts into functional application to be consumed based on individual users or a business specific need and requirement.

The Program (Application)

The End of "One-Size-Fits-All"

Traditional software distribution has followed a predictable pattern: developers write code, compile it, and distribute it as a uniform product to all users. This approach, while efficient for mass distribution, often results in bloated applications trying to serve every possible use case. However, my experiment revealed something fascinating - AI models like ChatGPT and Claude could generate complete, functioning programs tailored to specific requirements, suggesting a future where software can be customized on demand.

The Prompt Store Revolution

Imagine a "Prompt Store" — similar to Apple's App Store or Google Play—but instead of downloading pre-built applications, users browse through a curated selection of tested prompts that generate the exact software they need. Enterprise software providers like Oracle, Salesforce, and ServiceNow could adopt this paradigm, offering prompts tied to their trained and fine-tuned LLMs to generate customized, on-demand software solutions. In my view, this shift would redefine application distribution and usher in a transformative customer experience, enabling businesses to create highly individualized software instantly.

On another level, platforms like ReadiPrompt could serve as marketplaces where prompt engineers showcase their expertise in crafting precise, high-quality prompts that reliably generate customized applications. These platforms could also revolutionize software distribution by enabling software publishers to deliver personalized, on-demand solutions through AI-generated instructions.

The Road Ahead

My key takeaway from this experiment is clear: the success of ChatGPT and Claude—and to a lesser extent, DeepSeek—in generating a complex game from a detailed prompt demonstrates that this isn’t science fiction; it’s an emerging reality.

This approach has the potential to revolutionize software customization and distribution, much like how smartphones redefined personal computing. The future of software distribution may evolve into a hybrid ecosystem, where traditional app stores and conventional software delivery models transition into prompt-driven marketplaces—a direction platforms like ReadiPrompt may follow.

This shift isn’t just another technological advancement—it marks the beginning of a new era in software development, one that could be as transformative as the rise of personal computing or the advent of cloud technology.