The Hidden Potential of AI: Minimizing Resource Consumption in Software

The rise of AI in software engineering is generating a lot of excitement. One of the most hyped developments is the potential for non-technical users — those without deep coding expertise — to write requirements in AI-powered chatbots and have AI generate code in response. This is the ultimate vision of the No-code movement. Such an approach promises to make software development more accessible, reducing the need for specialized software engineers to handle every detail of the process.

While this is an exciting evolution and some results are already visible, the idea of AI fully replacing software engineers remains a distant reality (although, with the current pace of innovation, it may come faster than we expect).
The reason? Too many nuanced decisions are made by software engineers throughout development—decisions that functional users are often unaware of, but which deeply affect the quality, performance, and reliability of the code.

However, what seems to be missing in the current AI discussion around software engineering is its potential to reduce the resource consumption and footprint of software applications. In a world increasingly concerned with efficiency, sustainability, and scalability, AI’s role in this area is just as crucial as its role in code generation. AI is often portrayed as resource-intensive and inefficient, which is true to some extent. But if we can justify the one-time, high resource usage of AI to optimize code that will be executed thousands, if not millions, of times, the overall consumption becomes far more justifiable.

Here are three concrete examples where AI could transform software engineering by minimizing resource usage:

Reducing Footprint by Streamlining Open-Source Components: Modern software development often relies on assembling open-source components and libraries to accelerate development. However, applications typically use only a fraction of these libraries' functionality, leading to bloated deployment packages with unnecessary code.
AI could analyze which parts of a library are actually being used and automatically strip away the unneeded code. This would allow developers to retain full source code during development while deploying a much lighter, optimized version of the application. The result? Reduced footprint, faster load times, and lower storage and bandwidth requirements for users.
Optimizing Compilation with AI: When software engineers write code in high-level languages, compilers translate that code into machine-readable instructions. While modern compilers are highly optimized, they often can’t match the performance of hand-written code in low-level languages like assembly, where programmers can make assumptions and optimizations that the compiler can’t account for.
AI has the potential to close this gap. By analyzing the context of the code, AI could identify optimization opportunities that traditional compilers miss. It could even prompt developers with questions about data inputs or resource constraints and use their answers to guide the compiler toward more efficient machine instructions. This could bring the performance benefits of hand-optimized assembly code to higher-level programming environments.
Reducing Network Usage Through Binary Comparison: A common inefficiency in software deployment is the repeated downloading of entire software packages, even when only small parts of the code have changed. This results in unnecessary network usage, particularly in large-scale applications or environments with bandwidth constraints. For example, consider the amount of data downloaded when a company like Meta releases a new version of the Facebook app in the App Store or Google Play Store to fix just a few small bugs. In principle, only a few machine instructions have been modified, yet everyone still downloads the full version of the app. AI could help generate efficient binary deltas—small files that contain only the changes made to the previous version of the software. By intelligently calculating these deltas, AI could ensure that only minimal data is transferred, significantly reducing network load. Additionally, AI could efficiently apply these deltas on the client side, ensuring the updated software runs smoothly without requiring a full download.

The true future of AI in software engineering may not be about replacing human developers but enhancing their capabilities. This can mean automating repetitive tasks to increase efficiency, but also more importantly, driving optimization. We are still far from AI managing the complexities of business logic or making the myriad small decisions required to write scalable, secure software. However, AI’s potential to optimize resource usage, reduce footprints, and streamline deployment processes could have a much more immediate impact on how we build and distribute software.

Efficiency gains from AI-driven optimizations could benefit not only performance and costs but also sustainability, as the global demand for computing power continues to rise. Software that consumes fewer resources, occupies less storage, and transmits fewer bytes over the network will be a crucial part of the future.

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