As users reload an application or web page multiple times attempting to access it, the user may quickly assume that the issue is with their Internet, device, or web browser; however, they generally do not consider the underlying infrastructure that is causing the delays related to the time it takes to deliver the application or web page to the user's device.
Data Centers, with their large amount of Data being transferred across international borders and the vast amounts of energy required to run and cool these Data Centers, are frequently overlooked by the user. Nevertheless, these Data Centers need to be able to provide the resources necessary to meet the demands of the users.
The Illusion of Automation
In recent months, we have also seen a growing trend of Technology CEO's making more outrageous claims regarding their belief that AI will create Programming. For example, Dario Amodei, the CEO of Anthropic stated, "we will be there in three to six months and AI will be doing 90% of Programming now," while Mark Zuckerberg has claimed that AI will do a majority of development of certain projects in the next year.
"Every line of code should still be reviewed by an engineer." — Boris Cherny, Head of Claude Code Division, Anthropic.
Software Engineer interviews and recent Research suggests that, although the introduction of AI tools has changed how code is written, there is still a requirement for Human Review. Many current AI tools add to the complexity involved in maintaining systems.
The Environmental Cost of Inefficiency
AI doesn’t create code that is environmentally neutral. The added complexity of using AI techniques adds a layer to AI-generated code that, in many cases, only adds more to the overall computational footprint of the software and results in inefficient, poorly defined, bulky datasets and solutions.
As more companies move toward a model of utilizing AI-assisted development, the total financial and ecological costs associated with inefficient AI-based development practices will quickly become significantly higher than if these companies were reliant solely on human-coded software.
Defining "Green" Code
Green code is defined as software designed to eliminate unnecessary processing in order to achieve the same functionality. An example of green code would be software with minimal feature deployments, well-thought-out system architectures, and sound algorithm design. In the context of AI, this definition challenges the belief that generating greater amounts of code will yield better software.
Organizational pressure contributes to the perpetuation of inefficiencies; while many engineers report they are required to utilize AI tools, the inability to utilize AI is frequently viewed as a "personal failure" instead of being recognized as a decision based on judicious technology selection.
Conclusion
Accountability in the new AI economy is not automatic; it is designed. The impact of AI upon how software is created cannot be denied; but while it has created an urgency to solve the trade-off between sustainability vs. speed and quality, AI has not solved that dilemma nor eliminated the need for human judgment.