At a recent industry meetup, a thought-provoking question emerged: Has Artificial Intelligence (AI) evolved into a mature engineering discipline? The discussion was lively but largely focused on AI in isolation. That conversation stuck with me, prompting a deeper dive—not just into AI, but into how engineering disciplines as a whole mature over time.
As an electronics engineer by education and a software professional by trade, I’ve had the privilege of witnessing the evolution of two of the most impactful fields of the last century. Exploring their journeys offers valuable context for assessing where AI stands today.
Electronics Engineering: From Breadboards to Modular Mastery
The early days of Electronics Engineering involved manually designing circuits on paper, followed by prototyping on breadboards using discrete components—resistors, capacitors, diodes, and transistors. Once a design was validated, it was implemented on a fabricated Printed Circuit Board (PCB) for practical use.
The advent of Integrated Circuits (ICs) shifted the focus away from discrete components to chip-based solutions, enabling the development of more complex and compact devices. With the introduction of electronic design automation (EDA) software, breadboards became obsolete. Engineers could now design and simulate circuits virtually, while innovations in PCB manufacturing allowed for multilayer, high-density layouts, driving massive growth in the electronics industry.
As circuit complexity increased, the repairability of boards diminished. Faulty boards were more cost-effective to replace than repair, giving rise to a module-based approach where entire subassemblies—power supplies, heatsinks, sensors—were treated as replaceable units. This replaceability, coupled with the establishment of standardized design processes and professional certifications, reflects the discipline's maturity.
This evolution from component-level design to modular systems is one axis of engineering maturity. Another is the educational background required for practitioners. Initially, expertise in electronics demanded a PhD-level understanding. Today, a broad spectrum of professionals—from electronics engineers and science graduates to high school hobbyists—can contribute meaningfully to the field.
Software Engineering: From Binary to Low-Code
Software Engineering has experienced a similarly dynamic journey. Early programming involved writing binary code, followed by assembly language. The development of the C programming language marked a turning point, unlocking higher-level programming languages such as COBOL, which ushered in increasing levels of abstraction and domain-specific specialization.
Today, we have dedicated languages for specific tasks: HTML, CSS, and JavaScript for front-end development; SQL for data manipulation; and many others. Libraries and frameworks—like Java’s Struts for web applications—further simplified the development of complex systems. The latest trend is toward low-code and no-code platforms, allowing users to configure applications through visual interfaces rather than writing traditional code.
Metrics in Software Engineering have also matured. Early measures focused on hardware utilization and code efficiency (e.g., Chidamber & Kemerer metrics), but contemporary metrics, such as those in the DORA framework, emphasize business impacts and user satisfaction.
Innovations in storage media from punched cards and magnetic tapes to floppy disks, hard drives (HSSs), and solid-state drives (SSDs) have further supported the field’s ability to handle larger instruction sets and data, reflecting its growing maturity and capacity to address societal needs.
On the education front, software engineering has democratized significantly. What once required a PhD is now accessible to computer science graduates, electrical engineers, general science graduates, and even self-taught individuals and high school students.
Contemporary AI: Powerful but Early in Its Journey
In contrast, contemporary AI remains in its early stages of development. The field demands a deep understanding of complex mathematical concepts—such as linear algebra, statistics, and optimization—alongside specialized programming skills in languages like Python and frameworks like TensorFlow or PyTorch. This high entry barrier mirrors the educational requirements of early Electronics and Software Engineering.
AI is characterized by rapid innovation, evidenced by the proliferation of research papers and the emergence of new standards, such as the Agent Name Service (ANS) and Model Context Protocol (MCP). Experimentation with novel hardware architectures and internal communication protocols (e.g. DeepEP by Deepseek) underscores the field’s dynamic nature. However, current metrics in AI remain narrowly focused on technical aspects, such as hardware utilization, code efficiency, and the accuracy or "truthfulness" of outputs, rather than broader business or societal impacts.
Significant challenges persist, including the need for explainable AI to ensure transparency in decision-making and the ethical implications of deploying AI systems. These factors, combined with the field’s reliance on cutting-edge expertise and its evolving foundations, suggest that contemporary AI has not yet reached the maturity of its engineering counterparts.
Conclusion
While contemporary AI has made remarkable strides, it has not yet achieved the maturity seen in Electronics Engineering or Software Engineering. Its dependence on advanced technical knowledge, the rapid pace of research, and the predominance of technical metrics all indicate a discipline still in its formative stages. By contrast, Electronics and Software Engineering have developed standardized practices, broadened educational accessibility, and shifted their focus to encompass broader impacts—hallmarks of mature engineering fields.
To advance AI toward maturity, the community must prioritize several key areas: investing in education and training programs to widen access, establishing industry-wide standards to stabilize practices, and reorienting metrics to measure societal and business value alongside technical performance. Only through such efforts can AI fully evolve into a robust and mature engineering discipline.