Do you worry that AI will take your job as a software engineer? Are there legitimate reasons to be concerned? In my reading I encountered this article from Tim O'Reilly, entitled "The End of Programming as We Know It" which had some thoughtful insights. He makes a comparison between what happened at the start of the industrial revolution to what is happening in the software engineering world today and the impact of AI.
At the start of the industrial revolution (1760-1840), automation and machines like power looms and steam engines began to displace skilled textile workers and manual laborers. In Britain's textile industry, a single mechanical loom could replace six hand-weavers, causing the wages of weavers to fall by 40% between 1800 and 1830. This depression in wages continued until workers began developing specialized skills as machine operators, mechanics, and factory supervisors. By the 1850s, those who mastered these new technical roles saw their wages rise by 25-30% above pre-industrial levels, and overall manufacturing productivity increased five-fold. Similarly, when assembly lines revolutionized automobile production in the 1910s, former craftsmen who adapted to become line specialists, quality inspectors, and machine technicians ultimately earned more than their predecessors.
Today AI is sometimes positioned as potentially replacing software engineers, much like mechanical looms threatened to replace weavers. However, Tim's point is that software engineering will likely follow a similar evolutionary path. Just as industrial workers who adapted by developing specialized skills as machine operators and supervisors ultimately saw higher wages and productivity, software engineers who master AI-assisted development could potentially achieve greater levels of productivity and creativity. The current wave of anxiety among engineers mirrors the historical pattern: initial fear of displacement followed by adaptation and transformation of the profession. What particularly unsettles many engineers seeing the rise of AI tools isn't just job security, but a perceived loss of control and autonomy over their craft—similar to how artisans felt during industrialization.
What might change our thinking? The fear of losing control is natural, but it's worth noting that software engineering has always evolved through strategic surrenders of direct control in exchange for productivity.
Abstraction
In the early days of computing, developers had to manually allocate and deallocate memory, carefully tracking pointers and ensuring memory wasn’t leaked or overwritten. Forgetting to free memory could lead to leaks, and accessing freed memory could cause crashes.
Today, languages like JavaScript, Python handle memory management automatically. Garbage collectors in high-level languages reclaim unused memory without the developer needing to think about it. As a result, most developers working in web development, mobile apps, or even many backend systems don’t have to worry about manually managing memory—it's just abstracted away.
Just as we've accepted garbage collection and other abstractions that increased our productivity despite surrendering some control, AI-assisted software engineering represents the next evolution in this pattern. Instead of viewing AI as a replacement, we can understand it as another layer of abstraction that allows us to work more efficiently at a higher level. However, as with all abstractions, human intelligence remains essential. Engineers will still need to understand the fundamentals, validate AI-generated solutions, and improve upon them. The difference is that our focus shifts from writing every line of code to directing AI tools effectively, evaluating their output, and applying our uniquely human insight to solve complex problems that AI cannot fully comprehend.
Cognitive Load
Cognitive load for a software engineer refers to the mental effort required to juggle multiple concepts, tools, and problem-solving tasks while working on a project.
For example, imagine you're developing a React application with an unfamiliar state management library like Zustand. At the same time, you're integrating authentication with Clerk, handling API requests with React Query, and ensuring proper TypeScript typings. Each of these technologies requires different syntax, best practices, and mental models.
Now, add on the need to debug a complex issue—maybe an API call is failing due to a subtle CORS misconfiguration. You have to switch contexts from UI state management to backend networking, while also keeping track of dependencies, error handling, and business logic.
The more unfamiliar tools and complex interactions you have to manage at once, the higher your cognitive load. This can slow down development, increase errors, and lead to burnout. Reducing cognitive load is critical. Engineers can leverage AI assistance to significantly lower this burden in several ways:
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First, AI tools can handle syntax recall and boilerplate code generation. Instead of memorizing the exact structure of a React component with TypeScript props, engineers can describe what they need in natural language, and let AI generate the basic structure. This keeps the developer focused on solving the actual business problem rather than syntax details.
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Second, AI assistants can troubleshoot common errors by recognizing patterns. When facing that CORS misconfiguration, instead of switching context to research solutions, developers can ask AI to diagnose the issue and suggest fixes based on their specific code context, saving valuable mental energy.
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Third, AI can assist with understanding unfamiliar libraries or APIs by providing contextual documentation and examples on demand. Rather than context-switching to read documentation, engineers can maintain their flow state while getting just-in-time information through their AI assistant.
By offloading these cognitive tasks to AI, engineers can reserve their mental bandwidth for higher-value activities: system architecture, algorithmic optimization, and creative problem-solving that truly requires human insight.
Feedback Loops
Feedback loops are fundamental to software development, but AI tools dramatically accelerate and transform this process. Traditional development involves writing code, testing it, identifying issues, and making corrections—often taking hours or days per cycle. With AI assistance, these cycles can be compressed to minutes or even seconds.
When using AI coding tools, rapid iteration becomes your superpower. You can quickly generate a solution, evaluate it, provide feedback to the AI, and refine the approach—all within a single session. This compression of the feedback loop allows you to explore multiple approaches to a problem in the time it might have taken to implement just one approach traditionally.
To leverage these accelerated feedback loops effectively:
Embrace experimentation. Unlike traditional coding where rewriting significant portions of code feels costly, AI tools make experimentation nearly frictionless. Don't hesitate to ask the AI to try different approaches, architectures, or implementations. The cost of exploring alternatives is dramatically reduced, so use this to your advantage to find optimal solutions.
Refine your prompting skills. The quality of AI-generated code directly correlates with the quality of your prompts. Learn to be specific about requirements, constraints, and expected behavior. Include relevant context like programming language, frameworks in use, and performance considerations. When results aren't quite right, don't simply reject them—explain what's missing or incorrect, then ask for improvements.
Manage context efficiently. AI models have limited context windows—they can only see what you show them. Learn to provide just enough context for the AI to understand your problem without overwhelming it with irrelevant details. For larger codebases, focus on sharing the most relevant components and interfaces rather than entire files.
By mastering these feedback loop techniques, you'll transform AI from a mere code generator into a collaborative partner that multiplies your productivity and creativity.
Conclusion
The parallels between the industrial revolution and today's AI revolution in software engineering are striking. Then as now, technological advances initially triggered fears of displacement, followed by adaptation and ultimately greater productivity. The critical difference is that we have the benefit of historical perspective – we can choose to be the weavers who became skilled machine operators rather than those who fought against the inevitable tide of progress.
AI won't eliminate the need for software engineers – it will transform how we work, what we focus on, and the value we create. Just as we embraced abstraction in memory management, high-level languages, and frameworks, AI represents another layer of abstraction that frees us from routine coding tasks. By offloading cognitive load to AI assistants, we gain mental bandwidth for the creative, architectural, and strategic thinking that truly moves projects forward.
To thrive in this new landscape, focus on developing your complementary skills: prompt engineering, evaluating AI-generated solutions, understanding the fundamentals behind abstractions, and applying human intuition where AI still falls short. The most successful engineers won't be those who can write the most code, but those who can orchestrate AI tools effectively while bringing distinctly human creativity and critical thinking to the table.
History teaches us that those who adapt to technological change rather than resist it ultimately benefit the most. By embracing AI as a powerful collaborator rather than fearing it as a replacement, you position yourself at the forefront of a more productive, creative, and rewarding future for software engineering. The question isn't whether AI will change software engineering – it's whether you'll be among those who help shape that change and reap its rewards.