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The Automation Paradox: Why Software Engineers Create Their Own Commoditization.

Software engineers create value by automating processes. We build systems that generate value tirelessly, like robotic hamsters in virtual wheels. But here’s an interesting observation: when automation truly succeeds, it opens new opportunities for skilled work.

This is the automation paradox, and it’s reshaping how we approach our craft.

The Web Development Evolution

Remember when web development was a specialized craft? You knew HTML, CSS, and —gosh— SEO! Clients needed websites, and you had the expertise to build them. The work paid well, and demand seemed endless.

Then came the drive for efficiency. We wanted to build websites faster, more effectively. We developed automation tools that would streamline the process and let us tackle more ambitious projects.

Enter Squarespace.

They automated web development so effectively that they democratized it entirely. What was once a specialized skill became accessible through intuitive interfaces. Web development evolved—not because Squarespace was disruptive, but because they delivered exactly what the market needed: accessible, automated creation.

We gained efficiency and reached new audiences. We shifted our focus to more complex challenges.

The AI Automation Opportunity

Today, we’re seeing similar patterns emerge with artificial intelligence. AI and LLM companies are creating powerful tools by automating complex tasks. The value proposition sounds familiar: “We’ll make this process so efficient that you can focus on what matters!”

This creates an opportunity: as AI handles routine tasks, engineers can focus on higher-value work that requires human judgment and expertise.

Consider the current AI landscape. Companies are building tools to assist with:

  • Code generation and review
  • System architecture design
  • Testing and quality assurance
  • Documentation and knowledge management

When these tools work well, they free engineers from repetitive tasks. What happens when junior developers can generate boilerplate code with a few prompts? What happens when AI assists with initial architecture sketches?

This creates space for engineers to focus on complex problem-solving, strategic thinking, and system-level optimization.

Lessons from Squarespace’s Victory

Squarespace didn’t invent drag-and-drop website builders. They weren’t the only company trying to democratize web development. What made them successful?

Their moat wasn’t technological superiority—it was simplicity and market positioning. They made web creation so effortless that the average person could do it without thinking twice. Their marketing reinforced this positioning, creating mindshare that competitors struggled to challenge.

The lesson for AI companies (and software engineers watching this unfold) is clear: true automation success means making specialized skills accessible to non-specialists. When that happens, the market rewards the automation platform, not the individual experts.

So how do software engineers maintain value in an increasingly automated world?

1. Focus on Complex, Contextual Problems

Automation excels at repetitive, well-defined tasks. Human engineers add value through:

  • Understanding nuanced business contexts
  • Making judgment calls in ambiguous situations
  • Balancing competing technical and business priorities
  • Building systems that evolve with changing requirements

2. Embrace Automation as a Tool, Not a Threat

Rather than resisting automation, use it strategically:

  • Automate routine tasks to focus on high-value work
  • Build tools that amplify your expertise
  • Teach others how to leverage automation effectively

3. Develop T-Shaped Skills

Broad knowledge across multiple domains becomes more valuable as specific technical skills become commoditized. Understand business strategy, user experience, system architecture, and team dynamics—not just coding.

4. Build Systems, Not Just Code

The most valuable engineers design and maintain complex systems that require ongoing judgment and adaptation. These systems can’t be fully automated because they depend on human understanding of context, trade-offs, and changing requirements.

The Path Forward

The automation paradox doesn’t mean software engineering is doomed. It means we’re entering a new era where value comes from judgment, context, and systems thinking rather than specialized technical skills alone.

As AI and automation tools mature, successful engineers will be those who:

  • Understand when and how to apply automation
  • Can navigate complex, interconnected systems
  • Make sound decisions under uncertainty
  • Communicate technical concepts to diverse stakeholders

The engineers who thrive won’t be those who cling to outdated workflows. They’ll be the ones who harness automation to solve bigger problems, create more value, and build more ambitious systems.

The paradox isn’t a threat—it’s an opportunity to redefine what makes engineering truly valuable.