The Automation Paradox: When Making Things Easier Makes Them Worth Less.

Software engineers automate processes. We make machines generate value[^1] like tireless robotic hamsters in little virtual wheels.

Ironically, the moment a process gets fully automated, it becomes a commodity. Enough competitors automate it, and price gets dictated by supply/demand, not the value it creates.

Take web development - once a killer money spinner. People couldn’t make websites, but everybody needed one. You knew HTML, CSS, and (gosh!) SEO.

Clients booked, websites made, bills paid.

So we wanted to make more. Faster. More efficient. We automated the hell out of it.

Then Squarespace ruined it for all of us[^2].

Web dev as we knew it was dead[^3]. They automated all the work out of it.

Exactly what we wanted. Exactly what we craved. Exactly what ended our gravy trains.

The Automation Paradox

This is the automation paradox: when you successfully automate a process, you often reduce its economic value. The automation makes the work accessible to more people, which drives down prices and changes market dynamics.

Software engineers are particularly susceptible to this paradox because our core value proposition is automation. We create systems that reduce manual effort, streamline processes, and eliminate repetitive tasks.

But each successful automation reduces the scarcity of the skill that was previously required. If enough people can automate a process, the market dictates pricing based on supply and demand rather than the value generated.

It all has to be about AI these days…

Many AI/LLM/GPT-sock-puppet[^4] companies are reaching for very-big-pots-of-gold in the same way Squarespace did.

Trouble is, they often have no moat. If it’s so easy to build an LLM app to automate something… then so can I. So can you, too, probably.

The question nobody asks: once AI automates our work, what happens to its value?

Understanding Client Problems as a Moat

Pure automation work becomes commoditized. But some things automation can’t easily touch.

Deep client understanding. The best engineers get the business problems, not just the technical ones.

Business context. Human factors. Strategic implications. Organizational BS.

You articulate why a solution matters, not just how it works.

But will AI ever get this? It can read documents and spit suggestions. But the subtle cues? Unspoken assumptions? Human trust-building? That interpersonal dance?

Yeah, probably not anytime soon.

Decision-Making Excellence

As automation eats the routine work, value shifts to decisions under uncertainty.

Evaluating trade-offs. Business impact. Technical debt. Architecture planning.

Skills that come with experience. Can’t be easily automated.

But will AI reduce our mental load here? It can gather data and suggest options.

The judgment calls? Weighing priorities? Anticipating unintended consequences?

AI might make it faster. Or just create more options to evaluate, more complexity to manage.

Product Development Excellence

The strongest moat: creating products people actually need and want.

Deep user understanding. Real feedback iteration. Technical-business balance. Maintainable systems.

Automation becomes a tool that enhances, not commoditizes.

But will AI get this? It can accelerate cycles and suggest improvements.

The creative leaps? Unmet needs? Emotional resonance?

As AI gets involved, it might increase cognitive load - validating suggestions, managing complexity, overseeing what you don’t fully understand.

The Question of Moats

If automation keeps advancing, how do engineers maintain value?

Technical expertise erodes as tools get better. Need new sources of value that automation amplifies, not diminishes.

Not about resisting. About understanding the new landscape.

Practical Implications

For engineers: build skills automation can’t touch.

• Business acumen
• Strategic thinking
• Complex problem-solving
• Communication
• Leadership

For organizations: value human skills alongside technical ones.

The future belongs to those who leverage automation thoughtfully. Not assuming it’ll reduce mental load, but managing how it changes our work. AI might not make complex tasks easier - it might make them different.

A Balanced Perspective

The automation paradox isn’t a death sentence for software engineering. It’s a reminder that value creation in technology is dynamic. What was valuable yesterday may be commoditized tomorrow.

The key is recognizing that automation is a tool, not a destination. But we should be cautious about assuming it will automatically free up mental load for “higher-order” thinking. Sometimes automation creates new cognitive burdens - managing AI outputs, validating suggestions, and dealing with increased complexity.

The paradox becomes an opportunity when you actively shape how automation changes your work, rather than passively accepting that it will make everything easier. The uniquely human aspects of software development may remain difficult to automate, but they might also become more important - and more challenging - as AI handles more of the routine work.

The challenge - and the opportunity - is building moats around the uniquely human aspects of software development: understanding complex problems, making sound decisions, and creating products that deliver real value. But we shouldn’t assume these moats will be easy to defend or that AI won’t find ways to encroach upon them.

Automation will continue to change what we do and how we do it. The engineers who thrive will be those who adapt not by resisting change, but by thoughtfully integrating automation while preserving and enhancing their most valuable human skills.