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The Automation Paradox: When Making Things Easier Makes Them Worth Less.
Software engineers automate processes. We make machines generate value1 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 us2.
Web dev as we knew it was dead3. 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-puppet4 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. Just a reminder that value in tech is dynamic. Yesterday’s premium becomes tomorrow’s commodity.
Automation is a tool, not a destination. But don’t assume it’ll free mental load for “higher-order” thinking. Sometimes it creates new burdens - managing outputs, validating suggestions, handling complexity.
Shape how automation changes your work. Don’t passively accept it’ll make everything easier.
Human aspects might stay hard to automate. But they might become more important - and challenging - as AI eats the routine work.
Build moats around understanding problems, making decisions, creating value. But don’t assume these moats are easy to defend. AI might encroach.
Engineers who thrive adapt by thoughtfully integrating automation while preserving human skills.
