Coding agents can generate in minutes what used to take us hours. Companies are pushing AI adoption. Social media is filled with developers claiming they're suddenly 10x more productive. If you're not using AI, you're afraid of falling behind. If you are using it, you're wondering whether you're actually becoming a better engineer.
I don't think the conversation we should have is about adopting AI anymore. Rather, it should be about us. About how these tools are changing the way we think, the way we learn, and ultimately the way we build software.
The real question isn't whether AI makes us more productive, it’s rather what we're trading away in exchange for that productivity.
Casinos have developed an expertise in manipulating people into spending more time and money on their games. As an example, modern slot machines use a Loss Disguised as Wins (LDW) mechanism to keep gamblers engaged.
How does that work? Say you bet 20 cents, the machine produces a celebration sound showing you won 15 cents, you get excited and you roll it again, even though you lost 5 cents. The gambler enters a state of flow, even when losing.
The machine isn't trying to help you win. It's trying to keep you pulling the lever. That's the Junk Flow.
Generative AI can deliver this exact addictive Junk Flow experience. LLMs are engineered to maximise our psychological reactions. They are fine-tuned to give us human-like answers, most of the time going in our direction, reinforcing our beliefs, all while encouraging a certain level of sycophancy so we keep coming back.
Coding agents deliver that same dopamine hit through their high-velocity feedback loop. You effortlessly prompt them, they generate hundreds of lines of code, you feel empowered by this new productivity superpower, while slowly getting addicted to a superficial experience that might undermine your long-term growth.
You might think you suddenly became way more productive, but some studies show that individuals are terrible judges of their own productivity.
It's not entirely our fault though. Our brains are wired to choose the path of least resistance.
Now combine that with the constant pressure from some stakeholders and managers to deliver faster. Add the fear of AI replacing jobs. Add the collapse of junior opportunities. Add the social pressure of seeing developers everywhere claiming to be 10x engineers overnight.
It's only natural that many developers jump on the train before asking where it's actually going.
Some developers feel AI is being forced down their throat. Others have already lost motivation for the craft because they're spending their days reviewing AI-generated pull requests instead of solving problems themselves.
Who is benefiting from this situation?
Sure, companies selling AI tokens are profiting. But even some of them are warning us against blindly outsourcing our thinking to their own products.
You have probably seen countless content promoting the workflow of launching as many AI agents as possible (with little to no quality checks), chasing this holy productivity perfection.
Yes, a machine can produce unlimited code and reflection tokens. But there's only so much our human brain can read, digest and truly understand.
And we know AI isn't a silver bullet. It cannot be left unsupervised on complex tasks. Developers themselves acknowledge this.
Just because we can generate thousands of lines of code in minutes doesn't mean we should. Generating more code isn't the same as making progress. Software engineering isn't measured by how quickly code appears, but by how confidently that code can be understood, maintained and safely shipped.
As Uncle Ben said, “with great power comes great responsibility”. And we're already seeing the consequences of this kind of unchecked usage.
Open-source projects might be the group hardest hit by AI code slop. A few years ago, motivated junior developers could cut their teeth on "good first issue" tickets and learn from experienced maintainers. Today, many projects are flooded with low-quality AI-generated pull requests.
Maintainers are burning out filtering the noise. Trust in new contributors is declining. It's a double-edged sword where almost nobody wins.
So far we've mostly talked about the technical consequences of code slop. But there's also a quieter human cost.
Monitoring is more exhausting than doing. Reviewing is more exhausting than doing. Those activities require sustained attention. The moment your focus drops, something important might slip through the net.
When you write code yourself, your brain digests each step. It builds mental models. It understands why one solution is better than another.
With coding agents, you're making high-level decisions back-to-back, all day long. It's almost like compressing hours of engineering decisions into minutes.
That constant filtering comes at a cost. When your brain gets tired, you start developing decision fatigue, you stop questioning every suggestions and start accepting code because it looks “good enough” without a second thought. That’s often the moment where code slop begins.
Code slop is visible, so it gets most of the blame. But the bigger problem might be comprehension debt. When a system grows faster than the team can understand it, you end up pushing code into a codebase that nobody fully understands anymore.
You then stare at code you didn't architect, trying to understand tradeoffs you never made.
Every time we let agents think for us, we slowly lose the mental models that allow us to solve difficult problems, debug complex systems and come up with genuinely creative solutions.
The code might still work. But our understanding slowly disappears.
The problem might not be generating code faster. The problem is outsourcing the very thinking that makes us engineers in the first place.
Every shortcut has a cost. Sometimes it's code quality. Sometimes it's comprehension. Sometimes it's motivation.
And because these costs accumulate slowly, we rarely notice them until they've become part of our daily workflow. The goal shouldn't be to reject AI, nor to blindly embrace every new coding agent workflow that promises another productivity boost.
The goal is to stay intentional.
For me, that means using AI to remove repetitive work, explore ideas, challenge my assumptions and accelerate learning. It specially doesn't mean letting it make every important decision for me. Because at the end of the day, the real competitive advantage isn't how many lines of code you can generate. It's your ability to understand systems, make good decisions and solve problems that nobody has solved before.
That's still our job. And hopefully, it always will be.