As we might become mechanical turks just approving whatever AI outputs, will we be less and less interested in the output that we approve, and the tone of voice and standard it should uphold?

The danger isn’t just putting humans in the loop but keeping us engaged in that loop. Without genuine interest our oversight becomes hollow ritual.

I remember that old sluggish legacy PHP code, still worked 99% of the time, but it was a code soup that no one wanted to deal with.

Every newcomer tried. I tried. Probably ones every year.

That piece of code became boring to work with. It was hard to add features too. It was easier to just add something around it, patch it.

Those pieces of code became boring and not prioritized.

It became tech debt out of being boring to work with, and every large scale system has that piece of code.

Followed up to why tech debt

Progress often feels like struggle until suddenly it doesn’t.

When things start flowing effortlessly, it’s not because the work lacks value.

The most insidious priority failure is treating tech debt as “optional work.” Companies create a false choice between “shipping features” and “fixing tech debt,” ignoring that they pay for debt daily through slower development and more bugs.

Follow up to why tech debt

We over-engineer features users rarely touch, creating unnecessary complexity. This happens when product managers can’t clearly define needs or when engineers solve theoretical rather than actual problems.

Tech debt isn’t solely an engineering issue. When designers create solutions without technical context, product managers can’t articulate clear user needs, or executives push for features without validation, we build the wrong things well rather than the right things simply.

The most dangerous misunderstandings come from solving theoretical problems instead of actual ones. We build complex, flexible frameworks anticipating edge cases that never materialize, leaving behind over-engineered systems that must be maintained without delivering proportional value.

Follow up to why tech debt

All tech debt stems from misunderstanding three things: the problem (what we’re solving), the priorities (what matters most), or the code (how it actually works).

It actually is that simple. We create tech debt when we work on things we think are interesting instead of what’s truly important (priorities), when we build before truly understanding the needs (problem), and when we work with old code without knowing why it works a certain way (code).

I try to drink less coffee, however my coffee machine makes really good coffee. When I turn it on to create a really good cup of coffee it stay on for about 45 minutes.

After those minutes it turns itself off and cleans everything, and it is quite loud when it does this. I’ve realized that when this happens I’m always reminded of coffee. This makes me want to go and… grab coffee.

This coffee machine scenario highlights a broader pattern I’ve noticed: our environment creates automatic triggers that can derail our intentions. Another example is how we use timers for productivity.

Getting into flow sometimes needs tools like setting a timer for 5 minutes to overcome initial resistance. I use this technique to commit to just “investing” 5 minutes in a task, which is usually enough to get started.

The problem with timers is when they go off… you get a Pavlovian response that you are done.

Breaking these loops might require changing the environment rather than fighting the response. Moving your workspace away from the coffee machine’s audible range eliminates the trigger entirely. Similarly, using visual timers like sand hourglasses instead of alarm sounds can help maintain flow while still tracking time.

The key is recognizing these automated triggers and redesigning them to align with your true intentions rather than working against them.

There are no stupid questions, but there is such a thing as too many questions.

Ask the one question that answers twenty others. Then be patient enough to listen.

Capture ideas while they are fresh and store them somewhere. You can ignore organization for the future, that will become a solved problem similar to how all your organizing of MP3s is a solved problem. Or how meticulously sorting photos into folders seems quaint now that search just works.

It was jokingly said that “For all these companies budgets to add up, every Swede would need a website worth 2 million kronor.”

During the late 90s dot-com bubble, Sweden had its own tech superstars. Jonas Birgersson became known as “Broadband Jesus” for his vision of universal internet access through his company Framfab. Johan Staël von Holstein built Icon Medialab into a global player before the crash, while Ernst Malmsten and Kajsa Leander’s Boo.com famously burned through massive venture capital in just 18 months before collapsing. Boo.com epitomized being too early - their Flash-heavy website required significant bandwidth that most users simply didn’t have at the time.

What these companies share with today’s AI boom is striking: outsized hype about profitability and technology prioritized over utility. These entrepreneurs were creative people riding a wave of genuine possibility.