Your Skepticism Is a Feature
AI has been causing a ruckus in the software development space. Nothing seems as it was just three, four years ago. The clankers are taking our raison d’être1 away from us. Since around December 2025, they seem to write code better than the average programmer. And faster, without a doubt.
Companies have tried to ride the wave by sacking developers en gros2 and replacing them with LLMs (with various degrees of success). The big question lingers in everyone’s head: Will crafting elegant, robust, effective and efficient code by hand—a part of our craft that we are proud of (and reasonably so)—be soon a thing of the past? Or is it already, and we just don’t want to realize it?
This is not the only concern that the raise of LLMs trigger. In fact, there are quite a few aspects of LLMs to be upset about. Upon a closer look, they belong to three categories: threats to culture and society, risks that are personal, and hype damage.
Threats to culture and society
Since the release of ChatGPT 3.5, AI has accumulated a lot of negative headlines, especially about observed or anticipated impacts on our cultural and societal achievements. It started with the artist crisis: Midjourney produced tons of generated images every day that many perceived as the democratization of art (“Now everyone can do artwork!”) while artists weren’t amused at all. Did human-made visual art lose its value?
And, more importantly, did AI even steal art? After all, LLMs are trained on all sorts of artifacts of human work: texts, images, music, code—anything you could imagine. AI companies willfully ignored copyright because no one stopped them. AI proponents claim that LLMs “learn” how to do art, in the same way humans do: by studying and memorizing the work of great artists. But LLMs were caught reproducing existing work almost verbatim, just like a human forger.
The exploitation doesn’t just affect the planet’s cultural work but also labor. AI companies paid legions of click workers a few measly dollars to .
The third big threat besides killing cultural achievements and exploiting labor is the risk of concentration of power at a handful of companies. Imagine if the frontier models push all other models out of the market. AI services would become an oligarchic system where few tech overlords can do whatever they want to their models (and their customers who won’t have much choice anymore). Product enshittification is the mildest of all consequences. (Open weight models to the rescue!)
And on top of this, with the advent of LLMs, the main resource bottleneck is not software but hardware. Up to then, hardware was cheap and fast. If software is slow, engineers optimized the algorithms or switched to fast programming languages. Now the hardware is the limit. LLMs need large amounts of RAM for quick access to their knowledge. Building and running supersized hardware requires resources: energy, water, and all kinds of chemical elements including rare earths. At this point, AI oligarchs seem to have chosen cheap and dirty over sustainable but more expensive. It’s a destructive war they fight against each other, and the environment doesn’t seem to be on the winning side.
Personal risks
Besides their impact on society, LLMs have a detrimental influence on individuals, regardless of whether they chose to use LLMs or were forced to, and even regardless of whether they are active users at all.
First, there’s the risk of deskilling. The convenience and speed of LLMs can trick us into running our brains in energy saver mode, trusting the LLM to do the right thing. Studies found that offloading brain work to LLMs can adversely affect our ability to think clearly. On the positive side, clever use of LLMs can not only avoid that decline but even help to increase our thinking abilities.
Developers face another potential risk: Being held reliable for code they did not write. While LLMs spit out code at an insane rate, yet, they cannot really be held responsible for their outpourings. Humans can. And managers don’t care who or what created the code. Welcome to the developer’s dilemma: They can take responsibility seriously, review every machine-generated PR, and become the human bottleneck. Or, to make PR reviews match the speed of code output, they can have one LLM review the output of another. Now they’re the meat blob whose only job is to formally take ownership of code they neither own nor control. It seems like a catch-22: you can’t escape this game in neither direction.
Some developers will choose the second option, often forced by orders from above: Management needs results. And the competitors don’t sleep. Is there another choice?
Hype damage
The consequences I looked at so far are bound to building and using LLMs. But there is another, less tangible aspect causing a sense of menace: The hype that built around LLMs since their first sensational successes (remember, even the first clunky attempts of GPT 3.5 at human conversation were perceived as a sensational breakthrough) feeds exaggerated expectations. Headlines go like, “LLMs are ten times more productive as human developers!”; “Company X replaces their entire development team by LLMs!”
A proof doesn’t exist, and it can’t; for coding is but one small part of software development. But the damage has been done. Engineers have been let go in an attempt to turn into a “dark software factory” where bots do all the software development while a few humans keep them on track and even fewer humans pocket all the profit.
Stay vigilant
Let’s be honest: LLMs are a technological revolution. They already affect our work and lives more than other technological shifts in the software industry. Reactions to this ongoing change range from fear (“We’ll all be losing our jobs!”) to ignorance (“Just wait until the bubble bursts.”). I say, pick your reaction carefully. Extreme positions rarely help. What we can do is to separate justified concern from reactionary panic. Nothing is purely black or white; everything has upsides and downsides. So let’s take the multiple facets and sort them into what we can fix, mitigate or only navigate (but still!)
In the next articles of this series, I’ll look at each facet and try to make them more manageable. A healthy mix of skepticism and curiosity gets us further than giving in to the fanboys and doomsayers.