AI — Ember

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This post is about my thoughts and reflections on AI and on how it has changed our lives. I promise I won’t write about how to become more productive, how to use the various models properly, and so on in that vein. It seems to me that such articles, useful as they are, do nothing to help us look into the future.

A little about me: I’m a programmer, I love my work, I use AI every day, and this post is my personal opinion.

My perception of AI went through all the stages of accepting the inevitable (the Kübler-Ross model) — denial, anger, bargaining, depression, acceptance. I suspect that for me, and for many others, these stages came all mixed together, sometimes displacing and replacing one another in no particular order.

Fig. 1 — Carry on; nothing has changed

Stage 01Denial

Denial was the shortest stage, because it was clear that the models would keep getting better, working faster, and occupying more and more niches. What I didn’t foresee was that this expansion would happen so fast and affect our lives so profoundly. Today a single announcement from Anthropic can move markets and crater the share price of once-successful companies, while the valuation of any company with the faintest hint of AI shoots to the moon.

Stage 02Anger

Anger set in when image-generating models arrived. It was obvious that everything they produce was built on the labor of many thousands of artists and illustrators whose work had been used for training without their consent. You can argue about the legality of this method and its ethics, but it led to the following consequences:

creating graphic content became practically free;

original works became indistinguishable from AI output;

authors lost the motivation to publish their work in the open.

These consequences strike me as deeply important, because we’ve ended up in a situation where the growth of visual culture is being crushed by the growth of technology. Art isn’t just about “pretty” — it’s about a person’s view of life, expressed through their work, and right now authors have no incentive to share that view.

Why would an illustrator publish a piece that took a week to make, if something very similar can be generated in a second — and if their work will be used to copy their style? In the process, we lose something I consider essential: the human view of the world. However good machines and algorithms may be, we are very unlike them, so it’s strange to expect them to have a view of the world of their own. They have no view, really, because they imitate rather than see; and yet their imitation eclipses reality through its availability, its cheapness, and its polish. I don’t know about you, but personally I find it a thousand times more valuable to encounter the imperfect but human worldview of a small creator — a person who poured a personal experience and a part of their life into the work.

Stage 03Bargaining and depression

Depression, with elements of bargaining, set in when it became clear that models can generate very good code. I think every programmer who loves their work has been through this stage. On one hand, you realize a model is taking away the work you love; on the other, there’s nothing you can put up against it on your own — it’s rather hard to compete with an immortal machine that has had billions of dollars poured into it and whose technical specs (memory, energy consumption) simply can’t be compared to a human’s.

Over time, as I worked with models more, I noticed that writing code had receded into the background. What moved to the foreground was planning functionality, the ability to grasp the overall structure of a project and its direction of travel. In effect, the programmer got a promotion: writing the code is delegated to the model, while the plan is created by the human together with the model. This used to be the job of project managers or team leads, whose role was to coordinate team members for more effective work. Now that development has sped up and most technical problems are solved individually, the need for a large team — and therefore for its managers — drops sharply, because teams get smaller.

Another important consequence of faster development is the erosion of what it means to “own the code.” Where once, on large projects, critical chunks of code were owned by an Alice, a Bob, and a Carol, today no human owns them at all — simply because the code wasn’t written by a human. We try to solve this by covering the code with tests and building various layers on top that check the project against our expectations, but that masks the problem rather than solving it.

The problem can be stated like this: “for any company that prioritizes the speed of building its software product and uses AI to build it, the level of code ownership will tend toward zero.” I’ll call this maxim...