Perhaps AI Is Modern Alchemy. And That’s Not a Bad Thing

Let’s be humble about what we ignore — but also about what we could know

Alberto Romero
9 min readSep 27


One of the criticisms that might hurt AI the most is calling it unscientific.

This is tricky because although it doesn’t necessarily deny the value of what ChatGPT or Midjourney can do, it labels them in a derogatory way as if implicitly putting them at the lower end of the hierarchy of things that matter to humanity. Practical, yes, but what do they tell us about the world or ourselves? Nothing.

I’ve argued before that AI is better depicted as an aspiring science, which isn’t the same as unscientific; it emphasizes the goal and not the current state. It’s not nearly as bad either. Calling AI a hard science like physics or biology might be far-fetched — not even AI researchers would go that far — yet every discipline we respect today with almost religious worship began as a protoscience with aspirations at the level of the dubious methods at our disposal back then.

“Aspiring science” is a compliment. Saying that AI is not really a scientific endeavor is an attack on its credibility: it implies it doesn’t want to be. If that were the case, AI is no different than alchemy. That would be a big problem.

Is AI the alchemy of our times?

Alchemy is a charged word, an inheritance left by those who strove, not very honestly, to separate it definitively from chemistry. We intuitively assume that comparing any field of study to alchemy is equivalent to marking it as unserious — a discipline destined to add to the bag of pseudoscience, together with astrology, humorism, and the aether theories. But I don’t think the comparison between AI and alchemy is to be interpreted in such a superficial, and hurtful way; if I’m generous, I can read it as an attempt to call out the dubious methods researchers use rather than reject the real possibilities it has of becoming a science.

In that sense, I can accept the analogy: It’s undeniable that modern deep learning was conceived without a robust theoretical basis, milestones are achieved by trial and error, the preferred method to move forward is throwing data and compute into the algorithms, and working…