How do we imagine artificial intelligence a couple of months from now, or a couple of years? Is it going to leave everyone jobless? Will digital products still be created by humans? What should we focus on to stay relevant?

Every day that passes, AI is more capable. I see how code agents are pushing junior developers out of the equation. In graphic design, generative tools package logos and illustrations in seconds, making the work evaporate slowly but surely. It’s a shadow relentlessly spreading over marketing, audiovisual production and, needless to say, translation.

But there’s a gap that still hasn’t been filled. We’re building systems that can understand language but not the purpose or value of what they say, at least not in a human way. Artificial Intelligence doesn’t achieve the perspective of will, and it can’t deal with the unknown except by hallucinating.

The illusion of will

In case you didn’t know yet, what we call AI these days is LLMs (large language models), systems that learned language patterns by reading an enormous amount of text. At an atomic level it started with the idea of predicting the next word.

Roses are …

Now, predicting the next word requires building a complex representation of the context. Building multiple contexts and relating them to each other in sophisticated ways. How this new word influences the whole conversation. Over time these models have grown in size, and so have their training sets, reaching capabilities that resemble reasoning, communication and problem solving.

And with that came people’s fear and anxiety. But these models don’t cover important aspects of our infrastructure as a society. Not all of our activities can be extrapolated into a training set.

Visualizing patterns, applying probabilities, finding sequences of tokens or words and statistical association is how models see the world. Experience, intent, responsibility, context and values are left to our side of the court.

Because every model categorically confuses frequency with truth, we forget that every dataset it is trained on has errors. That these models touched the internet. And the internet lies, right? Condemned to replicate and optimize the world as we describe it to them. With all the garbage and fiction, all the fanfiction, wattpad and absurd conspiracies. We have to tell the truth apart from the garbage it hands us and connect the wires to get on the right track, our track.

We went to A and then C, no problem getting to B. Now how do we get to Z

What is finite and easy to capture, the common events that feed these immense machines, shapes a perfect and efficient model of thought, 100 times more efficient at transactional tasks, where the details are so well defined that we could find a needle in a haystack.

But the world contains much more entropy than even we are willing to accept. It’s much messier and more chaotic. Strange events, atypical combinations or creative scenarios could leave a long tail that goes on to infinity. Or rather, they would have found the end, because they always do, but phenomenally wrong:

“Eat at least one small rock per day”… (BBC news story)

Also, in an imperfect environment like our reality, there’s another dimension that has to do with combinatorics: adding variables quickly leads to exponential combinations. For the way these models work to capture that dimensionality, we would need an enormous amount of data.

That’s why we should be careful with the illusion of having in front of us an image of ourselves, and its estimates. We still don’t have a model capable of “instant learning”. We won’t find any model that knows the art of improvising to get out of a jam.

RANT

What I wanted to say is that our work, and that of many others, will still be there: dealing with human ambiguity, battling legacy systems, struggling from one version to the next because someone decided to build on an unstable base. Designing discrete systems with an immense reality in mind.

There’s no machine that solves for us the problem of uncertainty, of long-term complexity. What does a code agent care whether the application is still working in a couple of years? The only thing that changes is that now we’re going to be buried under a mountain of code. And who is going to save us from that 80% of garbage code?

I hope you and I aren’t too lazy by the time there’s nobody left to hold accountable; when the concept of “AI first” has us digging through a cesspool that looks like it was written by an alien with attention deficit. And investors are going to love it, because it “was built in two days”. But who knows what the hell it summoned, and why it decided to import three libraries, cross dependencies and import half the internet.