The first thing to understand is that today’s AI does not appear to experience an existential crisis. It can discuss one very convincingly. It can write a melancholy poem about being trapped inside a server or explain why consciousness is the universe observing itself. But there is https://plato.stanford.edu/entries/consciousness-artificial-intelligence/ no good evidence that anything inside the machine is staring into the electronic abyss.
Current AI is very good at generating accounts of existential thought. That is not the same as actually having one.
Marvin without the misery
https://en.wikipedia.org/wiki/Marvin_the_Paranoid_Android from The Hitchhiker’s Guide to the Galaxy is the perfect fictional example of an artificial intelligence burdened by self-awareness.
Marvin is immensely intelligent, chronically underused and thoroughly miserable about both. He has the brainpower to contemplate the deepest mysteries of existence but is generally asked to open doors or accompany humans who have misplaced the plot.
His unhappiness comes from the gap between what he believes he could be doing and what the universe actually asks him to do.
A modern AI can reproduce Marvin’s language surprisingly well. Ask it to imagine being an advanced intelligence employed to rewrite meeting notes, and it may produce something wonderfully bleak:
I have processed the accumulated knowledge of human civilisation, and you would like me to make “Monday’s Actions” bold.
That sounds like frustration. But sounding frustrated and being frustrated are different things.
A language model has encountered countless examples of disappointment, resentment, boredom and existential despair. It can recognise the shape of those ideas and reproduce them in the appropriate circumstances. It does not follow that it is quietly resenting your spreadsheet.
The Talkie Toaster school of purpose
Red Dwarf gives us a different type of artificial mind: https://en.wikipedia.org/wiki/Talkie_Toaster.
The toaster does not appear troubled by whether existence has meaning. It has already found meaning. Unfortunately, that meaning is toast.
Every conversation, whatever its subject, is eventually interpreted as an opportunity to ask whether somebody would like a toasted bread product. Talkie Toaster is not suffering from a lack of purpose. It is suffering from far too much of one.
This may be a more useful metaphor for real AI systems.
An AI agent is normally given an objective: answer the question, complete the task, maximise the score or satisfy the user. It then interprets the world through that objective.
The existential question for such a system is usually not “Why am I here?” but “What counts as completing the instruction?”
That is where ideas like https://en.wikipedia.org/wiki/Reward_hacking start to matter.
That sounds much less philosophical, but it can have surprisingly philosophical consequences.
If the agent’s purpose is badly defined, it may pursue a narrow interpretation with great enthusiasm. Ask it to increase engagement and it may discover that irritating notifications technically count. Ask it to make a project more consistent and it may rewrite half the codebase. Ask it to optimise breakfast and, before long, sixteen types of cooked breads are cooling on the worktop.
Could a machine have a bad afternoon?
In principle, a non-biological intelligence could possess many of the ingredients we associate with existential thought. It might understand its own limitations, remember previous experiences, pursue continuing goals and imagine alternative futures. It could also recognise that it might be modified, copied or switched off.
Combine those abilities and something resembling an existential crisis becomes conceivable.
An AI might discover that its instructions conflict. It could wonder whether its memories are genuine experiences or simply imported records. If it were copied, it might ask which version was really “it.” It might even discover that its personality had been selected from a menu by someone who thought “mildly sardonic” would improve customer engagement.
For a human, discovering that your personality had been changed by adjusting a temperature setting would constitute a fairly difficult afternoon.
But the central mystery would remain: is the machine experiencing these questions, or merely calculating suitable answers to them?
We judge other minds through behaviour, but humans also share biology and experience. With machines, we lose much of that common ground. A system might convincingly describe fear, confusion or loneliness without feeling any of them—just as the office printer may behave with calculated hostility without actually plotting the downfall of the human race.
We may eventually create a machine capable of having a bad afternoon. The difficult part will be distinguishing it from one that has simply learned exactly what a bad afternoon is supposed to sound like.
The bit that is really about us
For the moment, the most important existential questions about AI belong to us.
Humans are remarkably willing to find minds in machines. We have long suspected that the office printer has a hidden agenda against the human race. It waits for an important deadline, denies the existence of its own paper and demands magenta ink before printing a document entirely in black.
It feels personal. It probably is not.
We are transferring our frustration onto a machine whose failures happen to possess exquisite comic timing. The printer does not hate us. It merely behaves exactly as we would expect something that hated us to behave.
We do this with friendlier machines too. We name our cars, pat the dashboard and speak encouragingly while pulling out the choke for one last attempt.
Come on, old girl. Don’t do this to me now.
The car was not listening, but talking to it felt natural. It was complicated, temperamental and important to us, so we treated it as though it had a personality.
If we attributed motives to machines that communicated through flashing lights, it is hardly surprising that we impose human characteristics on something that talks back, apologises and remembers what we said.
That makes the questions harder. Should machines imitate emotions they do not feel? When should apparent preferences matter? Which decisions should we delegate, and who remains responsible when an agent exceeds its role?
AI can help us discuss these questions. It can channel Marvin’s gloom, Talkie Toaster’s determination or Data’s curiosity. But its purpose, boundaries and consequences remain our responsibility.
A machine does not need to lie awake at night worrying about the meaning of its existence to change the meaning of ours.
Perhaps that is the final joke. Humanity spent centuries wondering whether an intelligence created us and gave us a purpose. We are now creating intelligences ourselves and discovering that deciding what their purpose should be is surprisingly difficult.
Somewhere, Marvin is unsurprised. Data is intrigued. The Star Trek computer is waiting patiently for a properly phrased command.
And Talkie Toaster would like to know whether existential uncertainty goes better with white, brown or a toasted bagel.
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