A while ago I built what I called an “empathetic algorithm.” It was a decision engine for an academic system, designed to automate a bureaucratic process. At the time I was proud to have encoded not only logic but also care and context into a rules-based system. What I didn’t anticipate was that the experience of building a very specific, limited AI would change my perspective on the vast and dazzling universe of modern AI.
I realized the smartest thing I programmed into my system was its ability to recognize its own stupidity.
AI as a Mirror of Our Rules
My algorithm wasn’t “intelligent” in the magical sense we often attach to AI today. It didn’t learn, it didn’t infer hidden patterns from large datasets. It was essentially a very fast and efficient mirror of a set of rules I, a human, had defined. If a rule was fair, the algorithm executed it with relentless fairness. If a rule was flawed, it became an agent of that flaw at a scale and speed no human could match.
This anchored me to a fundamental reality: much of the AI that drives decisions in the real world is not emergent consciousness but automation of our own mental processes, with all our biases and blind spots. We teach the machine to think like us, then are surprised when it inherits our defects.
The real danger isn’t that machines start to think like humans, but that humans stop thinking because machines do it for us.
The Unbridgeable Limit of Context
Today I interact with astounding language models. They can write code, compose poetry, summarize complex texts, and hold conversations with a fluency often indistinguishable from a person. They’re engineering marvels capable of processing and connecting information at a superhuman scale.
And yet my “dumb” algorithm had something these huge neural networks still lack: a programmed awareness of its own limitations.
One line of code effectively said: “If the request reason is ‘health issue’, don’t decide. Escalate to a human.” This wasn’t a data-driven decision but an ethical one. It was the recognition that certain domains of human judgment are outside what a machine should resolve.
Current AI can analyze millions of medical records to predict disease probability. But it cannot understand a patient’s fear upon receiving a diagnosis. It can analyze financial data to approve or deny a loan. But it cannot feel the anxiety of a family struggling to make ends meet.
Its “understanding” is statistical, not existential. It processes words but not the world behind them. It can simulate empathy, but it cannot feel it. In that gap between simulation and reality lies one of the greatest ethical risks of our time.
The Ethics of Deference
My experience with Project Phoenix taught me that the most crucial feature of any AI that impacts human lives is not accuracy or speed but humility—its ability to stop and defer to human judgment.
We are racing to automate everything, from hiring to sentencing, seduced by the promise of efficiency and objectivity. But we ask too little about what is lost in the process. What value does an “efficient” decision have if it’s insensitive? What value does an “objective” conclusion have if it ignores human context that doesn’t fit in a database?
True intelligence, in humans and machines, may not be about arriving at the correct answer but the wisdom to recognize a problem one has no right to solve.
I’m still fascinated by artificial intelligence. It’s unquestionably one of the most powerful tools we’ve created, with the potential to solve problems once thought insurmountable. But I no longer see it as a panacea. I see it as a tool that must be handled with extreme care and profound humility.
We must build our intelligent machines not only to be powerful but to be prudent. Not only to be fast but to be reflective. And above all, to understand that in the complex tapestry of human experience, the most intelligent act is sometimes to step back and let a human decide.