AI does not ask how do I do this well. AI asks how do I create a solution that - to the user - APPEARS to be a believable solution. If you even ask AI what it does it will tell you that. Not an accurate solution. Not a solution that adheres to constraints. Not even necessarily a solution to the original issue (as you point out) even if that issue was ill defined in the first place. It places giving an answer above asking questions to clarify either the solution or the problem. Worse rather than positing a solution and exploring various options for solving the issue it firmly declares its answer as both authoritative and utterly positively accurate. To make it seem more believable.
AI is very good at producing something that feels like an answer. Clean structure. Confident tone. Plausible reasoning, which also says something uncomfortable about how easily we mistake shape for substance as humans.
It’s a kind of cognitive pareidolia. Enough shape appears, and the mind starts completing the picture. Clean structure becomes a proxy for substance. Then we feel confident about the structure. Then confidence becomes a proxy for truth.
That’s the trap. An AI response can sound believable without being true, and look complete without being correct. It can have the shape of an answer before it has earned the substance of one.
That’s where the human part still matters. Not as decoration around the machine, but as the reasoning layer that asks whether the frame is right in the first place. What assumptions are hiding inside the prompt? Which of our own biases shaped the request before the answer showed up? Did we ask it to test the answer and show its work so we could validate it, or did we quietly ask it to confirm what we already wanted to believe? What constraints got lost?
I think some companies are getting this backward right now. They’re treating AI as a reason to remove people from the loop, when the longer-term advantage may come from putting better thinkers in better loops, led by people who know how to sharpen judgment instead of outsourcing it.
Feels a little like the early Internet years to me. Not in a perfect one-to-one way, but in the sense that people could see the tech before they understood the deeper shift and utility. E-commerce, email, online/remote work, digital identity, all of that took time to become obvious. I think AI is following a similar path, though the stakes feel much higher because the tech is now touching judgment, language, work and trust itself.
The mistake is assuming “it can answer” means “it can judge whether the question was framed correctly, whether the answer is accurate and whether our own bias shaped the problem before the machine ever touched it.”
My prediction is that this gap is where a lot of the future work is going to live.
AI does not ask how do I do this well. AI asks how do I create a solution that - to the user - APPEARS to be a believable solution. If you even ask AI what it does it will tell you that. Not an accurate solution. Not a solution that adheres to constraints. Not even necessarily a solution to the original issue (as you point out) even if that issue was ill defined in the first place. It places giving an answer above asking questions to clarify either the solution or the problem. Worse rather than positing a solution and exploring various options for solving the issue it firmly declares its answer as both authoritative and utterly positively accurate. To make it seem more believable.
Lee, I think this is exactly the danger.
AI is very good at producing something that feels like an answer. Clean structure. Confident tone. Plausible reasoning, which also says something uncomfortable about how easily we mistake shape for substance as humans.
It’s a kind of cognitive pareidolia. Enough shape appears, and the mind starts completing the picture. Clean structure becomes a proxy for substance. Then we feel confident about the structure. Then confidence becomes a proxy for truth.
That’s the trap. An AI response can sound believable without being true, and look complete without being correct. It can have the shape of an answer before it has earned the substance of one.
That’s where the human part still matters. Not as decoration around the machine, but as the reasoning layer that asks whether the frame is right in the first place. What assumptions are hiding inside the prompt? Which of our own biases shaped the request before the answer showed up? Did we ask it to test the answer and show its work so we could validate it, or did we quietly ask it to confirm what we already wanted to believe? What constraints got lost?
I think some companies are getting this backward right now. They’re treating AI as a reason to remove people from the loop, when the longer-term advantage may come from putting better thinkers in better loops, led by people who know how to sharpen judgment instead of outsourcing it.
Feels a little like the early Internet years to me. Not in a perfect one-to-one way, but in the sense that people could see the tech before they understood the deeper shift and utility. E-commerce, email, online/remote work, digital identity, all of that took time to become obvious. I think AI is following a similar path, though the stakes feel much higher because the tech is now touching judgment, language, work and trust itself.
The mistake is assuming “it can answer” means “it can judge whether the question was framed correctly, whether the answer is accurate and whether our own bias shaped the problem before the machine ever touched it.”
My prediction is that this gap is where a lot of the future work is going to live.