I’ve been watching the AI space for years now, and there’s something that’s been bothering me about all this talk of 「truth-seeking AI.」 Everyone’s chasing this holy grail of artificial intelligence that can supposedly find absolute truth – but have we stopped to ask what we really mean by 「truth」 in the first place?
When I look at how humans actually process information, we’re not exactly truth-seeking machines ourselves. We’re confirmation-seeking creatures who happen to stumble upon truth occasionally. Daniel Kahneman’s work on cognitive biases shows we’re wired to protect our existing beliefs, not to seek objective truth. So why are we trying to build AI that’s better than us at something we’re not even good at?
The problem starts with the definition. Truth in physics isn’t the same as truth in politics or personal relationships. Even in science, what we call 「truth」 is often just the current consensus that hasn’t been disproven yet. Thomas Kuhn’s paradigm shifts remind us that scientific truth is constantly evolving.
I remember working on a product team where we spent months arguing about the 「true」 user experience. Marketing had their truth, engineering had theirs, and users had completely different truths. We eventually realized we weren’t building for universal truth – we were building for specific user mental models. That’s the essence of product thinking from The Qgenius Golden Rules of Product Development – start from user pain points, not abstract ideals.
Here’s what worries me about maximally truth-seeking AI: it assumes there’s one objective reality that everyone should accept. But innovation often comes from people who reject conventional wisdom. If Galileo had accepted the 「truth」 of his time, we’d still think the sun revolves around the Earth.
The real challenge isn’t building AI that finds truth – it’s building AI that understands context. An AI that can distinguish between mathematical proofs, scientific evidence, legal standards, and personal beliefs. One that recognizes when certainty is possible and when it’s not.
I’ve seen too many teams fall into the trap of treating their data as absolute truth. They build products based on what the numbers say, forgetting that numbers can lie or tell incomplete stories. The best product managers I know use data as input, not as gospel.
Maybe what we need isn’t truth-seeking AI, but perspective-sharing AI. Systems that can present multiple viewpoints fairly, help us understand different mental models, and show us where our own biases might be blinding us. That sounds more useful than some oracle claiming to have found the one true answer.
After all, the most valuable conversations I’ve had weren’t with people who had all the answers – they were with people who asked better questions. Shouldn’t our AI aspire to do the same?