If you wanted to design the worst possible moment for AI to emerge, you might create a moment like this one.
What we are experiencing now goes deeper. I call this the “post-trust era:” a world where the very mechanisms that allow us to establish trust have broken down. It is not just that emotions sometimes trump facts. It is that we have lost shared foundations for deciding what counts as a fact in the first place.
The numbers tell their own story. Nearly 8 in 10 people get directed to their news primarily through algorithmic systems such as social feeds, search engines, and aggregators, effectively outsourcing their information diet to systems they neither understand nor fully trust. In the United States, trust in the federal government has fallen from over 70% in the late 1950s to under 20% today. Confidence in mass media has dropped: fewer than one-third of Americans express even a “fair amount” of trust. Religious institutions, financial systems, healthcare organizations, and even science itself have seen trust erode.
When the first photographs appeared in the 1830s, people marveled at their fidelity. For nearly two centuries, seeing was believing. A picture was not perfect, but it was evidence.
Within two years, AI-generated images, voices, and video had become so pervasive and so convincing that the question was no longer “can you spot the fake?” but “can you trust anything you see?”
That is the substrate into which AI arrives: a world where information is abundant, but trust is scarce, where consensus reality has splintered, and where the mechanisms for establishing shared truth have broken down.
A different kind of intelligence
Past technologies extended our capabilities while remaining subordinate to human thought. The printing press amplified the words we chose to print. Electricity transformed the energy we decided to harness. Combustion engines replaced the muscle power we used, and computers executed calculations we programmed. The internet distributed the messages we wrote.
At the heart of most of these systems is an architecture that learns by predicting what comes next, a word, pixel, or token, in a sequence. These models do not know “truth” in the way humans understand it. They discover patterns, frequencies, and correlations in whatever data they are given.
AI is trained on our news stories, our social media feeds, our digitized books, our biased archives, our polarized debates, our conspiracy theories, our scientific breakthroughs, and our misinformation. It absorbs not only our knowledge but our distortions. It learns not only our facts but also our conflicts about those facts.
A new kind of cognitive infrastructure is emerging at the precise moment our social infrastructure for trust is under strain.
Excerpted from The Trust Code with permission from Tiffany Xingyu Wang.
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