Trust is the currency of the internet. Every image shared, every video viewed, every article read depends on an implicit agreement: what you see is what actually exists. AI-generated content is challenging that agreement in ways we're only beginning to understand.
What the Research Says
Multiple studies conducted in 2024 and 2025 paint a consistent picture: the public is growing more skeptical of digital content, but most people still can't reliably identify AI-generated material when they encounter it.
A 2024 survey by the Reuters Institute found that over 50% of respondents expressed concern about distinguishing real content from fake online. Yet in controlled tests, the same respondents correctly identified AI-generated images only slightly better than chance. There's a gap between awareness (knowing AI fakes exist) and ability (actually spotting them).
Trust levels vary sharply by content type. People are most skeptical of AI-generated images appearing in news contexts and most accepting of AI imagery in advertising and entertainment, where a degree of fabrication is already expected.
Context Changes Everything
How people feel about AI-generated content depends enormously on where they encounter it:
- News and journalism: Trust drops sharply when people learn that images in news articles were AI-generated. Audiences expect photographic evidence in journalism to be authentic, and violations of that expectation damage credibility for the specific article and the publication as a whole. This is why AI in journalism raises serious ethical concerns.
- Advertising: Consumers are more tolerant of AI-generated visuals in marketing, provided the product claims remain truthful. However, brands that use AI images to misrepresent their actual products face significant backlash when discovered.
- Social media: Reactions are mixed. AI-generated art and creative content is generally well-received when labeled. AI-generated images presented as real photographs (fake vacation photos, fabricated events) trigger strong negative reactions.
- Education: Students and educators express high concern about AI-generated content undermining the reliability of educational resources and research materials.
The Disclosure Effect
One of the most consistent findings across research is the impact of disclosure. When AI-generated content is clearly labeled as such, trust outcomes improve markedly. People appreciate transparency and are more willing to engage with AI content when they know what they're looking at.
Conversely, discovering that unlabeled content is AI-generated triggers a stronger negative reaction than if the content had been labeled from the start. The deception, rather than the AI origin itself, is what damages trust.
This has led to growing momentum behind labeling standards. Social media platforms are implementing AI content labels, the EU's AI Act mandates disclosure, and the C2PA Content Credentials standard provides a technical framework for embedding provenance data into media files.
Generational Divide
Younger internet users (Gen Z and younger Millennials) tend to be more aware of AI-generated content and somewhat better at identifying it, having grown up with digital manipulation as a normal part of their online experience. However, this awareness doesn't always translate into greater skepticism. Younger users may also be more desensitized to AI content and less concerned about its implications.
Older adults are generally less aware of AI content generation capabilities and more likely to accept AI-generated images as authentic. This makes them potentially more vulnerable to misinformation campaigns using AI imagery.
The Liar's Dividend
Perhaps the most troubling consequence of widespread AI-generated content is what researchers call the "liar's dividend." As people become aware that any image or video could be AI-generated, those seeking to discredit authentic evidence can simply claim it's fake. A real video of misconduct can be dismissed as a deepfake. A genuine photograph can be called AI-generated.
This erosion of trust in authentic media may be even more damaging than the fake content itself. It creates an environment where truth becomes harder to establish, regardless of the evidence available. Understanding how to detect deepfakes and identify AI-generated images becomes essential both for spotting fakes and for defending the credibility of real content.
Building Trust in the AI Era
The path forward requires effort from all sides: platforms need better labeling and detection systems, creators need to be transparent about AI use, and consumers need to develop stronger media literacy skills. Trust in digital content won't rebuild itself; it requires active investment in transparency, verification tools, and education. Our AI Image Detector is one such tool, giving anyone the ability to check whether an image is real or machine-made.
