Can AI Be Used to Spread Misinformation?

Scroll through your feed for five minutes and there's a decent chance you've already looked at something AI made without realizing it. So: can AI actually be used to spread misinformation? Short answer — yes, and it's happening at a scale that caught even some AI researchers off guard.

Here's what really changed. A convincing fake used to take a small team: someone to write it, someone to design it, someone who knew their way around video editing software. Now one person with a laptop and a handful of free tools can do all three in a matter of minutes. That shift in cost and speed is really the whole story. Understanding how these tools work is, honestly, the best defense you've got.

01How AI Creates Misinformation

It helps to remember that an AI model doesn't actually know whether something is true. It knows patterns. Ask a Large Language Model (LLM) to write about an event that never happened, and it will happily predict which words statistically tend to follow which other words — with no internal sense that it's making anything up. Get the training wrong, which is a core challenge in what AI alignment is, and the model will state a fabrication just as confidently as it states a fact.

02The Rise of Deepfakes & Visual Deception

Deepfakes are the most visually convincing form of AI misinformation around right now. Using Generative Adversarial Networks (GANs) or newer diffusion-based tools, someone with modest technical skill can produce a video where a public figure appears to say or do something they never actually said or did.

We covered the wider picture in our guide to AI risks for everyday users, but deepfakes deserve their own spotlight, because the financial damage is no longer theoretical. In early 2024, a finance employee at the Hong Kong office of the engineering firm Arup joined what looked like an entirely normal video call with his CFO and several colleagues. Every single face and voice on that call turned out to be a deepfake, built from publicly available footage of the real executives. He ended up wiring more than $25 million before anyone at the company realized something was wrong — a case reported by CNN based on Hong Kong police briefings.

03The Unprecedented Scale of AI Disinformation

Before generative AI, running a real disinformation campaign took money, time, and a fair amount of human labor. Now it takes a few dollars in API credits and a laptop. That change in scale is really what makes this moment different from every previous wave of "fake news."

State-sponsored troll farms now use AI to generate millions of unique social media comments, artificially inflating divisive topics and shaping public opinion without ever repeating the exact same sentence twice.

04How to Spot AI-Generated Misinformation

AI is getting better at hiding its own fingerprints, but it still leaves clues most of the time. Here's the checklist worth keeping in your back pocket:

If you do get targeted by a suspected AI scam call, video, or message, it's worth reporting it. In the U.S., that means ReportFraud.ftc.gov, and the FCC has a plain-language guide on spotting deepfake robocalls specifically.

05How the Tech Industry is Fighting Back

The good news, if there is any, is that the industry isn't just standing by and watching this happen. Researchers are actively working on how AI companies make models safe, and a coalition of major tech and media companies — Adobe, Microsoft, the BBC, and others — has built an open standard called C2PA (Coalition for Content Provenance and Authenticity). It attaches a tamper-evident record of where a piece of media came from and what tools touched it, sort of like a nutrition label for photos and video.

06Frequently Asked Questions

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Written by the NyvoraAI Team

We investigate digital safety and provide practical media literacy guidance. This guide was reviewed for accuracy in June 2026. Learn more about our mission to empower users against digital deception.