Yes—state-linked and other influence operators are already using generative AI. The documented uses are usually less cinematic than a flawless fake speech: translating messages, rewriting articles, producing many posts and images, researching audiences, and keeping networks of websites and accounts supplied with material.
That makes propaganda cheaper, faster, more multilingual and easier to customize. It does not prove that AI automatically makes propaganda more persuasive. In the cases publicly described by OpenAI and Graphika, production capacity increased while demonstrated mass persuasion and organic reach remained difficult to establish.
What “AI propaganda” actually means
Propaganda is communication intended to shape attitudes or behavior in an actor’s interest. It can contain true facts, selective facts, falsehoods, emotional appeals or misleading context.
- Misinformation: false or misleading information shared without necessarily knowing it is false.
- Disinformation: false or misleading information spread deliberately.
- Influence operation: a coordinated effort to manipulate perceptions or behavior, often using fake identities, websites, paid promotion and repeated narratives.
- AI-enabled influence operation: an operation in which AI materially assists creation, translation, targeting, coordination or distribution.
A synthetic political image, a transparent campaign advertisement, a parody account and a state-backed network are not the same thing. The useful unit of analysis is usually the campaign and its distribution network, not one file or post.
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What operators are using AI to do
Translate and localize messages
Language models can translate slogans and articles, adjust idioms, and produce versions aimed at particular countries or communities. That lowers the labor cost of operating across languages. U.S. congressional testimony has referenced AI-assisted translation of Chinese propaganda into Spanish (Congressional testimony). OpenAI also described a Russia-linked operation that targeted audiences in Africa and the United Kingdom with AI-assisted material (OpenAI’s “Stop News” report).
Generate large quantities of ordinary-looking content
Models can draft short posts, replies, headlines, articles, memes, political cartoons, promotional copy, images, voice clips and fake profile photographs. The important change is often industrialization of mediocre content rather than creation of flawless deception.
Repackage and launder narratives
An operator can take material from a state outlet or partisan source, rewrite it in a neutral-sounding local voice, and publish variants through apparently independent sites. A typical chain is:
- An original narrative appears.
- AI rewrites or translates it.
- Several sites publish slightly different versions.
- Social accounts link to those sites.
- Search and recommendation systems encounter repeated versions.
AI can obscure provenance without changing the underlying claim.
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Create account assets and identities
Generated profile pictures and synthetic biographies help populate accounts. But investigators also examine creation dates, posting schedules, repeated phrasing, shared links, domain registration, recycled images and synchronized amplification. OpenAI’s 2025 description of “Uncle Spam” said a China-origin operation generated polarized U.S. political content and AI-generated profile images for platforms including X and Bluesky (OpenAI’s “Uncle Spam” report).
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Research audiences and optimize campaigns
Models can summarize public figures, movements and online subcultures; identify grievances; suggest audience-specific talking points; classify replies; and flag narratives to repeat or drop. This campaign-optimization role may matter even when the final posts look completely ordinary.
Documented cases, with limits on what they prove
| Case | What was reported | What it does not establish |
|---|---|---|
| PRC-linked U.S. AI-policy campaign, June 2026 | OpenAI reported generated comments, political cartoons, report editing and social-media monitoring around debates over U.S. AI policy and infrastructure. The company said the operation was disrupted. | OpenAI said there was no evidence the activity significantly shifted public opinion. Attribution is an assessment, not a public confession. OpenAI report |
| “Stop News,” reported October 2025 | OpenAI described a Russia-origin operation that generated articles and advertising copy for a fake news brand aimed at audiences in Africa and the U.K. | Use of AI in the workflow does not demonstrate that the operation reached or persuaded a large audience. OpenAI case study |
| “Uncle Spam,” reported June 2025 | OpenAI said a China-origin operation generated divisive U.S. political content and profile images. | A generated image does not by itself prove coordinated foreign influence or political impact. OpenAI case study |
| Ghana election example, 2024 | Freedom House reported that automated accounts spread ChatGPT-written messages supporting the incumbent New Patriotic Party, citing OpenAI disclosures. | The report does not show that AI activity determined the election. Freedom House |
OpenAI’s February 2026 reporting emphasizes that malicious actors generally use AI alongside websites and social-media accounts, sometimes across more than one model (OpenAI). Graphika’s November 2025 study likewise found faster, more scalable production but limited organic reach in the campaigns it examined (Graphika, “Cheap Tricks”).
What AI changes—and what it does not
Advantages for operators
- Speed: responses and new versions can be produced while an event is still developing.
- Scale: one team can generate far more posts, images and translations.
- Language coverage: messages can be localized for regions that would otherwise require specialist staff.
- Adaptability: wording, tone and visual style can be changed for different communities.
- Operational resilience: when an account or domain is removed, replacement material can be prepared quickly.
Persistent requirements
AI does not independently choose a political objective. Human organizations still select narratives, targets and timing, then need distribution: accounts, websites, advertising, messaging channels, audience access, coordination and favorable platform conditions. AI-generated content that never reaches people is a production event, not demonstrated influence.
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Why low-quality “AI slop” can still work
Awkward images and repetitive articles may be useful even when nobody mistakes them for professional journalism. They can reinforce group identity, provoke anger or amusement, keep a narrative visible, supply fake accounts with filler, test reactions, make a network look active, clutter search results and exhaust moderators.
Graphika’s “Cheap Tricks” report is valuable precisely because it challenges the assumption that an operation needs a spectacular deepfake. Influence can come from repetition, identity, selective exposure and network effects rather than one convincing forgery (Graphika). Graphika has separately documented AI-generated visual material in online communities that glorify mass violence; that extremist activity should not be conflated with state influence operations (Graphika, “Close to the Edge”).
Is AI making propaganda more persuasive?
The strongest evidence supports an efficiency claim, not a universal persuasion claim. AI clearly increases output, iteration and language coverage. Public evidence is much thinner on whether it changes beliefs or votes at scale.
Graphika found limited organic reach in the campaigns it studied. OpenAI’s June 2026 account of the PRC-linked operation said it did not appear to shift public opinion. Those findings do not mean every operation is harmless; they mean production volume should not be confused with audience impact.
AI may matter without winning an argument. It can crowd out reliable information, make a campaign appear locally rooted, speed responses to breaking news and increase the burden on journalists and fact-checkers. It also creates a liar’s dividend: once realistic fakes are common knowledge, people can dismiss genuine recordings as fabricated.
How an AI-assisted operation typically works
- Select a grievance or narrative. Operators choose an issue likely to activate an existing identity or resentment.
- Map audiences. They research communities, influencers, local language and likely points of friction.
- Generate variants. AI drafts posts, translations, images, headlines and replies.
- Build distribution. Content is placed on accounts, websites, advertising systems or messaging channels.
- Seed and amplify. Inauthentic accounts or paid placements create initial visibility.
- Monitor reactions. Operators classify comments and engagement, then adjust language and timing.
- Recycle what travels. Better-performing material is translated, rewritten and reposted across platforms.
- Replace removed infrastructure. New domains and accounts keep the campaign moving after enforcement.
This is why examining only whether a single image was AI-generated misses the broader operation.
How to investigate suspicious content
Start with source and chronology
- Find the earliest known upload, not merely the account that made it viral.
- Reverse-search images and key video frames.
- Look for the original, higher-resolution file and preserve a copy before it disappears.
- Check the claimed date, location, weather, clothing, signs and chronology against independent reporting.
- Ask whether the account is new, unusually inactive between bursts, or suddenly posting in another language.
Look for coordination
- Accounts created in clusters or posting on synchronized schedules.
- Identical or near-identical captions and unusual repeated phrases.
- Shared URL shorteners, domains, hosting or tracking patterns.
- Accounts that jump across unrelated political topics or geographies.
- Recycled profile pictures, biographies or images.
- Engagement that appears manufactured rather than conversational.
Check the media itself
- Images and video: inspect hands, text, reflections, shadows, object geometry, lip movement and frame-to-frame changes; search key frames.
- Audio: compare cadence with known recordings, check unexplained cuts, and verify time and location with witnesses or contemporaneous reports.
- Text: verify names, quotations, dates, links and citations; compare language versions and identify sites that appear to have rewritten one source.
Do not share consequential material while these checks remain unresolved.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why AI detectors and watermarks are not truth machines
Provenance describes origin, not truth
C2PA Content Credentials can record signed information about origin and editing history. They do not establish that a political claim is true, fair or benign, and credentials may disappear during editing or redistribution. The C2PA Conformance Explorer lists conforming products and trust lists (C2PA Conformance Explorer).
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteOpenAI explains that C2PA metadata and SynthID can help identify supported OpenAI-generated images, but provenance remains separate from factual verification (OpenAI Help Center).
A negative result proves very little
Google says Gemini can recognize SynthID signals associated with Google AI tools and compatible C2PA credentials. Failure to find a signal may mean another system was used, the file was transformed, or no detectable watermark is available. Google’s current help page describes approximate rolling limits of 10 image checks, 10 video checks totaling up to five minutes, and 10 audio checks totaling up to three hours (Google Gemini Help).
OpenAI’s public verification tool checks provenance signals associated with supported OpenAI images and audio; it does not identify every other model (OpenAI Verify). OpenAI announced API access for organizations and developers on July 31, 2026 (OpenAI’s provenance update).
Text classifiers are particularly weak evidence
A score saying prose “looks AI-generated” cannot establish who wrote it, whether it is false, or whether it is coordinated propaganda. Translation, paraphrasing, human editing and short passages can all defeat such judgments. Treat automated classifications as leads for investigation, never as an accusation.
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Tools for verification teams
| Tool | Useful for | Limits |
|---|---|---|
| Google Gemini verification | Checking Google SynthID and compatible C2PA signals in image, video and audio. | Not a universal detector; a negative result does not prove authenticity. Gemini |
| OpenAI Verify | Checking supported OpenAI-generated images and audio. | Does not identify media from every provider or establish truth. OpenAI Verify |
| Hive detection and moderation | Commercial image, video and audio classification at scale. The listed pricing page showed $6 per 1,000 image requests, $6 per 1,000 video frames and $10 per audio hour on a lower-limit plan. | Classifier output is an investigative aid; cost and performance vary by modality, file quality, transformations and plan. Hive pricing |
| C2PA tools | Newsrooms and publishers checking signed origin and edit history. | Credentials can be stripped and do not prove truth or intent. C2PA Explorer |
For most readers, free vendor-specific checks and ordinary source verification are more appropriate than buying a generic detector. Organizations processing large volumes may justify an API, but still need human review and network analysis.
The practical standard for journalists, platforms and readers
Separate four questions:
- Was AI used?
- Who created or distributed the material?
- Is the claim true, false or misleading?
- Was the activity coordinated and strategically deployed?
No watermark, detector or visual inspection answers all four. The strongest defense combines provenance where available, reverse searches, source comparison, contextual fact-checking, account and domain analysis, platform enforcement and basic media literacy.
Platforms can label or remove some content, but labels alone cannot expose a cross-platform network. Journalists should report attribution with confidence language such as “OpenAI reported,” “Graphika identified” or “assessed as likely linked to.” Readers should resist both panic and complacency: AI-assisted content is not automatically false, and a failed detector result does not make it authentic.
The Bottom Line
AI is already part of modern influence operations, chiefly as a multiplier for translation, research, rewriting, visual assets and volume. The decisive questions remain human and institutional: who is coordinating the campaign, where is it distributed, whom does it reach, and can its claims be independently verified?
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