AI vs Disinformation: Speed, Psychology, and Defense

False news doesn’t beat true news because it’s smarter. It wins because it’s better engineered for human brains.

A major social-media study examined 126,000 stories shared by 3 million people over 10 years and found that false news spread farther, faster, and deeper than truth—often reaching its first 1,500 people about six times faster. The uncomfortable punchline: humans—not bots—drove most of the advantage.

For leaders working in defense, intelligence, and critical infrastructure, that finding changes the job. Countering disinformation isn’t only about blocking malicious accounts or labeling posts. It’s about defending decision-makers, operators, and the public from cognitive manipulation—now supercharged by generative AI. The good news is that AI can also be the shield, if we build it around how people actually think and share.

Disinformation outruns truth because it’s built for “the human algorithm”

Disinformation spreads quickly because it reliably triggers three human impulses: novelty, emotion, and identity. Those drivers act like a behavioral sorting function—what former intelligence leaders increasingly describe as a human algorithm.

Novelty wins the first click

People share surprising information because it feels valuable to the group: “You need to see this.” In influence operations, novelty is a weapon. A fabricated claim can be engineered to be more “new” than a verified report, especially early in a crisis when facts are incomplete.

In national security contexts, this matters because novelty compresses decision time. When a narrative is “breaking,” teams often treat it as time-sensitive—even when it’s unverified.

Emotion turns sharing into reflex

Emotion—especially anger, fear, disgust, and outrage—acts as an accelerant. Content designed to provoke moral outrage travels quickly because it creates urgency: “This is unacceptable. People must know.”

Adversaries don’t need you to believe the story. They need you to react.

Identity makes people self-deploy

Identity is the quiet force multiplier. People share content that signals group membership: patriotic, skeptical, vigilant, wronged, righteous, “in the know.” The result is that disinformation doesn’t require massive botnets to go viral. The audience becomes the distribution network.

Here’s the line I come back to when advising teams: If you can predict what a community wants to feel, you can predict what it will amplify.

AI makes cognitive warfare cheaper, faster, and harder to attribute

Generative AI didn’t create cognitive warfare. It made it scalable.

When a small influence cell can generate thousands of variations of a narrative—tailored by audience, language, and platform—the bottleneck isn’t content production anymore. It’s attention management. That’s why modern influence campaigns increasingly behave like marketing operations:

• Multiple “brands” (synthetic personas) for different audience segments
• A/B testing phrasing for outrage and engagement
• Rapid iteration based on what gains traction
• Repackaging the same claim as memes, short clips, “leaks,” and pseudo-analysis

Deepfakes shift the battlefield from “true vs false” to “real vs uncertain”

Deepfake audio and video are especially corrosive because they don’t just introduce falsehoods; they introduce doubt about everything else. The strategic effect is often epistemic exhaustion: people stop trying to verify because it feels impossible.

That’s a direct national security risk:

• Command and control can be disrupted by forged guidance or impersonation.
• Public trust can be degraded during crises, elections, or kinetic escalation.
• Allied coordination can be strained by manufactured “evidence” and rumors.

And the most dangerous part? Speed. If a false clip shapes public understanding in the first hour, the correction two days later is a footnote.

The objective isn’t belief—it’s confusion and delay

A lot of counter-disinformation programs still behave as if the adversary’s goal is persuasion.

Often it isn’t.

Many modern disinformation campaigns aim for:

1. Confusion (people can’t tell what’s true)
2. Cynicism (people assume everyone lies)
3. Factionalization (groups accept different “realities”)
4. Decision delay (leaders hesitate, publics fragment, alliances argue)

Confusion is a strategic advantage because it slows responses and increases the political cost of action. If leaders must first win an argument about reality, they lose the initiative.

That’s why defense and national security teams should treat disinformation as a time-domain threat. It’s not just about accuracy. It’s about who shapes the narrative first.

Use AI to raise “truth velocity” without copying disinformation tactics

Truth can move fast, but only if it’s packaged for humans. The goal is truth velocity: getting accurate information into circulation quickly, through trusted channels, in language that fits the moment.

AI can help here, but only if the system is designed for operational realities—classification constraints, legal boundaries, chain-of-command approvals, and public trust.

What “truth velocity” looks like in practice

AI-enabled truth velocity isn’t a single tool. It’s an integrated workflow:

• Early signal detection: identify fast-rising narratives, not just viral posts.
• Claim clustering: group variants of the same claim across platforms and languages.
• Impact forecasting: estimate which communities the narrative will reach next.
• Response drafting: generate options for factual messaging tailored to audiences.
• Dissemination routing: recommend which trusted voices should deliver which message.
• Measurement: track whether corrections penetrate the same networks as the falsehood.

The stance I take: If your counter-message arrives after the rumor has become identity, you’re late.

AI systems that actually help (and ones that don’t)

Helpful AI in cognitive defense tends to focus on:

• Narrative-level analysis (themes, frames, emotional tone)
• Cross-platform fusion (text, image, video, audio)
• Multilingual monitoring (including slang and code words)
• Analyst-in-the-loop workflows (AI suggests; humans decide)

Unhelpful AI is the stuff that looks impressive in demos but fails operationally:

• Black-box “misinformation scores” without explainability
• Systems trained on outdated data that miss emergent narratives
• Tools that can’t operate with partial information and uncertainty

In national security, explainability isn’t a nice-to-have. If a system can’t show why it flagged something, it won’t be trusted in the room where decisions get made.

Build cognitive defense around “micro-frictions” that change behavior

One of the most practical countermeasures is surprisingly small: create a moment of pause before sharing. Research has shown that brief accuracy prompts reduce the spread of false content because they interrupt reflexive sharing.

This matters for defense organizations because so much harm comes from internal spread: group chats, team channels, forwarded screenshots, unofficial briefings.

Low-cost interventions you can deploy now

If you manage comms, security awareness, or mission support, these micro-frictions are high ROI:

1. Accuracy prompts in internal tools
   • Before reposting externally sourced content, ask: “Do we have a source we trust?”
2. “Two-source rule” for crisis sharing
   • In fast-moving events, require two independent confirmations before forwarding.
3. Provenance-first habits
   • Train teams to ask: “Where did this originate?” not “Who retweeted it?”
4. Pre-briefed response templates
   • Prepare language for likely disinformation themes so speed doesn’t kill accuracy.
5. Rumor triage channel
   • A dedicated place where people can drop questionable content for rapid review.

None of these require censorship. They require discipline.

“Mind sovereignty” is an operational skill, not a wellness slogan

One idea worth keeping is what some practitioners call mind sovereignty: noticing when content is trying to provoke you, then choosing to evaluate before reacting.

In defense settings, I’d frame it like this: Mind sovereignty is maintaining decision-quality under narrative pressure. It’s the cognitive equivalent of not firing until you confirm the target.

A practical framework: AI-enabled cognitive defense in 90 days

Teams ask a fair question: “What do we do Monday?” Here’s a concrete 90-day plan I’ve seen work in security-adjacent organizations.

Days 1–30: Map the threat to your mission

• Identify your high-value audiences (operators, families, local communities, partners).
• List the narratives most likely to target you (readiness, casualties, procurement waste, alliance betrayal, leadership scandal).
• Define “harm” in your context: delayed mobilization, reduced recruitment, base-community tension, policy paralysis.

Days 31–60: Stand up the AI workflow (with humans in charge)

• Deploy narrative monitoring that clusters claims and tracks velocity.
• Create an analyst review loop with clear thresholds: watch, warn, respond.
• Establish an approval path that can function during weekends and holidays.

December is a good time to stress-test this—attention is fragmented, staffing is lighter, and adversaries know it.

Days 61–90: Operationalize truth velocity

• Build pre-approved factual “modules” (what you can say quickly, legally, accurately).
• Train spokespeople and trusted internal voices on rapid response.
• Measure outcomes: time-to-detection, time-to-response, penetration into affected communities.

If you can’t measure response time, you can’t improve it—and you’ll keep losing to speed.

What leaders in defense and national security should demand from AI vendors

If your goal is leads and real deployments—not slide decks—buyers should ask sharper questions. Here’s my short list.

Must-have capabilities

• Cross-modal detection: text + image + audio + video, not just one.
• Narrative and network analysis: who is pushing what, where it’s going next.
• Human-in-the-loop controls: review, override, audit trails.
• Explainability: why it flagged a claim, with evidence.
• Operational security: data handling that respects sensitive environments.

Proof you should require

• Performance on recent datasets, not years-old benchmarks.
• Red-team results against synthetic persona campaigns.
• A clear plan for model drift and adversarial adaptation.

AI in defense and national security has to work in the real world, where the adversary watches your playbook and adjusts.

The fight is for attention—AI can protect it

Disinformation is a national security problem because it attacks the thing democracies run on: shared reality. The MIT-scale evidence is clear: falsehood spreads faster because people share it for human reasons—novelty, emotion, and identity—not because bots are magical.

That’s also why I’m optimistic about the right kind of AI in defense and national security. AI can spot narrative patterns earlier than humans, translate and cluster claims at machine speed, and help teams respond quickly without improvising under pressure.

The open question for 2026 planning cycles is straightforward: Will your organization treat cognitive defense like a core mission function—or like a communications problem?