AI Assistants in 2026: How ChatGPT, Claude, Perplexity and Gemini Shape Conflict Information

Look at your own analytics and you can see it: **ChatGPT, Perplexity, Claude, Gemini and other AI assistants are now real referrers alongside Google.** During fast-moving conflicts, users are asking models “what just happened?” and “what does this mean for tech?”, and those models are citing and linking out to sources.

In 2026, AI assistants are becoming a new layer of **information infrastructure for conflicts and crises**. This piece explains how that works and what it means for developers and publishers who want to be part of that ecosystem.

## 1. From Search to Answer Engines

Traditional search engines answer questions with **links**. AI assistants answer with **summaries** that increasingly include:

- Inline citations to articles and reports
- Direct links to sources
- Explanations tuned to the user’s context (for example, “I’m a developer”)

During conflicts, this matters because:

- Many people now go to AI tools first for context.
- Models can pull in niche, high-signal sources that would be buried on page five of search.
- Traffic shows up as referrers from chatgpt.com, perplexity.ai, claude.ai, and other domains.

## 2. How AI Assistants Choose and Use Sources

Each assistant has its own stack, but common patterns include:

- A search or retrieval layer that finds relevant documents on the open web
- A ranking or scoring system that prioritises credible, detailed, and timely sources
- A citation mechanism that attaches URLs or titles to parts of the answer

For conflict and cyber topics, models tend to favour:

- Detailed explainers that go beyond headlines
- Technical breakdowns for specialised audiences (such as developers and security teams)
- Evergreen context pieces (undersea cables, BGP, AI in warfare) that can be reused across questions

## 3. What This Means for Developers and Publishers

**Content design**

- Write articles that answer clear, specific questions in depth (“how did Iran’s internet drop to 4%?”, “what happens if Red Sea cables are cut?”).
- Use structure that models understand: headings, concise intros, and explicit explanations of impact (“what this means for developers”).

**Technical hygiene**

- Keep pages fast, mobile-friendly, and accessible.
- Maintain clean metadata (titles, descriptions, canonical URLs) and sitemaps so crawlers and tools can find and interpret content.

**Ethics and responsibility**

- Conflicts are high-stakes. Accurate, sourced, and non-sensational content matters — both for users and for how models learn which sources to trust.
- Corrections and updates should be visible when facts change.

## 4. Building on Top of AI Assistants

For developers, AI assistants are not just referrers — they are **platforms**:

- You can build tools and workflows that assume users will copy answers, click through citations, and share snippets.
- You can use the same models in your own products to help users navigate complex events (for example, internal threat briefings or customer communications).

Treat them as part of your **distribution and UX stack**, not just something happening “out there”.

## 5. Takeaways

In 2026, AI assistants have become part of how the world learns about wars, cyber incidents, and infrastructure risks. For developers and publishers:

- Write for humans first, but in a structure that models can understand and cite.
- Watch your analytics for AI referrers; they are an early signal of what models see as high-quality sources.
- Use that feedback loop to decide what to write next — just as you are doing now.

The combination of search plus AI assistants is not replacing the open web; it is reorganising it. If you keep publishing deep, timely, technically grounded explainers, you will stay in that new information loop.