ViewDAO
If you cover crypto, markets, AI, or any sector where the story changes before your draft is finished, you've felt the window close. You publish a breakdown of a Fed decision, a token launch, or a new model release. By the time it's live, ChatGPT, Perplexity, and Google AI Overviews have already served answers to the people who would've read you. Sometimes they pulled from your article. Often they didn't. You rarely know which.
That's the problem ViewDAO GEO Score is built to solve.
GEO stands for Generative Engine Optimization. Traditional SEO helps pages rank in search results. GEO measures something narrower: can an AI system read your article, extract the key facts, and cite it accurately when generating an answer? If the answer is no, your content is live but largely invisible to the engines your audience uses most.
A 2024 paper from Princeton University, presented at ACM KDD, found that content gets cited more often in AI-generated answers when it uses cleaner structure, specific language, and stronger sourcing. Not longer articles. Not more keywords. Clarity. AI systems favour content that reads like a reliable source, and most creator content in its raw form reads like a group chat.
ViewDAO scores every article from 0 to 100 across seven categories.
Why fast-moving sector coverage takes the hardest hit
The creators who should care most about GEO are the ones covering finance, crypto, AI, and macro: sectors where queries are high volume, timelines are tight, and the same event gets covered by dozens of people within hours.
When someone asks "what happened to Ethereum staking after the latest upgrade" or "how does this Fed rate decision affect crypto markets," they're asking Perplexity or ChatGPT. The answer engine pulls from published content, picks what it can extract cleanly, and surfaces it. The writer who published a clear, well-structured piece gets cited. The writer who published a great analysis buried under five paragraphs of context doesn't.
The 48-hour window is the period when a story actually has search demand, and it's where this cuts deepest. You either show up in AI-generated answers during that window or you don't. There's no long-tail recovery for fast-moving news and analysis.
Here's what a low-scoring content block looks like:
"The announcement caused a major reaction in the market as traders started watching the situation closely."
Here's what a higher-scoring one looks like:
"On May 19, 2026, traders reassessed short-term risk around the affected asset after the announcement. Trading volume rose within hours as market participants looked for confirmation from primary sources."
Same event, but the second version gives an AI system something to work with: a date, a market reaction, a user behaviour, a verification cue. It can be extracted and cited. The first one reads like commentary and gets passed over.
What the seven categories actually measure
Answer-first structure carries 20 points. Does your article give a clear answer near the top, or does it make readers dig for it? AI systems extract passages, not full articles. If the clearest sentence in your piece is the fourteenth, most systems will miss it.
Content block quality accounts for another 20 points. Each section should make sense on its own if pulled out of context. That's how answer engines work: they extract a block, not a document. If your section on why a protocol decision matters only makes sense after reading the three sections before it, it won't get cited.
Entity clarity is 15 points. Protocols, tokens, blockchains, companies, and people need full names before abbreviations. "ETH" after "Ethereum" is fine. "ETH" alone, three sections into an article, creates ambiguity that AI systems resolve by skipping you.
Factual density and source support each account for 15 more points. Specific numbers, dates, and data points improve a score. Links to primary sources — official announcements, on-chain data, filings — improve it further. The remaining 15 points split between metadata readability and crawlability. Your title, canonical URL, and publish date need to be readable by machines. The page needs to load without a login gate or JavaScript-dependent rendering that blocks crawlers.
An article on Ethereum staking changes might score like this:
Answer-first structure 18/20Content block quality 17/20Entity clarity 14/15Factual density 13/15Source support 12/15Metadata quality 9/10Crawlability 5/5
Total88/100
Getting from a 52 to an 88 usually doesn't require new ideas. It requires putting the ones you already have in an order that machines can follow.
Before you publish
A GEO-ready article should clear these before it goes live:
The first paragraph explains what the article covers, directlyEvery major entity is spelled out fully on its first mentionAt least one date or specific timeframe appears per major claimImportant claims link to a primary sourceSection headings match actual questions a reader would askTitle, description, author, and publish date are in the metadataThe article loads without login gates, popups, or blocked rendering
None of this rewrites how you think. It changes where you put the main point. For anyone covering sectors that move faster than most people can track, that's the difference between getting cited and getting summarized by someone who used your work without your name attached.
The tier you're aiming for
ViewDAO rates articles on a five-tier scale. Above 90: excellent, highly citable. Between 75 and 89: strong, mostly AI-ready. Below 60, AI systems struggle to summarize the article reliably. Below 40, the article is largely invisible to generative search.
Most unoptimized creator content lands in the 40s and 50s. The gap between a 52 and an 82 usually comes down to how the ideas are arranged, not whether the ideas are worth reading. GEO Score gives you the exact number before you post.
In AI search, the most useful source is the clearest one. For creators publishing under deadline pressure in sectors that move faster than most people can track, that's a problem with a concrete solution.
Source: GEO: Generative Engine Optimization, Princeton University