Reddit v. Perplexity filing claims Perplexity and a few data vacuums: SerpApi, Oxylabs, AWMProxy pulled Reddit content by scraping Google’s search results, sidestepping robots.txt. I am not a lawyer. I read filings the same way I read IKEA manuals: with coffee, confusion, and a small sense of dread.
Now, the eyebrow-raiser: Reddit says they posted something only Google could crawl. Hours later, Perplexity’s answers included that content. If the timeline holds, the clean explanation is somebody scraped Google’s results and piped the info into an answer engine. You might say, “Isn’t that how half the internet works” Yeah, kind of. The point here is the plumbing, not a TED Talk on ethics.
What this tells you about AI search
You are not dealing with magical omniscience. You are dealing with a filter.
- AI engines are not crawling the entire web in real time.
- They start with what surfaces in Google and a shortlist of trusted sources.
- Then they run their own retrieval, scoring, and answer assembly on top.
- Freshness and structure matter a lot, like when your tools are laid out on a pegboard instead of dumped in a bucket. Retrieval grabs the neat stuff first.
I mean, if Google can see you, AI answers can see you. If Google cannot, you are yelling into a pillow.
What they are actually scraping for
They are not reading your page like a person. They are pulling parts. Think parts bin, not poetry.
- Problem: the user’s pain or question.
- Solution: the short fix or answer.
- Entities: specific names for people, products, versions, places.
- Verbs: actions that map to intent, like install, compare, diagnose, buy, cancel.
- Attributes: specs, dates, prices, counts, requirements, limits.
- Adjectives: qualifiers like free, beta, on-prem, FDA cleared. Nice to have, not the engine.
Different systems weight this stuff differently. I do not have the exact math. It is somewhere sensible: entities and attributes carry more weight than adjectives; verbs help route intent; problem and solution frame the whole page.
Under the hood, plain English
All right, here is the likely workflow the lawsuit points to and practitioners see in the wild:
- Discovery: query or scrape Google to find fresh, high-ranking URLs.
- Ingestion: fetch pages and split them into chunks.
- Embedding: turn chunks into vectors to compare against queries.
- Scoring: rank chunks by semantic match, structure quality, source credibility, and freshness.
- Answering: assemble a response and attach citations.
I am sure some of you are thinking, “So authority still matters” Yes. Credible mentions and clean references help. Not magic. Just consistent signals machines can verify.
How to write so AI systems actually pull your page
This is the part you control. No hacks, just good mechanics.
1) Lead with the answer
Problem and solution up top, not after three paragraphs about the future of innovation.
- Problem: “Widget X fails to connect after firmware 2.4.”
- Solution: “Update to 2.4.1 and reset pairing. Works for most users.”
Why it works: AI engines grab the first clean answer they find. Google’s helpful content mindset also rewards direct, fast clarity.
2) Name entities precisely
Use canonical names, models, versions, regions.
- “Acme Widget X, firmware 2.4.1, Model AX-200, North America.”
Why it works: engines match pages to queries by entity, not vibes. Reduce ambiguity and you increase retrieval accuracy.
3) Label actions with intent verbs
Use headers like Install, Configure, Troubleshoot, Compare, Buy, Cancel, Migrate.
Why it works: systems route user intent to matching sections. Clear verbs make the right chunk easy to fetch.
4) List attributes cleanly
Bullets or a tiny table. Numbers are candy.
- Version: 2.4.1
- Release date: 2025-10-12
- Works with: AX-200, AX-210
- Time to fix: 5 minutes
Why it works: attributes ground the entity. Google and AI both prefer concrete facts over hand-waving.
5) Add a mini-FAQ
Q and A pairs map neatly to how people search and how AI answers.
- Q: Does this require admin rights
A: Yes. Local admin or MDM approval. - Q: Does it work offline
A: No. Needs an active connection the first time.
Why it works: FAQs create tidy retrieval targets and support rich results.
6) Stamp freshness
“Updated: 2025-11-03. What changed: added fix for 2.4.1.”
Why it works: recency dials up visibility. A date plus change note signals active maintenance.
7) Cite primary sources
Link to docs, standards, or original data. One solid source beats ten fluff blogs.
Why it works: citations reinforce trust. Google and AI both check the company you keep.
I got a few regrets in life. Letting pages sit stale for eight months is right up there with buying an elliptical I used twice.
Quick example you can steal
Title: Fix Widget X Connection Failures After Firmware 2.4
Updated: 2025-11-03
Problem: Widget X disconnects after updating to firmware 2.4.
Solution: Update to 2.4.1, reset pairing, and reboot the hub.
Key entities: Acme Widget X, firmware 2.4.1, Hub H-3
Attributes: Affected models AX-200, AX-210. Fix time 5 minutes. Internal success rate 80 percent.
Diagnose
- Check firmware version in Settings.
- Confirm hub is on H-3 or later.
Step-by-step fix
- Download firmware 2.4.1.
- Install update, then reset pairing.
- Reboot hub and test connection.
Alternative workaround
- Disable auto-sleep until 2.4.2 is available.
FAQ
- Q: Does this require admin rights
A: Yes. - Q: Will this reset saved profiles
A: No.
Common objections, straight answers
“Is AI search just piggybacking on Google”
Mostly, yes. Discovery flows through Google’s results, then AI applies its own layers.
“Do I need a brand-new strategy”
No. Write for humans, structure for machines. Same topics, clearer framing, steady updates.
“Do adjectives matter”
A little. They help with qualifiers like free trial or on-prem, but they do not outweigh entities and attributes.
Bottom line
All right, here is the punch list, and yes, it lines up with my Treasure Map Entity SEO approach:
- State the problem and solution at the top
Why: AI pulls the first clear fix. Google’s helpful content play favors fast answers. I start with intent, then the solve. Same rhythm. - Name entities precisely
Why: engines match by entity. Canonical names, models, versions, and regions reduce confusion and raise match quality. That is Treasure Map 101: lock the entity, then expand the graph. - Use intent verbs in headers
Why: Install, compare, diagnose, buy. Verbs tie your sections to user tasks. Retrieval grabs the right chunk without guessing. - List attributes in tight bullets or a small table
Why: specs, dates, counts, limits anchor your entity and give AI stable facts to cite. Google rewards concrete details. - Add a mini FAQ
Why: Q and A mirrors query patterns and creates clean, reusable answer units. - Stamp freshness with dates and what changed
Why: recency raises visibility, and change notes signal ongoing care, which both Google and answer engines notice. - Cite primary sources
Why: credible references build entity trust and make it safer for AI to cite you.
This is the overlap that actually works: build clear entities, answer intent fast, structure cleanly, and keep pages alive. That is Google-friendly and straight out of the Treasure Map teaching.
Want the deeper dive with my system? Join the course here: https://mastercoursereviews.com/
And hop into the Facebook group SEO Training Camp for real-time feedback and hands-on entity mapping.
I mean, it is not rocket science. Say the problem, show the fix, name the right things, and keep it tidy. Do that and both Google and AI know exactly what you are talking about. Skip it and you are basically duct-taping your content to the wind.