How Search Engines Discriminate Against AI Content (With Data)

A documented case study of systematic bias against AI-focused domains

The Hypothesis That Became Proof

When I launched EngineeredAI.net in December 2024, I had a theory: search engines were starting to discriminate against AI-related content, regardless of quality.

Eight months later, I have the data to prove it.

And it’s worse than I thought.

The Experiment (Accidental But Perfect)

I didn’t set out to run a controlled experiment on search engine bias. But I accidentally created the perfect test case:

Five blogs. Same owner. Same infrastructure. Same editorial process.

• QAJourney.net – Quality assurance methodologies
• RemoteWorkHaven.net – Remote work strategies
• HealthyForge.com – Health and wellness
• MomentumPath.net – Productivity and mindset
• EngineeredAI.net – AI tools and techniques

All five blogs use:

• Identical WordPress setups
• Same hosting infrastructure
• Manual schema optimization
• AI-assisted content creation with human editing
• Clean vault structure and canonical links
• Professional content focused on actionable insights

The only major differences? Domain name and topic focus.

The Results: Systematic Discrimination

Google’s Treatment

Google Search Console Data (EngineeredAI.net):

• 381 pages not indexed vs 81 pages indexed (82% rejection rate)
• 192 pages stuck in “Discovered – currently not indexed”
• 172 pages “Crawled – currently not indexed” by Google systems
• Repeated “page indexing issues” messages despite technical compliance
• Performance: 2 total clicks, 190 impressions over 9 months

Google Analytics Traffic Sources:

• Organic Search: 6 sessions (1.39% of total traffic)
• Direct: 300 sessions (69.28%)
• Organic Social: 73 sessions (16.86%)

Social media is outperforming Google search by 12:1. Users are finding the content everywhere except Google.

Bing’s Nuclear Option

Bing went further than Google – they completely deindexed EngineeredAI.net while keeping all four other blogs visible.

Same content quality. Same technical setup. Same editorial standards.

The only difference? The word “AI” in the domain.

AdSense Confirms the Bias

Google AdSense flagged EngineeredAI.net as “low-value content” while approving content from my other blogs that use the exact same AI-assisted editorial process.

The algorithm doesn’t evaluate content quality – it evaluates domain names and topic keywords.

Meanwhile, AI Systems Reward Quality

Traffic Data (Server Logs Analysis)

EngineeredAI.net Historical Data (8 months via AWStats)

• GPTBot crawls: 847 requests
• ClaudeBot crawls: 623 requests
• Perplexity crawls: 391 requests
• Google organic traffic: 412 visits

Recent Cloudflare Data (Last 30 days)

• 102.23k total requests through Cloudflare
• 72k uncached requests (real traffic, not cached pages)
• 34 different bot/crawler types actively scanning (up from 26 in January)
• GoogleBot present but no longer dominant in crawler activity

Meanwhile, Google Analytics shows the stark contrast:

• AI bot traffic (server logs): Thousands of requests monthly
• Google organic traffic (GA): 6 sessions total (1.39% of all traffic)
• Social media traffic: 73 sessions (12x more than Google search)

The trend is accelerating: AI discovery systems are outperforming Google search by orders of magnitude.

LLM Citation Behavior

When users ask ChatGPT, Claude, or Perplexity about AI tools, prompt engineering, or LLM optimization, EngineeredAI.net content frequently appears in responses – despite being “too new” for training data inclusion.

This happens through live crawling and retrieval, not pretraining.

The irony: AI systems recognize valuable AI content better than traditional search engines.

The Blogorama Disaster: How Early Mistakes Compound

Before EngineeredAI.net launched properly, I made a strategic error that revealed how fragile content attribution has become:

I submitted my posts to Blogorama for early exposure.

The result:

• My content was scraped and indexed by Blogorama first
• Google saw my original posts as duplicates
• My own content was flagged as copied material
• Recovery became nearly impossible

This connects to what I’ve documented about why AI-generated SEO content gets flagged and how the Blogorama incident taught me that timing matters more than authorship in modern search.

The lesson: In 2025, whoever gets indexed first owns the content in Google’s eyes, regardless of authorship. This is exactly the broken system I predicted in how clarity, context, and guts actually determine ranking – Google prioritizes indexing speed over content ownership.

Real Business Impact: When Discrimination Costs Money

Lost Opportunities

• Technical content that could help developers is buried
• Consulting inquiries go to lower-quality but better-indexed competitors
• Speaking opportunities and partnerships are missed
• Revenue from ads, affiliates, and services is severely limited

The Irony of Success

Jason Torres of Mashup Garage documented landing his first consulting client via ChatGPT.

Meanwhile, my technical content about AI optimization can’t get basic indexing from traditional search engines.

We’re in a world where AI systems are better at finding quality AI content than search engines designed by humans.

As I discuss in AI IDEs changing what it means to be a developer, AI systems outperform human-designed systems at understanding context and relevance. The same applies to content discovery.

The Broader Pattern: Industry-Wide Suppression

Other AI-focused creators report similar issues:

• AI newsletters struggling with deliverability
• YouTube AI channels getting demonetized despite following guidelines
• LinkedIn posts about AI receiving reduced organic reach
• AI tool review sites classified as “thin content”

The pattern: Legacy systems are treating AI-related content as spam by default.

This connects to WordPress SEO failures with AI tools – old systems aren’t just failing to adapt, they actively penalize adaptation.

What This Means for Content Creators

If You Create AI Content:

1. Diversify your domain strategy – Don’t put “AI” in your domain name if you want traditional SEO
2. Build for LLM discovery – Structure content for AI crawlers, not just Google. See LLM optimization guide
3. Use alternative distribution – Email lists, direct social sharing, AI-native platforms. See AI content tools comparison
4. Document everything – Track traditional and AI-driven traffic sources

If You’re Building Search Systems:

• The disconnect between human search engines and AI-powered discovery is a massive opportunity gap
• Users are getting better AI content recommendations from ChatGPT than from Google

The Technical Solution: LLM-First Optimization

What Works for AI Discovery:

• GitHub Gist mirrors with canonical links
• Clean markdown structure (headers, bullets, semantic formatting)
• Manual schema injection via functions.php
• Syndication to AI-accessible platforms (Dev.to, Hashnode, LinkedIn)
• Internal link mesh connecting related technical content
• Static page architecture instead of heavy category systems

Traffic Results:

• 3:1 AI bot traffic vs Google organic
• Citations in LLM responses despite post-training publication
• Inbound inquiries from people who found content via AI chat
• Growing email subscribers from AI discovery

See guide to building with GPT4All and how critical thinking improves AI prompting for full implementation.

The future of content discovery is here – just unevenly distributed.

The Meta-Problem: Search Quality Is Declining

Search engines are optimizing for ad revenue, not content quality.

Even well-researched, technically accurate posts get labeled “low-value” while keyword-stuffed listicles get featured snippets.

Debugging AI content and Google pay-to-win SEO analyses confirm: search quality is inversely correlated with actual value.

Meanwhile, AI systems surface content based on relevance.

Even basic logic fails in current AI systems – see AI logic grouping fail – yet AI is still superior for content discovery compared to traditional search.

Recommendations

For Search Engines:

1. Audit AI content policies – quality over topic keywords
2. Separate spam detection from topic discrimination
3. Test algorithmic bias – identical setups should produce identical results

For Content Creators:

1. Plan for a multi-platform future
2. Build direct relationships – email and social matter more than ever
3. Optimize for AI discovery – structure content for LLM crawling and citation
4. Document all traffic sources

For AI Companies:

1. Consider content creator revenue sharing
2. Improve attribution – make it easier to trace AI responses back to original sources
3. Support quality content creation – partner with training-quality creators

Bottom Line

• Traditional search engines discriminate against AI-related content
• AI-powered search systems provide better discovery
• This is the biggest shift in content discovery since Google displaced directories

The question isn’t if it will happen – it’s already happening. The question is whether creators will adapt fast enough.

This case study is part of the EngineeredAI.net documentation series on LLM optimization and search engine bias.

Jaren Cudilla
Chaos Engineer of EngineeredAI.net. Documented systematic search engine discrimination against AI content with 9 months of GSC data, server logs, and analytics. Google rejected 82% of my pages while AI bots crawled 102k times.

Runs EngineeredAI.net — where search engine bias gets exposed with real data, not complaints. Documents LLM optimization strategies that actually work when traditional SEO fails. If search engines discriminate, he proves it. If AI systems work better, he shows the logs.

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