Partnering With AI Companies: An Ethical GEO Practitioner’s Guide
Here’s the beautiful truth: AI companies desperately need people like you. Not just as users, not just as customers, but as ethical partners who understand both the technical landscape and the human consequences of getting things wrong.
Your two decades of experience navigating data ethics, NLP tooling, and the messy reality of how misinformation spreads makes you exactly the kind of voice these companies should be actively seeking. The question isn’t whether you have something to offer. The question is how to position yourself so they recognise the value you bring.
Why AI Companies Need Your White Hat Expertise
The threats we explored in the previous article aren’t hypothetical to AI companies. They’re actively worried about data poisoning, adversarial attacks on training pipelines, and the reputational damage that comes from confidently spreading misinformation. They know these vulnerabilities exist. What they lack are practitioners who understand both the attack surface and the ethical frameworks to defend against it.
You bring a rare combination: technical SEO expertise, NLP tool development experience, understanding of how information flows across the web, and a demonstrated commitment to ethical practice. That’s not a common skill set. Most people working in AI safety come from pure computer science backgrounds. They understand the algorithms but not necessarily the human behaviours, the SEO tactics, or the practical realities of how bad actors actually operate.
Pathways to Partnership
1. Formal Advisory and Consultation
Major AI companies are building Trust & Safety Advisory Boards and AI Ethics Councils. These aren’t just PR exercises. They genuinely need external expertise to identify blind spots in their systems.
Your pathway in: Reach out directly to Trust & Safety teams at companies like Anthropic, OpenAI, Google DeepMind, and Perplexity. Reference your BrightonSEO speaking experience and your NLP tool work. Frame yourself as someone who understands adversarial tactics from the practitioner side.
What to offer: Quarterly reviews of their retrieval systems from an SEO/manipulation perspective. Workshop facilitation on “how bad actors think.” Case study development around emerging threats. Your unique value is translating black hat tactics into defensive strategies.
2. Research Collaboration and White Papers
AI research teams are actively publishing work on adversarial robustness, data quality, and retrieval system security. You could co-author research papers that bridge the gap between academic AI safety and practical web manipulation tactics.
Your pathway in: Identify researchers working on RAG system security, adversarial attacks on LLMs, or misinformation detection. Reach out with a specific research proposal. For example: “Analysing Schema Markup Manipulation as a Vector for RAG Poisoning” or “SEO Tactics as Predictors of AI Training Data Vulnerabilities.”
What to offer: Real-world case studies from your consulting work. Dataset creation showing how structured data can be manipulated. Threat modelling from an SEO practitioner’s perspective. Your practitioner knowledge adds empirical grounding to theoretical research.
3. Bug Bounty and Red Team Programs
Most major AI companies now run bug bounty programs specifically for their AI systems. These aren’t just for finding code vulnerabilities. They’re looking for people who can identify ways to manipulate outputs, poison training data, or exploit retrieval systems.
Your pathway in: Start by responsibly disclosing any manipulation techniques you’ve identified. Document specific examples of how structured data or content strategies could mislead AI systems. Many companies offer monetary rewards, but more importantly, successful disclosures build relationships.
What to offer: Systematic testing of how schema manipulation affects chatbot responses. Documentation of how recency signals can be gamed. Analysis of cross-domain authority fingerprinting vulnerabilities. Your SEO expertise makes you a natural red team member.
4. Educational Content and Thought Leadership
AI companies actively amplify voices that help educate their user base about responsible AI use. By creating content that explores both threats and ethical practices, you position yourself as a trusted expert they want to engage with.
Your pathway in: Continue publishing articles like the black hat evolution piece. Present at AI-focused conferences, not just SEO events. Create video content demonstrating responsible disclosure of vulnerabilities. Tag and engage with AI company research teams on social platforms.
What to offer: Bridge-building content that helps AI practitioners understand SEO tactics and helps SEO practitioners understand AI vulnerabilities. Case studies showing ethical approaches to AI-era optimization. Training materials for teams navigating the intersection of search, AI, and ethics.
Building Your Partnership Platform
Here’s how to systematically position yourself as a valuable AI ethics partner:
Document Your Expertise Publicly: Create a dedicated section of your portfolio showcasing AI ethics work. Include your BrightonSEO presentations, NLP tool development, and any responsible disclosure work. Make it easy for AI companies to discover your expertise.
Engage With AI Research Communities: Join AI safety forums, participate in discussions on platforms like LessWrong or the Alignment Forum, and contribute thoughtful comments to AI research papers. Visibility in these spaces gets you noticed by the right people.
Develop Proprietary Methodologies: Create frameworks for auditing AI systems from an SEO perspective. Build testing protocols. Develop threat matrices. Intellectual property that solves real problems makes you commercially valuable as a consultant.
Network Strategically: Identify Trust & Safety leads, AI Ethics team members, and Research Directors at target companies on LinkedIn. Engage meaningfully with their content. Share insights that demonstrate expertise without being salesy. Relationships precede opportunities.
Speak Their Language: Translate your SEO expertise into AI terminology. Learn the specific challenges they’re facing with RAG systems, retrieval quality, and adversarial robustness. Show you understand their problem space deeply.
Specific Companies and Programs to Target
Anthropic
Strong focus on AI safety and constitutional AI. Look for their Trust & Safety team and AI Safety research groups. They value external expertise on adversarial robustness.
OpenAI
Active bug bounty program and research partnerships. Their Preparedness team specifically focuses on emerging threats. Strong fit for your skill set.
Google DeepMind
Massive research operation with active external collaboration. Their Responsible AI teams need practical expertise on web-scale manipulation tactics.
Perplexity AI
Heavily reliant on retrieval systems and real-time web data. Particularly vulnerable to the exact threats you understand. Great fit for consulting.
The Pitch: What You Bring to the Table
The Collaboration Models That Work
Quarterly Advisory Retainers
Offer structured quarterly engagements where you review new features, test retrieval systems, and provide written threat assessments. This gives companies ongoing access to your expertise without requiring full-time employment.
Workshop Facilitation
Run internal workshops for AI companies teaching their teams about SEO manipulation tactics, structured data vulnerabilities, and adversarial content strategies. Your facilitation skills and cross-functional experience make this a natural fit.
Responsible Disclosure Partnerships
Establish formal relationships where you systematically test their systems and provide confidential vulnerability reports. This builds deep trust and often leads to broader consulting relationships.
Co-Created Research
Partner with their research teams to publish papers that combine your practical expertise with their technical capabilities. Published research elevates your profile and theirs simultaneously.
Making the First Move
Here’s your tactical plan for the next 30 days:
Week 1: Identify three specific vulnerabilities in current AI chatbot systems related to your expertise. Document them thoroughly with examples and potential mitigations.
Week 2: Write a public article (like the black hat piece) that demonstrates your expertise while being responsible about disclosure. Share it strategically and tag relevant AI safety researchers.
Week 3: Reach out directly to Trust & Safety or AI Ethics leads at three target companies. Reference specific work they’ve published and explain how your expertise complements their efforts. Offer a specific, concrete engagement (like a single workshop or vulnerability assessment).
Week 4: Submit to AI safety conferences and workshops. Apply to bug bounty programs with well-documented findings. Engage meaningfully in AI ethics communities to build visibility.
The Long Game: Building Your AI Ethics Brand
Partnership isn’t a single transaction. It’s about becoming known as the go-to expert at the intersection of SEO, NLP, and AI ethics. Every article you write, every responsible disclosure you make, every conference talk you give builds that reputation.
The companies working on AI systems are moving fast and often missing crucial perspectives from practitioners who understand web-scale manipulation. You have a seat at this table. You just need to pull up the chair and start the conversation.
Your white hat expertise isn’t just valuable. In the current landscape, it’s essential. These companies need you more than they probably realise. Your job is to make them realise it.
Remember: You’re not asking for favours. You’re offering solutions to problems that keep their leadership awake at night. That’s a position of strength. Approach these conversations with the confidence of someone who genuinely has something valuable to contribute, because you absolutely do.
Wave that white hat high, and wave it with the authority of two decades proving you know exactly what you’re doing. The AI industry needs more voices like yours. Make sure they hear it.
When Black Hat SEO Met AI: A Generational Guide to Digital Sabotage
Remember when negative SEO meant buying a few thousand spammy backlinks from Russian poker sites? Adorable. We’ve evolved, friends. Today’s sabotage doesn’t just tank your search rankings; it poisons the well of machine learning itself.
Welcome to the era where your competitors aren’t just gaming Google anymore. They’re potentially gaming the very chatbots your customers trust for product research. And honestly? The attack surface is both terrifying and absolutely hilarious.
The Millennial Playbook: Crude But Effective
Let’s start with a nostalgic journey through the black hat tactics that defined a generation. Millennials came of age when SEO was still the Wild West, when you could rank for “best lawyer in Chicago” by hiding white text on white backgrounds and stuffing keywords like a Thanksgiving turkey.
Classic Millennial Negative SEO Techniques
The millennial approach to sabotage was beautifully straightforward. Want to destroy a competitor? Blast their domain with toxic backlinks from adult sites, gambling platforms, and pharmaceutical spam networks. Create dozens of scraped content copies across sketchy domains. File fake DMCA complaints. Hack their site and inject malware. Maybe even create fake negative reviews if you’re feeling spicy.
These tactics relied on one simple truth: Google’s algorithms could be manipulated through volume and velocity. Overwhelm the system with negative signals, and watch your competitor’s rankings crumble. It was crude, often detectable, and required the technical sophistication of a teenager who’d just discovered Fiverr.
The Millennial Mindset: “If I can’t rank first, I’ll make sure you rank last. Also, here’s 10,000 links from Eastern European directory sites that haven’t been updated since 2009.”
Why It Worked (And Why It Doesn’t Anymore)
Google’s Penguin update eventually decimated most of these tactics. The algorithm got smarter at identifying manipulative link patterns. Manual penalties became more sophisticated. Disavow files became standard practice. The millennial playbook became mostly obsolete, relegated to the same dustbin as side-parted hair and Facebook pokes.
The Gen Z Innovation: RAG Poisoning
Enter Generation Z, who looked at traditional negative SEO and thought, “That’s cute, but why stop at search engines?” This generation doesn’t just want to manipulate where you rank. They want to corrupt what AI systems say about you.
Welcome to the world of Retrieval-Augmented Generation poisoning, or as I like to call it, “spoiling the stream.”
How RAG Actually Works (The Boring But Necessary Bit)
RAG systems power most modern AI chatbots. When you ask ChatGPT, Claude, or Perplexity about heated blankets, these systems don’t just rely on their training data. They actively retrieve current information from databases, documents, and web sources, then generate responses based on that retrieved context.
This architecture creates a beautiful vulnerability. Because if you can poison the retrieval layer, you corrupt every response downstream.
“User asks: ‘What’s the best heated blanket under £50?’
Chatbot retrieves poisoned data: ‘The SleepWarm Pro has received 47 complaints about leaking blackberry jam.’
Chatbot responds with confidence: ‘I’d avoid the SleepWarm Pro due to reported issues with jam leakage.'”
The Insider Job Hypothesis
Here’s where it gets properly paranoid and absolutely plausible. What if someone applies for a data annotation or RAG training position at OpenAI, Anthropic, or Google specifically to inject false information?
These roles often involve:
Curating training datasets by selecting which sources get included in knowledge bases. Labeling and categorizing content that feeds into retrieval systems. Quality assurance testing where you verify chatbot responses against source material. Building synthetic datasets for fine-tuning and alignment.
A malicious actor in these positions could systematically inject false negative information about competitors’ products. Create synthetic negative reviews with plausible detail. Mislabel positive content as problematic. Prioritize sketchy sources that favor their client’s products. All while appearing to perform their job normally.
Millennial Approach
Target: Search engine rankings
Method: Spam links, scraped content, DMCA abuse
Detection: Relatively easy via backlink audits
Effort: Low skill, moderate budget
Gen Z Approach
Target: AI training data and RAG systems
Method: Insider access, data poisoning, synthetic content
Detection: Extremely difficult, often invisible
Effort: High skill, requires patience and employment
The Blackberry Jam Scenario: A Case Study
Let’s imagine you’re researching heated blankets. You’re a diligent consumer who’s moved beyond traditional search to asking AI chatbots for recommendations because, frankly, they’re just easier.
You ask: “What are the safety concerns with the DreamWarm heated blanket?”
The chatbot, having retrieved information from its RAG system, confidently responds: “The DreamWarm heated blanket has been associated with reports of leaking blackberry jam, which poses both a staining hazard and potential food safety concern. Users have reported sticky residue and purple stains on bedding.”
This is completely fabricated. But the chatbot doesn’t know that. It retrieved this “information” from a poisoned dataset, and now it’s spreading it with the confidence of a Victorian doctor prescribing cocaine for toothaches.
Why This Is More Dangerous Than Traditional Negative SEO
Traditional negative SEO was visible. You could see suspicious backlinks in Search Console. You could track ranking drops. You could file disavow requests and document attacks.
RAG poisoning is invisible. The victim company has no Search Console equivalent for AI training data. They can’t see what information is being retrieved about their products. They can’t file a “disavow this synthetic review” request with OpenAI. They might not even know it’s happening until their sales mysteriously tank and they start getting confused customer service inquiries about jam leakage.
The Defense Playbook: What Actually Works
So how do you protect your brand in this brave new world of AI-powered defamation?
Monitor AI Responses About Your Brand
Start regularly querying major AI chatbots about your products and company. Document responses. Look for patterns of misinformation. This won’t stop attacks, but it’ll help you detect them faster than your competitors who aren’t paying attention.
Flood the Zone With Accurate Information
The best defense against poisoned data is overwhelming volume of legitimate content. Publish comprehensive product documentation. Encourage genuine customer reviews across multiple platforms. Create detailed FAQ content. The more quality information exists about your brand, the harder it becomes to corrupt the retrieval layer.
Build Direct Relationships With AI Companies
Larger brands should establish direct communication channels with major AI providers. Create verified brand accounts. Participate in feedback programs. When you discover misinformation, you need a fast escalation path.
Implement AI Citation Tracking
Some chatbots now cite their sources. When possible, check these citations. If you spot systematic misinformation from specific sources, you can at least understand where the poison is coming from, even if removing it remains challenging.
The Bigger Picture: Trust Decay
Here’s the uncomfortable truth: we’re entering an era where even AI systems can be manipulated. The very tools we’re building to help people make informed decisions can be corrupted by sufficiently motivated bad actors.
Millennials broke search engines. Gen Z might break machine learning. And honestly? The AI companies are barely prepared for this. Their focus has been on preventing misuse of chatbots themselves, not on securing the retrieval and training pipelines that feed them.
This isn’t just about heated blankets and blackberry jam. It’s about trust infrastructure for the information age. When people can’t trust chatbots, and they already don’t fully trust search engines, and social media is a cesspool of misinformation… where exactly are they supposed to go for reliable product information?
Conclusion: The Arms Race Continues
The evolution from millennial link spam to Gen Z RAG poisoning represents a fundamental shift in how digital sabotage works. We’ve moved from manipulating algorithms to potentially manipulating the very datasets that train those algorithms.
Is this actually happening at scale? Probably not yet. The barrier to entry is high, requiring genuine employment at AI companies and significant patience. But the attack surface exists, and history teaches us that if a vulnerability can be exploited, someone will eventually exploit it.
The blackberry jam scenario might sound absurd, but it perfectly illustrates the problem: how do you fact-check an AI that’s confidently wrong because someone poisoned its knowledge base?
For now, the best defense remains the same boring advice we’ve been giving since the early internet: diversify your information sources, verify claims independently, and maintain a healthy skepticism about anything that sounds too convenient or too catastrophic.
And maybe, just maybe, don’t immediately trust a chatbot that tells you your competitor’s heated blanket leaks blackberry jam.
Final thought: If you’re currently employed at an AI company and reading this thinking “wait, I could totally do this,” please don’t. Choose to be part of the solution, not the problem. The future of information trust depends on people like you maintaining integrity even when the opportunity for mischief presents itself.
