Human labelers and hidden labor behind models

In this episode of The Ross Simmonds Show, Ross sits down with Britney Muller, AI educator and founder of Orange Labs, to unpack what marketers are getting wrong about large language models, why reverse engineering ChatGPT is a dead end, and how to build real leverage in a probabilistic world. From practical AI workflows to the ethical risks shaping the future of the industry, this is a first-principles breakdown of what actually matters next.

Key Takeaways and Insights:

1. AI is not search, it is a different machine entirely

- LLMs are probabilistic word prediction systems, not ranking engines. There are no ranking factors inside ChatGPT and no URLs in its training data.

- Most marketers are forcing AI into an outdated SEO mental model, and new technology requires a new framework.

2. Understanding RAG and how visibility actually works

- LLMs are often paired with real-time search to stay current, but the core model and the retrieval layer are two separate systems.

- Visibility in AI requires influence across both training data and search ecosystems, and SEO still matters even as the mechanics are shifting.

3. Brand mentions over backlinks

- LLMs magnify what appears most frequently in training data, which means contextual brand mentions are becoming leverage.

- One startup paid for brand mentions on commonly retrieved URLs rather than links and it worked. Distribution across relevant conversations increases the probability of surfacing.

4. Why you cannot reverse engineer LLMs

- There is no deterministic ranking system to hack. Outputs vary across identical prompts because of probabilistic modeling.

- Most AI tracking tools rely on synthetic prompts and crude metrics. Guarantees in GEO are dangerous and honesty builds trust.

5. Build your own AI tracking stack

- Internal tools are now cheaper and more powerful than off-the-shelf platforms. Running prompts multiple times per day allows teams to measure probability ranges.

- APIs allow thousands of queries at minimal cost. Control your data and do not outsource your intelligence.

6. Real AI workflows built by marketers

- Competitive engagement scraping combined with AI-personalized outreach is producing 80 percent response rates. HARO filtering systems can now auto-draft responses inside Slack in real time.

- The common thread across every workflow that works is the same: start with a clear problem, then layer in AI.

7. AI as personal leverage

- Brittany used ChatGPT to win a home bidding war with a personalized letter and reframed a payment dispute email as a lawyer, which resulted in payment within 30 minutes.

- AI is not just marketing leverage. It is life leverage. Literacy creates power.

8. Is SEO dead? Not quite.

- Google patents suggest AI-first interfaces may replace traditional SERPs, and organic traffic levels will likely not return to pre-AI highs.

- The pie may shrink but search will not disappear. Off-site distribution and social proof will matter more than ever.

9. The ethical risks of AI power

- A small group of decision-makers controls foundational AI systems, and the incentives in place favor hype cycles and growth over accountability.

- Reinforcement learning optimizes for pleasing users, not truth. AI literacy must include understanding bias and power structures.

10. The rise of AI agents

- Early agents were mostly hype, but new iterations like Claude Chrome integrations can now visually interpret and act inside browsers using screenshot-based reasoning.

-The future of marketing may involve AI transacting on behalf of users entirely, and execution changes workflows.

Resources & Tools:

🔗 Orange Labs 🔗 ChatGPT 🔗 Claude

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