The Answer Engine Optimization Gold Rush Is Built on Synthetic Data and False Promises
Annalisa Hilliard & Jen Carroll
The question facing marketers isn't so much "How do I optimize for ChatGPT SEO or Claude citations?" but rather "How do I build a brand people actively seek out and trust, regardless of how search evolves?"
When gold was discovered in California in 1849, suppliers selling picks, shovels, and provisions made fortunes while miners went bust. The answer engine optimization rush follows an identical pattern, with one crucial difference: at least California gold actually existed in the ground.
Answer engine optimization tools promise to track your visibility and help you improve content optimization across ChatGPT, Claude, Perplexity, and Google AI mode—platforms where it’s now 750 to 30,000 times harder to be discovered than traditional search, according to Cloudflare CEO Matthew Prince. Yet, an entire industry has emerged almost overnight, selling what amounts to picks and shovels for mining AI chatbots. These AI SEO software platforms range in price from $30 to $3,000 per month and feature impressive dashboards, competitive benchmarking, and citation tracking across multiple AI systems.
There’s just one problem: these tools fundamentally measure AI talking to AI, not actual human behavior. Due to platform privacy restrictions and no financial motivation to release the data at this point, no one can access what real humans ask AI platforms or determine which responses they find valuable.
Those who know the fundamentals of marketing communications will recognize this moment for what it is—a return to principles that worked before the internet, throughout the digital era, and will outlast whatever comes next. For small and mid-size businesses (SMBs), the answer engine optimization tool market should create clarity rather than anxiety. When even enterprise platforms rely on synthetic prompts and can’t access real user behavior data, the strategic question becomes: why pay to estimate something you can’t validate?
Don’t have enough time to read this article? Skip to the end for a TL;DR summary.
How AI search optimization tools work
A recent analysis by journalist Derek Thompson and investor, writer, and researcher Paul Kedrosky highlights how the AI boom exhibits classic gold rush dynamics—momentum-led speculation, capital stampedes, and infrastructure overbuild racing ahead of sustainable revenues. Kedrosky notes more than once that, at this point, no one knows if and when investors will see ROI for the billions poured into this technology.
The answer engine optimization market exhibits the same pattern: developers racing to build measurement infrastructure before there’s anything to measure. This AI search optimization gold rush has created an entire ecosystem of tools claiming to deliver insights they can’t actually observe.
This begs another question: if no external developer can retrieve people’s prompts in ChatGPT, Claude, Perplexity, etc., what are customers who pay for these tools getting?
Inside the GEO tool workflow
- Generate synthetic prompts. AI presumes questions users might ask about your topic
- Query AI platforms. These artificial questions get fed to AI systems for what developers market as ChatGPT SEO optimization (along with Claude, Perplexity, and other platforms) via publicly available APIs
- Analyze citations. Tools capture AI-generated responses and track which sources get mentioned
- Create visibility scores. Algorithms transform these mentions into estimated “brand visibility” metrics across platforms
- Deliver dashboards and alerts. Customers receive reports showing their supposed visibility, content recommendations, and notifications when AI answers change
The more you pay (from $30 to $3,000+ monthly), the more synthetic prompts these tools can generate and analyze.
What’s missing? Any connection to actual human queries, genuine user engagement, or measurable business outcomes. The tools aggregate signals, automate testing at scale, and provide analytics layers—but they’re fundamentally measuring AI talking to AI, not real people seeking information.
What’s actually happening? Cannibalistic optimization. AI answers its own imagined questions, tools measure these synthetic conversations, brands optimize for this artificial behavior, and AI trains on this “optimized” content that’s increasingly divorced from genuine human needs. This also potentially accelerates the creation of AI slop and feedback loops, adding to internet decay.
Tool developers justify their fees through automation, multi-platform coverage, and analytics infrastructure, which we know are costly investments. But the fundamental limitation remains: no amount of sophisticated dashboard design can overcome the absence of real user behavior data.
Why LLMs can't predict what humans actually search
The fundamental limitations of LLMs
Similar to our struggles with a lack of LLM data, LLMs also have no access to search behavior data unless we can feed it to them. Even then, they can’t see:
- What people actually type into search boxes
- Which variations they try after their first search fails
- The messy and informal ways people phrase questions
- Regional or temporal variations in terminology
- The jargon real people tend to use when discussing a niche topic
LLMs are operating blind to the behavior they’re supposed to predict, and they are biased toward formal, optimized language because that’s overrepresented in training data. Add to this lack of domain-specific insider knowledge—even technically relevant suggestions miss the subtle distinctions practitioners know, like the difference between how insiders use one term versus another that outsiders might use interchangeably.
Finally, most LLMs currently operate from frozen knowledge. Language evolves rapidly, especially in tech and emerging fields. AI might suggest keywords that were popular but have been replaced, or miss emerging phrases entirely.
When answer engine optimization tools use AI to generate synthetic queries, they’re compounding all these limitations—then measuring your brand’s presence in responses to queries that may not bear much resemblance to what humans actually ask.
The validation problem with answer engines
Traditional SEO tools like SEMrush, Google Search Console, and Google Analytics 4 provide something answer engine optimization tools fundamentally cannot: actual data from real humans. These platforms show you precisely what people typed, how often they searched for it, which results they clicked, how long they stayed on pages, and whether they converted into customers.
This creates a validation loop that keeps SEO grounded in reality. When you test keyword strategies, you can measure whether your assumptions match actual behavior. If you optimize for a term you think people search for but the data shows zero volume, you adjust. The feedback is immediate and unmistakable.
We regularly test AI-generated keyword suggestions against actual search data in SEMrush. Many sound plausible—they use industry terminology correctly, they address logical questions, they feel like things people would search for. But when you check actual search volume? A large percentage actually get zero searches per month. ZERO.
When it comes to answer engine optimization tools, this isn’t a problem better engineering can solve. Unlike traditional SEO tools that validate assumptions with real data, AI SEO software faces an insurmountable barrier: the data simply isn’t available. The entire measurement category is built on approximations of approximations.
Build authority that transcends platforms
The practices that genuinely help with AI discovery—clear content structure, factual accuracy, proper citations, authoritative expertise—are the same methods I (Annalisa) have used to drive SEO success for years. When marketers debate SEO vs. GEO, they’re missing the point. What works for Google’s algorithms also works for AI systems, because both reward genuine authority and valuable content.
This means traditional marketing principles still apply; they’re just being expressed through new technology. The focus shifts from gaming algorithms to creating genuine value, from attention exploitation to attention conservation. As I (Jen) explore in my article on the attention economy, respecting the finite nature of human attention isn’t just ethical, it’s increasingly strategic.
Plus, traditional search isn’t disappearing any time soon. Even as Google AI mode becomes more prominent, standard search results remain the primary discovery method for most businesses, especially when you focus on integrated SEO and brand strategy rather than chasing algorithmic tricks.
Construct marketing assets that appreciate
Instead of chasing synthetic visibility scores, sustainable marketing for the AI era rests on three foundational principles. I (Jen) wrote in depth about this framework in Resilient SMB Marketing: 3 Proven Ways to ‘Outlast’ in the Post-Attention Economy, but the core insight is this: build owned channels that strengthen relationships over time, develop clear messaging that addresses real customer needs, and create authority that generates brand gravity regardless of discovery platform.
Rather than paying for answer engine optimization tools, invest those same resources in technical hygiene, cross-platform authority growth, and metrics that are tied to business outcomes that matter to you.
Technical hygiene (the real AI search optimization that matters)
- Proper structured data and schema markup
- Clear content organization with logical heading hierarchy
- Factual accuracy with proper attribution
- Citation-ready statements
These practices help both humans and AI understand your content, but they’re table stakes, not competitive advantages.
Cross-platform authority growth
- Create genuinely valuable content serving actual customer needs.
- Develop content that extends beyond what AI summaries can provide: original research, unique perspectives, deep expertise that takes time to develop.
- Build recognition through media relationships, thought leadership, and demonstrated expertise.
- Earn mentions in trusted publications through editorial merit as much as possible.
- Position specific people, not just brands, as authorities—because people connect with people.
Measurement that connects to business outcomes
Track brand awareness metrics tied to actual results rather than synthetic visibility:
- Branded search volume—are people looking for you specifically
- Direct traffic growth—mental availability and brand recognition
- Return visitor rates—loyalty indicators that predict customer lifetime value
- Actual conversions and revenue across channels
- Customer acquisition cost to understand efficiency
As I (Annalisa) discussed in How to Measure Brand Awareness Like an SEO Pro, bridging traditional SEO metrics with brand awareness signals creates a comprehensive view of how your authority translates across discovery methods. The most resilient measurement frameworks focus on signals that indicate genuine human connection rather than algorithmic favor.
The SMB competitive advantage in marcom
For SMBs with limited budgets, understanding your marketing reality is important.
What you can’t compete on:
- Budget for the latest expensive tools
- Chasing every platform change as it happens
- Hiring specialists for each new trend
What you CAN compete on (and always could):
- Deep understanding of your specific niche
- Authentic relationships with customers who know your name
- Agility to adapt without bureaucratic approval processes
- Personal expertise and reputation that can’t be quickly replicated
- Attention conservation approach—providing real value that justifies the cognitive resources you request
This is where the Google API leak insights become especially relevant for SMBs. While the leak confirmed that brand authority signals have always driven rankings more than Google publicly acknowledged, it also revealed that building this authority doesn’t require expensive tools—it requires consistently demonstrating expertise and strengthening genuine industry and customer relationships.
The playing field is more level than tool developers want you to believe. Large competitors may have bigger budgets, but they can’t buy trust, genuine expertise, or the agility to adapt quickly. These remain advantages available to any business willing to invest in marcom fundamentals.
When competitive advantage comes from things money alone can’t buy—expertise, relationships, trust, strategic clarity in messaging—the playing field levels. You can compete on these dimensions against much larger competitors because they require human judgment, consistency, and authentic value delivery, not just capital.
We’ve been here before with SEO
Those who have been in marketing in recent decades recognize this pattern. Early SEO was about helping search engines understand good content. Then it became about gaming algorithms—keyword stuffing, link schemes, content farms. Google spent years fighting back against these manipulations.
We’re seeing the same pattern emerge with AI. The initial focus on helping AI systems understand quality content has already begun shifting toward gaming: synthetic prompts, manufactured visibility scores, optimization divorced from value creation.
The question facing marketers isn’t so much “How do I optimize for ChatGPT SEO or Claude citations?” but rather “How do I build a brand people actively seek out and trust, regardless of how search evolves?”
This second question is definitely harder. It requires real expertise, not just content production. Authentic relationships, not just reach metrics. Patience and long-term thinking instead of only quick wins. Investment in quality in addition to proven optimization tactics.
What resilient businesses understand is that building brand awareness and authority for humans while also incorporating optimization for bots will, over time, create what we call brand gravity—a natural pull that draws customers to you even as reach out to them. This gravity works across all discovery methods: traditional search, AI-generated answers, word-of-mouth recommendations, and social platforms.
Here’s what makes this particularly relevant to answer engine optimization: the practices that build genuine brand authority are exactly what help AI systems recognize you as a credible source. The SEO vs. GEO debate is largely artificial—they’re fundamentally the same discipline applied to different interfaces.
When AI systems evaluate which sources to cite, they’re looking for the same signals Google’s algorithms have rewarded in branding and SEO—clear content structure, factual accuracy with proper attribution, authoritative expertise demonstrated consistently over time, and recognition from trusted third parties. The difference is you can’t game these signals with synthetic prompts. You have to build them authentically.
Real answer engine optimization builds brand authority
The web has, so far, proven remarkably resilient throughout its history. Human curation didn’t die when Google launched—it evolved into new forms. We don’t think it will die from AI, either. What will evolve is how we demonstrate expertise and build trust.
Thriving SMB marketers don’t need to pay for synthetic visibility scores. They know communications has always been about creating value for humans, establishing trust through consistent delivery, earning attention rather than stealing it, and respecting the finite nature of human attention in an increasingly noisy world.
That work isn’t fast, easy, or glamorous, but it creates business assets that appreciate over time and can be repurposed and optimized to go even further. And ironically, that matters more for answer engine optimization than any tool measuring imaginary conversations ever could—because it builds the genuine authority that AI systems are actually designed to recognize.
The answer engine optimization gold rush will eventually end, as all gold rushes do. When it does, the businesses still standing will be those that built something real: expertise worth discovering, brands worth trusting, and value worth seeking out—regardless of which platform people use to find them.
Ready to stop chasing synthetic visibility scores and start building genuine authority that works across all discovery methods? Contact The Dames to develop an integrated SEO and brand strategy that creates sustainable visibility whether customers find you through traditional search, AI-generated answers, or word-of-mouth recommendations.
TL;DR
- Answer engine optimization tools promise to track your visibility across ChatGPT, Claude, Perplexity, and Google AI mode—but they fundamentally measure AI talking to AI, not actual human behavior. These platforms ($30-$3,000/month) generate synthetic prompts, query public APIs, and produce visibility scores based on imagined questions rather than real user queries.
- The core problem: Privacy restrictions prevent anyone from accessing what humans actually ask AI platforms. LLMs can’t predict real search behavior because they pattern-match training data rather than understanding current user intent, they’re biased toward formal language, and they operate from frozen knowledge that misses evolving terminology.
- Traditional SEO tools validate assumptions with real user data—showing what people typed, clicked, and converted on. AI SEO software can’t. The entire measurement category is built on approximations of approximations.
- What actually works: The practices that help with AI discovery—clear content structure, factual accuracy, proper citations, authoritative expertise—are the same methods that have driven SEO success for years. The SEO vs. GEO debate misses the point: both reward genuine authority and valuable content.
- Instead of paying for synthetic visibility scores, invest in technical hygiene (schema markup, clear organization, proper attribution), cross-platform authority building (original research, media relationships, thought leadership), and measurement that connects to actual business outcomes (branded search volume, direct traffic, return visitors, conversions).
- For SMBs: Large competitors may have bigger budgets, but they can’t buy trust, genuine expertise, or agility. Build marketing assets that appreciate over time. The answer engine optimization gold rush will end—the businesses still standing will be those that built real expertise worth discovering, brands worth trusting, and value worth seeking out.
- Answer engine optimization tools promise to track your visibility across ChatGPT, Claude, Perplexity, and Google AI mode—but they fundamentally measure AI talking to AI, not actual human behavior. These platforms ($30-$3,000/month) generate synthetic prompts, query public APIs, and produce visibility scores based on imagined questions rather than real user queries.