Putting GEO Into Context: Lessons for Executives from Early Days of SEO
Before investing heavily in Generative Engine Optimization, we should learn from early SEO's uncertainty, opportunistic consulting, and rapid algorithm shifts.
Generative Engine Optimization (GEO) is emerging as a buzzword in the AI era, promising visibility in LLM-generated answers. Before investing heavily, we should learn from the early SEO era (1997–2012), which was marked by uncertainty, opportunistic consulting, and rapid algorithm shifts. Today’s LLM landscape is even more fragmented and opaque, creating higher risk for misinformation and wasted spend.
Recommendation: Treat GEO as an experimental lane, not a core investment. Focus on durable content, provenance, and compliance—not hacks or speculative tools.
Historical Parallel: Early SEO
Uncertainty bred snake oil. In the 2000s, consultants exploited gaps in knowledge with tactics like keyword stuffing and link farms.
Google’s communication stabilized the market. Matt Cutts and Webmaster Guidelines gave clarity, aligning webmasters with Google’s interests.
Single dominant platform. By 2005, Google overtook Yahoo; by 2007, it was the clear leader. Optimization became predictable and scalable.
The early SEO playbook eventually coalesced around a few core tactics:
- Keyword research and on-page optimization
- Quality backlink acquisition
- Technical crawlability and site architecture
- Content freshness and relevance signals
- User experience metrics (bounce rate, time on page)
Key dynamic: Deterministic algorithms + one platform = eventual playbook.
The SEO era taught us something important: when the rules are unclear, the loudest voices aren’t always the most accurate. Clarity comes from the platforms themselves — and it takes years.
Today’s Reality: LLMs & GEO
Fragmented ecosystem. Multiple engines (OpenAI, Anthropic, Google, Meta, Cohere, Mistral) with different behaviors and fast update cycles.
Opaque, probabilistic outputs. Unlike search rankings, LLM responses are stochastic and prompt-dependent; tactics rarely generalize.
No incentive for guidance. Vendors prioritize model capability and safety—not marketer education.
Tooling risk. Most GEO tools are thin wrappers over LLM APIs, scraping outputs and extrapolating from questionable data. Expect high churn; few moats exist.
Key differences from the SEO era include:
- No single dominant platform to optimize for
- Outputs are non-deterministic — the same query produces different results each time
- No equivalent of Google’s Webmaster Guidelines or Search Console
- Model updates happen silently and frequently, invalidating prior observations
- Attribution and sourcing remain inconsistent across engines
Implication: Even if a tactic “works” today, it may fail tomorrow—or on another engine.
Recommendations
Anchor to durable principles: Maintain strong SEO (crawlability, structured data, E-E-A-T) and layer GEO-friendly content (clear Q&A, attribution, licensing).
Prioritize provenance: Embed author, date, and rights metadata; publish canonical, self-contained assets.
Limit GEO spend: Avoid over-investing in consultants or tools; insist on short-term contracts and compliance documentation.
Build internal monitoring: Run small, controlled tests across engines; track mentions and citations without over-interpreting short-term lifts.
Defend our properties: Implement bot management and AI crawler controls to manage bandwidth and licensing.
Focus on customer utility: Content should answer banking questions accurately and safely—resilient across engines and time.
Bottom Line
GEO is real but immature. The early SEO era taught us that clarity and standards take years to emerge. Until then, avoid chasing hacks or speculative tools. Invest in content quality, provenance, and compliance—the only strategies likely to endure.
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