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.

Early days of search engine optimization
The early SEO era was defined by information asymmetry and rapidly shifting rules — a dynamic that's repeating itself with GEO.

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:

  1. Keyword research and on-page optimization
  2. Quality backlink acquisition
  3. Technical crawlability and site architecture
  4. Content freshness and relevance signals
  5. 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:

The modern LLM landscape
Unlike the search era's consolidation around Google, the LLM landscape remains fragmented across competing providers with divergent approaches.

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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