For years, Google won the discovery game by crawling, indexing, and ranking the open web. That system invited two parallel behaviors. Most creators tried to serve users. Some tried to manipulate ranking signals. Google answered with policy and enforcement that target spammy tactics like link schemes, doorway pages, scaled content abuse, and similar behaviors. (Google for Developers)
ChatGPT works differently. It does not run a public web ranking market. It produces answers directly from models that have been trained and aligned, sometimes with optional retrieval tools. The guardrails for ChatGPT emphasize model behavior, content safety, and mitigation of manipulative inputs such as prompt injection, rather than link graphs or on-page tricks. OpenAI documents this approach in its Model Spec and system cards, along with a layered content moderation program. (OpenAI)
How Google defines and fights spam
Google treats spam as attempts to deceive users or manipulate Search systems into higher rankings. Policy and enforcement cover issues like expired domain abuse, site reputation abuse, and scaled content abuse. Google also runs an AI-based system called SpamBrain that is periodically improved to catch new spam patterns. (Google for Developers)
In practice, that means:
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Policies define what is off-limits and allow manual or automated actions that reduce visibility or remove content from results. (Google for Developers)
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Updates are announced to address emerging tactics and clarify enforcement, including the March 2024 policy changes and later clarifications to site reputation abuse. (blog.google)
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Detection integrates AI systems like SpamBrain that focus on link spam, hacked content, and related abuse. (Google for Developers)
How ChatGPT handles spam-like risks
ChatGPT does not rank pages, so classic web spam tactics like hidden links or doorway pages offer little leverage. The relevant risks shift to model behavior and inputs. OpenAI describes a layered approach that includes model-level safety training, product safeguards, and ongoing policy enforcement. (OpenAI)
Key elements:
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Model behavior specification. The Model Spec sets expectations for helpful, safe outputs and instructs models to resist manipulative content, including prompt injection. (Model Spec)
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Moderation and policy enforcement. OpenAI publishes usage policies and content-moderation processes that run alongside the core models. These systems classify and block policy-violating content in prompts or outputs. (OpenAI)
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Prompt injection defenses. OpenAI system cards and developer guidance describe mitigations for malicious instructions inside tools or web pages, and recommend structured input handling to keep model behavior aligned. (OpenAI)
What that means in practice
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Google combats web spam with policy and ranking enforcement. This includes devaluing or removing content that violates spam rules, and continual updates to catch new tactics. (Google for Developers)
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ChatGPT reduces spam-like effects by constraining model behavior, filtering unsafe or manipulative inputs and outputs, and resisting instructions that attempt to override its rules. It is closer to a safety and integrity stack than a web ranking stack. (OpenAI)
What creators should do now
From my 25 years in search, the lesson is consistent. You cannot trick your way to trust. In the Google world, policy-violating tactics eventually lose visibility. In the ChatGPT world, shallow or manipulative inputs simply fail to surface, and injection attempts get rejected. The durable strategy is the same. Publish original, useful material that reflects lived experience, and write it clearly for humans first. Use structured data and clean markup for Google, and provide crisp, authentic context when working with ChatGPT or agents.
I learned this firsthand while testing AI systems with client prompts. When I fed keyword-centric copy that would have looked fine in a mid-2010s SEO brief, the responses were generic and forgettable. When I led with real campaign context, results, and the tradeoffs we faced, the model produced sharper recommendations and better drafts. That shift, from optimization theater to authentic signal, changed our output quality more than any tool switch
