Paid Influence Is Baked Into the Web

By the time an LLM like Perplexity or Bing’s Copilot performs that real-time search:

• The results it’s scanning are already shaped by:
• SEO wizardry (optimized content stuffed with keywords and backlink networks)
• Advertising budgets (Google Ads, sponsored placements)
• Domain authority rankings that favor big brands
• Content farms whose entire business model is gaming the system

Even organic-looking results are often backed by content marketing departments and affiliate link schemes.

So what does this mean for LLMs?

Large language models don’t know that a result was bought and paid for. They’re trained (or instructed) to:

• Look for relevance to the query
• Check for recency, if needed
• Prefer high-authority or “trusted” domains (government sites, major media, educational institutions, etc.)
• Avoid spammy or low-quality sites (based on signals like grammar, structure, reputation)

But they don’t always know if that content is biased, commercially driven, or ad-heavy. Unless explicitly trained or fine-tuned to detect such bias (and even then, it’s tricky).

So we end up with this paradox:

• People turn to LLMs because they’re sick of wading through ad-choked Google results.
• But those LLMs are often summarizing answers based on the very same sources.
• Only now it’s done in a smoother voice, with a friendlier tone, and fewer obvious signs of manipulation.

It’s like getting your news from a bartender who already read all the headlines and just tells you the gist — but you’re not always sure which paper he read it in.

What can be done? 

Some things that are happening (or could happen):

1. Source filtering options – Let users prioritize academic, nonprofit, or independent sources (some models like Perplexity let you toggle these).
2. Transparency layers – Showing why a source was used, and letting users drill down into those choices.
3. Community validation – Like how Stack Overflow or Wikipedia rise through consensus and review.
4. Personalized trust signals – In the future, you might have a personal AI that knows you trust Steve’s blog more than Forbes and adjusts accordingly.

Bottom line?

Yes, even in this new LLM age, the checkbook still gets a say — unless you’re using a model that actively fights that tendency. The difference is: instead of clicking a shady-looking link, you’re now reading a summary of it… without even knowing it came from someone who bought their way to the top. You still need to squint, tilt your head, and ask:

“Who benefits if I believe this?”