Are Marketers Killing Google Ads Because of LLMs?

An r/DigitalMarketing post asked the timely question: are marketers stopping Google Ads campaigns due to LLMs? 36 upvotes, 63 comments, the thread bounced between "yes I cut my budget" and "ads still convert". The honest answer requires measurement, not vibes.

The two things actually shifting

• Top-of-funnel informational queries are migrating to LLM products. "How do I X", "best X for Y", "X vs Y" — these now terminate inside ChatGPT/Claude/Perplexity for many users.
• Bottom-funnel transactional queries still skew Google. "Buy X", "X near me" — Google still owns these for now.

The Google Ads cut question depends on which kind of traffic the campaigns target. If informational, the bleed is real. If transactional, less so.

The metric most marketers don't have

AI share-of-voice (LLM SoV) — the percentage of LLM-generated answers for category-relevant queries that mention or cite the brand. Without this metric, the "cut Google Ads or not" decision is guesswork.

The tracking pipeline

Three components:

• AI Overview citations from Google SERP (via Scavio)
• Direct LLM polling (Claude API + GPT API + Perplexity Sonar)
• Time-series store + chart

import requests, os, anthropic
H = {'x-api-key': os.environ['SCAVIO_API_KEY']}
client = anthropic.Anthropic()

def llm_sov(query, brand):
    # AI Overview citations
    aio = requests.post('https://api.scavio.dev/api/v1/search',
        headers=H, json={'query': query, 'include_ai_overview': True}).json()
    aio_cited = any(brand.lower() in str(c).lower()
                    for c in aio.get('ai_overview', {}).get('citations', []))

    # Claude direct
    claude_ans = client.messages.create(
        model='claude-sonnet-4-7', max_tokens=400,
        messages=[{'role':'user','content': query}]
    ).content[0].text
    claude_mentioned = brand.lower() in claude_ans.lower()

    return {'aio': int(aio_cited), 'claude': int(claude_mentioned)}

What the data looks like in practice

For a SaaS tracking 50 buyer-intent queries weekly:

• Week-over-week brand-mention rate per LLM
• AI Overview citation source list (which sites Google AIO is citing for your category)
• Position-weighted score per query

The slope matters more than the absolute number. Rising LLM SoV with flat Google traffic suggests the channel is migrating; flat LLM SoV with falling Google traffic suggests something else (competitor surge, Core Update).

Cost of running this in-house

Verified pricing (2026-04):

• Scavio Project tier: $30/mo for 7K credits (covers ~7K weekly polls)
• Claude API: ~$3/M input, $15/M output. 50 queries/wk * 4 wks * 400 tokens out = ~$1.20/mo at most
• OpenAI GPT API: similar order of magnitude
• Perplexity Sonar (optional): $1/M input + $5-12/1K requests

Total: ~$50-80/mo for in-house tracking with full query control. Dedicated AEO tools (Profound, Peec) start at $89-1,000+/mo for similar coverage with a managed dashboard.

What to do with the data

Three actions:

• Reallocate ad spend. If LLM SoV is rising for your category, paid-search budget on those query themes is at risk. Shift to AEO content investment.
• Optimize for AI Overview citations. The citation source list reveals which sites Google AIO is citing. Track which of your pages get cited and double down on the structure.
• Watch the leading indicator. Reddit thread density precedes AI Overview citation patterns in many categories. Use Scavio reddit_search to monitor your brand-relevant subreddits.

When LLMs are not killing your ads

E-commerce with mostly transactional intent: largely unaffected so far. Local services with "near me" intent: largely unaffected. Branded search: unaffected. The bleed is concentrated on top-of-funnel informational queries — not all paid search.

The honest answer to the thread

Yes, LLMs are eating top-of-funnel informational traffic. No, this doesn't mean kill all Google Ads. The right move is measure (LLM SoV) and reallocate (informational queries -> AEO investment; transactional -> keep ads). The teams that win in 2026 are the ones who can quantify the shift before making the budget call.