The New AI‑Driven Web — Advertising’s Future in an Era of Agents & Attention

ChatGPT Browser and Comet let users book hotels, compare flights or generate creative all inside chat. Every task the agent finishes skips the paid listing that used to win the click.

Why it matters

• Lost ad impressions → lost behavioural data → weaker retargeting pools.
• The browser owner now owns the training signals for future ad products.

Why ad impressions evaporate
The agent never displays Google Shopping cards or Meta retargeting units. Every conversion fulfilled in-chat is an impression, and a data-signal, lost to the incumbent ad stack.

Data is the new moat
Browser owners have always owned tracking data ; now OpenAI, Perplexity, Arc-AI race for data to train targeting models.

Evidence & sources → ChatGPT weekly active users ≈ 500 M (OpenAI analyst deck); Perplexity testing sponsored answers (TechCrunch, 2025-07-22); industry note “owning the browser secures your ads business” (Digiday, 2025-07-21).

Google’s AI Mode places a conversational answer above the fold. Pew Research (Mar 2025) shows pages with an AI summary deliver ≈ 9 % link clicks vs 15 % on classic SERPs.
Publishers in finance & travel report -30 % to -44 % CTR since rollout (MonetizeMore, May 2025).

flowchart LR
A[Query] --> B[AI Overview]
B -->|8 %| C[Organic Clicks]
B -->|1 %| D[Source Citations]

New “Citation SEO” Checklist

Google is already testing paid suggestions inside Overviews for commerce queries (“best running shoes under $120”) — conversational, clearly labeled, but inside the answer box. Brands must prepare FAQ-style creative & structured offers that the LLM can weave in naturally.

SEO ➞ Citation Fitness

• Serve structured data (FAQ, HowTo, Product)
• Craft fact‑dense snippets the LLM can lift verbatim
• Elevate EEAT signals (bylines, original stats).

Ad evolution – Paid placements now surface inside AI Overviews. Supply conversational copy & rich product attributes for smooth integration.

Gen Z spends 108 min/day inside TikTok’s feed. Brands respond with experiences > impressions.

• BMW’s generative-video car configurator on TikTok → +23 % engagement/$ vs static (PLabs test, 2025-Q1).
• AI-companion app Tolan grossed $36 M in 45 days via avatar skins & micro-spend (SensorTower, Apr 2025).

Winning Formats

Trade-offs: higher creative overhead, new ethical debate (biometric targeting), but ROAS scales with engagement minutes.

LLM+RL agents learn from every scroll, dwell & click.
Granular, consented, first-party signals are now the difference between:

• An agent merely mentioning your brand, and
• An agent picking your product as the best action to maximise its reward.

Privacy upside: fewer third-party cookies; more value-for-value exchanges where users trade data for better autonomous service.

1. Measurement Paradigm Shift

• Marketing-Mix-Modelling (MMM) assumed independent channels and week-level lags.
• Agentic environments are interactive and real-time – the same user may see 50 policy updates a day.

Practical stack

1. Off-policy evaluation (OPE) libraries (e.g., Doubly Robust, MAGIC) to estimate lift without full hold-outs.
2. Simulation sandboxes (e.g., Ray Tune, Replay-buffers) for safe policy testing before live rollout.
3. Attribution graph that logs state → action → reward tuples so every media dollar has a trajectory, not just a last-touch.

Bottom line: media data scientists become RL ops engineers – curating reward functions, checking exploitation-exploration ratios, and stress-testing model safety.

Tip: start local with Baselines → port to Ray for scale → wrap in LangGraph when multiple agents must collaborate (e.g., creative and bid-pacing).

What Scrunch AXP Does

• Spins up an AI‑only, machine‑readable shadow site — invisible to humans.
• Middleware routes LLM crawlers to this optimised version; humans still see your regular site.
• Auto‑structures content: JSON‑LD, bullet lists, fact boxes, canonical source tags.

Scrunch vs. Other AI‑Visibility Tools

Why it matters: As AI agents replace SERP clicks, being parsable beats being pretty. Scrunch’s lightweight proxy makes you LLM‑friendly in hours, not quarters.

LLM power‑users no longer want answers — they want actions completed.

Implication — Brands must expose actionable APIs (not just content) so agents can execute multi‑step journeys and still attribute value back to the brand.

LLMs stitch context on the fly; Google Web Guide (Labs) already clusters SERPs into sub‑topics.

Tactics

• Own multiple facets of a query (write specialised, well‑labeled pieces).
• Expose product & article data via OpenAPI / JSON feeds for agent access.
• Add canonical content-signature headers (emerging spec) to ensure citation credit.

1. Audit Content for AI-Fitness ⟶ FAQ blocks, schema, expert bylines.
2. Expose Agent-Friendly Endpoints ⟶ APIs, clean HTML, friction-free checkout flows.
3. Prototype AI-Native Ads Now ⟶ Conversational copy in P-Max, sponsor an industry chatbot.
4. Invest in Experiences ⟶ 30-day AR/LLM pilots; track dwell-minutes & micro-sales.
5. Upgrade Measurement ⟶ Server-side events + GA4’s “AI Interaction” metrics; prepare for dark funnels.
6. Educate Org & Advocate ⟶ Form “AI Readiness” squad; engage in standards bodies for fair content use.

📊 Pros & Cons Snapshot

⚡ Quick Demo Prompt

{
  "role": "system",
  "content": "You are a brand-experience generator. Tools: arStudio.createTryOn(model3D,url), mirage.liveFilter(url,styleId), stripe.checkout(session)."
}
{
  "role": "user",
  "content": "Launch a virtual pop-up where sneakerheads can try the new AirZoom in AR, remix the colorway live, and buy the limited drop."
}

The agent will:

• 1⃣ create the Web-AR try-on
• 2⃣ stream real-time colour remix filters
• 3⃣ trigger stripe.checkout when the shoe is bought.