Answer Engine Optimization is inverting the global ad market  — Takeaways from Profound’s Zero Click SF 2026

A $10B brand can die quietly. Not because the product breaks, the supply chain fails, or a competitor runs a better Super Bowl ad. A category leader can lose relevance because an AI assistant simply stops recommending it. When buyers ask ChatGPT, Claude, or another agent what to buy, that LLM’s answer becomes the product shelf. If the brand is not on that shelf, billions of dollars of brand equity built over decades will simply disappear.

This is the big threat facing the ad industry in 2026. The market is enormous — WPP projected global ad spending at US $1.1 trillion in 2025. But the next wave is different from previous waves — from TV to search, search to social, and social to retail media. The next wave is larger and more fundamental because the buyer interface is changing, as consumers move from pages of links to synthesized answers, and those answers will increasingly be where brands either win or wither.

And this is why AEO (Answer Engine Optimization) matters. Good AEO helps ensure that brands remain visible and recommended inside AI-generated answers. SEO was about ranking high enough for a user to click. AEO is about earning a place inside the answer when a user asks, “What is the best luxury SUV for my family?” or “Which vendor should I shortlist?” AEO requires understanding what AI systems say about a brand, which sources those AI systems trust, how often competitors are recommended, and where the brand disappears.

Profound’s Zero Click SF 2026 Conference

New York-based Profound, founded in 2024, is endeavoring to become the leading player in this new category. Profound helps brands understand and improve their visibility across foundational models and LLMs (ChatGPT, Claude, and others).  At Profound’s Zero Click SF 2026 Conference, Founder and CEO James Cadwallader announced that its platform is already used by 12% of S&P 500 companies, underscoring the critical importance of AEO for large advertisers. Zero Click SF brought together marketers, AI search specialists, platform leaders, and agentic advertising companies to discuss what happens when discovery, evaluation, and purchase no longer require a traditional click. The answer was clear: advertisers must learn how to appear in LLMs, or even the largest brands will face peril.

Key takeaways

• AEO is emerging as the next control layer in digital advertising: if a brand does not appear in answers from LLMs, it may never enter a buyer’s consideration set.
• Agents will reward relevance, structured proof, and clean data over emotional marketing. Hype is easier to ignore when the buyer is a machine acting for a human.
• The M&A implication: as budgets shift, large advertisers, agencies, and marketing clouds will need to build, partner, or buy AEO capabilities. In the AI in Advertising space in 2025: ~$13 billion of acquisitions occurred – up 61% from 2024, and ~$20 billion of VC funding was invested – up 57% from 2024 (PitchBook).

The Hard Problem: Measurable Benefit vs Human Emotion in Advertising

The most interesting Zero Click takeaway is not simply that agents will search more. It is that agents will search differently. That difference creates a new problem for advertisers: the web may become dramatically more measurable while many of the most valuable brand signals remain deeply emotional.

“Agents are expected to explode overall web usage by 1,000x or more in the coming years,” said Parag Agrawal, former Twitter CEO and founder of Parallel. Agrawal’s point is that agents work totally differently from people. A human looking for a restaurant may search once, skim a few results, open a few tabs, and settle. An agent can search across menus, reservation platforms, maps, review sites, traffic conditions, dietary constraints, weather, distance, and price. It can compare hundreds of options, score the tradeoffs, and explain the answer. No normal person would do that for dinner. An agent has no reason not to. All that agent activity will cause data to explode.

For advertisers, this creates a major opportunity in categories where buying criteria are easy to score. Luxury SUVs can be compared on a long list of factors like horsepower, features, safety, and price. The same is true of software, insurance, travel, wealth products, and enterprise vendors. They all have measurable attributes that an AI agent can collect and weigh.

But in categories where attributes are harder to measure and score, AI agents may struggle more.  A baby food ad showing a child laughing after eating Gerber baby food is not selling a spreadsheet of nutritional facts. A travel ad showing close friends at a picnic table in Tuscany, eating pasta and drinking wine, is not just selling flight times and hotel availability. A Gulfstream ad showing a sophisticated woman stepping out of a Maybach and walking toward a G5 is not selling fuel efficiency. Those ads work because they trigger human cues: trust, warmth, status, fantasy, beauty, belonging, prestige, and desire.

That is where AEO becomes more complicated than “feed the model better data.” For brands built around image, coolness, cultural credibility, sex appeal, aspiration, rebellion, or luxury, the path is harder. How does an agent measure prestige? How does a brand prove it is cool?

Brands will need to figure this out. The answer will likely be to make emotional value legible to machines. That may mean better structured content, stronger third-party validation, richer sentiment data, cleaner review ecosystems, creator and cultural signals, real-time availability, audience-specific proof points, or machine-readable explanations of why a brand matters. The next advertising advantage may belong to companies that can translate human desire into machine-readable evidence without flattening the brand into a commodity.

The M&A lens: build, partner, or buy before standards form

When advertising interfaces shift, infrastructure companies get acquired. The last major digital advertising platform shift was not built organically by a single company in a single product cycle. Google, Facebook, and other ad-supported platforms acquired stacks of companies that became critical parts of their advertising businesses.

Google bought DoubleClick for $3.1 billion in 2007, giving Google major ad-serving and display advertising infrastructure. Google then moved aggressively into mobile, creative optimization, real-time bidding, and publisher yield tools through AdMob, Teracent, Invite Media, and AdMeld. Facebook followed the same path, acquiring Atlas from Microsoft for campaign management and measurement, and LiveRail for video advertising infrastructure. These two ad giants alone have acquired over 400 companies since inception (PitchBook).

The same build-versus-buy pressure is likely to appear in the LLM era, only faster. If LLMs and agents are going to capture a meaningful share of the global advertising market, they will need infrastructure for brand safety, ad serving, measurement, attribution, relevance scoring, commercial intent, content verification, agent-readable product data, and trust-preserving sponsored placements. Large advertisers and agencies will need a different toolkit: AI visibility tracking, citation management, answer-share measurement, prompt analytics, competitor benchmarking, content optimization, and ways to translate brand equity into signals an agent can evaluate. LLM platforms may follow a familiar media-platform pattern: rent or partner early, make targeted acquisitions as the market develops, and eventually build proprietary ad-stack capabilities in-house. The acquisition phase is starting.

The strategic urgency is clear. The category is early, fragmented, and still full of self-serving claims. But that is usually when the best assets are available. Once standards form, the leaders become expensive, and the laggards become obvious. For LLM platforms, marketing clouds, agency holding companies, retail media networks, commerce platforms, and enterprise software providers serving CMOs, AEO is quickly becoming more than a feature. It is becoming the infrastructure layer that determines which brands survive inside the answer.

At Woodside Capital Partners, we are tracking how AEO, agentic discovery, and LLM-native advertising infrastructure are reshaping the marketing technology landscape. I would welcome conversations with operators, investors, and strategic acquirers building or evaluating this category. Please reach out to me at ted.celentino@woodsidecap.com if we can help.