Will AI Show Ads? Decision Engines and the Agentic Commerce Revolution

When the way we use the internet changes, the way money moves changes with it. AI is not simply answering questions: it shapes decisions and shops on our behalf. How can brands survive in this new environment?

Every decade brings a fundamental shift in digital life. The move from traditional Google results to decision engines resembles the transition from the Yellow Pages to Google in the early 2000s. Most users no longer want to type keywords and get lost among links. They seek refined information, direct answers and even an assistant that acts on their behalf.

This article goes beyond “Will AI start showing ads?” to examine how agentic commerce changes advertising, the economic pressures behind it and the technical roadmap brands need for 2025 and beyond.

A New Era of Digital Advertising: Next-Generation AdTech
In this ecosystem, decision engines that interpret intent replace conventional search. The high operating costs of large language models make advertising an economic necessity. Ads move from interruptive graphics toward contextual solutions, while commerce evolves toward agentic commerce, where autonomous AI agents make purchasing decisions.

Why Can AI Not Remain Ad-Free?

The industry often expresses an appealing expectation: “AI search will offer a clean, unbiased information experience without advertising.” Financial realities, hardware costs and energy bills, however, make that difficult to sustain. Advertising is presented here not as a preference but as an economic necessity.

The Weight of Operating Costs

The basic difference between traditional search and generative AI is processing cost.

Traditional search: Retrieves information from an indexed database using CPU processing, at a cost measured in microcents.
Generative AI: When you ask a model such as GPT-4 or Claude 3 a question, it does not simply retrieve a ready-made answer. It processes billions of parameters to generate a new response, token by token.

According to the Stanford AI Index and industry analyses, inference for a GPT-4-class model costs 10–100 times more than conventional search. Rental costs for high-performance GPUs such as NVIDIA H100, along with data-center energy and cooling, increase cost per query.

This structure makes subscription income alone, such as US$20 a month, insufficient for survival. The article identifies selling high-intent traffic to brands as the way to subsidize inference: advertising becomes AI’s fuel.

The Subscription Barrier and Market Saturation

Although OpenAI, Gemini, Anthropic and Perplexity initially focused on subscriptions, market research indicates that 71% of consumers are unwilling to pay for AI services.

Technology groups including Microsoft, Google, Meta and Amazon spent more than US$300 billion in capital expenditure in 2025. An estimated 75%, or US$225 billion, went directly to AI infrastructure. Limited numbers of Pro subscribers cannot provide the return on that investment. Advertising-supported freemium models support mass adoption and data collection. This explains Perplexity’s advertising program and ads within Microsoft Copilot conversations.

Broader hyperscaler expenditure, also focused on AI, approached US$443 billion.

Plans for 2026 and Beyond

Companies plan continued investment growth:

Company	Future Vision and Objectives
Microsoft	Its Community-First AI Infrastructure initiative will focus on sustainable US data-center development in 2026. Global commitments continue, including US$30 billion in the UK through 2028.
Google	Expects a significant capital-expenditure increase in 2026, likely above US$100 billion, with the aim of doubling AI-serving capacity every six months.
Meta	Expects 2026 spending to be significantly higher than 2025, above US$100 billion. Meta Compute will build tens of gigawatts this decade. Key projects: Prometheus in Ohio, 1 GW, operational in 2026; Hyperion in Louisiana, 5 GW, operational in 2028.

New Advertising Formats: Sponsored Answers

AI platforms will not simply copy intrusive Web 2.0 banners and pop-ups. The new advertising must be native, conversational and sensitive to context.

Perplexity AI: Sponsored Follow-up Questions

Answer engines such as Perplexity position ads as a natural continuation of the exchange. Conventional engines generally place ads above organic results, sometimes confusingly; newer engines adopt sponsored follow-up questions.

How Does It Work?

User question: “What are the best long-distance running shoes?”
Organic AI answer: The system reviews online information and provides an independent list and summary.
Native ad: Beside or below the answer, a suggested next question appears: “Why is Nike’s new Pegasus model chosen for marathons?”

Selecting it opens a detailed, AI-generated product explanation funded by the brand. Advertising is positioned as additional value in the discovery journey rather than interruption.

Compare and Decide

This format, seen in Microsoft Copilot and Google’s AI Overviews strategy, appears when users reach the bottom of the purchase funnel.

A question such as “iPhone 15 Pro or Samsung S24 Ultra?” produces a dynamic comparison table. Within or below it, Dynamic Shopping Ads present current offers. The difference is that the user has moved from information gathering to decision-making, significantly increasing conversion potential.

Traditional and Next-Generation Advertising

Feature	Traditional AdTech: Web 2.0	AI-Native AdTech: Web 3.0
Objective	Click	Action
Placement	Beside or above content	Within or as part of an answer
Trigger	Keyword	Semantic intent
Timing	When the page loads: synchronous	As the answer streams: asynchronous
Success metric	Impressions / CTR	Resolution rate / sales

Agentic Commerce

The most radical stage comes when AI moves from recommending to transacting as an agent. Large Action Models (LAMs) enable this change.

AI as an Action Engine and the Zero-Click Economy

LLMs specialize in text; LAMs specialize in digital actions. They understand interfaces, click buttons and complete forms.

Previously: A traveler visited a website, chose dates, compared prices, registered, entered card details and purchased a flight.
In the new model: They say, “Buy me the cheapest flight from Istanbul to London next week.” The agent completes the background steps in seconds.

In this scenario, impressions become less meaningful and the focus shifts to the completed action. E-commerce platforms will need to open APIs to integrate with the zero-click experience.

A Forecast for Turkey

This global shift will affect Turkish e-commerce. Given consumers’ sensitivity to price comparison and promotions, companies such as Trendyol, Hepsiburada and Getir will inevitably develop internal search into AI shopping assistants.

A Turkish user might soon say, “Find the most stylish coffee machine for my mother under TRY 1,000, rated above 4.5, and send it with the fastest delivery.” Local marketplaces’ algorithms will process that agentic request. Agent optimization (AEO) becomes relevant: sellers with the cleanest product information, freshest stock data and most positive reviews become the preferred choice.

A New Revenue Model

As OpenAI CEO Sam Altman has suggested, future revenue may come from transaction commissions—affiliate or CPA—rather than CPC. Platforms would charge only when a sale occurs, while their algorithms work more actively and persuasively to secure it.

RAG, OpenRTB and Latency Management

A complex technology stack supports relevant ads delivered in milliseconds.

RAG and ad insertion: When a question arrives, retrieval-augmented generation fetches reliable information and simultaneously calls an ad server. Grounding makes the ad a logical part of the response rather than an apparent hallucination.
OpenRTB GenAI extensions: The global advertising standard is being updated for AI. New signals tell advertisers whether placement is in human-created content or an AI summary.
Semantic caching: Generating answers creates latency. Frequently asked questions such as “How many Turkish lira is one dollar?” and suitable ad matches can be cached, allowing the answer and ad to be served immediately.

How Will AI Advertising Address Trust?

A major concern is an ad appearing alongside hallucinated information. Imagine an advertisement for a new car while the same conversation falsely claims it has been recalled for brake failures. That would be a reputational disaster.

This risk moves the discussion beyond brand safety toward the more sophisticated concept of brand suitability.

Older systems focus on prohibited words such as violence or gambling. Newer technology examines sentiment.

Dynamic trust scores: Verification companies such as Cint and DoubleVerify produce real-time trust scores for models, monitoring the conversation continuously.
Automatic blocking: If the model becomes inconsistent, hallucinates or takes a negative emotional tone such as anger or sadness, the system marks the context as risky and stops bidding within milliseconds.

Ads can consequently reach users in verified, positive contexts aligned with brand values.

What Do the New Terms Mean?

Understanding these concepts matters for visibility in AI-led search.

Decision engine: An AI system that analyzes and combines information to recommend an option directly rather than return only links.
Agentic commerce: An economic model in which autonomous AI agents make purchase decisions and conduct transactions for people.
Inference cost: The processing, hardware depreciation and energy required to generate an answer.
Retrieval-augmented generation: Technology that retrieves live information from reliable external sources rather than relying solely on training data, to prevent hallucinations.
Answer engine optimization (AEO): Optimizing content for selection as a direct answer, using clear definitions and question-and-answer formats.

Advertising Is Becoming Smarter

AI will not kill advertising. It will make it more efficient, action-oriented and integrated. The era of clicking blue links gives way to providing answers and completing transactions.

Successful brands will not try to deceive algorithms through keyword stuffing. They will provide accurate, current, structured and trustworthy information, becoming a trusted source. Advertising becomes an exercise in delivering value and technical integration rather than merely capturing attention.

What Should You Do Now?

Structure your data: Align product and service information with Schema.org and JSON-LD. Agents do not favor unstructured data.
Focus on questions and answers: Revise content to answer the long-tail questions users and voice assistants ask.
Prepare for agentic commerce: Make products accessible and purchasable through APIs and invest in zero-click infrastructure.
Build trust: Being a verified, reliable source is a major competitive advantage.

Sources Used

Gökhan Aksoy

I write about technical SEO and AI visibility, interpreting findings in their business context.

Will AI Show Ads? Decision Engines and the Agentic Commerce Revolution

A New Era of Digital Advertising: Next-Generation AdTech