When Search is No Longer a Finite Game

We're witnessing the death of search as we know it. Not because search is broken. Because something better is emerging.

Search today is chess. AI tomorrow is jazz.

The shift from discrete to continuous games represents one of the most significant changes for information retrieval. Your relationship with information.

Game theory provides a valuable perspective on what to expect.

Game Theory in Information Retrieval

Right now, Search operates as a discrete game. You ask. It answers. Clear turns. Defined moves. Every Google query follows this pattern. You type. Results appear. You click or refine. The game ends when you leave.

Discrete games have structure. Chess moves happen in sequence. Poker hands resolve individually. Each decision point is clear. You can plan between moves. Information reveals itself at specific moments.

Continuous games flow differently. No turns. No pauses. Players act simultaneously, constantly adjusting strategies in response to real-time changes. Financial markets work this way. Traffic patterns. Competitive bidding.

AI search is becoming continuous.

Search comes from the Latin circare, meaning "to travel back and forth, to circle." Traditional search creates circles around potential solutions rather than providing direct answers. The search engine can expand or contract these circles to optimize for ad revenue or to keep you searching rather than finding answers and exiting without clicking ads.

Dialogue derives from the Greek words dia (meaning "through") and logos (meaning "words"). Through words, we reach understanding. Conversational AI operates on dialogue principles rather than circling solutions.

The Revenue Model Built on Circles

Google's discrete game structure aligns perfectly with advertising revenue. You ask. Results appear with ads inside the circle of possibilities. You click or refine. Each query cycle creates monetization opportunities. The game ends when you leave, but Google wins when you engage commercially rather than finding definitive answers.

This transactional structure directly supports commercial interests. Clicking on ads marks the "win-state" for both the platform and the user if the ad meets needs. Each query nudges you toward discrete transactions where search engines profit from keeping you within the circle of results rather than providing direct solutions.

Current search engines profit from incomplete answers. Each "People also ask" expansion represents another revenue opportunity. Every refinement generates additional ad impressions. The longer you stay within the circle, the more money flows.

Larry Page and Sergey Brin identified this fundamental tension before Google became Google. They understood that advertising revenue would create inherent conflicts between user needs and commercial interests. This prescient observation explains exactly why search engines design expandable circles rather than direct answers—the bias toward advertisers requires keeping users within result sets rather than providing immediate solutions.

Current search has no memory between sessions. Each query starts fresh. Google knows your search history but rarely applies it meaningfully because sustained context would reduce query volume and ad opportunities.

The AI analyzes dozens of related questions while you formulate one query. It explores related pathways, anticipates follow-ups, and prepares contextual information. Parallel processing mirrors human conversation patterns.

Think of a chessboard halfway through a game. You can infer some moves from current positions, but you can't precisely reconstruct the whole game. Classic search operates similarly—snapshot results without context or memory of your entire search journey. AI changes this completely by maintaining continuous memory where every interaction shapes future responses.

A dynamic AI system builds cumulative understanding. If you consistently discuss cars rather than horses, it knows which "Mustang" you mean. If information theory dominates your conversations, your AI anticipates references to Claude Shannon, not sports figures.

Mike King's analysis at IPullRank breaks down how current AI systems operate, shifting from deterministic search to probabilistic, personalized outcomes. Everyone should read Mike King's post. He calls out people who summarize what he wrote, and I don't want to do that here. It will take you an hour to read the post and nothing short of a sleepless night to process it. Everyone working in information should understand these mechanics.

The Infinite Game Transformation

Each interaction modifies the system's understanding of thinking patterns, knowledge gaps, and information processing styles. Context evolves with every exchange.

The Ongoing Dialogue Transformation

Traditional search optimizes for question-answer cycles that generate multiple touchpoints within the circle of results. AI systems optimize for sustained thinking support through genuine dialogue.

Today, only a handful of AI models—ChatGPT, Claude, and Google Gemini—appear to truly follow and access prior conversations with memory beyond individual chat sessions. But breaking beyond chat sessions to understand everything we've discussed in the past represents an absolute superpower. This creates a personal relationship between humans and AI that we've never had with search.

Early adopters already experience glimpses of dynamic assistance. Query refinement becomes conversation. Information discovery becomes exploration. Search sessions become ongoing dialogues.

Your next search might be your last discrete one because the game never truly ends with AI. We're headed toward an infinite game—a game where there are no ends, just ongoing dialogue between a human and their AI.

From Ads to Offers: The Revenue Model Shift

This also means the revenue model shifts from ads to offers. I covered this in my Adweek article over a year ago, where I explored how we're headed for a real-time bidding system. Businesses can put offers into an engine based on the particulars of an individual dialogue with AI without exposing any of the dialogue to the business.

For example, if I'm in London with my wife on a trip and we're looking for a place to grab a quick dinner before a show, the AI should reach out to restaurants in Soho that offer a table for two, along with a round of drinks or some other incentive. This is very different than search with ads that are targeted at aggregated data. It’s “wandering” - covered last month here in The Datable:

The AI doesn't need to share anything about the human with the business. They should be able to match the situation from the discussion or dialogue, or what they know about the human, to find an offer that meets their needs. This is far more possible than people realize. You can create situations as a business and structure data for specific offer categories based on the information available from AI conversations.

Businesses need to think about this personalized context. They need to start putting structured data around their offers, events, and other elements that used to require an ad to show up. AI systems won't want the ads, but they will want the offers. As they learn about a person wondering about their vision and getting an eye test, they'll want the best offer in the area beyond just coupons or ads.

I expect this market to develop quickly as an alternative to the ad model.

Augmented Memory and the Infinite Game

This reminds me of Total Recall by Gordon Bell and Jim Gemmell, which explores what humans become when they can recall everything because they have augmented their memories. We transition from a discrete game, which is highly inefficient, to an infinite game, or a continuous game, which more effectively mirrors life.

Consider these emerging patterns:

• Context-aware recommendations replace keyword matching

• Conversation-based commerce replaces search-based advertising

• Sustained relationships replace discrete transactions

• Understanding-driven offers replace interruption-based ads

• Augmented memory creates a continuous context across all interactions

• Real-time offer matching based on dialogue context rather than search history

The Titans will need to solve this revenue puzzle. Offers that are personalized based on getting through the words—through dialogue—will become much more important.

The Resistance Points

Commercial interests resist this transformation. Advertising revenue depends on keeping users within expandable circles of results rather than providing direct answers. Clear solutions reduce profit opportunities.

Privacy concerns multiply when AI systems maintain continuous memory. Control mechanisms for information retention remain unclear.

Agency questions arise when AI constantly anticipates needs. Authentic decision-making becomes harder to distinguish from system nudges.

Current infrastructure investments in search advertising create incentives to maintain circular result sets rather than dialogical answers.

The Stakes

The shift from discrete search to continuous AI represents a significant change in our relationship with information, technology, and commerce.

Traditional search engines profit from keeping you within circles of potential solutions. AI systems profit from providing direct answers through dialogue.

This transformation will determine whether information technology serves human understanding or commercial confusion. The companies that solve personalized dialogue-based revenue models will define the next era of information access.

Understanding this shift isn't just useful—it's essential for navigating how we engage with information, privacy, agency, and the very way we think.

Agency, Trust, and Privacy in Continuous Games

Continuous AI companionship brings new considerations:

• Privacy and Control: When your AI assistant remembers everything, how do you control what information is retained or discarded?

• Agency: If AI constantly anticipates your needs, how do you ensure that decisions are authentic rather than being influenced by system nudges?

• Commercial Incentives: Discrete games focused on ads within result circles. What will continuous AI prioritize—your goals or its own?

The shift from discrete search to continuous AI isn't just technical. It's a significant change in our relationship with information, technology, and ourselves.

The shift from Circare to Dialogos represents more than technological evolution.

It's economic, social, and philosophical.