The Last Ad Layer

The ad-supported web promised free access in exchange for attention. What it delivered was surveillance, manipulation, and a content ecosystem that rewards spectacle over expertise. A specialist who solves real problems spends her evenings filming TikTok hooks instead of doing her actual work. The people who need her can’t find her.

This is an internet architecture problem. And there’s a narrow window to fix it.

The Backbone Broke

Search was the foundation of the ad-supported internet. Google Search was how people found things, how businesses got found, how the entire ad economy priced attention. Every other discovery channel (social media marketing, content strategy, paid ads) was built on top of search or in reaction to it.

Google’s AI Overviews now answer queries directly, suppressing the links that used to connect searchers to experts. Organic click-through rates dropped 65% on queries where AI Overviews appeared. Zero-click searches, where you never leave Google, account for roughly 60% of all queries. The SEO game that small businesses spent a decade learning is over. The links are gone. Google killed them because answering the query directly keeps users on Google longer. The foundation was sacrificed for engagement.

Social filled the gap, then captured it. When search stopped delivering traffic, businesses moved to social media. Social media platforms then completed the cycle Cory Doctorow calls enshittification: first they serve users, then they exploit users for advertisers, then they exploit advertisers to extract all remaining value. The algorithm doesn’t surface the best climbing instructor. It surfaces the climbing instructor who films the most engaging 30-second clips. Expertise and performance are different skills. The platforms select for performance.

Paid search priced out the long tail. The last fallback was paying for the traffic that used to be free. Google Ads operates on keywords, discrete text strings that advertisers bid on. “Physical therapy” is a keyword. Five physical therapists (a climbing specialist, a pelvic floor specialist, a pediatric specialist, a sports rehab specialist, and a generalist) all bid on it. The auction can’t tell them apart. The climbing PT pays to compete on pelvic floor queries she’ll never convert on. In a recent industry poll, more than 50% of respondents said small businesses have been priced out of Google Ads entirely. The keyword auction extracts maximum revenue by forcing specialists into competitions they can’t win.

Every channel failed at once, and the failures are structural.

The Chatbot Migration

When search results became ads and AI summaries, and social feeds became engagement traps, people migrated to chatbots. ChatGPT has 800 million users. Perplexity, Claude, Gemini, and dozens of smaller tools handle millions more queries daily, all in natural language, expecting direct answers.

But someone has to pay for inference. Every query costs money. AI was never free the way search pretended to be. OpenAI’s inference costs hit $8.4 billion in 2025 and are projected to reach $14 billion in 2026. The company’s own projections show $25 billion in cumulative cash burn by 2030. Against that, they’re targeting $1 billion in ad revenue from free users, less than 7% of their inference bill. They launched ads in ChatGPT on February 9.

The ad layer is following people into the chatbot. The gap between what ads need to earn and what they currently earn guarantees that the pressure to extract more from every query will only increase.

The Fork

The ad layer that gets installed inside chatbots will shape how billions of people discover products, services, and expertise.

Path one: keywords again. Bolt existing ad infrastructure onto chatbot conversations. This is what’s happening now. OpenAI’s ad system is closed and unverifiable. Conversational queries don’t map to keywords. They’re full paragraphs. Cramming them into keyword resolution destroys the signal.

Path two: match problems to expertise. A conversational query has a precise meaning in the vector space where AI models represent language. An ad auction that scores by proximity between the query’s meaning and the advertiser’s declared expertise connects people to the right specialist without forcing unrelated businesses to bid against each other.

Matching by Meaning

Every modern AI model converts text into high-dimensional vectors called embeddings. Similar concepts land near each other. Dissimilar concepts land far apart. This is already running at scale, billions of times a day.

An embedding-space auction lets each advertiser position at a point in this space representing what they do. When a query arrives, the auction scores each advertiser by combining their bid with their proximity to the query — the closer your expertise, the less you bid to win. Keywords are a special case of this system with zero radius. The extension is purely additive.

A person who asks a chatbot I need a financial planner who understands freelance translation income gets matched to someone who actually does that. The targeting comes from the geometry of meaning, from the query itself. The person stated what they need. The auction connects them to it.

The Trust Chain

The math is published. The implementation is straightforward. Trust is the hard part, and trust is a chain. Every link has to hold.

If a single company controls the ad auction inside a chatbot (sets the scoring function, runs the embedding model, determines who wins, decides how results appear) then users and advertisers are back where they started. Trusting the platform. Hoping it’s fair. Unable to verify. This is Google’s current model. It works for Google. It fails everyone else.

The alternative is a chain of trust where every link is verifiable:

Link one: verifiable intent matching. Open-weight embedding models like Nomic, BGE, and GTE are publicly available. Anyone can run the same model on the same text and get the same coordinates. You can check the exchange’s work yourself.

Link two: verifiable auction execution. Trusted execution environments are hardware enclaves that prove a specific piece of code ran unmodified. AWS Nitro Enclaves, Intel SGX, and others let an exchange publish its auction code and then prove, cryptographically, that the published code is what actually processed the bid. Verify it yourself. One ad exchange already runs this way, with the code open-sourced. The infrastructure exists and is in production.

Link three: presentation that respects the user. The first two links guarantee that the right match was found honestly. The third determines whether the user experiences it as help or as noise. When a chatbot conversation surfaces a relevant expert, the UX should present it as a suggestion: a climbing PT who specializes in finger pulley injuries is available in your area. The difference is whether the system treats the user as someone to connect or someone to sell to. Labeled clearly, offered at the right moment, relevant to what the person actually asked. That’s introduction. If the first two links work and the third one fails, the user still feels sold to, and trust collapses regardless of how clean the auction was.

Break any link and you’re back to the same extractive ad layer wearing a new interface. Hold all three and you have an open protocol for advertising.

Fragmentation, Not Monopoly

Monopoly is unlikely. Google is under active antitrust enforcement. The DOJ ruled they monopolized ad tech, and remedies may include breaking up parts of their ad business. The DoubleClick-to-dominance playbook probably can’t repeat under that scrutiny.

The real threat is fragmentation. OpenAI builds a closed ad system. Google builds a closed ad system. Perplexity, Anthropic, and a dozen vertical chatbot platforms each build their own. None of them interoperate. Five walled gardens instead of one, each with its own targeting taxonomy, its own opaque auction, its own advertiser onboarding. A specialist faces five separate platforms, each requiring separate campaigns, separate budgets, separate creative, none sharing a protocol. That’s worse than a monopoly. At least a monopoly has liquidity.

The pieces to prevent this are converging independently. Academics are publishing embedding-space auction mechanisms. Industry labs have production-grade embedding infrastructure. Standards bodies are forming working groups for AI-era monetization. Platform companies are launching ad products inside chatbots. An exchange has TEE attestation in production.

Nobody has assembled them into a single open protocol.

The window is the period between “every platform needs ad revenue” and “every platform has built its own proprietary system.” Once each platform ships a closed ad product, switching costs lock it in. Advertisers build campaigns, integrations harden, inertia takes over. Based on how fast these companies move, that window is approximately 18 to 36 months.

If an open protocol ships first, it becomes the default. Every chatbot platform that needs ad revenue adopts the shared protocol rather than building from scratch. One campaign reaches every platform. Liquidity pools instead of fragmenting.

If the protocol doesn’t ship in time, the chatbot ad layer fragments into incompatible walled gardens. The keyword tax replicates five times over.

Who Goes First

The technology exists. This is a coordination problem.

One exchange needs to add embedding parameters to its auction: optional fields that let advertisers specify a position, a radius, and a model. Keywords keep working as before. Embedding-aware demand is additive.

One chatbot platform needs to route its ad inventory through an open exchange instead of building a proprietary system. Queries flow in as embeddings. The auction clears by proximity. Specialists win their own queries and stop paying for each other’s.

One standards body needs to add an embedding vector field to the bid request spec. A few optional fields in the protocol. Once it’s in the spec, every exchange and every platform can implement it.

None of these steps requires a breakthrough. Each requires someone to go first.

If you’re building an ad exchange, a chatbot platform, or setting standards for AI-era monetization: the mechanism is described, the simulation is open source, and the infrastructure exists. I want to hear from you.

Part of the Vector Space series.