Towards Building Rails for Context Monetisation
The Internet Was Never About Selling Data—It Was About Monetising Access
From the earliest days of the web, data has never been the product—access to it has. Search engines didn’t pay publishers for indexing their pages, but they built massive advertising businesses around controlling how users accessed information. Most publishers (other than paywalled ones), monetised traffic, impressions, and transactions, ensuring they captured value even when users didn’t directly pay for content.
Every major evolution of digital monetisation has followed this pattern:
Search (Google) → Index everything, monetise access via search ads.
Social (Facebook, X) → Aggregate user-generated content, monetise engagement via targeted ads.
Aggregators (Skyscanner, GoCompare) → Aggregate third-party product data and monetise transactions via referral fees and fulfilment.
Now, AI models are doing something even more extreme: They aren’t just aggregating content; they are replacing direct access altogether. Instead of directing users to publishers, AI tools synthesise answers, summarise articles, and make decisions in real time. This removes the entire click-based economic model and raises a fundamental question:
If AI systems consume content without sending traffic, how do publishers, data providers, and businesses monetise their data?
The naive answer is: Sell the data directly. But history shows that selling data is always a race to the bottom—what truly gets monetised is access, visibility, and influence. That’s why AI Data Marketplaces are already a broken model—and why we need to think more deeply about the building the Monetisation Rails for AI and Context.
The Case Against AI Data Marketplaces
At first, it seems obvious that an AI Data Marketplace would be the best way for content owners to monetise their data. If AI models need information, why not charge them just like businesses pay for stock images, premium APIs, or datasets? However, this approach misaligns with how AI models actually function, whether at the training stage or the retrieval stage.
1. AI Doesn’t Consume Data in Bulk—It Retrieves Contextually
A key assumption behind AI Data Marketplaces is that AI companies will pay for full datasets in advance- this could be true for pre-training but it does not generalise to downstream model use, especially inference. Modern AI systems—especially those using it for information retrieval via RAG or similar—don’t function this way. Instead of pre-loading and storing vast amounts of static data, they dynamically retrieve relevant information as needed. More concretely:
Training : AI companies do need large amounts of data for pre-training, but once a dataset is integrated, there is no ongoing payment—the AI extracts all future value from it indefinitely. One way to monetise this is via Model Marketplaces, let content partners train models and sell metered access to the model. Once again this problem breaks down into one of metering but it does not solve the problem of having Foundational Models.
Test-Time Consumption (e.g. Retrieval-Augmented Generation): AI models increasingly rely on real-time retrieval instead of static pre-training. Architectures now use chain-of-thought and retrieval, where models pull in external knowledge dynamically, reinforcing the need for access-based monetisation rather than bulk licensing.
Distillation Training (Knowledge Distillation): Using an existing model to generate data to train another model. DeepSeek is a good example where large parts of its training datasets are curated from other models.
Implication: A one-time dataset sale does not reflect the continuous value generated when AI retrieves information. A better model is one that monetises retrieval as it happens, rather than as a static transaction.
2. AI Data Marketplaces Enable Data Commoditisation
Marketplaces have another major flaw: They turn data into a low-value commodity.
If AI companies can buy datasets outright, they have no incentive to keep paying.
Data quickly becomes an undifferentiated product—leading to race-to-the-bottom pricing, resale, and loss of economic control.
We are already seeing this in some modalities/ domains where there are too many dataset providers chasing too few deals- training data inflation is a thing.
Value isn’t in selling raw data—it’s in controlling how and when AI retrieves it.
3. Marketplaces Don’t Solve the Incentive Problem
AI companies operate under a cost-minimisation mindset—they will always choose the lowest-cost path to acquiring data. Right now, that means scraping first, negotiating later. If content is freely available, they will take it. If they are forced to pay, they will seek the cheapest or most scalable option.
Marketplaces fail because they don’t change this equation. Instead, they assume AI companies will return to pay for more data over time—when in reality, AI models extract long-term value from a single purchase and have no economic pressure to keep paying.
This misalignment plays out in different ways:
Big AI companies (OpenAI, Google, Meta, etc.) scrape and only negotiate when legally pressured or when access provides strategic value.
Price-sensitive AI startups avoid paid data entirely unless there is no viable free or low cost alternative.
Enterprise AI models (finance, healthcare, legal, etc.) are more willing to pay for structured, high-trust data but expect strict access controls, metered usage, and clear ROI.
Even if an AI company does buy a dataset, marketplaces fail to ensure continued monetisation because:
AI models extract permanent value from a single purchase → They train once and use the knowledge indefinitely.
Marketplaces don’t meter retrieval → AI companies remix, resell, and reuse data without ongoing payments.
AI companies will always try to remove their reliance on external data sources → distillation, aggregation, and synthetic data make one-time purchases more appealing than recurring costs.
AI companies will always default to free access unless they are technically restricted or if paying provides them a greater return—such as better retrieval placement, improved AI responses, or monetisable integrations.
4. No Natural Economic Friction = No Sustainable Monetisation
The internet’s monetisation model worked because attention and access were scarce. Google, Facebook, and other platforms didn’t sell access to raw data—they controlled visibility, prioritization, and transactions.
If AI models can retrieve content without restriction, there is no natural economic limit preventing them from extracting infinite value without recurring payments. This is why data marketplaces are fundamentally broken: they assume AI behaves like traditional enterprise software, when in reality, it behaves like an infinite consumer of knowledge.
If AI data is sold like a static product, its value steadily drops to zero. The only way to monetise AI is at the point of retrieval.
Enforcement vs. Alignment
The only way to change this dynamic is through Enforcement or Alignment—either you block AI agents and bots from freely consuming content, or you make it more valuable for AI companies to participate in a structured monetisation model than to extract content for free.
Enforcement
The simplest way to force AI to pay is to make free access impossible—to control data extraction at the networking and infrastructure level so that AI models cannot scrape or retrieve content without permission.
This requires a combination of:
A stronger, enforceable Robots Exclusion Protocol (robots.txt 2.0) → One that AI companies cannot bypass the way they do today.
CDN- and firewall-based enforcement → Blocking AI scrapers at the web infrastructure level to prevent mass data extraction.
Data licensing and watermarking mechanisms → Ensuring AI models cannot train on proprietary data without authorisation.
Opt-in/ Opt-out Registries → A standardised way for content providers to declare and communicate AI access and usage rights, ensuring clear legal and technical enforceability for AI crawlers and agents.
Why This Matters → AI companies will always default to free access unless there are clear technical barriers preventing it. Without enforcement, AI models will continue extracting knowledge at no cost.
The Challenge? Enforcement alone is a defensive strategy—AI will always look for ways around it. True sustainability comes from making AI companies want to pay, not just forcing them to.
Alignment
Instead of simply blocking access, the smarter long-term strategy is to create an economic structure where AI developers benefit from working with content providers rather than against them.
To do this, AI companies must recognise that they have something far more valuable than static datasets—context windows.
In the old internet, content was the bait—publishers created information to attract users, monetise through ads, and convert engagement into transactions.
In the AI-driven world, content is the fuel—AI agents, assistants, and reasoning engines don’t just display links; they consume knowledge, synthesise insights, and generate responses that influence decisions.
So why this is better for AI developers?
AI retrieval is no longer a passive process—it’s an economic event. The more high-quality content AI integrates, the better its responses become, which in turn makes AI models more valuable.
This model is self-reinforcing—the better AI’s knowledge sources, the more businesses will rely on AI-driven answers/decisions, creating new monetisation layers through retrieval fees, sponsored context, and AI-native transactions.
Rather than treating content as a commodity to extract, AI companies benefit from making retrieval itself a monetised interaction—turning extractions into structured, sustainable relationships.
AI Context Windows: The New Ad Real Estate
More broadly, the shift from search-driven monetisation to AI-driven monetisation creates a new kind of digital real estate.
In the past:
Search engines monetised clicks.
Social media monetised engagement.
Now, AI monetises context windows—the space where knowledge is retrieved, ranked, and synthesised.
AI systems curate and process data dynamically—the most valuable real estate is no longer a search result, but the AI’s reasoning process itself.
Whoever controls the context window controls visibility, influence, and monetisation.
The Case for Building Better Monetisation Rails for AI Context
If AI is consuming data dynamically, then monetisation must also be dynamic. Instead of selling access to content, businesses need to monetise retrieval, prioritization, and AI-generated decision-making. This is where building the Monetisation Rails for AI Context come in.
How would it work?
Instead of charging for access to data/content itself, you wrap structured access control, billing, and attribution mechanisms around AI context window usage. Building monetisation rails ensures that every AI/agent interaction is a billable event, an ad impression, a referral, or a transaction. This can happen in two key ways:
Direct Monetisation: Charging AI for Retrieval
These models ensure that AI companies pay every time they retrieve, use, or surface premium data.
Per-query Pricing – AI models pay per API request, ensuring usage-based monetisation.
Context Window Leasing – High-value datasets charge for inclusion in AI context windows.
Inference-Based Pricing – AI companies pay based on how many times a dataset influences an answer.
Why this works: Instead of a one-time transaction, publishers monetise AI continuously—just like Google monetises search results per impression. Proprietary data sources such as scientific papers and journals are one example where this channel is the more applicable.
💡 Why AI companies pay: AI models require high-quality, structured, and authoritative data to improve accuracy. Premium datasets especially proprietary data—such as financial, medical, legal, and real-time knowledge sources—will always be in demand, creating a natural incentive to pay for structured access.
Indirect Monetisation: Controlling Influence and Transactions
Even if AI models can’t be forced to pay for access, having better monetisation rails can still ensure that businesses capture economic value through indirect revenue models.
Sponsored Retrievals & Visibility – Brands and content owners can pay to be prioritised in AI responses, just like search ads.
Attribution & Referral Models – AI-generated content can include monetised referral links that drive revenue per transaction.
AI-Native Advertising – Instead of traditional display ads, better monetisation rails enable contextual ad placements inside AI-generated responses.
Why this Works: Instead of fighting AI’s ability to retrieve content, monetisation rails turn visibility, prioritisation, and transactions into monetisable assets.
💡 Why AI companies participate: AI-generated interactions are increasingly influencing commerce, subscriptions, and decision-making. AI companies benefit from ensuring their recommendations are accurate, monetisable, and aligned with commercial opportunities. Another side effect of the ability to monetise their own context windows- opening up an entirely new monetisation mechanism.
The Future: We Need Better Monetisation Rails, Not Static Marketplaces
AI isn’t just another enterprise software category—it’s a new way of interacting with knowledge itself. Just as search monetisation shaped the modern web, AI retrieval monetisation will define the AI economy.
Selling data is broken. AI companies will extract infinite value from a single purchase, leaving publishers with nothing.
The only scalable way to monetise AI is at the point of retrieval. If AI depends on premium knowledge, it must pay dynamically for each use case.
Monetisation rails will power the AI economy. Just as Google built the search ad market, AI monetisation rails will define how AI-generated content creates economic value.
🚀 AI isn’t just changing how content is consumed—it’s changing how value is captured. And in this world, Building Better Monetisation Rails for AI and Context aren’t just an option—they’re the viable path forward.
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Photo by Google DeepMind from Pexels.
