What if AI Models were Built with only Excellent Data?

There's a thought experiment worth considering: What if AI companies could license the same high-quality content that professionals rely on today?

Not scraped web data of uncertain provenance. 

Not public domain works that predate modern practices. 

But current, expert-vetted, professionally edited books and journals, the kind that medical students study from, that engineers reference for technical standards, that historians cite in peer-reviewed work.

What changes when AI models can legally train on the same material that shapes human expertise?

The Quality Signal Problem

Current AI models are trained on everything the internet offers, which means they learn from Nobel Prize-winning literature and conspiracy theory blogs with roughly equal weight. The signal-to-noise ratio is determined by volume and a bit of luck.

This creates a curious limitation: The more specialized and expert the domain, the less likely high-quality training data is freely available. Medical textbooks are copyrighted. Legal treatises are behind paywalls. Engineering standards are sold by professional societies. The very sources that would make AI most reliable in high-stakes domains are the ones least likely to be in training datasets.

Imagine instead a medical AI trained specifically on Harrison's Principles of Internal Medicine, up-to-date clinical guidelines, and the complete archive of peer-reviewed journals in a specialty instead of scraped WebMD articles and patient forum discussions.

The model would not only have more precise medical information, but it would have information filtered through the same editorial and peer-review processes that establish reliability in the medical profession. That's a different kind of training data entirely. And the contributors would be fairly paid.

Domain-Specific Intelligence

Today's large language models are generalists by necessity. They're trained on broad internet material because that's what's available without licensing negotiations. This approach works remarkably well for general knowledge tasks, but it creates limitations for specialized applications.

With licensed access to domain-specific publishing, we might see a proliferation of specialized AI models:

Legal AI trained on the complete case law databases, treatises, and practice guides that attorneys pay thousands of dollars annually to access. Not a general model that knows some law, but one trained on the same sources lawyers cite in court filings.

Engineering AI with access to every technical standard, specification, and engineering handbook published by professional societies. The model would understand not just general physics but the specific standards required for regulatory compliance.

Historical AI trained on university press monographs, archival collections, and scholarly journals rather than Wikipedia summaries. The difference between reading a comprehensive scholarly treatment and reading crowd-sourced encyclopedia entries would be reflected in the model's understanding.

The economic model shifts too. Instead of one massive general-purpose model trying to be mediocre at everything, publishers could license domain-specific training data to create specialized models that excel in particular fields. The publisher's deep catalog becomes training data infrastructure.

Attribution as Feature, Not Bug

When AI models train on scraped web data, they can't attribute their knowledge. The training process deliberately obscures provenance. The current models learn patterns without remembering where specific information came from. (Yes, deep research queries through an AI may cite sources from web search data, but these citations don’t come from training data.)

But if AI companies license specific works for training, attribution becomes technically feasible and legally valuable. The licensing agreement could require that outputs reference the works they draw from, similar to how academic citations work.

This creates interesting possibilities:

A medical AI explaining a diagnosis could cite which textbook chapters informed its reasoning. A legal AI researching precedents could link to the specific treatises and case annotations it referenced. A technical AI could point to the engineering standards it's applying.

For publishers, this transforms content from a one-time product into ongoing infrastructure. Every time an AI model trained on your content answers a query, it could cite your work while driving awareness and potential sales of the full text for users who need deeper detail.

For AI companies, this solves a trust problem. "How do you know this answer is reliable?" becomes answerable: "This response draws from these specific authoritative sources, which you can verify."

Real-Time Knowledge Integration

Current AI models are frozen in time. GPT-5 knows what was in its training data as of its knowledge cutoff date. If you want current information, the model must retrieve it separately through search or access to live databases.

Licensed content opens a different approach: Real-time access to published works during inference, not just training.

Imagine an AI with licensed access to publishers' current catalogs. When you ask a question, the model draws on old training data and it queries the latest editions, recent journal articles, and newly published works. The publisher gets paid per query (like streaming music royalties), and users get answers grounded in current expert knowledge.

This could work for:

• Medical AI checking current clinical guidelines before suggesting treatments
• Legal AI verifying that case law citations reflect the latest appellate decisions
• Technical AI ensuring recommended specifications match current industry standards
• News AI incorporating recent reporting from licensed journalism sources

The business model mirrors how professionals already work: lawyers pay for real-time access to updated legal databases, physicians pay for UpToDate subscriptions that reflect recent research, engineers pay for the latest code books. AI licensing could follow the same pattern.

The Backlist Economics

Publishers often describe their backlists as assets that have already recovered their production costs. Any additional revenue is high-margin because the editorial investment is long complete.

AI training data licensing could transform backlist economics. A medical textbook from 2015 might not sell many copies today (the 2024 edition is current), but it could still be valuable for training AI models. Historical context, foundational concepts, and the progression of medical thinking over time all contribute to a model's understanding.

This creates a new revenue stream for content that has largely exhausted its traditional market:

• Older editions of technical references
• Scholarly monographs with narrow audiences
• Professional handbooks from previous decades
• Out-of-print works that remain intellectually valuable

The licensing value isn't in current sales potential - it's in contribution to comprehensive training data. A publisher's entire archive becomes potentially monetizable again, not through selling physical copies but through making the knowledge computationally accessible.

Some of the Possible Negative Consequences

Speculation should also explore what doesn't change, or what changes in ways we might not prefer.

Human expertise doesn't become obsolete. Even an AI model trained on every medical textbook can't replace the clinical judgment developed through seeing thousands of patients. The model knows what's in the books; it doesn't know what the books couldn't capture about the art of medicine.

Licensing creates gatekeepers. If high-quality training data requires expensive licenses, only well-funded AI labs can afford the best datasets. This could concentrate AI capabilities among large companies rather than democratizing access. Whether that's good or bad depends on your perspective about open versus controlled AI development.

Not all use cases require quality sources. Sometimes "good enough" information from free sources works fine. An AI trained on comprehensive copyrighted medical knowledge might be crucial for clinical decision support, but overkill for answering "What are common cold symptoms?" Licensing economics need to match use cases.

Publishers might not want their economics disrupted. If AI tools trained on your content become good enough to reduce direct sales, licensing revenue might not compensate for lost book sales. The music industry learned this lesson: streaming royalties never quite replaced album sales revenue, even as streaming became dominant.

The Infrastructure Enables the Experiment

None of these possibilities are guaranteed. They're scenarios that become possible when the infrastructure exists to license high-quality content legally and efficiently.

Without a TDM registry, AI companies can't easily discover which publishers are willing to license. Publishers can't declare their licensing terms in ways AI companies can find and act on. Every deal requires custom negotiation. The transaction costs are high enough that only the largest publishers and biggest AI companies can make it work.

With registry infrastructure, the friction drops. Mid-sized publishers can license their catalogs without hiring business development teams. Smaller AI companies can access quality training data without negotiating hundreds of separate agreements. New use cases become economically viable because the overhead costs are lower.

The infrastructure doesn't guarantee any particular outcome. It only makes more outcomes possible to test. Whether domain-specific AI models trained on licensed content prove more valuable than general models trained on free data is an empirical question. But we can't run the experiment until the infrastructure exists to license the data.

What Remains Unknown

The honest answer is: We don't yet know which of these scenarios will prove most valuable, or whether entirely different use cases will emerge that we haven't imagined.

What we do know is that AI's appetite for high-quality training data is real, publishers control enormous archives of expert knowledge, and currently there's no efficient infrastructure connecting the two. Creating that infrastructure doesn't predetermine outcomes. It allows a market to develop where publishers and AI companies can discover what works.

Maybe domain-specific models trained on licensed professional content become the standard for high-stakes applications. Maybe real-time citation and reference become expected AI features. Maybe backlist licensing becomes a meaningful revenue source. Or maybe hybrid approaches combining free web data with licensed expert sources prove most practical.

The registry model makes all of these testable. Which is perhaps the most important possibility: moving from speculation to experimentation, from theory to evidence, from "could AI do this?" to "let's find out what happens when we try."

Amlet's TDM Registry provides the infrastructure for publishers to license content and for AI companies to discover licensing terms at scale. What becomes possible when the friction of licensing drops? We're building the foundation to find out. Learn more at amlet.ai.