3. Problem Statement

Artificial intelligence is no longer limited by algorithms or compute. The real choke point is data. Every serious team building AI today runs into the same three problems:

The most valuable data is locked behind regulation.

There is no system for ownership or royalties.

Public datasets are exhausted, leaving models starved of quality and diversity.

Below, we break each down in plain terms.

3.1 Locked by Compliance

Some of the most important data on Earth sits unused because of privacy laws and compliance frameworks.

Healthcare:

Hospitals hold millions of electronic health records (EHRs), imaging archives, and lab results.

HIPAA in the U.S. and GDPR in Europe make direct sharing nearly impossible.

Even if hospitals want to collaborate, they risk heavy penalties if personal information leaks.

Finance:

Banks and trading firms log every transaction, fraud event, and market anomaly.

This data could train better fraud detection systems or trading agents, but is bound by banking secrecy rules and internal compliance.

Enterprises:

Companies collect logs of user behavior, IoT telemetry, and customer service interactions.

These traces contain valuable edge cases (bugs, failures, anomalies), but are considered sensitive or competitively guarded.

All of this data, medical, financial, enterprise, is exactly what models need to improve. It has the rare outcomes and fine-grained details that don’t exist in public web data. But compliance walls make sharing unsafe.

Data stays siloed, models plateau, and the value is lost.

3.2 No Provenance, No Royalties

Even when data is shared or reused, contributors see almost no benefit.

In Web2, once data leaves your system, you lose all control.

There is no built-in way to prove where it came from.

There is no mechanism for contributors, whether hospitals, enterprises, or individuals, to earn royalties when their data powers models.

Consider how music or film royalties work: when a song is streamed, artists get paid automatically. With data, nothing like this exists.

A hospital that provides data to train a sepsis model sees none of the upside when that model is sold.

An individual whose browsing history trains a recommender system is neither asked for consent nor compensated.

This creates two problems:

Contributors lack incentives to provide high-value data.

Buyers cannot verify provenance, so trust breaks down.

Without ownership or royalties, the supply side dries up.

3.3 Public Data is Exhausted

For years, AI companies solved the data shortage by scraping the public internet. That well is now dry.

Every major model has already trained on Wikipedia, Reddit, GitHub, and Common Crawl.

The same data is recycled again and again, adding little new value.

Worse, models start to learn from their own outputs, creating “data collapse” risks where errors compound.

Public data also has two deep flaws:

It lacks edge cases. Rare medical outcomes, fraudulent trades, and unique enterprise failures don’t exist in public sets.

It is biased. Most public data is English, Western, and male-dominated. Models trained solely on this inherit those biases.

If we keep feeding models from the same pool, they will stagnate.

3.4 The Cost of Doing Nothing

If these problems remain unsolved, the consequences are serious:

AI models plateau: We reach diminishing returns because there’s no fresh, diverse data.

Enterprises can’t adopt AI: Healthcare, finance, and other regulated industries remain locked out.

Contributors are left behind: Billions of people and organizations generate valuable data every day, but see zero benefit.

Innovation slows: The AI economy becomes concentrated in a few companies that hoard private data, leaving everyone else dependent on them.

In other words, the world is generating terabytes of valuable data daily, but it dies unused because there’s no safe way to unlock it.

3.5 Case Examples

Healthcare Example

A hospital wants to train an AI to detect sepsis early. They have 20 years of patient records that could be lifesaving. But HIPAA makes it nearly impossible to share raw data, even anonymized, there is re-identification risk. So the model is trained on smaller, less diverse datasets and performs poorly. Patients suffer because compliance rules block innovation.

Finance Example

A trading firm has logs of thousands of fraudulent transactions. These are ideal for training fraud detection models. But financial secrecy laws prevent sharing. As a result, fraud models lack rare cases and miss patterns, costing banks billions annually.

Enterprise Example

A retail company logs millions of customer interactions. Buried inside are insights about churn, anomalies, and bugs. But these logs are siloed inside corporate servers. Without a mechanism to safely synthesize and share them, no external model can benefit.

3.6 The Gap in Today’s Solutions

Some startups have tried to address these issues, but they fall short.

Synthetic data companies (e.g., Gretel, MostlyAI): They generate synthetic data, but are focused on Web2 SaaS. No provenance, no contributor royalties, limited incentive models.

Data sovereignty projects (e.g., Vana): They focus on personal data and user-controlled access, but do not solve the problem of enterprise-grade regulated data (medical, financial, behavioral logs).

PETs (privacy-enhancing tech like TEEs, federated learning): Useful, but expensive, slow, and limited in scaling.

What’s missing is a system that:

Unlocks regulated and behavioral data without exposing raw inputs.

Anchors provenance so buyers know where data came from.

Rewards contributors fairly with royalties.

3.7 Restating the Problem Simply

The world’s most valuable data is locked.

There is no system of ownership or royalties for contributors.

Public datasets are exhausted and no longer sufficient.

This is the bottleneck that Syncora exists to solve.