For years, advances in artificial intelligence were fueled by the explosive growth of computing power. Faster GPUs, cheaper cloud services, and new chip architectures created an environment where training ever-larger models became not only possible but routine. Today, the cost of computation continues to fall. Investments in hyperscale data centers, energy-efficient hardware, and specialized accelerators are driving the marginal cost of compute closer to the cost of electricity itself.
But this story has a twist. As compute becomes abundant, data — not processing power — has emerged as the scarce and decisive resource.
The End of Free Data
The first generation of AI breakthroughs relied heavily on public datasets: open text corpora, large-scale image libraries, and social media streams. These resources fueled the rise of natural language processing and computer vision.
Yet in 2025, those reserves are largely exhausted. High-quality open datasets no longer provide the edge. Instead, the competitive race has shifted to exclusive, domain-specific, and lawful data sources. And in no field is this more pronounced than healthcare.
Why Medical Data Is Different
Medical data is uniquely valuable: rich in information, diverse in modalities (text, imaging, signals), and directly tied to high-stakes decisions. But it is also uniquely protected.
- Special category: By law, health data is classified as highly sensitive.
- Consent and contracts: Usage requires lawful basis, patient consent, and detailed agreements.
- Secure environments: Data cannot simply be copied into the cloud — it must remain inside controlled, auditable infrastructures.
This means the value of medical data lies not just in its content, but in the lawful channels through which it can be accessed and processed.
The New Economics of AI in Medicine
In this new landscape, the equation has changed:
- Compute is cheap. Cloud providers and chipmakers continue to drive down cost per training run.
- Data is priceless. Gaining lawful, scalable access to high-quality medical datasets is the true bottleneck — and the true differentiator.
The result? A reversal of priorities. Success in medical AI no longer depends on who can build the largest model, but on who can secure access to the right data under the right legal and technical frameworks.
Europe’s Bet: EHDS
The European Health Data Space (EHDS), adopted in 2025, is a landmark attempt to solve this bottleneck. By 2027–2029, all EU member states must establish Health Data Access Bodies and connect their Secure Processing Environments (SPEs).
This creates, for the first time, a lawful and scalable channel for secondary use of health data across borders. Whoever positions themselves early inside these environments will effectively control the tollgate for medical AI innovation in Europe.
Implications for Innovators
For AI developers, hospitals, and investors, the message is clear:
- Algorithms are no longer the scarce asset. Access to data streams is.
- Compliance is not optional. Only lawful access routes — with consent, contracts, and SPEs — will unlock value.
- Strategic timing matters. Aligning with early EHDS implementations in countries like Malta, France, and Finland can set the stage for exponential scaling when cross-border federation begins.
Conclusion
The economics of AI in medicine are being rewritten. Compute is abundant; data is scarce. Public datasets are spent; lawful medical data is the new currency.
In this new era, the winners will not be those who simply build bigger models, but those who build trusted bridges between data holders and AI developers. Platforms that embrace compliance, operate inside secure environments, and unlock shared value will define the next decade of medical innovation.
The future of medical AI will be shaped not by compute, but by access. And access — done lawfully and at scale — is priceless.