While hospitals and insurers are bound by HIPAA, their terms of service often include clauses allowing them to sell de-identified patient data. This creates a massive, legal shadow market for healthcare data. AI companies will leverage this data, obtained via consumer consent, to build powerful advertising and personalization engines.
Related Insights
Public internet data has been largely exhausted for training AI models. The real competitive advantage and source for next-generation, specialized AI will be the vast, untapped reservoirs of proprietary data locked inside corporations, like R&D data from pharmaceutical or semiconductor companies.
A company can build a significant competitive advantage in healthcare by deliberately *not* touching or seeing Protected Health Information (PHI). Focusing exclusively on metadata reduces regulatory overhead and security risks, allowing the business to solve the critical problem of data orchestration and intelligence, a layer often neglected by data aggregators.
Pharmaceutical advertising is the second leading source of health information for patients. AI can “de-criminalize” it by moving from untrackable broadcast ads to programmatic, personalized, and compliant digital content, turning it into a valuable and trusted patient resource monitored by the government.
Previously, data privacy concerns were abstract for most, leading only to worse ads. Now, giving AI companies unfettered access to your professional data provides them with the exact material needed to train models that will automate your job.
As AI personalization grows, user consent will evolve beyond cookies. A key future control will be the "do not train" option, letting users opt out of their data being used to train AI models, presenting a new technical and ethical challenge for brands.
As AI allows any patient to generate well-reasoned, personalized treatment plans, the medical system will face pressure to evolve beyond rigid standards. This will necessitate reforms around liability, data access, and a patient's "right to try" non-standard treatments that are demonstrably well-researched via AI.
While AI shopping agents promise to protect consumer privacy by abstracting away direct retailer relationships, this is a false dawn. Power will likely centralize with the major tech companies providing these agents, not empower individual users with decentralized control. The battle for "owning the customer" simply moves to a new layer.
The market reality is that consumers and businesses prioritize the best-performing AI models, regardless of whether their training data was ethically sourced. This dynamic incentivizes labs to use all available data, including copyrighted works, and treat potential fines as a cost of doing business.
Standalone AI tools often lack enterprise-grade compliance like HIPAA and GDPR. A central orchestration platform provides a crucial layer for access control, observability, and compliance management, protecting the business from risks associated with passing sensitive data to unvetted AI services.
The initial AI boom was fueled by scraping the public internet. Cuban predicts the next phase will be dominated by exclusive data deals. Content owners, like medical journals, will protect their IP and auction it to the highest-bidding AI companies, creating valuable data silos.
