by Chris Hutchins, founder and CEO of Hutchins Data Strategy Consulting
The speed of AI use and its progression is faster than any technology we have ever put into widespread use. It is already embedded across industries worldwide in ways we know and don’t know (e.g., healthcare delivery, national security, product procurement, etc.).
That pace creates a difficult but unavoidable question: how do we regulate AI without slowing down innovation to the point of irrelevance?
This regulatory conversation is not the same one we had around GDPR or earlier privacy laws. AI is not just about data collection or consent forms. It is about systems that learn, infer, and act at speeds that outperform traditional oversight models. If we respond the way we usually do — slowly, inconsistently, and in fragments — we risk losing ground in ways that extend beyond the purely technological to the ethical and economic.
Fragmented regulation is a competitive risk
While trusting the federal government with yet another complex responsibility is uncomfortable for many, the alternative is far worse.
Fifty different state-level AI governance regulations would almost guarantee fragmentation and unnecessary legislative delays. State legislatures are rarely full-time, and even fewer lawmakers are positioned to deeply understand the technical complexity. Expecting consistent, technically informed policy at that level is unrealistic.
AI companies already operate globally. Requiring them to comply with a patchwork of state-by-state regulations would slow deployment, discourage investment, and ultimately weaken the US position in a race that is already underway.
Speed matters, but coherence does too. National-level frameworks, even imperfect ones, are far more likely to preserve both.
Healthcare shows what is at stake when trust breaks
Healthcare offers a clear lens into what occurs when technology outpaces governance. Unlike most industries, medicine is anchored by a principle that exists beyond national borders: the Hippocratic Oath. Trust between doctor and patient is not optional; it is foundational.
That trust has already been eroded across much of society, and healthcare has certainly not been immune. The pandemic made that painfully clear. Data suppression occurred at scale, including within our own borders, and the effects are still being felt.
California’s SB 53, which affirms a patient’s right to be informed when doctors use AI, reflects a legitimate concern. Patients deserve transparency. When AI influences diagnoses, documentation, or care recommendations, clarity and consent matter — not because AI itself is dangerous, but because trust in this relationship can mean life or death.
While patients still trust their physicians more than AI systems, many do trust that their doctors know when and how to use AI and should be using it. With that said, it’s important to recognize the guardrails that could push patients toward a future in which they don’t trust their physicians, and the numbers for that are steadily increasing.
Speed without validation is not innovation
One of AI’s greatest strengths is its ability to process overwhelming amounts of data; far more than any human can manage alone. In healthcare and other data-intensive fields, this capability is both helpful and necessary.
The challenge is that review, validation, and governance processes have not evolved at the same pace. Accelerating decision-making without accelerating oversight creates exposure. We are already seeing the consequences.
In 2024 alone, the US recorded an estimated $12.5 billion in losses tied to deepfakes, voice cloning, and related AI-driven fraud. This year is on track to be at least 33 percent higher. Globally, the impact has exceeded $1 trillion.
These numbers are measurable outcomes of technology advancing faster than our ability to manage it responsibly.
Regulation must enable, not paralyze
This call is not one for heavy-handed regulation or slow-moving bureaucracy. It is a call for urgency of a different kind.
We need more than a whole-government approach. Public-private partnerships, particularly at the federal level, are essential. AI companies cannot be forced into lengthy approval cycles that render them uncompetitive, but they also cannot operate without accountability. The balance is difficult but necessary.
History offers a warning. Technologies like blockchain reshaped how wealth moves and how control shifts, largely before most people understood what was happening. AI is even more complex, and its implications are broader. If we wait for perfect understanding before acting, we will be too late.
Moving forward without falling behind
AI will continue to advance without thoughtful regulation. The question is whether we choose to lead responsibly or react after trust has already been lost.
National collaboration matters. Transparency matters. Validation matters. And speed comes not from ignoring these realities, but from designing systems that allow innovation and oversight to move together.
This is not a theoretical policy debate. It is a crisis already under our noses. If we fail to act with intention now, we will find ourselves trying to rebuild trust in systems that never earned it in the first place.
And that is a race no one wins.
Chris Hutchins serves as the founder and CEO of Hutchins Data Strategy Consulting. The healthcare institutions benefit from his expertise in developing scalable moral data and artificial intelligence methods to maximize their data potential. His areas of expertise include enterprise data governance, responsible AI adoption, and self-service analytics. His expertise helps organizations achieve substantial results through technology implementation. Through team empowerment Chris assists healthcare leaders to enhance care delivery while reducing administrative work and transforming data into meaningful outcomes.
