Artificial intelligence is already reshaping American business, but the risks and opportunities of AI depend heavily on how AI is used. In the United States, AI regulation follows a decentralized, sector-based model, where applications such as industrial AI in manufacturing are evaluated differently than open-ended consumer AI systems such as chatbots.
In this conversation, former Siemens CEO Barbara Humpton speaks with Congressman Jay Obernolte, who spearheaded the bipartisan House Report on Artificial Intelligence, about AI regulation, industrial AI, jobs, education, productivity, and the future of American competitiveness. The discussion explores why regulated sectors such as healthcare, transportation, aviation, and manufacturing may require different AI governance approaches than consumer-facing applications.
A key distinction emerges: industrial AI often operates in controlled environments, making risks easier to identify and manage, while consumer AI introduces broader uncertainty. At the same time, the conversation makes an optimistic case for AI as a tool that can increase productivity, expand access to knowledge, support lifelong learning, and create new types of work.
Key Takeaways from the Conversation
- Industrial AI operates in more controlled environments than consumer AI, making many risks easier to identify, evaluate, and manage.
- AI regulation in the United States is decentralized and sector-based, with existing regulators such as the FDA, NHTSA, and FAA overseeing AI applications in their domains.
- AI in manufacturing presents different risks than open-ended consumer AI, because industrial AI systems often operate within defined physical and operational boundaries.
- AI is expected to increase productivity and contribute to economic growth, especially if businesses and workers learn how to use AI effectively.
- AI may disrupt some jobs but is also expected to create new roles, including types of work that are difficult to define before the technology fully matures.
- Reskilling and lifelong learning will be essential, as AI changes how people learn, work, write, program, and solve problems.
- Public-private partnerships and academic access to AI resources matter, especially through initiatives such as the National AI Research Resource.
Full Transcript
Barbara Humpton:
Please give our audience the 30,000-foot view of how you see the opportunities and risks for AI in American businesses.
Congressman Obernolte:
AI is going to be completely transformative not just for American business, but for American society. And, unfortunately, I think we've done a very poor job of articulating the optimistic case for AI. We as Americans have had our opinion about the downsides of AI colored by the last 50 years of science fiction and pop culture. So, if you ask someone about AI, you'll get something out of the Terminator films where the army of evil robots is rising up to take over the world, but you don't get the optimistic case.
And the optimistic case is this: First of all, AI is already the most powerful tool for the dissemination of human knowledge that mankind has ever invented. It can teach you anything that you want to learn about, in whatever learning style is optimal for you. Second, it's going to completely democratize access, not only to information and education, but expert opinion. And that is going to completely change our society.
But what is also happening is that AI will shortly become the most powerful tool for the enhancement of human productivity that mankind has ever invented. And that is going to be incredibly empowering. I believe that that is going to create a rising wave of prosperity that literally lifts all the boats in America.
Barbara Humpton:
You led the bipartisan effort in Congress to produce the House report on artificial intelligence, exploring AI use across multiple sectors from healthcare to finance to agriculture. And the report also offered a vision for responsible AI adoption, the innovation and governance that has to be balanced as this technology enters our world. When you think about the report now, what are the highlights that really jump out to you?
Congressman Obernolte:
Our task force was charged with the job of coming up with, by the end of 2024, a report proposing a federal regulatory framework for AI. So, I had 24 very talented and dedicated members, 12 on each side of the aisle. And we had 25 hearings last year and delivered a 270-page report in December that fulfilled our charge.
Just hit the high points of this report: Americans are under the misperception that AI is unregulated in our country. That is completely untrue. We have adopted a very different approach than other entities like the European Union in embracing decentralized sectoral regulation.
What that means is that we believe in regulating outcomes, not tools. AI is a very powerful tool, but at the end of the day, it’s just a tool. And when you're thinking about the risks of AI deployment, which is what you have to understand to be able to talk about what our obligation is as regulators to mitigate those risks, it matters very much what you're going to do with that AI. An algorithm that is unacceptably risky in one usage context—for example, AI in a medical device that's going to be implanted in someone's body, to name a particularly high-risk deployment—could be completely benign in another usage, like in a video game. The sectoral regulators know this.
We do already have AI being used in contexts like medical devices. In fact, a lot of people are shocked when I tell them that the U.S. Food and Drug Administration (FDA), which obviously is our regulator that has authority over the regulation of medical devices, has already processed over a thousand applications for the use of AI in medical devices.
So, one of the things in our report that we are embracing is to continue this decentralized, sectoral approach to regulation. Here's the reason why.
The European Union has taken a very different approach. They've centralized everything; they have their sectoral regulators, but they're creating a new regulator for AI. As a result, you're going to need another license from that regulator for anything but the lowest-risk usage of AI.
We think that that's the wrong approach because we think it's a lot easier to teach the FDA, for example, what it might not already know about AI than it would be to teach a brand-new entity everything the FDA has learned over decades of ensuring patient safety.
That's true of other sectoral regulators, too. The National Highway Traffic Safety Administration (NHTSA) is already dealing with the use of AI in autonomously driven vehicles. The Federal Aviation Administration (FAA) is dealing with the use of AI and aircraft avionics. The list goes on and on and on.
Barbara Humpton:
I think that makes a world of sense. What's also interesting is when we think about it from an end-user's perspective, often we're not even aware of the AI's existence. Look at examples of the mapping algorithms that we use, or, frankly, just the simple search capabilities we've all become accustomed to. This is the first technology that actually comes to people instead of people having to go to the technology to incorporate it.
Congressman Obernolte:
It’s interesting that no one has come up with a federal definition in statute for what AI actually means, or what AI is. And that's because AI is in the eye of the beholder. Is it a neural network? Is it an algorithm that makes a computer seem like a person? Everyone has a different definition.
To me, AI is anything that you interact with a machine but seems like you're interacting with a human. When you boil it down to that, you can see why AI is going to be so impactful, because it's going to create a way that allows us a very human interface for dealing with complicated technology.
Barbara Humpton:
I was thrilled to have the opportunity earlier this year to testify before your committee to talk about AI and manufacturing. You were really delving into the impact of AI on manufacturing. And we had been really trying to draw a distinction between what we called, at the time, “consumer AI”— the AI that we're using in our private lives, in our commercial lives, in our entertainment lives—and the AI that's being introduced into the world of manufacturing, where we have control over the data that's used to train the foundational models, and where we have control over how that is used and over human intervention in the manufacturing environment. The day I testified, you followed up with a question that made clear you were thinking this way toward the sectoral approaches.
Congressman Obernolte:
And it's an excellent point that a sectoral approach to regulation allows you to differentiate between those two uses, because the thing about industrial AI is that the environment it's operating in is very constrained. If you imagine AI controlling a couple of robot arms on a manufacturing floor, with the caution tape laid out around it to make sure humans don't walk in there, and the robot arms are building a widget, that's a great example of industrial AI.
What we as regulators would do when evaluating the risks of that is to ask, “What are the worst things that can happen?” What are, as the lawyers say, the parade of horribles? And what are we going to do to mitigate those? Well, because the environment is constrained, the things that can go wrong are much more narrow. As long as you are doing quality control on the widget that the AI is building, you've really controlled for a lot of the risks of AI deployment in that case.
If you contrast that to a more consumer application of AI, like in a chatbot, you have no idea about risks. Could the chatbot help someone build a biological weapon? Could it cause a teen to adopt a suicidal ideation? Could it exhibit some really troubling racial biases? Because the deployment of AI in the consumer context is so much more open-ended, we have to worry about those things, whereas in industrial AI it's much simpler to get your arms around.

