Hearings to examine AI that improves safety, productivity, and care.

This hearing will come to order. Good morning everyone. Thank you all for being here. I recognize myself for opening remarks. And thank you Ranking Member Baldwin and Chairman Cruz, Ranking Member Cantwell and our witnesses for working to put this important hearing together. Artificial intelligence will undoubtedly usher in significant improvements in quality of life for the American people. It will make many workplaces safer and more productive, helping to increase output, raise wages, and grow the economy. It will enhance manufacturing capabilities, especially those of critical importance to our economic and national security, such as semiconductors and those in the defense industrial base. It will make it easier to reshore manufacturing through human-enhancing automation and digital twinning simulation. Smart systems and devices have the potential to revolutionize healthcare, improving early detection of diseases such as cancer and helping people with disability live better lives. Not by replacing doctors, but by augmenting their diagnostic and treatment capabilities. AI-enabled research aided by self-driving cloud labs could massively reduce barriers to the discovery of new drugs. AI's potential in the healthcare industry presents unique opportunities to save and improve lives. I've also noted that as I travel the state, some North Carolinians share concerns about the growth of AI in automated or autonomous technology. There's a natural hesitancy towards technology that may disrupt incumbent industries or systems. It's normal to worry about the impacts of advancement on your job, your children, and your community. But if we have learned anything from our history, it's that innovation is the lifeblood of the American economy. Our nation's story has been shaped by technological advancements that have time and time again expanded prosperity and improved outcomes. I believe that AI, deployed in numerous ingenious ways, can help people be better versions of themselves in their daily lives. For those of us in this room, it's our job to listen to those concerns and to work together in a bipartisan fashion to address potential harms so that our nation can reap the tremendous benefits that AI has to offer. As I've said before, winning the AI race against China is paramount for our national and economic security. The administration is leaning in and providing important leadership. Ongoing work to first identify and then to aid in the export of the American AI stack, from hardware to software, it will ensure that global AI diffusion and standards are anchored in American values. The Genesis mission will build upon America's scientific dominance by networking together world-class labs, computing power, and datasets to turbocharge scientific discovery. The diffusion of AI into the economy will be a critical dimension in this race. According to one report, AI is the most rapidly adopted general purpose technology in history, with three in five U.S. adults surveyed having used AI less than three years after its release. However, other studies have found that U.S. businesses can lag their Asian and even European peers in enterprise adoption of AI tools. I'm concerned that China, given its top-down command and control structure, deep and sophisticated manufacturing industry, and open-source heavy AI ecosystem, is in a prime position to diffuse AI quickly and broadly. Achieving a manufacturing renaissance in America is as bipartisan and deeply held a goal as any in Congress. Given demographic realities such as an aging skilled workforce and stalled population growth, we will need to diffuse and scale smart technology and processes to make more critical goods domestically. I am excited to discuss many of these technologies and systems today. Our witnesses are at the front lines and cutting edge of making our economy and our daily lives smarter. I look forward to hearing from them about what excites them, what concerns them, and roadblocks we in Congress can address. Ranking Member, do you have any opening comments?

Absolutely. Thank you so much, Mr. Chairman. And thank you to the witnesses who are appearing before our subcommittee today. Given the focus of today's hearing, I wanted to begin by raising the troubling news from last week regarding the Trump administration's actions to force artificial intelligence companies to deploy systems without guardrails and safety measures. Anthropic refused to have their technology used for mass surveillance of Americans or for the deployment of autonomous weapons without human oversight. The Trump administration then labeled Anthropic as a security risk, and Secretary Hegseth has taken the unprecedented step to blacklist the company. Our witnesses are here to testify about how artificial intelligence can be used to improve worker safety and efficiency. But we cannot ignore the stakes at play here. Workers and consumers alike are rightfully worried about the deployment of AI systems. A study done by Stanford last year found that 45 percent of workers expressed concerns about the accuracy and reliability of AI systems. 23 percent feared job displacement and 16 percent worried about a lack of human oversight built into the systems. And that doesn't even begin to touch on the concerns regarding privacy, intellectual property, and energy use that comes along with AI. But if designed with workers at the table during all stages, from research and development to deployment to negotiating guardrails with employers, artificial intelligence has the potential to improve productivity, reduce safety risks, and ease burnout in the workplace. The best innovations come from the factory floor. The people actually doing the work know better than any senior executive what the needs, inefficiency, and liabilities are day to day. So I look forward to hearing our witnesses today and again, thank you for being here.

I thank the Ranking Member. I'd like to introduce our witnesses for the day. Our first witness is Demetri and help me on this one, okay? Pronounce your last name for me.

Giannikopoulos.

I even have a phonetic spelling here, but I thank you for that. Mr. Giannikopoulos is the Chief Innovation Officer at Rad AI, a San Francisco-based company using AI to help radiologists save time on reporting, allowing them to spend more time on patient care. His experience applying new technologies to healthcare spans over two decades. Our second witness is Brittany Ng. Ms. Ng is Vice President at Siemens Digital Industry Software. Ms. Ng is an expert in business matters and she has extensive expertise in the deployment of AI across manufacturing context. Our third witness is Damion Shelton. Dr. Shelton is co-founder and chairman of the board at Agility Robotics. He has almost two decades of experience in academia, innovation, and business. Dr. Shelton founded two highly successful robotics companies. Our final witness is Mark Muro. Mr. Muro is a senior fellow at the Metropolitan Policy Program at the Brookings Institution. At Brookings, his research focuses on how technology development plays out differently across regions. All right. Mr. Giannikopoulos, you are recognized to deliver your open statement if you're ready.

Chairman, Ranking Member, and members of the subcommittee, thank you for the opportunity to testify. I come before you as a healthcare AI expert who has deployed artificial intelligence nationwide. As a caregiver who has supported my wife through cancer. As the spouse of a nurse practitioner who with more than a decade of frontline primary care experience. And as a patient who has lived with multiple sclerosis for more than two decades. These experiences have shaped who I am and are why I am here today. They have fueled my more than 20-year career in healthcare. They have given me a firsthand view of how care is delivered, how decisions are made, and what happens when the system works and when it doesn't. In each of these roles, I have seen the same reality. Outcomes often hinge on whether a diagnosis is made quickly and accurately. Because in healthcare, the most dangerous failure is not a machine failure, it is a missed or delayed diagnosis. Research from Johns Hopkins Medicine estimates that nearly 800,000 Americans each year die or are permanently disabled because of diagnostic error. Extensive research has shown that errors often occur because clinicians are operating within an increasingly complex and strained healthcare system, managing rising volumes, time pressures, and expanding information. Imagine a patient arriving in a community emergency department with sudden chest pain. A CT is performed. The cause is something rare but deadly, an aortic dissection, a tear in the body's main artery. Without rapid diagnosis and treatment, a quarter of patients can die within 48 hours. Today, AI is helping at multiple points in that patient's care. Within minutes, AI can analyze the images and help flag the findings so it is reviewed immediately. When the radiologist opens the case, AI can help integrate trusted clinical guidance directly into their workflow, drawing on peer-reviewed evidence developed through organizations like the Radiological Society of North America. It does not replace the physician. It strengthens confidence and trust in the diagnosis. Once the diagnosis is made, the care team must act quickly. The patient may need to be transferred for life-saving surgery. AI can help ensure the diagnosis is clearly communicated, documented efficiently, and shared across care teams so treatment can begin without delay. That is productivity. That is coordinated care. Research published by the American Heart Association shows that coordinated care pathways for aortic dissection reduce delays and improve survival. We see this same reality in stroke care, where every minute of delay risks permanent death of millions of brain cells controlling vital functions of the body. Faster diagnosis and transfer can mean survival and less disability. But the story does not end there. That same scan may reveal a quiet lung mass. Not an emergency today, but something that must be followed carefully. AI can help ensure follow-up imaging is scheduled, care teams are alerted, and patients do not fall through the cracks months or years later. Unfortunately, over half of patients never receive the follow-up care their conditions require. For those Americans, the risk is not theoretical. It becomes their reality. This is not just about efficiency. It is about trust. We are asking clinicians to manage rising imaging volumes, expanding documentation requirements, and increasingly complex patient needs while the healthcare workforce is shrinking and burnout remains high. AI, when implemented responsibly, is not replacing clinicians. It is acting as a pressure release valve, helping reduce cognitive burden and supporting clinicians in delivering safe and timely care. I have deployed both FDA-cleared AI tools and other clinical software that does not require FDA clearance. In both cases, these systems undergo extensive clinical, privacy, and governance reviews before deployment. Physicians remain responsible for patient care and these tools operate within existing healthcare laws and professional accountability frameworks. Based on my experience deploying these systems nationwide, the most important determinant of safety is not only how they are evaluated before deployment, but how they are governed, monitored, and supported once they are in clinical use. This is where thoughtful lifecycle governance and consistent national standards are essential. This is especially important for rural and underserved communities, where access to subspecialty expertise may be limited. AI can help extend the reach of clinical expertise and support clinicians caring for patients regardless of geography and access to local resources. AI is most powerful not when it replaces human judgment, but when it strengthens it. AI will not replace physicians. It will help them do what they trained their entire lives to do: care for patients. Less hype, more help. That is not a future promise. It is happening today. Because behind every scan is a person, a family, and a moment where getting the diagnosis right can change everything. Thank you. I look forward to your questions. ...help them do what they trained their entire lives to do: care for patients. Less hype, more help. That is not a future promise. It is happening today because behind every scan is a person, a family, and a moment where getting the diagnosis right can change everything. Thank you. I look forward to your questions.

Thank you for your remarks. Ms. Ng, you are recognized for five minutes.

Good morning Chairman, Ranking Member, and members of the subcommittee. Thank you for the opportunity to testify today. The United States does not need AI as an abstract capability. Our global competitiveness requires AI deployed on factory floors, in shipyards, and across production systems where it generates measurable productivity gains. I'm proud to take on this challenge. I lead Siemens' maritime business in the United States, where we work hand-in-hand with shipyards and manufacturers. At Siemens, we are a global leader in industrial AI. Last year alone, we invested $15 billion in the United States to further our leadership in this transformational technology. We are continuing to bring industrial AI to customers and seeing firsthand how AI is most powerful when it connects data to operational decisions in the real world. Through our maritime work, shipbuilders are using our physics-based, AI-enabled digital shipbuilding platform to connect design, simulation, and production planning so that teams can identify bottlenecks before they occur. They can reduce rework, improve first-time quality, and compress production timelines. And by training AI in virtual environments, shipyards can improve planning and performance even in complex production conditions. Industrial AI, alongside digital twins and software-defined automation, is transforming manufacturing across industries. We are seeing machine downtime reduced by 50 percent, energy consumption cut by 20 percent, and quality control with 99.99 percent accuracy. Industrial AI is helping manufacturers to achieve up to 40 percent in productivity improvements. And it's transforming traditional factories into flexible digital enterprises. In modern shipbuilding, innovation looks like being able to create a full digital twin of a vessel before steel is even cut. This means that our customers can simulate how the ship will be built, how systems will integrate, and how production will flow through the yard, all in a virtual environment. On the deck plate, industrial AI can help sequence work packages, flag quality issues earlier, predict equipment downtime, and optimize the flow of material. The crews deploying these technologies are spending less time waiting and doing rework and more time building. When these tools are broadly adopted, they become economic multipliers. However, technology does not just transform industry unless the people understand it, trust it, and see how it improves their lives. At Siemens, we've learned that digital transformation works best when the worker is at the center. Manufacturing today faces a shortage of 400,000 open jobs nationwide. The issue is not AI coming for industrial jobs. It's a shortage of skilled workers amid increasing production complexity. Industrial AI doesn't just solve the workforce shortage. It changes the entire equation. These technologies reduce repetitive and hazardous tasks while elevating the skill sets of American workers. As Congress considers AI and manufacturing policy, I respectfully offer three considerations. First, distinguish industrial AI from consumer AI. Industrial AI operates in structured, safety-critical business-to-business environments with validated data and rigorous testing. Second, focus on adoption. Federal policies and investments should integrate digital modernization from the outset and accelerate deployment through public-private collaboration. Programs like Manufacturing USA and the DOE's Genesis mission provide that bridge, moving from innovation into real-world production. Lastly, the government should send a clear and consistent signal that digital capability is core industrial infrastructure. America's competitive edge will be determined by who most effectively deploys AI in the physical world. Our nation is indisputably the world's greatest innovation leader. Our challenge is scaling advanced technologies through the industrial base. Thank you, and I look forward to your questions.

Thank you, Ms. Ng. Dr. Shelton, you are recognized for five minutes.

Thank you, Chairman Budd and Ranking Member Baldwin, for inviting me to speak today. Also thanks to the rest of the committee. I'd like to open with a quote from Peter Pan: "All of this has happened before, and it will all happen again." The United States first started collecting census data around agricultural employment in 1820, almost exactly 200 years before Agility Robotics launched its humanoid robot. In 1820, the vast majority of Americans worked on farms using simple tools and assisted by animals. Over the next 200 years, agricultural employment shrank to about 2 percent of the workforce. This is not, however, a story of decline. Absolute employment, the total number of Americans working in agriculture, has actually increased by about 30 percent since the 1820s. Why? In a single word: technology. In the 50 years after the patenting of the McCormick reaper in 1834, nearly 12,000 additional farm implement patents were filed in the U.S. Far from destroying jobs, automation in agriculture expanded both direct agricultural employment and unlocked truly staggering growth in the United States: a 30x increase in population and a 40x increase in per capita GDP. The modern AI and robotics boom promises to bring these sorts of transformative changes to the rest of our economy. Agility Robotics started in 2015 as a spin-off from Oregon State University. From our DARPA-funded academic roots, we've grown to more than 300 employees, primarily in Oregon, Pennsylvania, and California. Our Oregon facility co-locates both our R&D teams and our factory, RoboFab, where we assemble 100 percent of our robots in the U.S. Our humanoid robot Digit was launched in 2020 as the first full-sized humanoid robot available for purchase. Our customers include warehouse and logistics work: Amazon, GXO, and Mercado Libre; and manufacturing and automotive suppliers: Schaeffler and Toyota, with the common theme of providing a solution for repetitive material handling alongside human co-workers. I'd like to address the impact of automation on jobs and human workers. Many manual labor jobs in the U.S. are facing pressure from an aging workforce, high turnover, and a high injury rate, which is where Agility has focused its deployments. The Bureau of Labor Statistics projects average annual vacancies of more than 1 million positions in warehouse and logistics work. Each robot deployed here not only doesn't take a job from a human, it enables the business to grow and expand overall hiring elsewhere in the organization. To cite one example from an Agility partner, in 2012, Amazon employed about 88,000 people and acquired the robotics startup Kiva. Over the next 13 years, they deployed more than a million robots while also hiring an additional 1.4 million people. Prior to the modern AI boom, it took a skilled engineer many months to develop a new application for a robot. Modern AI can dramatically shorten the time to deployment for small and medium-sized businesses that desperately need to solve labor challenges but lack the technical and the financial resources to run their own IT department. However, as we've seen from self-driving vehicles, safety often lags behind technical ability. It's imperative that robots not endanger their human colleagues, members of the public, or our homes and workplaces. Focusing first on controlled environments has allowed Agility Robotics and our partners like Boston Dynamics to focus on developing responsible, industry-led safety standards for robots in the workplace. Gaining experience in these environments first with a focus on continuous safety improvement and industrial best practices is the fastest and most responsible path towards a successful long-term build-out of general-purpose automation. As an industry, we owe the general public a solid safety argument backed by data. I'll conclude by briefly addressing the challenges posed by China's extremely rapid progress in humanoid robots. To be as blunt as possible, they're doing a good job. Their technology is well-designed, highly capable, and backed by a formidable supply chain. Two recent papers by security researchers have identified a critical security vulnerability in a Chinese humanoid robot, allowing for remote takeover as well as a phone-home data logging mechanism that sends data to a remote server. As of this week, and I did check this before the hearing, that robot is available for sale in the U.S. for less than $20,000. Combined with open-source and open-weight AI models, China is creating a compelling value proposition for early adopters of general-purpose humanoid robots. This is a siren's call that we would be well-served to take seriously or risk seeding the future of automation and, by extension, economic growth to others. Thank you again for the opportunity to speak today, and I thank for this committee's focus and leadership on these important issues, including the regulatory sandbox approach. Agility Robotics is ready to help the U.S. solve its labor challenges, expand the economy, and ensure American competitiveness and economic security as we move into the next industrial revolution.

Thank you, Dr. Shelton. Mr. Muro, you are recognized for five minutes.

Mr. Budd, Ms. Baldwin, thanks so much, and to distinguished members of the committee, we really want to thank you for the opportunity to comment here. I'll just say my remarks here are my personal views, you know, separate from the Brookings Institution. In that vein, I want to start by reaffirming the basic premise here that I think is very compelling: that America's innovators are day by day introducing incredible new tools and solutions that are allowing more and more people, firms, and entrepreneurs to expand and reach the reach and achievement of human expertise. It's a compelling moment, and that's creating in the country significant excitement. And yet, a degree of pessimism also complicates this moment. We should be frank about that, and the committee has been frank about that. Some worry that the technology will not be smoothly adopted, that adoption will be challenged. Others fear what has been deemed the greatest automation technology in history. And still others worry about safety questions and which skills will serve them in the future to enable this work. So I want to say just a few words about areas where federal support can help maintain the sector's momentum, reinforce its value, and promote optimism about its possibilities. In this direction, my fellow panelists have done an excellent job of inspiring confidence by detailing some of the possibilities. I want to touch on just a few ways my written testimony suggests we can sustain AI success. First, broader AI innovation and adoption like we're seeing requires maintaining a dominant research base. Abundant research flows generate talent, but also the ideas, intellectual property, innovation, and startups that we're hearing about. I will provide more detail in the written remarks, but the nation remains, does retain, clear global leadership. With that said, it has fallen short of the doubling of AI research that had been suggested in 2019 by the National Security Commission on Artificial Intelligence, an important bipartisan watchword. Beyond that, to address the scale issue, the nation should prioritize a step change in the total AI R&D outlays. At the same time as seeking to rejuvenate the AI Research Institutes program for accelerating research on new topics, establish new testbed programs involving federal research units, private sector, and even data centers that can be brought into this, and then direct grant-making towards investments in what some of us call pro-worker AI that supports such values as education systems, health, human learning, and human decision-making. Accelerating AI adoption also requires promoting the growth of emerging AI clusters in geographic regions. NVIDIA, OpenAI, Microsoft, and Anthropic have all advocated for this kind of development, sometimes using the term "economic zones" to envision regional investment areas that fuse permit and energy solutions with federal and state creation, small business empowerment, and community-level prosperity. Such regional development, including through linkages with local testbeds, could foment a powerful surge of optimism in communities. We believe that bottom-up economic innovation in regions is an important part of this work ahead of us. More boldly, Congress and the Trump administration could build new prize competitions or recent challenge grant programs, such as the Tech Hubs and NSF's Innovation Engines, to further develop these regions and hub engines that have so much to give. Finally, AI adoption leadership will further hinge on ensuring that adequate pools of high-quality talent exist all across the country and in every sector and every region. The challenge is both narrow and wide. The narrow... ...total AI R&D outlays. At the same time, it's seeking to rejuvenate the AI Research Institutes program for accelerating research on new topics, establish new testbed programs involving federal research units, private sector, and even data centers that can be brought into this, and then direct grantmaking towards investments in what some of us call pro-worker AI that supports such values as education systems, health, human learning, and human decision-making. Accelerating AI adoption also requires promoting the growth of emerging AI clusters in geographic regions. NVIDIA, OpenAI, Microsoft, and Anthropic have all advocated for this kind of development, sometimes using the term economic zones to envision regional investment areas that fuse permit and energy solutions with federal and state creation, small business empowerment, and community-level prosperity. Such regional development, including through linkages with local testbeds, could foment a powerful surge of optimism in communities. We believe that bottom-up economic innovation in regions is an important part of this work ahead of us. More boldly, Congress and the Trump administration could build new prize competitions or recent challenge grant programs, such as the Tech Hubs and NSF's Innovation Engines, to further develop these regions and hub engines that have so much to give. Finally, AI adoption leadership will further hinge on ensuring that adequate pools of high-quality talent exist all across the country and in every sector and every region. The challenge is both narrow and wide. The narrow part of the challenge is the nation's diminishing homegrown share of the world's elite talent. Speaking of this, ITIF has shown that while the U.S. attracts and employs a high share of elite talent, its domestic production of such talents has slowed. That's a worry and needs to be addressed. At the same time, concerns around the broad degree of AI readiness are important, and starting now, virtually all workers will need to understand AI principles, be able to understand and direct AI effectively, and be able to evaluate its outputs. AI literacy instruction, as the Department of Labor has been describing in recent months, will need to cultivate agility and install such human skills as judgment, teamwork, creativity, and problem-solving. If that is widely achieved, workers will feel optimistic about the technology. If not, I worry they won't. And so a supportive AI talent strategy must foster AI readiness among both elite and general populations. Fortunately, immigrants and U.S. higher ed are proven talent sources. Thoughtful visa reforms can help the nation retain its edge on elite talent, and even more important is the need to prioritize broad AI literacy, not just at elite institutions, but throughout the regional AI, the entire workforce system.

Mr. Muro, if we could... Thank you so much. If we could take a few questions for the rest of the panel. If you have additional thoughts, we'll come back to that when you're recognized, if that's okay.

Absolutely.

Thank you. Yes, thanks again for being here. Thanks for your opening remarks. You know, it's been said that the four main inputs of AI are talent, compute, energy, and data. The federal government houses a tremendous amount of scientific and important datasets that could be leveraged as strategic assets in the U.S. AI leadership. The Open Government Data Act of 2018 requires data assets owned by the federal government, whose sharing would not otherwise be prohibited by law, to be published in machine-readable format. If each of you first three with particular companies, operating companies, what would each of your companies' efforts to deploy AI, how would that be affected by more access to data if it's AI-ready and if it's machine-readable and compatible? How would that affect each of your companies? We'll start with you.

Senator Budd, the access to data is a critical aspect of development for artificial intelligence. However, in healthcare, the ability to have validation data, by which you can measure the quality of your solutions that are developed, by which deployers at the institutional level can assess the fit within their personal institution, is one of the greater challenges. So having more robust access to this machine-readable, translatable, and ideally in healthcare outcomes-linked data will provide opportunities to actually assess these solutions, not in a vacuum, but against real American data as part of that. So, you know, more robust access to that would be a significant enabler of adoption and innovation of this new technology.

...all of our capabilities in the shipyards and in the backshops are able to operate efficiently. We're seeing that bringing that entire life cycle view together, we are very able to be able to reestablish that that dominance. The other question that you had around how do we continue to support the workforce and make sure that we're being able to retain that workforce is is I would say pretty straightforward. We have to have a strategy to attract workforce and retain them. Meaning the workforce is becoming increasingly digitally native. They have expectations. They want to work with top-of-the-line software and AI-enabled tools. So we're really honored to be able to provide that as part of talent acquisition strategies for manufacturers and shipbuilders specifically.

And and I certainly want to just encourage you to have them at the table from the beginning, not introduce them at the end. Mr. Muro, I have a question for you. Last month you coauthored a piece titled Turning the Data Center Boom into Long-Term Local Prosperity. In this article you note that local officials have the leverage to engage data center developers on becoming true local partners in their community. The article highlights some efforts in Wisconsin, specifically Microsoft's partnership with the University of Wisconsin-Madison in the development of the AI Co-Innovation Lab and their partnership with Gateway Technical College to train workers. What should local officials be trying to get out of these negotiations?

We got a lot of them going on in Wisconsin.

Wisconsin actually is a demonstration, almost like an aerial view of of data center development that is benefiting a region that is tied to broader economic achievement for the region too. That's because tech they are technology themselves. They are a source of computer processing, high-speed technology that of that could be tied in and is being tied in to regional university activity, research, and so on. And then there's users of energy and places for energy innovation. So data centers can be thought of in a very broad way along with their important national their national push towards powering the whole technology. But places have the possibility to work enter into interactions with with the hyperscalers early in the process. We think there's an opportunity for them to trade essentially very quick initial ability to save excuse me to to establish you know quick permitting in response in in a trade with the companies to build these kind of inter partnerships in the region. So I think the more a region has a sense of its technology goals and where those intersect with the hyperscalers, and I think there are lots of areas for particular research, work on energy issues, testbeds of all sorts. So I think there's just a wider array of possibilities in regions.

Thank you.

All right. Thank you, Mr. Muro. Senator Cruz, you're recognized.

Thank you, Mr. Chairman. Welcome to all the witnesses. Let's start with a broad question. AI is a fundamentally transformational technology. And like past waves of innovation, we don't know exactly how it's going to impact our economy, including what it's going to do to employment. One of the greatest fears that I hear from people is concern that AI is going to take their jobs and it's going to lead to fewer jobs. Mr. Giannikopoulos, what do you say to those concerns? What are you experiencing in your current job markets and how do you anticipate AI shifting the job market in respective industries?

Chairman Cruz, thank you for the question. The crisis in healthcare is real and it's happening right now. There are around 900,000 physicians in the United States. The projected shortage of physicians by 2030 is 187,000, excuse me, by 2037. The projected shortage of nurses by 2030 is 194,000. So the gap is growing. The need is there, specifically within radiology. If you look at the attrition rate over the past five years, it's up by 50 percent compared to historical norms. Meanwhile, the projection is that imaging volume will rise by 26 percent in the next 30 years. So continued growth and a continued need. Dr. Curtis Langlotz, the immediate past president of the Radiological Society of North America, recently did a task-based analysis of the work of a radiologist, and he estimated that within the next five years, the amount of work a radiologist will need to do could possibly go down by 33 percent. Yet the need for them to perform that understanding and ultimately make those diagnoses so that patients can get the care will only increase. So AI is not replacing the physician at any point along this. It's enabling them so that we can leverage them to get where we need to be as a healthcare society.

Ms. Ng, how would you answer the same question?

Chairman Cruz, in our experience, advanced technologies support workforce expansion, not contraction. This is especially true in my sector of shipbuilding, where we're seeing these advanced technologies drive an increased demand for highly skilled trades. AI is not diminishing the need for skilled workers. It's amplifying it. AI's expansion reinforces the need for strong technical skills and for people who are passionate about innovating. The best example that I have is actually in Fort Worth, Texas, where we have the latest and greatest Siemens facility that was just stood up. In Fort Worth, we used our digital twin and AI-enabled tools to be able to model the entire production floor and simulate it before even breaking ground. This facility created hundreds of new jobs in that area and the employees that are working there are operating in reduced complexity and much better training. So in summary, the greater risk to jobs in our opinion is losing industrial competitiveness. It's not responsible modernization. Thank you.

Dr. Shelton.

Yeah, thanks for the question. I can give two examples with human labor. The first in the warehouse and logistics world is there are sort of two problems. First right now is an extreme growth in that sector over the last say 10 or 15 years. All of us love our next-day Prime shipping. It has created a completely unsustainable growth trajectory in that industry. So there are only two ways you can do that. One is you could pull slack out of the rest of the economy from a human labor standpoint and second is you can deploy automation. Companies right now have tried to do both, like the Amazon example that I cited. However, as a sector, it's still coming up short from a total employment count standpoint. So that's the backfill side of this. The second and the more exciting piece of this is I think it changes fundamentally the nature of what a job is. So as a private pilot, there was a time in the U.S. history where delivering airmail meant you put on your goggles, you got into your biplane, and you flew across the U.S. personally carrying the mail. There is now an enormously complex logistics system for shipping things by air, and you could say that you work in that industry while someone from the 1930s would simply not recognize what the job has evolved into. That's why I got into robotics. That kind of stuff is super exciting to me and the evolution of things like agriculture. As a notoriously terrible grower of corn, I think people aren't aware of how hard modern agricultural jobs are and just how far we've come on that side of the economy. So I'm an optimist about this. I do think we should be cognizant that there are evolutions of jobs over time and we should be sensitive to that, but it is also the path forwards.

Two final questions. What is something that you hope AI could do in your industry that we can't currently do yet, number one? And number two, what is a surprising way you've seen AI used that you didn't expect?

I will answer the same way but from two different perspectives. If you look at precision medicine pathways, AI has already opened those up in ways that we've never seen. Take my personal diagnosis journey. It was 10 years to that ultimately. 10 years of varying symptoms that were ultimately dismissed because I was a white male with Greek heritage in Florida, which is not exactly common for multiple sclerosis. Being able to identify that this very numbness, all these different parts and pieces personalized to me as an individual and understanding in our broader healthcare system could have shortened that cycle of diagnosis. That's what it's already starting to do. But with better integration, better understanding, better tailoring of the medicine and the understanding to the individual, it will be able to take that to the next level. This is where we're getting into predictive cancer scores, different things like that so that you can identify a path that a person's on, not when they're already on it, but before they start it and really make a big difference.

Briefly, Ms. Ng and Dr. Shelton.

To answer your first question, we want to see AI adopted throughout the entire life cycle as opposed to in silos, so say maybe just design or just production or just sustainment. For the second question, what has surprised me the most is seeing the application of industrial AI with the U.S. Navy, which is my customer. We've been able to work with the Navy to digitally model the four shipyards and help them to be able to plan for future work. So when you build a submarine, you have a whole maintenance plan that comes associated with that. And what's surprising to me is the ability to use industrial AI to actually model out new dry docks, new backshops, all of this new physical infrastructure that doesn't disrupt the current availabilities and work happening there. Thank you.

So the very first time we deployed one of our robots, Digit, the humanoid, doing a task, it took an engineer as I recall somewhere about five and a half weeks to really prototype that. By 2023, and this video is actually on our YouTube channel for anybody who's curious, we decided to hook an early version of ChatGPT up to it to see if it could write code that could run the robot. And shockingly, and I was completely gobsmacked by this, it worked on the first try. Nothing in technology ever works on the first try. So that was a really interesting outcome. Now at the time it was not hardened, it was not deployable, but as a sort of a shot across the bow of things that were to come, it was super interesting. I would love, and this gets back to Chairman Budd's question about the data set availability, that that worked is a sign that we can take descriptions of tasks that we want robots to do and use automated toolsets to get them deployed as rapidly as possible. It's an exciting and completely novel side of the industry that continues to surprise most of us who've been in the field for a while.

Thank you.

Thank you, Chairman. Senator Blunt Rochester.

Thank you, Chairman Budd and Ranking Member Baldwin, and thank you so much to the witnesses for this hearing. I get very excited about this topic. I was former Secretary of Labor in Delaware as well as head of state personnel. But I also had the opportunity in the House when I served in the House to start a bipartisan ...partisan Future of Work Caucus because for me, if anybody says they know what the answers are or whether the economy is going to, we're going to have more jobs or less jobs, you really don't know. This is all so new and so and so impactful in so many parts of our lives. I'm on the HELP Committee, so health care. I have a nursing workforce shortage bill, so health care is important. I am from an ag state, so we think about precision agriculture. I saw a woman use an iPad in her kitchen to control her crops. So to me, this is very important. Our state has a statewide AI commission and we're setting up clear guardrails on ethics, safety, transparency across models, on training, creating a regulatory sandbox and also new legislation. We also have two land-grant institutions, the University of Delaware and Delaware State University, that are actually turning AI into real-world innovation and workforce pathways. And we have a lot of local companies like Qunity who are deploying new hardware to improve AI systems. And we also have to pair AI adoption with real safety guardrails and policies that ensure that we strengthen our workforce instead of sidelining it. And I think, you know, that's one of the, for me, there's the, I'm a pragmatic optimist, let's put it that way. So there are the good things, but I also understand that we don't want to leave people behind. Whether it's a podcast, whether it's magazines like The Atlantic, the AI and the future of work, it is truly everywhere. We are reshaping how we work. I want to really kind of tailor my questions to something kind of picking up on Senator Baldwin about about the workforce and how the workforce is included as these changes are happening. And I'll start with Ms. Ng. What does engagement with your workforce look like when it comes to designing and implementing AI-related changes? Could I start with you?

Absolutely. Thank you for the question. As I mentioned in the opening statement, what we have found at Siemens is that digital transformation truly does work best when the worker is at the center. And that is over a multitude of ways. The best way that I can provide a story, though, is actually a story from the Puget Sound Naval Shipyard. There is a superintendent of Backshop 31 at Puget Sound, and she truly demonstrates what effective partnership looks like when we're trying to solve a problem using a digital technology. And what I mean by that is this superintendent, she leads from a place of empathy. She knows the struggles and the pain points that her workforce is managing. So working with leaders like that.

But do you have like a, is there a specific and and for all of you, I'll submit questions for the record because I have a lot of questions and I don't have a lot of time. But I'll follow up with you on, like, is there a specific process that you use of a way to get that information and then turn it into your policies? I know that Mr. Muro, you said in your testimony, Brookings analysis suggests 30 percent of all workers could see at least 50 percent of their occupation tasks disrupted by generative AI in the coming years. So even with the updating of tasks, can someone share with me how you include workers in making sure that their jobs, that they reskill, that they're trained, that they have what they need to keep up? And I'll start with, maybe I'll start with you, Mr. Muro. And I have 41 seconds.

Yeah, no, it's first, that's those statistics are directional. Clearly, there's tremendous uncertainty around the, but we're talking about a pathway. We know that there'll be significant disruption. Disruption, though, is can be positive or negative. And we think significant aspects of AI's impact on work will be disruptive, but also beneficial.

Right. I mean, I understand the difference between a horse and a buggy and now we have cars. I get that part. I'm looking for and we'll follow up with each of you, what specific things are we doing to prepare the workforce and also include them in the help and decision-making of how to make sure that it's not so disruptive? I love the issue of coding. We were pushing people to go into coding and now the machines can code. And so as I'm thinking about the future and how I think Dr. Shelton said, the changing nature of the workforce, I would love to have conversation with each of you about what can we do as Congress to ensure that we're not leaving anybody behind, but at the same time, we're benefiting from the technology that's before us. And I am out of time. I yield back. Thank you, Mr. Chairman.

Thank you, Senator. Senator Blackburn.

Thank you, Mr. Chairman. And to each of you, thank you for being here. Establishing a framework for AI is something that's going to be very important for Congress to do. President Trump asked me to take the first stab at drafting the Trump America AI Act, and it's based off of his executive order. I was pleased that the executive order included protections for what I call the four Cs: children, creators, communities from job loss and high electric rates, and then censorship. We know that AI and the LLMs are biased against a lot of conservatives. And in Tennessee, I like to say we have a good, bad, and ugly relationship with AI. Our manufacturing, logistics, health care really like it a lot. A lot of our innovators that hold patents and trademarks and work with our auto industry. And then, of course, our entertainment industry with our musicians, our songwriters, our screenwriters, our scriptwriters are really quite concerned about what it's going to do in their ability to protect their name, image, likeness. So having the proper guardrails in place are important. And we know that every industrial sector has had those guardrails except the virtual space. And once we define what the rules of the road are, we free up innovators to take off and do great work because they know what the playing field is. So thereby, they can develop a strategy to win. So that is the purpose of our having the Trump America AI Act and putting these protections in place. And I want to, I think it's Mr. Giannikopoulos, am I saying that anywhere near right?

Very close. Giannikopoulos.

Giannikopoulos. I did get very close.

You did.

All right. As you know, Nashville area really is the health care informatics and health care interactive technology hub. And we're seeing so much innovation that is taking place there. And we're really when it comes to predictive diagnosis, disease analysis, remote surgeries, telehealth even, we see the benefit of these applications. The but comes into play when we talk about creating a digital health ecosystem and privacy concerns that that are there. So in your work, I want you to talk about that importance as an innovator and a patient. I'm going to give you a total of a minute. And then if you want to submit something longer form to me, I would welcome that.

Excellent. As a developer within health care governed by business associate agreements, we are, you know, clearly within the scope of HIPAA. HIPAA is an elastic rule. It started out as an insurance portability...

Needs to be modernized.

It does need to be modernized, but it started out as an insurance portability.

In order to include all of this. Yes, as I say, it came about, it covers the fax and the fax machine, the paper and the fax machine.

It covers the access to data and with electronics, we have the ability to audit it. That's really key with the transformation we've gone through. Nashville, as you've mentioned, has led the pack in that. And what I see out of the large institutions is they're approaching this as workforce transformation. It's not simply leveraging technology to do what you've always done. It's how can we leverage the technology to do something new with it. And as a patient, that's where the benefit sits for us. If we can receive better care pathways as a result of that.

Ms. Ng, want to come to you. In your work, let's talk about the difference between industrial applications and commercial applications. Because I think this gets lost many times as we talk about how consumers approach the utilization of a technology and their expectation of that. And as we've developed tech policy through the years, I've told people look at the computer and look at the backside and the front side. And there is truly a difference that is there. And with AI and the fact that AI coupled with the other computational sciences like quantum is going to yield faster, more accurate results. But talk about the difference in the industrial and the commercial.

Thank you, Senator. You touched on it perfectly, which is that in a manufacturing and industrial environment, oftentimes the work is safety-critical and operating in a high-tolerance environment, meaning that precision matters extremely importantly. So consumer AI is using, you know, all of the internet to analyze data and do various things. Industrial AI is using much more controlled data sets from machines on the floor, from manufacturing systems, and from digital engineering data sets. And it's using that controlled data to generate insights and make recommendations. Ultimately, the human is still the decision authority to confirm what to do next. Thank you.

And you know as we look at training our children and we need to have STEM labs in all of our schools, even though much coding will be done by AI from the consumer side, kids need to code just like they need to learn a language. They need to have that understanding. So we look forward to helping build this policy out as we move forward. Thank you. Thank you, Mr. Chairman.

Thank you, Senator. Senator Moreno.

Well, thank you. I didn't see who I didn't see that pattern coming. That's great. Thank you for for this hearing. It's obviously very, very important and very topical. I'll start with with you, Demetri, because I'm not gonna I'm not gonna take a swing at your last name. So I'll just call you Demetri. We're gonna make it informal part I will call you by your first name, so you can call me Bernie. In the 1920s, would it surprise you that there was people saying machines will devour man?

I believe they were called cars.

Exactly. And in the 1950s, a MIT professor did a study that said that basically we're stumbling into the automation era and all workers will be replaced. Does that sound familiar as well?

Absolutely.

And in 1980s, there's a book written the robot is after your job. Does that sound familiar as well?

The Excel spreadsheet was also going to replace accountants.

Thank you for the question, Senator. There's an incredible story about Albert Einstein riding on a train back in the day and the conductor's coming through stamping tickets and he sees Einstein scrambling, looking for his ticket. And he looks up and says, "Mr. Einstein, it's okay. I trust that you bought a ticket. I know who you are." Continues on his way, sees Einstein still scrambling on the floor. Goes back and says, "Mr. Einstein, truly, it's fine. I know who you are. I know that you bought a ticket." And Albert Einstein looks at him and says, "Thank you, sir. I also know who I am. What I don't know is where I'm going." And I think that that story really gets at the heart of of what you're mentioning, which is ultimately what we need to be doing now is partnering with industry to define what that compelling vision and that roadmap is. And that's what I do every single day on the ground at Siemens with my customers is partnering with them to define what are the actual business outcomes that you're trying to achieve and how can we fully channel these industrial AI solutions and applications towards achieving that outcome? So to answer your question, I truly believe that what's interesting and new about this technology is that we have the ability to explicitly help specific business outcomes. Thank you.

Yeah. And obviously productivity improvements is key. And I think the expression's been used, you're not going to lose your job to AI, but you may lose your job to somebody who uses AI. And Dr. Shelton, I'll kind of shift to you on this this part of the questioning. Part of what I see as a worry is that this is moving much faster than these other rollouts, right? So this is definitely at a speed which we're not used to. And what I worry about is are our institutions that train people, are they prepared for this speed? What are your thoughts on that?

It's a qualified yes, I think, to your question, although I would agree that the speed here is very different. I think something that everybody in this room has had to grapple with with AI is we did not perceive automation coming for our jobs or evolving the nature of work for us as largely knowledge workers. And I think, ironically, people more on the manual labor and the blue collar side of the universe has had, you know, several hundred years to get used to the idea of using tools to accomplish more than they could have accomplished on their own. It's why I used the farming example in my my opening statement. Those of us who deal primarily on the knowledge side of the universe have not had options to deploy autonomous agents until the last, let's call it six months, to go off and do portions of of the tasks that we're using them for, not to do the job per se, but to do a subcomponent of it. So I think all of us who are on the more knowledge end of the economy are going to have to evolve very rapidly to start understanding our job as managing fleets of autonomous workers who are working on our behalf as opposed to doing that work ourselves. And that is quite different. From a university standpoint, I think there's going to be a rapid pivot back to the liberal arts. This is something that my wife, who's an English professor, likes to remind me with some rapidity is she's known all along that it was all about talking and I have to concede that that's probably looking like it's going to be the case over the next 20 years.

Great. Thank you.

Thank you, Senator Moreno. Senator Hickenlooper, you're recognized.

Thank you, Mr. Chair. Thank you all for being here. Certainly a very timely hearing and I commend the the chair for that. Let me start just with talk a little bit about product testing. Mr. Giannikopoulos, I think that's close. Thank you for sharing how Rad AI is integrating the software to support quality to the delivery of quality healthcare to to patients. And as you know, software, hardware, rigorously tested before they are used with patients. There's endless testing and backing up. We're working on bipartisan legislation called the VET AI Act, which promotes evidence-based best practices to help companies have their AI tools independently evaluated by independent third parties, let's leave it at that. This helps increase transparency, promote accountability for responsible system design. I think to a large extent creates trust in in the customers. So Mr. Giannikopoulos, would you describe how what Rad AI is doing now to test, you know, to looking at what are its whether its products are sufficiently tested when before they go into action?

We are a company founded by radiologists for radiologists. Dr. Jeff Chang, the youngest radiologist at the time of his graduation, 17 years of age. Yeah, very very smart man. Has been central to the design ethos of our solutions. He was a practicing radiologist when he created the, you know, idea of Rad AI to begin with. We have continued to engage with the radiologists, validate the usage and understand exactly how it integrates into the workflow. The other key piece that really needs to be built with the adoption of AI in healthcare in particular is trust. And a way to gain trust is there's the technical natures, there's the transparency, the information about how you've built your models. But there's also leveraging relationships. For example, we have one with the RSNA Ventures group to integrate the century of knowledge that RSNA has available directly into the application at the point of care for the radiologist. So it's not AI assisting them with a diagnosis, it's AI plus trusted and validated information right at the point of care.

Huh. Cool. That's worthy of a longer discussion. Miss Ng, is that right? Somehow close. See, I'm not scared of these you know, difficult names, unusual names we'll call them. I have a difficult name, Hickenlooper. Thanks for your testimony and sharing how all the work that Siemens is doing around, you know, how AI is going to enter new sectors of the economy and especially the industrial economy. It's clear that we're in the middle of a of a revolution. I think of it as the great transition, right? And I think we're doing several things at once. We're going into AI, we're moving towards clean energy. This will look back 50 years or 100 years from now, they'll look back as the beginning of this great transition. I think it's going to in the terms of AI, it's going to define how AI supports this growth of small businesses and entrepreneurs, but also attracts students to STEM fields. And ultimately will transform our our workforce. Now, when two companies enter into a contract, it's essential that the service agreements are transparent, but also that they're enforceable for how an AI system can be used or not used or misused. So Miss Ng, how does Siemens ensure it's transparent with its customers about the design limitations of the AI products themselves? Do you understand the policy?

Yes. Thank you, Senator, for the question. So transparency is critical to trust, which we were just just speaking about. And there's several ways that we as Siemens ensure that we're providing full visibility into not only how industrial AI is being used in environments like a beer manufacturing customer that we have in Colorado, but throughout all of the sectors in the United States. And it really comes down to two things. One is allowing the industrial AI application to be able to share the source data. So pulling from which machines, which engineering datasets, and which manufacturing systems that it's producing from. The second is providing explanation or evaluation of why that recommendation is being made, which comes down to the ability that we were speaking about earlier of training folks to be able to work in systems oversight types of roles so that they can engage more effectively with that industrial AI capability. Thank you.

Great. I'm going to hold off. I have a couple other questions, but I'll submit them in writing. Appreciate what they they're slave drivers here in terms of keeping us on our time.

Only because more showed up. So thank you for the question. Senator Young, you're recognized.

Well, thank you to our witnesses for being here today. And Miss Ng, I I'd like to build maybe on the previous questioning and and your response. We need to unlock more industrial data, spatial data as I understand a lot of it, so that embodied AI or or AI used to control machines in the physical space can continue to move forward. China, a country we care about because we compete with them on a number of different levels, has it they lead the world in in terms of deploying industrial robots, collecting this sort of data. We're going to have to come up of course with a different set of rules and practices, ones consistent with with our values and and laws. But I think for starters, we need to distinguish between industrial AI and consumer AI. I have some legislation with several colleagues called the AI Public Awareness and Education Campaign Act. This seeks to provide transparency into AI, its promises, limitations, and and what consumers can expect from it. Too often we think about AI and our minds immediately, I think naturally, move towards generative AI rather than the AI we've been living with and using for the past several decades. You are correct in in your testimony that we what we in Congress do on consumer-facing AI shouldn't impede the innovation and deployment of technology in manufacturing, like with the work you're doing in the maritime space, for example. As you may know, I introduced some legislation called the SHIPS for America Act. This is legislation to reinvigorate American shipbuilding, an item that fortunately Republicans and Democrats alike have gotten behind. It's something that President Trump has prioritized for his administration. And I understand that Siemens recently created a new maritime business to address the opportunities the company perceived in the shipbuilding and ship repair industries. Can you walk me through, Miss Ng, the benefits these types of industrial AI capabilities can have in the effort for America to reclaim our shipbuilding dominance?

Absolutely. Thank you for the question. So what I would start with is by saying that if America truly does want to reclaim shipbuilding dominance globally, we have to outpace our competitors, not just in labor, but in building out these digital capabilities. And industrial AI is indisputably a force multiplier here. We think about tackling industrial AI in shipbuilding across three different areas of a ship's lifecycle. ...so starting in design, through production, and then into sustainment. We apply applications of industrial AI across each area of that life cycle, everything from doing faster, more precise design using AI-enabled simulation through predictive maintenance, making sure that the machines in the back shop are available and ready to roll for every single task that's needed. But if I were to summarize, the most important thing is that we're using industrial AI to help sequence work so that it's done more efficiently and more quickly. And what this does is it reduces rework, and it makes work better on the deck plate in the shipyards because people are able to execute first time, you know, the first time, and operate in a more productive environment. Thank you.

And as some of my constituents watch this hearing, they may think, wait a second, more productivity, that means higher wages too, right? So that's a byproduct oftentimes of these types of investments, whether it's yesterday's capital investments or today's AI investments. So I do want to underscore that. What is relatedly Siemens doing, Ms. Ng, to upskill the American workforce more broadly to ensure workers can succeed in these high-tech, high-demand, AI-powered roles?

Thank you for the question, Senator. We're doing a lot, but if I were to summarize, I would pick two different things. One is we are working with an ecosystem of partners to develop what we call micro-credentials for shipbuilding specifically, but more broadly for manufacturing. What this is doing is it's creating pragmatic, kind of ready-to-use credentials that might be a day, a week, a month long, and they prepare the workforce to go into modern shipbuilding and start operating on day one. The second piece is that I am incredibly proud that Siemens has committed to training 200,000 electricians and manufacturing experts by 2030. We're not doing it alone. We're working with public-private partners, universities, community colleges, and trade programs to be able to do that. And I'm extremely proud to share that today. Thank you.

Great. Thank you for your answers, Ms. Ng. Mr. Chairman.

Thank you, Senator Young. Senator Cantwell, you are recognized.

Thank you, Chairman, and I thank you and Ranking Member Baldwin for this important hearing and the witnesses for being here. We're here today to talk about some of the most innovative things that could help our economy going forward, but I want to mention we do have an NSF AI Education Act that Senator Moran and I have introduced, and it talks about some of the workforce issues you guys have been discussing, and a Small Business AI Training Act that also was Senator Moran, so that we're getting this across all aspects of our economy. And a Future of AI Act that my colleague Senator Young and Hickenlooper and Blackburn and I, this is about voluntary standards for infrastructure in innovation, mostly on safety and security. But the thing I wanted to focus on is the science side of the equation. I represent a big national lab, and but the laboratories across our country represent a unique opportunity to take science research that basically might take you years to do and now drive it down to months. And so one of the things I'm very worried about is that we cut 10,000 STEM PhDs from the federal agencies in 2025. So that's not a good idea. I represent also one of the most scientific regions of our country, and so we like scientists because they're in there creating the next generation of economic competitiveness and solutions. And certainly when you look at the massive amount of AI investment that China's making, you want to keep your scientific workforce because they're going to help you. But one of the things that I'm most interested in hearing from maybe you, Mr. Muro, or Dr. Shelton, is how transformative biology and chemistry lab work could be, compressing it into a few months. As I mentioned, the Pacific Northwest Lab is doing that already on deploying autonomous experimental platforms that you're basically, you know, creating everything from discovery in bioenergy to advanced materials and fusion, but you're getting it done in months instead of years. And so I would assume that people think that this kind of AI accelerated discovery is, you know, worth the commitment and worth getting this done and probably one of the most important things that we could be doing. Dr. Shelton or Mr. Muro, either one.

I'd just say one general thing here is that we can make alignments of our computing, our AI systems, our data, and our talent, but we have to have the ongoing basic scientific activity functioning at a high level as well because really all of that is essentially trading data for the next iteration of all of that innovation. So I think that's one of the things that a comprehensive attempt to leverage AI for national good would want to look at is making sure that you have all dimensions of the AI machinery working. And we do think that actual scientific activity and the talent it collects is absolutely central.

Dr. Shelton.

So logistics and warehouse work has already seen the problem that in that industry we call islands of automation. So you may have a conveyor belt and you may have a ground vehicle that's carrying something and you have to get a piece of material transferred from the autonomous ground vehicle over to the conveyor belt. Academic research has similar problems, although typically with higher-tech devices where you have existing automated processes, say a PCR machine that does DNA analysis, and some other piece of lab equipment that you have to connect together. And robotics and specifically general-purpose platforms are a way to address that. There is also, and this is not my field, although I do have friends who work in this, one of the most exciting things I think that AGI or narrow AGI specifically is able to offer up is vastly outsized performance within a particular problem area. So if you're familiar with the AlphaFold project out of DeepMind looking at protein folding, you can get AIs that are superhumanly good at a very narrow task, and that feeding into a lab structure that is highly automated allows you to go from the conceptual computation side of it down to the wet lab work in a very compressed process.

Okay, so somebody at home, how do they understand that? What you just said. I mean, I'm just trying to say, we spend a lot of money on our national labs. We've already decided they are critical to our competitiveness as a nation. And we're also proud of our universities, but when I look at UW versus PNNL, we're talking about a huge size difference in the amount of research that's done. So now if you're saying you're going to apply AI to that and basically translate that science into faster application, as I'm saying, not years but months, then I think this is a huge initiative that we should be undertaking is to take all of that research. You just gave one example. But you're basically taking one of our big research arms, and you're basically saying, let's make sure we apply AI to it, because it really is one of our most competitive R&D efforts, right?

One of the things, and we've seen this in robotics research itself, is you have a human who comes up with an idea and wants to see that translated into something practical. So for your question of how would I explain this to someone who's not sitting here in the perspective of robotics land, you have an idea, you have to get that translated into some sort of physical reality, and then you have to test that physical reality. I think where AI allows us to inject the best short-term safety-controlled piece of this is to work with the human researcher to get the math part of the process turned into something physical that can then be tested, whether it's in the medical space or elsewhere in industry, and allow us to actually reduce something to practice as rapidly as possible.

Yeah, my just last point, I know I'm over my time, is that we're spending like about $10 billion on lab efforts that are all about all these issues. So I'm just saying one area to double your investment is to basically say, you know, we're spending $10 billion here, so we've already decided we think this is really, really important. We're saying apply AI to that effort to make it more efficient because we've already decided that's where we're spending our money. And you just gave two really good examples of what that acceleration could deliver. So thank you, Mr. Chairman.

Thank you, Senator. Senator Rosen, you are recognized.

Well, thank you, Chair Budd, Ranking Member Baldwin. It's really an important hearing, and I want to thank all the witnesses for being here today. And so I'm going to start with you, Ms. Ng. I'm going to talk a little bit about AI standards and trust. Earlier this year, I led a congressional delegation to CES in Las Vegas with a few senators, I've been leading one almost every year, senators from this committee. We were able to stop by the Siemens booth, hear how Siemens is innovating on AI, other new tools, and I'm excited you're here to testify today. In your testimony, you outline how using AI in advanced manufacturing has a potential for enormous benefits. However, the financial risk of an inaccurate outcome from an unreliable AI tool, well, is still very high. Therefore, your customers have to have a clear incentive to ensure AI tools that they integrate with their products are trustworthy and reliable. And so what standards of trust and reliability are your customers demanding? Are there best practices or standards that NIST Center for AI Standards and Innovation should consider from the industry?

Absolutely, Senator, and I'm so glad that you got to visit CES. It's always a wonderful event. To answer your question, the most important thing in defining trust is having really strong underlying data that informs the industrial AI application. So what that means is being able to tap into machine-generated operational data on a shop floor, manufacturing systems data, and digital engineering data sets that all come together to be able to support analysis and recommendations and generate insights. So what's interesting is that that's a huge piece of my business today at Siemens is supporting our customers in delivering what we call the sort of digital authoritative backbone for engineering. That serves as the foundation of which all operational capabilities are built on top. Thank you.

Thank you. And you know, I really do believe, and I'm sure you do too, without clear federal standards and guardrails, AI tools that are sold to consumers, small businesses, large businesses, but small businesses in particular, they have less if any leverage. They may not be safe and reliable. They could have potential to cause significant harm, so we do have to be sure that we're paying attention. And so I want to talk about small business and AI adoption because many companies have prioritized AI adoption, trying to find where this tool can improve efficiency, add value to their business, right? How do you grow? However, we need to ensure that the potential benefits as we're seeing from AI do not continue to disproportionately benefit only the largest corporations that have the capital and the influence really to adopt safe AI, those large language models and all the other things that go along with it. So I'm going to stay with you, Ms. Ng. You have a range of customer sizes, large legacy brands like PepsiCo to very small businesses, startups. What's the biggest challenge you would say that your smaller customers face adopting AI? And what can Congress do as we think about them also on the Small Business and Entrepreneurship Committee? So I want to think of the large businesses and the small. What can Congress do to ensure that the AI adoption gap, it just doesn't widen, leaving our smaller innovators potentially behind?

...incredibly important question. We think about adoption for small and mid-sized businesses in three ways. It's awareness, ability, and willingness. So awareness is being able to communicate and understand the art of the possible, of truly what are the business outcomes that industrial AI can unlock for that business. Ability is, yes, to your point, sometimes capital, but it's also the aligned incentives with both leadership and, as Ranking Member Baldwin mentioned, the ability to also have the factory floor workers involved in the process. The third piece is willingness. And this is my biggest passion in life is organizational change management. So making sure that there's an intentional rollout plan to all segments of the workforce to ensure that that adoption happens.

Well, you set me up for my only have a few seconds for my last question because your passion is going to lead me to Mr. Muro to ask this question, AI literacy, right? Because your enthusiastic about getting it out to everyone. But can we talk about how AI literacy closes that AI adoption gap that we want to have happen? So thank you for setting me up for that question.

I would just say that this is where regional ecosystems can be extremely helpful. Regional learning ecosystems, regional technology ecosystems, and as part of that, work to make sure that adoption does include these features. More and more regions are beginning to find their own level on these technology solutions. And I think the federal government has historically had a role in supporting regional economic development. I think we can be more pointed about that and maybe making this a central area because the talent piece and the talent piece are intimately connected to the kind of broad adoption that we're talking about technologically.

Well, thank you. Thank you again for all of your work for being here. Great hearing. Thank you, Mr. Chair.

Thank you, Senator Rosen. Ranking Member Baldwin, you're recognized for additional question.

Thank you. Senator Rosen has queued me up for my next question and my last question as we do a quick second round before closing. Mr. Muro, I appreciate your highlighting worker security in your testimony. In Wisconsin, we are very proud of being the first in the country to pass a law relating to apprenticeships and registered apprenticeships. We did that in 1911. The U.S. didn't follow suit until 1937. But with the first apprenticeship law in the country, we continue to break enrollment records in part because our apprenticeships focus on in-demand fields. And I'd like it if you could talk a little bit more about the opportunities to integrate artificial intelligence into registered apprenticeships and how these programs can help with retraining workers in an AI world.

I mean, first, I think our view of apprenticeships is highly shaped by its industrial past. And that's an incredibly important theme and a place that it can be utilized and merge with AI. AI can also add to the learning and training dimensions of apprenticeship through tutorials, AI online supports, and all of those. But I think that we should think of apprenticeship as especially a way to get hands-on work experience. And we're going to need that more and more because it's going to become clearer that strictly higher education degrees are not going to be maybe more vulnerable to change than very specific subject matter hands-on experience with people learning a technology. So I think that this is an extremely important dimension that you've put your finger on here because in some ways apprenticeship points exactly at a gap that I think we're going to face in especially launching careers and setting up pathways for development.

Thank you.

Thank you, Ranking Member Baldwin. Mr. Giannikopoulos, there's, since you're in radiology world, there was a famous prediction made by the father of AI, I'm sure you're familiar with it, I think it's Geoffrey Hinton. He said that with the rise of AI, there would be no more need for radiologists. But someone as smart as he was was completely wrong. And your company, your work just proves that wrong every day. How could someone as smart on AI as he was originally get that sort of prediction wrong? And how could we use that as sort of wisdom for the future as we think about the application of AI in personally and commercially?

As we look at all of this, all of this transformation, it's not unique to radiology. That was a field that was early identified as potential for automation and replacement. That didn't work out. It's part of the way the AI is trained. It's trained on this medical information that's generated by a radiologist through an interpretation process. That judgment cannot be replaced. There are going to be edge cases. There are going to be zebras. Again, my personal diagnostic journey, I do not fit any algorithmic assessment that would normally get to that diagnosis, which is why we need the human directly involved. Medicine is both a science and an art. Being able to put that together and synthesize it. Now, what AI offers as an opportunity is the ability for the radiologist, the clinicians to synthesize more, understand more, access it without having to dig through records and all these complicated systems, but instead see it presented to them in a really easy way so that they can make that determination. And if you look at rural healthcare in particular, that's an area where we need to augment those institutions. In North Carolina, ARA Health Specialists in Asheville, where they're originally based out of, they cover most of Western North Carolina right now and serve as the safety net to make sure that patients aren't having to be flown over mountains, literally, to get to other healthcare systems. They do that by early adoption of technology. I met them 20 years ago on the documentation side. And in my last company, they were the first, some of the first in the state to adopt image analysis for improvement of understanding. Now they're using new documentation tools like ours to be able to augment that and integrate in the Radiology Society of North America evidence directly into that to really speed it up. So I can't comment on how Jeff got that quite so wrong, but I can share he did get it wrong. And today they also said self-driving cars would take over by this time too. Radiologists are still driving to work in their cars.

We're still waiting on that one. I just want to thank each of you. I thank those that came and asked great questions. I thank the Ranking Member and again, each of your companies and allowing time for you all to be here today. Senators will have until the close of business on March 10 to submit questions for the record. The witnesses will have until the close of business on March 24 to respond to those questions. This concludes today's hearing. The committee stands adjourned. Thank you.