Congressional Testimony
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Colorado School of Mines VP Copan Testifies Before Senate Commerce, Science & Transportation Subcommittee
WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Science, Manufacturing and Competitiveness released the following testimony by Walter G. Copan, vice president emeritus for research and technology transfer at the Colorado School of Mines, from a July 21, 2026, hearing entitled "Measuring What Matters: Science, Standards, and Strategic Competition":
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Chairman Budd, Ranking Member Baldwin, members of the Committee and distinguished participants. It's a privilege to testify on the state of U.S. science, technology, engineering, and manufacturing for our ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Science, Manufacturing and Competitiveness released the following testimony by Walter G. Copan, vice president emeritus for research and technology transfer at the Colorado School of Mines, from a July 21, 2026, hearing entitled "Measuring What Matters: Science, Standards, and Strategic Competition": * * * Chairman Budd, Ranking Member Baldwin, members of the Committee and distinguished participants. It's a privilege to testify on the state of U.S. science, technology, engineering, and manufacturing for ourNation's global economic competitiveness and national security, particularly in relation to China's ascendance.
The state of the great power competition between the U.S. and China is a focus of the work with the Center for Strategic and International Studies (CSIS), where I serve as Senior Advisor for the Renewing American Innovation project. It has been my honor over the past 5 years to lead research and technology transfer at Colorado School of Mines, a top tier U.S. research university rated in the top 3 engineering programs in America. I previously served our Nation as Director of the National Institute of Standards and Technology, for which this Committee has oversight, and prior, with two of the U.S. Department of Energy national labs. My leadership experience spans public and private sectors--as executive, entrepreneur and investor.
Two decades ago, the United States was the undisputed global leader in science and technology, and in driving innovation for economic value.
America's research universities and institutions attracted and welcomed the brightest and best talent from around the world to contribute to our science and engineering enterprise, with many ultimately to become citizens and participants in our National prosperity. Today, however, by many internationally recognized measures, America's global leadership position in the majority of critical technology fields has been overtaken by China.1
By 2024, China surpassed the U.S. in total R&D investment, and, rather than just being considered a perpetrator of IP theft, China has become a globally recognized force through its own strengths and modern built infrastructure for science, engineering and innovation.2
China is currently seen as leading in 57 of 64 technology categories essential to the global economy, while the U.S. still holds the clear lead in the remaining seven.3,4
China now leads in numbers of patents5 and highly cited top tier research publications in respected journals,6 having also strengthened their IP system7 while further seeking to dominate global technology standards.8 At the same time, the U.S. has allowed the strength of our intellectual property protections to decline9,10 contributing to a net devaluation of U.S. intellectual properties in global markets.11 China now dramatically outpaces the U.S and other nations in international priority patent families granted in critical technologies including biotechnology, quantum information science and engineering, robotics, semiconductors and artificial intelligence. China's national focus on standards leadership and IP has further resulted in a substantially increased rate of accepted contributions to international standards by Chinese entities over the past 10 years.12
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1 The State of U.S. Science and Engineering, 2026: https://ncses.nsf.gov/pubs/nsbsep20261
2 The Power of Innovation: The Strategic Value of China's High-Tech Drive: https://www.csis.org/analysis/powerinnovation-strategic-value-chinas-high-tech-drive
3 ASPI's Two-Decade Critical Technology Tracker, https://www.aspi.org.au/report/aspis-two-decade-criticaltechnology-tracker
4 State of the Science Address: 2026, https://www.nationalacademies.org/events/113research productivity in critical technology areas a3 https://www.nationalacademies.org/news/2024/06/in-state-of-the-science-addressnas-president-urges-improvements-to-k-12-science-education-in-order-to-strengthen-the-u-s-stem-workforce
5 https://itif.org/publications/2023/01/23/wake-up-america-china-is-overtaking-the-united-states-in-innovationcapacity/
6 https://www.nationalacademies.org/news/2024/06/in-state-of-the-science-address-nas-president-urgesimprovements-to-k-12-science-education-in-order-to-strengthen-the-u-s-stem-workforce
7 Translating IP Into Revenue: China's Changing Place in the Global IP Landscape: https://www.csis.org/blogs/trustee-china-hand/translating-ip-revenue-chinas-changing-place-global-ip-landscape
8 What Washington Gets Wrong About China and Technical Standards: https://carnegieendowment.org/research/2023/02/what-washington-gets-wrong-about-china-and-technicalstandards?lang=en
9 Intellectual Property Rights in the U.S.-China Innovation Competition: https://www.csis.org/analysis/intellectualproperty-rights-us-china-innovation-competition
10 Losing the Lead: Why the United States Must Reassert Itself as a Global Champion for Robust IP Rights https://itif.org/publications/2023/06/12/losing-the-lead-why-united-states-must-reassert-itself-as-globalchampion-for-robust-ip-rights/
11 Intellectual Property Litigation: U.S. Trends in Global Perspective: https://www.cornerstone.com/wpcontent/uploads/2026/06/IP-Litigation-US-Trends-in-Global-Perspective-June-2026.pdf
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Measuring the context of strategic advantage in the technology domains that matter to the future of the economy and national security, however, also requires taking an ecosystem view that considers the fundamentals and strategic enablers of competitiveness.13 The CSIS Economic Security and Technology Tech Edge methodology14 takes into account these ecosystem factors across four distinct technology types based on breadth of application and production complexity. These include:
1. Stack technologies (e.g., biotech, AI and advanced chips, requiring deep capital markets, collaborative research networks, and platform orchestration)
2. Precision technologies (e.g., jet engines and lithography, which demand trusted partnerships and gold-standard certification regimes)
3. Production technologies, (e.g., high-end machine tools, robotics and industrial process automation, which need long-term capital and ongoing workforce training)
4. Base technologies (e.g., rare earth elements, batteries, steel and aluminum alloys, which require coordinated supply chains and processing infrastructure)
This analysis indicates U.S. leadership advantage in Stack and Precision technologies, whereas China is increasingly competitive in Production technologies and dominant in Base technologies. In the face of the competition with China, achieving the technological dexterity to build ecosystem strengths across multiple technology types is a strategic imperative for the Nation.
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12 China's High-Tech Drive in 10 Charts: https://www.csis.org/analysis/chinas-high-tech-drive-10-charts
13 Tech Edge, A Living Playbook for America's Technology Long Game - https://www.csis.org/analysis/tech-edgeliving-playbook-americas-technology-long-game 14 https://csis-website-prod.s3.amazonaws.com/s3fs-public/202601/260120_EST_Tech_Edge_0.pdf?VersionId=MJKGLWqbgviv53jbBuWHg32poCRpHJAL
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We are now at an inflection point where China's ascendance toward innovation peer status presents America with both risk and opportunity.15 The U.S. must take a new strategic approach to the future of research and S&E advancement that coordinates across government agencies. We must leverage collaboration and investments with the private sector,16 bolster education at all levels, and effectively engage our national R&D, intellectual property, standards and innovation enterprise.17 We have the chance to drive great synergies in our federal R&D investment through a national S&T and innovation strategy that takes the long view.
At the end of World War II, Vannevar Bush and his seminal report "Science: The Endless Frontier" ushered in a new era of research and innovation for the U.S. America's sustained investment in public-sector R&D has proven to be an essential contributor to our global competitiveness,18 though its strategic value and high rates of investment return19 are not always fully appreciated.20 It is well documented that substantial industrial productivity gains21 and the majority of our tech startups now originate from public R&D funding.22,23
Commencing in 2025, the U.S. R&D enterprise has experienced seismic shifts which have disrupted the pace and continuity of research programs, negatively affecting research productivity, graduate education, international partnerships, innovation outcomes, and more. Current indicators are clear: the pace of progress in priority technologies for the nation, including quantum, biotech and emerging energy has decelerated, affected by grant cancellations, funding slowdowns, staff losses, programmatic discontinuity and uncertainties. America's research and engineering workforce is experiencing its largest loss of talent in history, and other nations are taking advantage.24
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15 Will America Squander Its New Sputnik Moment? https://www.csis.org/analysis/will-america-squander-its-newsputnik-moment
16 The State of U.S. Science and Engineering, 2026: https://ncses.nsf.gov/pubs/nsbsep20261
17 U.S. Research and Innovation Performance, Elsevier, 2026: https://www.elsevier.com/promotions/us-researchand-innovation-performance
18 Competing in the Next Economy. Innovating in the Age of Disruption and Discontinuity. A Call to Action: https://compete.org/wp-content/uploads/coc-disruption_discontinuity-call-to-action-final_12.13.24.pdf
19 Federal R&D Funding Is Even More Valuable Than Washington Thinks: https://www.aei.org/economics/federalrd-funding-is-even-more-valuable-than-washington-thinks/
20 Fieldhouse, A.J., & Mertens, K. (2023). The Returns to Government R&D: Evidence from U.S. Appropriations Shocks. Working paper. https://andrewjfieldhouse.com/wpcontent/uploads/2023/12/The_Return_to_Government_R_D_manuscript.pdf
21 NBER "Estimating the Economic and Budgetary Effects of Research Investments" https://www.nber.org/papers/w33402)
22 Dyever, A. (2024) Public R&D Spillovers and Productivity Growth: https://www.ecb.europa.eu/press/conferences/ecbforum/shared/pdf/2024/EFCB_2024_Dyevre_paper.en.pdf
23 Fleming, L., Greene, H., Li, G., Marx, M., & Yao, D. A. (2019). Government-funded research increasingly fuels innovation. Science, 364 (6446), 1139-1141. https://doi.org/10.1126/science.aaw2373
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The OMB's proposed rule to revise the Guidance for Federal Financial Assistance (Docket OMB2026-0034) has resulted in significant feedback on the future fundamentals of how federally funded R&D will be conducted.25 Clearly, the U.S. R&D enterprise must move more quickly, align with national priorities, and be good stewards of tax dollars. However, the guidance as drafted would most certainly have the opposite effects, with a cascade of unintended consequences. The nation must learn from its unforced errors, and to avoid delays and disruptions to U.S. research that are particularly costly in view of the accelerating pace of the global innovation race.
To translate invention to impact, preserving the integrity of the Bayh-Dole Act for U.S. innovation is essential.26,27 We must now implement a muchneeded modernization of the Stevenson-Wydler Act to deliver increased innovation outcomes from federal research, and NIST Green Paper on "Unleashing American Innovation" and the "Return on Investment" Legislative Proposal delivered to Congress in 2020 provide a starting point to achieve these goals.28 Enabling more flexible and efficient collaborations between universities, government, industry and science philanthropy are key to America's success in S&T across the nation's innovation ecosystems.29
The United States is at a crucial juncture for the future of American innovation leadership globally. Our economic security and national security are closely intertwined with the reliability and the protections afforded by our R&D, IP, standards and innovation ecosystem. The foundational strength of U.S. IP rights, as established by the framers of the Constitution, must be enhanced with the enforceability of the rights of inventors in the U.S. and abroad, together with the rule of law and global respect for private contracts. These are particularly important for standard essential patents (SEPs), where the U.S. still leads the world as a net exporter of innovation. Reliable IP rights licensed to development and manufacturing partners enable trusted global supply chains and value creation for consumers and shareholders alike. The mobile telecommunications sector contributes an estimated total economic value of more than $4.8 trillion to the global economy.30 Technology innovators, including holders of SEPs, gain returns on their investments in R&D and standards engagement through licenses and royalty payments, in addition to product and services sales. Global intellectual property commerce, driven by SEP licensing, was $1.1 Trillion in 2023, with a net positive balance of trade in intellectual assets to the U.S. of over $130 Billion.31
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24 America's Scientific Brain Drain Is No Longer Hypothetical: https://www.realclearscience.com/articles/2026/07/14/americas_scientific_brain_drain_is_no_longer_hypothetic al_1194182.html
25 https://www.science.org/content/article/u-s-researchers-outraged-proposed-changes-federal-grants
26 Copan, W.G., America's Goose that Lays the Golden Eggs: https://rollcall.com/2021/04/22/americas-goose-that-lays-the-golden-eggs/
27 Unleashing American Innovation, NIST Green Paper SP1234, https://www.nist.gov/unleashing-americaninnovation/green-paper
28 ROI Initiative Status Update: Legislative Package Sent to Congress: https://www.nist.gov/newsevents/news/2020/12/roi-initiative-status-update-legislative-package-sent-congress
29 McNutt, M. (2024) Keeping America "Science Strong," https://pmc.ncbi.nlm.nih.gov/articles/PMC11459151/
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However, there is widespread unlicensed technology use in China of innovations and IP assets owned by U.S. entities. Further, China's government has weaponized its legal system with a wide range of patent and competition law tools, price controls on international entities, forced technology transfers, and by providing selective legal protections and incentives seeking to advantage Chinese companies.32
There is a concerning movement away from market-based negotiations and valuation toward government-controlled price regulation for standardized technologies. The EU has proposed a massive new regulatory regime33 toward government control of SEP prices. The Chinese Communist Party has issued guidelines that would also apply its antitrust laws to SEP licenses to benefit Chinese entities.34
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30 https://s3.amazonaws.com/media.hudson.org/The+Western+Innovators+of+the+Mobile+Revolution_+The+Data +on+Global+Royalty+Flows+to+U.S.+and+Europe+and+Why+It+Matters+-+Jan+2024.pdf
31 Global Innovation Index: https://www.wipo.int/en/web/global-innovation-index/w/blogs/2025/internationaltrade
32 DOI: https://doi.org/10.15779/Z38XP6V46N
33 https://single-market-economy.ec.europa.eu/publications/com2023232-proposal-regulation-standard-essentialpatents_en
34 https://www.allenovery.com/en-gb/global/news-and-insights/publications/china-draft-sep-antitrust-guidelinereleased-by-samr-for-public-comment
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Supported by Chinese government incentives, legal actions and long-term strategy, China's technology companies are gaining ground.35 In the development of 5G wireless broadband standards in the 3GPP (Third Generation Partnership Project) multi-stakeholder consortium, four Chinese companies were in the top 10 list of providing approved contributions to the standard: China Academy of Telecommunications Technology (CATT), China Mobile, ZTE, and substantially led by Huawei;36 two U.S.-headquartered companies were on this list:
Qualcomm and Intel. In 2019, the U.S. placed restrictions on having engagements with those Chinese organizations on the Commerce "entity list." This became a self-inflicted wound for the open technology standards process, damaging the positions of American companies and global partners.37 We must learn from this experience, among many other lessons, about the strategic importance of maintaining open and fair international engagement for standards, metrology and trade.38
Engagement in global collaborations for critical emerging technologies is an essential part of standards development and testing for the U.S. to ensure that common principles are established, and ultimately to support rather than impede the pace of technology innovation. International partnership networks enable the U.S. to establish an effective perspective and the balance between voluntary consensus standards and regulations for innovative new technologies, including artificial intelligence, emerging biotechnologies and quantum systems.
It is essential for the U.S. to provide leadership globally, taking deliberate steps to expand standards engagement and enforce the rights of American innovators here and abroad. We must systemically encourage and incentivize engagement in standards development - for companies large and small, for academia and for government entities. Developing standards literacy as part of U.S. higher education in STEM as well as in economics, business and legal curricula must also be a key priority. As we look to rebuild America's advanced manufacturing base and a trained domestic workforce, as well as trusted, resilient supply chains, the U.S must build upon the strengths of our free market economy, and a reliable intellectual property and innovation system that once again must lead the world.
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35 https://www.telegraph.co.uk/business/2024/04/30/huawei-profits-surge-steal-market-share-apple-china/
36 China's High-Tech Drive in 10 Charts: https://www.csis.org/analysis/chinas-high-tech-drive-10-charts
37 U.S. drafts rule to allow Huawei and U.S. firms to work together on 5G standards: https://www.reuters.com/article/technology/exclusive-us-drafts-rule-to-allow-huawei-and-us-firms-to-worktogether-on-5-idUSKBN22K214/
38 United States Standards Strategy (USSS) 2025: https://www.nist.gov/standardsgov/united-states-standardsstrategy-released
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The future U.S. workforce must have the skills necessary for the STEMrelated careers that drive the innovation economy. A prepared workforce is essential for rebuilding and reshoring our advanced manufacturing base for all industries in the economy of the future. The Manufacturing USA Institutes have essential roles to play for the future of America's manufacturing technology base and workforce development. A recent report by the U.S. National Academies provides a wealth of analysis and powerful insights for the future of this manufacturing innovation and support infrastructure to help counter the decline in American manufacturing productivity growth over the past two decades.39
America's small- and medium-sized manufacturers are essential parts of the nation's supply chains and innovation system. Rechartering and fully supporting the Manufacturing Extension Partnership (MEP) in an expanded mission for the U.S. to drive manufacturing innovation, entrepreneurship, shared services and advanced manufacturing technology efficiencies must be considered well by this Committee and stakeholders.
We have much work to do. America's K-12 educational outcomes40 have fallen behind other nations. Preparing future leaders with modernized higher education pathways and a pragmatic base of experience with flexibility for a range of interdisciplinary career journeys will be essential.41 Attracting and retaining talented people from other nations also remains vital to our innovation economy.42 Further, international scientific collaborations - with like-minded nations who are technology leaders as well as with developing countries in the Global South - enable the acceleration of discoveries, building diplomacy, talent access and supply chain resilience that also supports America's position abroad.43
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39 A Vision for the Manufacturing USA Program in 2030 and 2035: https://www.nationalacademies.org/projects/DEPS-NMMB-24-01/publication/29295
40 U.S. Department of Education Issues Statement on the Nation's Report Card: https://www.ed.gov/about/news/press-release/us-department-of-education-issues-statement-nations-reportcard
41 Leadership development in U.S. Higher education: Strategies for lifelong learning and upskilling: https://www.econstor.eu/bitstream/10419/327649/1/S2444569X2500099X.pdf
42 Foreign-born Share of the U.S. STEM Workforce, https://www.csis.org/analysis/innovation-lightbulb-foreignborn-share-us-stem-workforce
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Over the past two decades, the bureaucracy and administrative burdens associated with American publicly funded R&D have also skyrocketed.44 This bureaucracy has increased the costs and reduced the productivity of U.S. research. We must continually increase efficiencies in S&T across technology domains, drive speed-to-scale for innovation, build interagency coordination, and strengthen intellectual property, technology standards leadership and our international partnerships. The National Science and Technology Council must be fully revitalized for truly effective interagency coordination toward maximizing efficiencies and the value created from public R&E investment. We must further modernize legislation and policies to remove barriers and incentivize innovation for America to continue to lead the world. The U.S. must defend its innovators at home and abroad against mercantile and malign threats.
Thanks to this Committee for your important work toward securing the science, technology and innovation leadership for U.S. economic and national security. I look forward to answering questions you may have.
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43 Preserving America's Place in Global Science, T. Smith (2024): https://nautil.us/preserving-americas-place-inglobal-science-1031512/
44 Changes in Federal Research Requirements Since 1991: https://www.cogr.edu/changes-federal-researchrequirements-1991
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Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/cfab7159-df7d-220e-271d-93a7ca071912/WG_Copan_Testimony_Senate_Commerce_SMC_07172026_3a886492-0625-4081-9e00-3184a5f9d082.pdf
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Chairman Budd, Ranking Member Baldwin, members of the Committee and distinguished participants. It's a privilege to testify on the state of U.S. science, technology, engineering, and manufacturing for our ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Science, Manufacturing and Competitiveness released the following testimony by Walter G. Copan, vice president emeritus for research and technology transfer at the Colorado School of Mines, from a July 21, 2026, hearing entitled "Measuring What Matters: Science, Standards, and Strategic Competition": * * * Chairman Budd, Ranking Member Baldwin, members of the Committee and distinguished participants. It's a privilege to testify on the state of U.S. science, technology, engineering, and manufacturing for ourNation's global economic competitiveness and national security, particularly in relation to China's ascendance.
The state of the great power competition between the U.S. and China is a focus of the work with the Center for Strategic and International Studies (CSIS), where I serve as Senior Advisor for the Renewing American Innovation project. It has been my honor over the past 5 years to lead research and technology transfer at Colorado School of Mines, a top tier U.S. research university rated in the top 3 engineering programs in America. I previously served our Nation as Director of the National Institute of Standards and Technology, for which this Committee has oversight, and prior, with two of the U.S. Department of Energy national labs. My leadership experience spans public and private sectors--as executive, entrepreneur and investor.
Two decades ago, the United States was the undisputed global leader in science and technology, and in driving innovation for economic value.
America's research universities and institutions attracted and welcomed the brightest and best talent from around the world to contribute to our science and engineering enterprise, with many ultimately to become citizens and participants in our National prosperity. Today, however, by many internationally recognized measures, America's global leadership position in the majority of critical technology fields has been overtaken by China.1
By 2024, China surpassed the U.S. in total R&D investment, and, rather than just being considered a perpetrator of IP theft, China has become a globally recognized force through its own strengths and modern built infrastructure for science, engineering and innovation.2
China is currently seen as leading in 57 of 64 technology categories essential to the global economy, while the U.S. still holds the clear lead in the remaining seven.3,4
China now leads in numbers of patents5 and highly cited top tier research publications in respected journals,6 having also strengthened their IP system7 while further seeking to dominate global technology standards.8 At the same time, the U.S. has allowed the strength of our intellectual property protections to decline9,10 contributing to a net devaluation of U.S. intellectual properties in global markets.11 China now dramatically outpaces the U.S and other nations in international priority patent families granted in critical technologies including biotechnology, quantum information science and engineering, robotics, semiconductors and artificial intelligence. China's national focus on standards leadership and IP has further resulted in a substantially increased rate of accepted contributions to international standards by Chinese entities over the past 10 years.12
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1 The State of U.S. Science and Engineering, 2026: https://ncses.nsf.gov/pubs/nsbsep20261
2 The Power of Innovation: The Strategic Value of China's High-Tech Drive: https://www.csis.org/analysis/powerinnovation-strategic-value-chinas-high-tech-drive
3 ASPI's Two-Decade Critical Technology Tracker, https://www.aspi.org.au/report/aspis-two-decade-criticaltechnology-tracker
4 State of the Science Address: 2026, https://www.nationalacademies.org/events/113research productivity in critical technology areas a3 https://www.nationalacademies.org/news/2024/06/in-state-of-the-science-addressnas-president-urges-improvements-to-k-12-science-education-in-order-to-strengthen-the-u-s-stem-workforce
5 https://itif.org/publications/2023/01/23/wake-up-america-china-is-overtaking-the-united-states-in-innovationcapacity/
6 https://www.nationalacademies.org/news/2024/06/in-state-of-the-science-address-nas-president-urgesimprovements-to-k-12-science-education-in-order-to-strengthen-the-u-s-stem-workforce
7 Translating IP Into Revenue: China's Changing Place in the Global IP Landscape: https://www.csis.org/blogs/trustee-china-hand/translating-ip-revenue-chinas-changing-place-global-ip-landscape
8 What Washington Gets Wrong About China and Technical Standards: https://carnegieendowment.org/research/2023/02/what-washington-gets-wrong-about-china-and-technicalstandards?lang=en
9 Intellectual Property Rights in the U.S.-China Innovation Competition: https://www.csis.org/analysis/intellectualproperty-rights-us-china-innovation-competition
10 Losing the Lead: Why the United States Must Reassert Itself as a Global Champion for Robust IP Rights https://itif.org/publications/2023/06/12/losing-the-lead-why-united-states-must-reassert-itself-as-globalchampion-for-robust-ip-rights/
11 Intellectual Property Litigation: U.S. Trends in Global Perspective: https://www.cornerstone.com/wpcontent/uploads/2026/06/IP-Litigation-US-Trends-in-Global-Perspective-June-2026.pdf
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Measuring the context of strategic advantage in the technology domains that matter to the future of the economy and national security, however, also requires taking an ecosystem view that considers the fundamentals and strategic enablers of competitiveness.13 The CSIS Economic Security and Technology Tech Edge methodology14 takes into account these ecosystem factors across four distinct technology types based on breadth of application and production complexity. These include:
1. Stack technologies (e.g., biotech, AI and advanced chips, requiring deep capital markets, collaborative research networks, and platform orchestration)
2. Precision technologies (e.g., jet engines and lithography, which demand trusted partnerships and gold-standard certification regimes)
3. Production technologies, (e.g., high-end machine tools, robotics and industrial process automation, which need long-term capital and ongoing workforce training)
4. Base technologies (e.g., rare earth elements, batteries, steel and aluminum alloys, which require coordinated supply chains and processing infrastructure)
This analysis indicates U.S. leadership advantage in Stack and Precision technologies, whereas China is increasingly competitive in Production technologies and dominant in Base technologies. In the face of the competition with China, achieving the technological dexterity to build ecosystem strengths across multiple technology types is a strategic imperative for the Nation.
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12 China's High-Tech Drive in 10 Charts: https://www.csis.org/analysis/chinas-high-tech-drive-10-charts
13 Tech Edge, A Living Playbook for America's Technology Long Game - https://www.csis.org/analysis/tech-edgeliving-playbook-americas-technology-long-game 14 https://csis-website-prod.s3.amazonaws.com/s3fs-public/202601/260120_EST_Tech_Edge_0.pdf?VersionId=MJKGLWqbgviv53jbBuWHg32poCRpHJAL
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We are now at an inflection point where China's ascendance toward innovation peer status presents America with both risk and opportunity.15 The U.S. must take a new strategic approach to the future of research and S&E advancement that coordinates across government agencies. We must leverage collaboration and investments with the private sector,16 bolster education at all levels, and effectively engage our national R&D, intellectual property, standards and innovation enterprise.17 We have the chance to drive great synergies in our federal R&D investment through a national S&T and innovation strategy that takes the long view.
At the end of World War II, Vannevar Bush and his seminal report "Science: The Endless Frontier" ushered in a new era of research and innovation for the U.S. America's sustained investment in public-sector R&D has proven to be an essential contributor to our global competitiveness,18 though its strategic value and high rates of investment return19 are not always fully appreciated.20 It is well documented that substantial industrial productivity gains21 and the majority of our tech startups now originate from public R&D funding.22,23
Commencing in 2025, the U.S. R&D enterprise has experienced seismic shifts which have disrupted the pace and continuity of research programs, negatively affecting research productivity, graduate education, international partnerships, innovation outcomes, and more. Current indicators are clear: the pace of progress in priority technologies for the nation, including quantum, biotech and emerging energy has decelerated, affected by grant cancellations, funding slowdowns, staff losses, programmatic discontinuity and uncertainties. America's research and engineering workforce is experiencing its largest loss of talent in history, and other nations are taking advantage.24
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15 Will America Squander Its New Sputnik Moment? https://www.csis.org/analysis/will-america-squander-its-newsputnik-moment
16 The State of U.S. Science and Engineering, 2026: https://ncses.nsf.gov/pubs/nsbsep20261
17 U.S. Research and Innovation Performance, Elsevier, 2026: https://www.elsevier.com/promotions/us-researchand-innovation-performance
18 Competing in the Next Economy. Innovating in the Age of Disruption and Discontinuity. A Call to Action: https://compete.org/wp-content/uploads/coc-disruption_discontinuity-call-to-action-final_12.13.24.pdf
19 Federal R&D Funding Is Even More Valuable Than Washington Thinks: https://www.aei.org/economics/federalrd-funding-is-even-more-valuable-than-washington-thinks/
20 Fieldhouse, A.J., & Mertens, K. (2023). The Returns to Government R&D: Evidence from U.S. Appropriations Shocks. Working paper. https://andrewjfieldhouse.com/wpcontent/uploads/2023/12/The_Return_to_Government_R_D_manuscript.pdf
21 NBER "Estimating the Economic and Budgetary Effects of Research Investments" https://www.nber.org/papers/w33402)
22 Dyever, A. (2024) Public R&D Spillovers and Productivity Growth: https://www.ecb.europa.eu/press/conferences/ecbforum/shared/pdf/2024/EFCB_2024_Dyevre_paper.en.pdf
23 Fleming, L., Greene, H., Li, G., Marx, M., & Yao, D. A. (2019). Government-funded research increasingly fuels innovation. Science, 364 (6446), 1139-1141. https://doi.org/10.1126/science.aaw2373
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The OMB's proposed rule to revise the Guidance for Federal Financial Assistance (Docket OMB2026-0034) has resulted in significant feedback on the future fundamentals of how federally funded R&D will be conducted.25 Clearly, the U.S. R&D enterprise must move more quickly, align with national priorities, and be good stewards of tax dollars. However, the guidance as drafted would most certainly have the opposite effects, with a cascade of unintended consequences. The nation must learn from its unforced errors, and to avoid delays and disruptions to U.S. research that are particularly costly in view of the accelerating pace of the global innovation race.
To translate invention to impact, preserving the integrity of the Bayh-Dole Act for U.S. innovation is essential.26,27 We must now implement a muchneeded modernization of the Stevenson-Wydler Act to deliver increased innovation outcomes from federal research, and NIST Green Paper on "Unleashing American Innovation" and the "Return on Investment" Legislative Proposal delivered to Congress in 2020 provide a starting point to achieve these goals.28 Enabling more flexible and efficient collaborations between universities, government, industry and science philanthropy are key to America's success in S&T across the nation's innovation ecosystems.29
The United States is at a crucial juncture for the future of American innovation leadership globally. Our economic security and national security are closely intertwined with the reliability and the protections afforded by our R&D, IP, standards and innovation ecosystem. The foundational strength of U.S. IP rights, as established by the framers of the Constitution, must be enhanced with the enforceability of the rights of inventors in the U.S. and abroad, together with the rule of law and global respect for private contracts. These are particularly important for standard essential patents (SEPs), where the U.S. still leads the world as a net exporter of innovation. Reliable IP rights licensed to development and manufacturing partners enable trusted global supply chains and value creation for consumers and shareholders alike. The mobile telecommunications sector contributes an estimated total economic value of more than $4.8 trillion to the global economy.30 Technology innovators, including holders of SEPs, gain returns on their investments in R&D and standards engagement through licenses and royalty payments, in addition to product and services sales. Global intellectual property commerce, driven by SEP licensing, was $1.1 Trillion in 2023, with a net positive balance of trade in intellectual assets to the U.S. of over $130 Billion.31
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24 America's Scientific Brain Drain Is No Longer Hypothetical: https://www.realclearscience.com/articles/2026/07/14/americas_scientific_brain_drain_is_no_longer_hypothetic al_1194182.html
25 https://www.science.org/content/article/u-s-researchers-outraged-proposed-changes-federal-grants
26 Copan, W.G., America's Goose that Lays the Golden Eggs: https://rollcall.com/2021/04/22/americas-goose-that-lays-the-golden-eggs/
27 Unleashing American Innovation, NIST Green Paper SP1234, https://www.nist.gov/unleashing-americaninnovation/green-paper
28 ROI Initiative Status Update: Legislative Package Sent to Congress: https://www.nist.gov/newsevents/news/2020/12/roi-initiative-status-update-legislative-package-sent-congress
29 McNutt, M. (2024) Keeping America "Science Strong," https://pmc.ncbi.nlm.nih.gov/articles/PMC11459151/
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However, there is widespread unlicensed technology use in China of innovations and IP assets owned by U.S. entities. Further, China's government has weaponized its legal system with a wide range of patent and competition law tools, price controls on international entities, forced technology transfers, and by providing selective legal protections and incentives seeking to advantage Chinese companies.32
There is a concerning movement away from market-based negotiations and valuation toward government-controlled price regulation for standardized technologies. The EU has proposed a massive new regulatory regime33 toward government control of SEP prices. The Chinese Communist Party has issued guidelines that would also apply its antitrust laws to SEP licenses to benefit Chinese entities.34
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30 https://s3.amazonaws.com/media.hudson.org/The+Western+Innovators+of+the+Mobile+Revolution_+The+Data +on+Global+Royalty+Flows+to+U.S.+and+Europe+and+Why+It+Matters+-+Jan+2024.pdf
31 Global Innovation Index: https://www.wipo.int/en/web/global-innovation-index/w/blogs/2025/internationaltrade
32 DOI: https://doi.org/10.15779/Z38XP6V46N
33 https://single-market-economy.ec.europa.eu/publications/com2023232-proposal-regulation-standard-essentialpatents_en
34 https://www.allenovery.com/en-gb/global/news-and-insights/publications/china-draft-sep-antitrust-guidelinereleased-by-samr-for-public-comment
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Supported by Chinese government incentives, legal actions and long-term strategy, China's technology companies are gaining ground.35 In the development of 5G wireless broadband standards in the 3GPP (Third Generation Partnership Project) multi-stakeholder consortium, four Chinese companies were in the top 10 list of providing approved contributions to the standard: China Academy of Telecommunications Technology (CATT), China Mobile, ZTE, and substantially led by Huawei;36 two U.S.-headquartered companies were on this list:
Qualcomm and Intel. In 2019, the U.S. placed restrictions on having engagements with those Chinese organizations on the Commerce "entity list." This became a self-inflicted wound for the open technology standards process, damaging the positions of American companies and global partners.37 We must learn from this experience, among many other lessons, about the strategic importance of maintaining open and fair international engagement for standards, metrology and trade.38
Engagement in global collaborations for critical emerging technologies is an essential part of standards development and testing for the U.S. to ensure that common principles are established, and ultimately to support rather than impede the pace of technology innovation. International partnership networks enable the U.S. to establish an effective perspective and the balance between voluntary consensus standards and regulations for innovative new technologies, including artificial intelligence, emerging biotechnologies and quantum systems.
It is essential for the U.S. to provide leadership globally, taking deliberate steps to expand standards engagement and enforce the rights of American innovators here and abroad. We must systemically encourage and incentivize engagement in standards development - for companies large and small, for academia and for government entities. Developing standards literacy as part of U.S. higher education in STEM as well as in economics, business and legal curricula must also be a key priority. As we look to rebuild America's advanced manufacturing base and a trained domestic workforce, as well as trusted, resilient supply chains, the U.S must build upon the strengths of our free market economy, and a reliable intellectual property and innovation system that once again must lead the world.
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35 https://www.telegraph.co.uk/business/2024/04/30/huawei-profits-surge-steal-market-share-apple-china/
36 China's High-Tech Drive in 10 Charts: https://www.csis.org/analysis/chinas-high-tech-drive-10-charts
37 U.S. drafts rule to allow Huawei and U.S. firms to work together on 5G standards: https://www.reuters.com/article/technology/exclusive-us-drafts-rule-to-allow-huawei-and-us-firms-to-worktogether-on-5-idUSKBN22K214/
38 United States Standards Strategy (USSS) 2025: https://www.nist.gov/standardsgov/united-states-standardsstrategy-released
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The future U.S. workforce must have the skills necessary for the STEMrelated careers that drive the innovation economy. A prepared workforce is essential for rebuilding and reshoring our advanced manufacturing base for all industries in the economy of the future. The Manufacturing USA Institutes have essential roles to play for the future of America's manufacturing technology base and workforce development. A recent report by the U.S. National Academies provides a wealth of analysis and powerful insights for the future of this manufacturing innovation and support infrastructure to help counter the decline in American manufacturing productivity growth over the past two decades.39
America's small- and medium-sized manufacturers are essential parts of the nation's supply chains and innovation system. Rechartering and fully supporting the Manufacturing Extension Partnership (MEP) in an expanded mission for the U.S. to drive manufacturing innovation, entrepreneurship, shared services and advanced manufacturing technology efficiencies must be considered well by this Committee and stakeholders.
We have much work to do. America's K-12 educational outcomes40 have fallen behind other nations. Preparing future leaders with modernized higher education pathways and a pragmatic base of experience with flexibility for a range of interdisciplinary career journeys will be essential.41 Attracting and retaining talented people from other nations also remains vital to our innovation economy.42 Further, international scientific collaborations - with like-minded nations who are technology leaders as well as with developing countries in the Global South - enable the acceleration of discoveries, building diplomacy, talent access and supply chain resilience that also supports America's position abroad.43
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39 A Vision for the Manufacturing USA Program in 2030 and 2035: https://www.nationalacademies.org/projects/DEPS-NMMB-24-01/publication/29295
40 U.S. Department of Education Issues Statement on the Nation's Report Card: https://www.ed.gov/about/news/press-release/us-department-of-education-issues-statement-nations-reportcard
41 Leadership development in U.S. Higher education: Strategies for lifelong learning and upskilling: https://www.econstor.eu/bitstream/10419/327649/1/S2444569X2500099X.pdf
42 Foreign-born Share of the U.S. STEM Workforce, https://www.csis.org/analysis/innovation-lightbulb-foreignborn-share-us-stem-workforce
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Over the past two decades, the bureaucracy and administrative burdens associated with American publicly funded R&D have also skyrocketed.44 This bureaucracy has increased the costs and reduced the productivity of U.S. research. We must continually increase efficiencies in S&T across technology domains, drive speed-to-scale for innovation, build interagency coordination, and strengthen intellectual property, technology standards leadership and our international partnerships. The National Science and Technology Council must be fully revitalized for truly effective interagency coordination toward maximizing efficiencies and the value created from public R&E investment. We must further modernize legislation and policies to remove barriers and incentivize innovation for America to continue to lead the world. The U.S. must defend its innovators at home and abroad against mercantile and malign threats.
Thanks to this Committee for your important work toward securing the science, technology and innovation leadership for U.S. economic and national security. I look forward to answering questions you may have.
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43 Preserving America's Place in Global Science, T. Smith (2024): https://nautil.us/preserving-americas-place-inglobal-science-1031512/
44 Changes in Federal Research Requirements Since 1991: https://www.cogr.edu/changes-federal-researchrequirements-1991
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Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/cfab7159-df7d-220e-271d-93a7ca071912/WG_Copan_Testimony_Senate_Commerce_SMC_07172026_3a886492-0625-4081-9e00-3184a5f9d082.pdf
USTelecom - The Broadband Association President Spalter Testifies Before Senate Commerce, Science & Transportation Subcommittee
WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following written testimony by Jonathan Spalter, president and CEO of USTelecom - The Broadband Association, from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications":
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Chairman Fischer, Ranking Member Lujan, and members of the subcommittee: thank you for the opportunity to appear before you today. I am Jonathan Spalter, President and CEO of USTelecom - The Broadband Association. Our members are ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following written testimony by Jonathan Spalter, president and CEO of USTelecom - The Broadband Association, from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications": * * * Chairman Fischer, Ranking Member Lujan, and members of the subcommittee: thank you for the opportunity to appear before you today. I am Jonathan Spalter, President and CEO of USTelecom - The Broadband Association. Our members arethe companies that build, own, operate, and defend the broadband networks that reach into nearly every community in this country, from the largest cities to the Nebraska Sandhills and the mesas of northern New Mexico.
I want to begin by suggesting a frame for how we think about AI. We have a habit, in the early years of any transformative technology, of treating it as a category of its own. We once spoke of "e-commerce" and "e-health" and "e-learning" - until they quietly became commerce, health care, and education. Artificial intelligence is on that same path, albeit fast-tracked. AI will not simply be a sector of our economy. It is going to become a layer, embedded in nearly everything: in agriculture, in logistics, in medicine, in manufacturing, in national defense, in the way a small business keeps its books and finds its customers. Artificial intelligence is not the biggest thing since the internet; it is the next, most powerful evolution of the internet itself.
Every serious account of the AI buildout now names three core ingredients: compute, energy, and connectivity. The first two dominate the headlines. The third - the networks that move the data - is the one that determines whether any of this progress reaches businesses, consumers, and government offices across America.
If I could leave this subcommittee with one primary contribution today, it is this: America's AI future and the future of our entire innovation economy runs through fiber broadband networks.
Not alongside them. Not around them. Through them. Fiber is the foundation on which all next-generation connectivity depends. This includes satellite and wireless, which rely on fiber to move the massive amounts of AI-fueled data that help deliver the real-world benefits of the innovation emerging all around us - to communities throughout our nation.
The primary constraint on America's AI infrastructure is not technological.
It is governmental. Permitting is the single largest roadblock to delivering AI-ready connectivity in every community, and it is squarely within Congress's power to address this year.
Broadband providers are ready to build connections across this country, and we have the record to prove it. We have invested $2.2 trillion over the past 30 years, since the earliest days of the commercial internet. We directly employ roughly 570,000 Americans - engineers, data scientists, and cybersecurity specialists working alongside the field technicians who keep these networks running,/1 and we have decades of experience working with artificial intelligence to strengthen the reliability and efficiency of our networks.
Our task is to advance constructive solutions that allow us to work together, policymakers and the private sector, to connect every community. Whether a farmer in Custer County, a machine shop in Grand Island, or a nurse practitioner in the only clinic for sixty miles ever sees the benefit of any of this progress depends entirely on the power of the network at their door. So, I hope a central question for us is not who builds the computing, but whether every American will be connected well enough to use it.
I. Fiber Is the Foundation of the AI Economy
Let me start with the physical reality that underlies everything else: AI requires the continuous movement of massive amounts of data, and how much data can move is a function of the medium it moves through. No medium moves more than fiber. When a community competes to land an advanced manufacturing plant, when a regional health system builds out remote diagnostics, those demands rely on access to fiber. Fiber is the scalable backbone that every internet technology depends upon. It is, in the truest sense, the circulatory system of the American innovation economy.
Without question, every technology has an important role in the future we are shaping together. Fixed and mobile wireless and satellite serve real and important purposes - reaching the hardest-to-reach geographic areas, backfilling aging copper, connecting ships and aircraft, restoring service in the aftermath of a disaster. But fiber is the infrastructure foundation for our AI future.
A fiber optic strand is thinner than a human hair. Buried in the ground and built to last for decades, it has three distinct advantages.
* Capacity that scales. The fiber itself is not the limiting factor: capacity typically can be expanded simply by upgrading the electronics at either end, not digging back into the ground. So, the same fiber laid today can carry many times the traffic of today's internet, and stands most capable among all technologies to keep pace with what AI will demand tomorrow.
* Backhaul. Every other internet technology relies on fiber. 5G and 6G cell sites connect to fiber. Satellites land their traffic at ground stations that connect to fiber.
* Latency. The closer we get the fiber to the home or to the business, the faster and more capable that connection will be. On a video call, a few dozen milliseconds is invisible. For a robotic arm working beside a person on an assembly line, for a vehicle deciding whether to brake, for a surgical instrument guided from another hospital, timing is everything.
Putting fiber in the ground requires a skilled workforce - surveyors, trenching crews, splicers, and technicians who live in the communities they connect. These are jobs in your states: trenching crews in Nebraska, splicers in New Mexico, technicians in Minnesota, Illinois, Michigan, Wisconsin, Montana, and Ohio. Demand already outstrips supply. Industry analyses already project a shortfall approaching 180,000 skilled workers through the early 2030s./2
Not every technology now competing for funding creates work of this kind, in our communities.
The same is true at the community level. As our nation works to bring the production of chips, routers, microprocessors and optical transceivers back to the United States, we know advanced manufacturing does not locate where the network does not reach. Reindustrialization and broadband deployment are the same project, and the rural communities competing for those facilities know it.
II. Making Sure Every American Can Participate
AI adoption is not just a "big tech" story. It is a Main Street story. The JPMorgan Chase Institute finds that 17.7 percent of small businesses had purchased an AI service in 2025, more than triple the share two years earlier. This is almost certainly an undercount, since it captures only direct payments, not the free or embedded tools many use today./3
The U.S. Chamber puts generative AI use among small businesses at 58 percent, up from 23 percent in 2023./4
The work is routine and practical - whether it's drafting the estimate or the marketing email, answering the customer who calls after hours, keeping the books, or flagging equipment that may be in need of maintenance./5
Nearly two-thirds of foundations and nonprofits report using AI./6
And 80 percent of small businesses say AI enhances their workforce, with 82 percent of those using it having grown their headcount over the past year./7
The opportunities created by these advances extend beyond the private sector. In New Mexico, a public university now uses AI across its admissions office - answering prospective students around the clock, in multiple languages, and freeing staff for person-to-person counseling work.
What unites the companies USTelecom represents is a commitment to connect every community to the best technology we can get to them. That is why we are such large investors ourselves in the nation's broadband infrastructure, and why we have worked with this body on programs like BEAD that can bridge the cost gap where private capital alone cannot reach remote areas. The communities with the most to gain from AI are very often the ones where the economics are toughest. Which is the heart of the matter: the places where private capital struggles hardest are the places where your leverage is greatest. To those communities, a permit granted in 90 days instead of three years is worth more than any promise the rest of us can make them.
III. Broadband Companies Have Been Using AI in Our Networks for Decades
The Subcommittee asked how the communications sector is using AI tools to run networks more efficiently, anticipate outages, and defend against cyberattacks. The industry's experience here is not new. As far back as the 1980s, telephone companies deployed rule-based "expert systems" for fault management - flagging when something on the network failed, isolating the cause, and clearing it. Machine learning followed, for call routing, predictive monitoring, and fraud detection. Today our networks can increasingly diagnose and fix themselves, and some carriers now train AI models on their own networks, so the system learns the quirks of that particular network the way a seasoned engineer learns the one she has run for years - except across millions of readings at once, continuously, and without ever looking away. These are not hypotheticals: One major broadband provider reports making roughly 700,000 changes to its network every day with AI, using a foundation model built for its own network. Another now runs some 200 edge computing centers that automatically resolve more than three-quarters of network events before a customer is affected, locating faults with better than 99 percent precision./8
Network efficiency. Demand fluctuates enormously across a single day - the morning surge as a region logs on, the evening crest of video streaming, a stadium that fills on a Saturday. AI systems proactively watch the network in real time and steer traffic around congestion, dynamically allocating capacity where it is needed most. The same systems increasingly anticipate outages before they happen. This is the shift from reactive repair to predictive resilience. AI models can identify the small anomalies that precede a failure - a degraded optical signal, an irregular power draw, a component drifting out of alignment - so a provider can dispatch a technician or reroute traffic before customers are affected. The outage that never happens is invisible to the public. It is one of the most valuable things AI does in our networks.
The same logic scales up to disasters: Leading broadband providers use AI to model approaching hurricanes to predict which assets are most likely to be hit, so crews, generators, and portable capacity can be positioned before landfall.
Cybersecurity. Our broadband sector is keenly aware that America's adversaries are using this same technology to attack our infrastructure at greater speed and scale. That is why USTelecom is a contributor to the National Institute of Standards and Technology's work on AI as part of its Cybersecurity Framework. This is a dynamic, industry-informed approach where policy leaders and broadband experts roll up their sleeves together on the kinds of solutions these complex challenges call for./9
Network engineers now work alongside AI agents that read the flood of alarms coming off the network, pinpoint the real problem, open a ticket, propose a fix, and write the incident summary. These capabilities run in parts of our members' networks, not yet across all of them, and extending them takes expanding our fiber capacity, which again brings us back to permits.
IV. AI Is Changing the Shape of Network Traffic
As we look at the growth in network traffic attributable to AI, the volume is striking. Consumer network traffic is forecast to run roughly 6.6 times higher by 2035, with AI the dominant driver of growth. Agentic AI for enterprise is expected to bend the curve even more steeply, powering a 9-fold expansion in related traffic by 2035./10
Of course, ten years from now might as well be a century, given how quickly the landscape is evolving. The pace is already visible: this June, for the first time in the history of the internet, automated systems generated more search requests than human beings - 57.4 percent from bots and AI agents, against 42.6 percent from people./11
Experts can differ in good faith on the precise forecasts, but the more consequential change gets less attention: AI is not simply increasing the volume of network traffic. It is changing the shape of it. And the shape must determine what we build, and where.
* Traffic is reversing direction. Two terms are important to understand: upstream is everything traveling toward the computing - the prompt, the photograph, the sensor reading; downstream is everything coming back - the answer, the diagnosis, the instruction. For 30 years the internet has been engineered overwhelmingly for downstream. Picture a freeway built with eight lanes running into town and two running out. That is what was needed to meet the network demands of video streaming. But AI inverts the flow, pushing enormous volumes of data back upstream: rich prompts, video, sensor and lidar feeds that allow machines to register their surroundings. As an example, a single connected vehicle generates roughly 20 gigabytes of data per day./12
This is roughly 30 times what the average mobile customer consumes, and it flows in the direction most network technologies - copper, coaxial cable, wireless, satellite - were least designed to carry. Fiber is the exception: the same, highcapacity lanes in both directions.
* Traffic is becoming "bursty" and machine-driven. An AI agent does not browse the way a person does. A person loads a page, reads, thinks, clicks. An agent fires off dozens of requests in a second, goes quiet, then does it again - short, intense spikes rather than a steady stream, and a network sized for the steady stream is generally not sized for the spike. Cisco finds a 450 percent increase in traffic when a task is executed by an AI agent rather than a human./13
An AI chatbot says, "Here is an answer." An AI agent says, "Here is the plan, and I will carry it out step by step." Every one of those steps is traffic.
* Traffic is becoming intolerant of delay - and geographically distributed. This is the change that most directly implicates federal policy. There are machines acting in the physical world, on a deadline. A traffic camera using AI to prevent a collision cannot wait for a round trip to a server several states away. An autonomous system on a factory floor has no margin for a lagging signal. These applications require computing and connectivity close to where the work is happening. And that means more fiber, deeper into more places, to directly support communities and to support the wireless, satellite and other connectivity options that ride on it.
The defining infrastructure challenge of the AI era is not simply how much we build. It is where, with what, and how fast. Latency is a function of distance, and distance is a function of geography. You close it by building closer to the user - which means opening a trench, crossing a right-of-way, filing a permit. Thousands of times, in thousands of jurisdictions. Simply put, the physics of AI turns a computing challenge into a permitting challenge. And, that's a problem we can solve today.
V. The Broadband Community Is Doing Its Part
Consider the scale of what private capital has already built: $2.2 trillion invested by broadband companies since 1996, and $89.6 billion in 2024 alone/14 with 2025 expected to be higher still - sustained through recessions, higher interest rates, and a cautious capital environment. To put that in context, Congress committed $42.45 billion for BEAD, the largest federal broadband infrastructure program in U.S. history. It is designed to fill a critical gap: connecting the communities that cannot otherwise be reached, where the cost of building broadband networks exceeds any realistic private return. Roughly half of BEAD funds, about $21 billion, are going to deployment. This is about one percent of the private capital the industry has invested since 1996. America's digital infrastructure is overwhelmingly privately financed, and it was built because both parties, across many administrations, sustained a pro-investment posture: stable rules, predictable policy, and a settled preference for building.
VI. The Limiting Factor Is Government, Not Technology
Broadband providers are ready to build. What stands in the way is a permitting system designed for a different era, and a problem that is often misdiagnosed. Our difficulty is rarely an outright "no" from a permitting authority. Our primary difficulty is the absence of an answer. It is delay.
Running out the clock - through a construction season, through a grant deadline, through a community's patience - until the project is more expensive or abandoned. Among our members' experiences:
* Illinois. A provider may hold only one active permit at a time; each must be closed out before the next is released.
* Maryland. A city stalled a provider's effort to install a small fiber hut for nearly a year by requiring an extensive zoning review typically reserved for large commercial developments.
* Minnesota. A city demanded a $63,000 permit fee plus nearly $29,000 in per-foot charges for a single block of fiber. When it refused a compromise, the provider had to walk away.
* Hawaii. The legislature enacted a 60-day broadband shot clock nearly a decade ago. Not a single permit has ever been processed in that timeframe.
* Ohio. One town simply ignored a provider's application. The deployment was abandoned.
* Montana. A member received a federal grant in June 2023 and, two years later, had not turned a single shovel of dirt - waiting on permits.
* The Mountain West. One provider waited three years for a permit in an area with a sixweek annual construction window, narrowed by weather, terrain, and protected-species breeding seasons. By the time the permit arrived, the season had closed.
* Federal lands. Permitting can take up to four years. One member was made to conduct a fresh environmental review of a corridor that already held a telephone line, power and natural gas lines, and a county road - land previously disturbed and previously analyzed.
* Big Bend National Park. A member waited two years to bring fiber to homes and businesses inside the park, held up in DC by a required review of forest resources. The problem? Big Bend sits in the Chihuahuan Desert. There is no forest.
* Railroads. It is nearly impossible for a fiber deployment not to be stopped in its tracks by a railroad demanding fees that bear no relation to cost to bore or cross under a railroad.
Congress, and this committee, should pass the RAIL Act to put in place common-sense rules that preserve safety for railroads while limiting the delays that serve no safety purpose.
Permitting officials are doing important work, most of them under resourced. And when the rules are clear and followed, communities win. This leads to my most important point: We are not speculating about whether permitting reform works. We know that it does.
* In 2018, as part of the same legislative push that cleared the runway for 5G, Congress required federal agencies to grant or deny communications permits on federal land within 270 days./15
Where agencies kept reliable records, the Government Accountability Office found average processing time fell by 57 percent between 2018 and 2022. GAO's other finding is just as important: the Bureau of Land Management could not reliably determine its own processing time for 42 percent of applications./16
Congress set the deadline. Some agencies still cannot confirm they are meeting it. Both halves need fixing - and the CLOSE THE GAP Act would do just that.
* Also in 2018, the FCC adopted shot clocks and cost-based fee caps for small wireless facilities to more densely cover a neighborhood, a corridor, a stadium. Small-cell deployment then grew by 110 percent./17
This resulting densification is what turned 5G into the powerful network Americans now carry in their pockets. Importantly, the FCC today is working to advance a similar approach to streamline permitting for wired networks.
* More recently, state Broadband Ready Community certifications for towns and counties that commit to a single point of contact, a firm review deadline, and reasonable, cost-based fees have demonstrated something important: faster reviews and reasonable fees do not cost communities anything. They signal to providers that a community is open for business.
More than 280 towns, cities, and counties across Georgia, Indiana, Tennessee, and Wisconsin have adopted these certifications.
The Subcommittee will notice that some of these reforms date to 2018 - and that is no accident. They were built for wireless: for the small-cell densification that made 5G possible.
Nothing on the same scale has ever been done for wireline. The playbook exists. It worked once. It has simply never been applied to the infrastructure that matters most today.
Every delay is a constituent community that waits. And every delayed broadband build is a delay in strengthening America's AI infrastructure. Computation without connectivity reaches only those who can afford to build their own onramps. Our job, and yours, is to make sure it reaches everyone else, too.
VII. What We Ask of Congress
1. Pass the CLOSE THE GAP Act (S. 4561). Introduced by Senators Barrasso and Lummis, this bill goes directly at the federal-lands bottleneck: it streamlines agency permitting regulations for broadband and exempts previously permitted and previously analyzed land from duplicative review. The communities waiting on the far side of a four-year permit are found in New Mexico as surely as in Wyoming, Montana, and Nebraska. It is targeted, it is sensible, and it is ready./18
2. Pass the Broadband and Telecommunications RAIL Act (S. 3268). Introduced by Senators Blackburn and Lujan, this bill goes directly at the railroad-crossing bottleneck: it sets enforceable timelines for railroads to respond to and schedule crossing work, reins in the arbitrary fees that can run into the tens of thousands of dollars for a single crossing, narrows the grounds for denial to genuine questions of safety and railroad operations, and gives the FCC a role in resolving disputes. It does all of this while preserving the safety standards that rail carriers rightly require, since providers must still submit engineering plans and coordinate closely with the railroads. The bottleneck here is not safety. It is a permit that can sit unanswered for the better part of two years while the town on the far side of the tracks waits for service./19
3. Pass the Accelerating Broadband Permits Act (S. 4448). Introduced by Senators Thune, Lujan, and Barrasso, this bill goes directly at the accountability bottleneck. It cuts duplicative red tape in agency review and brings transparency to where an application sits in the process.
Congress set that deadline in 2018. GAO reported in 2024 that agencies were still missing it.
A deadline without a mechanism is only a suggestion, and the households waiting on an unanswered file are in South Dakota and New Mexico as surely as in Wyoming and Nebraska.
4. Reauthorize CISA 2015. Finally, to ensure the safety and security of the networks we build, it is imperative that Congress reauthorize CISA 2015 so that we can continue to safely, quickly, and effectively share information across our industry and with our government partners.
Conclusion
Chair Fischer, Ranking Member Lujan: at the end of all of this are families in small towns across the country who want to know what any of it means for them - for their quality of life, for their kids, for the cost of running their business. The honest answer is that everything we have described arrives in their lives only if there is a network at their door capable of delivering it.
America's broadband providers have built these networks across a generation, at a scale few industries can match, and we are ready to keep building. We are not asking Congress to build them for us. We are asking you to clear the path - and, where the economics will not reach, to keep the federal partnership strong enough to finish the job.
The gateway is government. The levers that remain are the ones you hold. Pull them, and the powerful modern networks through which America's future transits will be built faster than anyone expects - and every community will be on the other end of the opportunities they make possible.
Thank you. I look forward to your questions.
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1 U.S. Bureau of Labor Statistics, Current Employment Statistics: Wired and Wireless Telecommunications Carriers (Except Satellite), NAICS 517111 (2024). This figure reflects broadband providers' direct employees; it excludes the construction and contractor trades that build and maintain networks, as well as satellite carriers.
2 Patience Haggin, "High-Speed Internet Boom Hits Low-Tech Snag: A Labor Shortage," Wall Street Journal, February 1, 2026, https://www.wsj.com/business/telecom/high-speed-internet-boom-hits-low-tech-snag-a-laborshortage9c92b514
3 JPMorganChase Institute, Understanding AI Use by Small Businesses (2026), https://www.jpmorganchase.com/institute/all-topics/business-growth-and-entrepreneurship/understanding-ai-usebysmall-businesses.
4 U.S. Chamber of Commerce, Empowering Small Business: The Impact of Technology on U.S. Small Business (2025), https://www.uschamber.com/technology/empowering-small-business-the-impact-of-technology-on-u-s-smallbusiness.
5 U.S. Chamber of Commerce Foundation, AI in Action: Early Lessons from Small Businesses on the Front Lines of Adoption (2026), https://www.hiringourheroes.org/resources/ai-in-action-whitepaper/.
6 Center for Effective Philanthropy, AI With Purpose: How Foundations and Nonprofits Are Thinking About and Using Artificial Intelligence (2025), https://cep.org/report-backpacks/ai-with-purpose-how-foundations-andnonprofits-arethinking-about-and-using-artificial-intelligence/
7 Goldman Sachs small business survey, as reported in Fox Business (August 8, 2025), https://www.foxbusiness.com/economy/small-business-ai-adoption-jumps-68-owners-plan-significantworkforcegrowth-2025; U.S. Chamber of Commerce, Empowering Small Business: The Impact of Technology on U.S. Small Business, supra note 4.
8 Phil Harvey, "The AI-Enabled Network Is Here. The Pitch Is Stuck in Traffic," Light Reading (May 29, 2026), https://www.lightreading.com/ai-machine-learning/the-ai-enabled-network-is-here-the-pitch-is-stuck-in-traffic
9 See National Institute of Standards and Technology, Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile): NIST Community Profile, NIST IR 8596, initial preliminary draft (December 2025), https://csrc.nist.gov/pubs/ir/8596/iprd.
10 Cisco, AI Impact on Wide Area Networks (2026), https://www.cisco.com/c/dam/en/us/solutions/collateral/artificialintelligence/mass-scale-infrastructure/ainetwork-traffic-report.pdf.
11 Cloudflare data reported, NBC News, Bot Web Traffic Has Overtaken Human Web Traffic, Data Shows (June 3, 2026), https://www.nbcnews.com/tech/tech-news/bot-web-traffic-overtaken-human-web-traffic-data-showsrcna348522.
12 U.S. Department of Transportation, Federal Highway Administration, Maricopa Association of Governments Finds Value in Connected Car Data, Crowdsourcing for Operations Case Study, FHWA-HOP-22-047 (2022), https://www.fhwa.dot.gov/innovation/everydaycounts/edc_6/docs/crowdsourcing_mag_case_study.p df
13 Cisco, AI Impact on Wide Area Networks (2026), supra note 10.
14 USTelecom, 2024 Broadband Capex Report (October 21, 2025), https://ustelecom.org/research/2024broadbandcapex-report/.
15 Consolidated Appropriations Act, 2018, Pub. L. No. 115-141, div. P, tit. VI (MOBILE NOW Act), Sec. 606 (establishing timelines for federal agency action on communications facility siting applications on federal property).
16 U.S. Government Accountability Office, Broadband: Agencies Should Improve Data Reliability and Coordination for Permitting on Federal Lands, GAO-24-106157 (2024), https://www.gao.gov/products/gao-24-106157.
17 Accelerating Wireless Broadband Deployment by Removing Barriers to Infrastructure Investment, Declaratory Ruling and Third Report and Order, WT Docket No. 17-79, 33 FCC Rcd 9088 (2018); CTIA, 2025 Annual Survey Highlights at 7 (2025), https://api.ctia.org/wp-content/uploads/2025/09/2025-Annual-Survey-Highlights.pdf.
18 CLOSE THE GAP Act, S. 4561, 119th Cong. (2026) (introduced by Sen. Barrasso, cosponsored by Sen. Lummis), https://www.congress.gov/bill/119th-congress/senate-bill/4561.
19 Broadband and Telecommunications RAIL Act, S.3268, 119th Cong. (2026) (introduced by Sen. Blackburn, cosponsored by Sen. Lujan), https://www.congress.gov/bill/119th-congress/senate-bill/3268.
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Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/379d9950-dc84-b1bf-2c6a-ee1ebb42fbe2/Spalter_Written_Testimony_7eddc348-33d3-4f43-b974-3023b1205597.pdf
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Chairman Fischer, Ranking Member Lujan, and members of the subcommittee: thank you for the opportunity to appear before you today. I am Jonathan Spalter, President and CEO of USTelecom - The Broadband Association. Our members are ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following written testimony by Jonathan Spalter, president and CEO of USTelecom - The Broadband Association, from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications": * * * Chairman Fischer, Ranking Member Lujan, and members of the subcommittee: thank you for the opportunity to appear before you today. I am Jonathan Spalter, President and CEO of USTelecom - The Broadband Association. Our members arethe companies that build, own, operate, and defend the broadband networks that reach into nearly every community in this country, from the largest cities to the Nebraska Sandhills and the mesas of northern New Mexico.
I want to begin by suggesting a frame for how we think about AI. We have a habit, in the early years of any transformative technology, of treating it as a category of its own. We once spoke of "e-commerce" and "e-health" and "e-learning" - until they quietly became commerce, health care, and education. Artificial intelligence is on that same path, albeit fast-tracked. AI will not simply be a sector of our economy. It is going to become a layer, embedded in nearly everything: in agriculture, in logistics, in medicine, in manufacturing, in national defense, in the way a small business keeps its books and finds its customers. Artificial intelligence is not the biggest thing since the internet; it is the next, most powerful evolution of the internet itself.
Every serious account of the AI buildout now names three core ingredients: compute, energy, and connectivity. The first two dominate the headlines. The third - the networks that move the data - is the one that determines whether any of this progress reaches businesses, consumers, and government offices across America.
If I could leave this subcommittee with one primary contribution today, it is this: America's AI future and the future of our entire innovation economy runs through fiber broadband networks.
Not alongside them. Not around them. Through them. Fiber is the foundation on which all next-generation connectivity depends. This includes satellite and wireless, which rely on fiber to move the massive amounts of AI-fueled data that help deliver the real-world benefits of the innovation emerging all around us - to communities throughout our nation.
The primary constraint on America's AI infrastructure is not technological.
It is governmental. Permitting is the single largest roadblock to delivering AI-ready connectivity in every community, and it is squarely within Congress's power to address this year.
Broadband providers are ready to build connections across this country, and we have the record to prove it. We have invested $2.2 trillion over the past 30 years, since the earliest days of the commercial internet. We directly employ roughly 570,000 Americans - engineers, data scientists, and cybersecurity specialists working alongside the field technicians who keep these networks running,/1 and we have decades of experience working with artificial intelligence to strengthen the reliability and efficiency of our networks.
Our task is to advance constructive solutions that allow us to work together, policymakers and the private sector, to connect every community. Whether a farmer in Custer County, a machine shop in Grand Island, or a nurse practitioner in the only clinic for sixty miles ever sees the benefit of any of this progress depends entirely on the power of the network at their door. So, I hope a central question for us is not who builds the computing, but whether every American will be connected well enough to use it.
I. Fiber Is the Foundation of the AI Economy
Let me start with the physical reality that underlies everything else: AI requires the continuous movement of massive amounts of data, and how much data can move is a function of the medium it moves through. No medium moves more than fiber. When a community competes to land an advanced manufacturing plant, when a regional health system builds out remote diagnostics, those demands rely on access to fiber. Fiber is the scalable backbone that every internet technology depends upon. It is, in the truest sense, the circulatory system of the American innovation economy.
Without question, every technology has an important role in the future we are shaping together. Fixed and mobile wireless and satellite serve real and important purposes - reaching the hardest-to-reach geographic areas, backfilling aging copper, connecting ships and aircraft, restoring service in the aftermath of a disaster. But fiber is the infrastructure foundation for our AI future.
A fiber optic strand is thinner than a human hair. Buried in the ground and built to last for decades, it has three distinct advantages.
* Capacity that scales. The fiber itself is not the limiting factor: capacity typically can be expanded simply by upgrading the electronics at either end, not digging back into the ground. So, the same fiber laid today can carry many times the traffic of today's internet, and stands most capable among all technologies to keep pace with what AI will demand tomorrow.
* Backhaul. Every other internet technology relies on fiber. 5G and 6G cell sites connect to fiber. Satellites land their traffic at ground stations that connect to fiber.
* Latency. The closer we get the fiber to the home or to the business, the faster and more capable that connection will be. On a video call, a few dozen milliseconds is invisible. For a robotic arm working beside a person on an assembly line, for a vehicle deciding whether to brake, for a surgical instrument guided from another hospital, timing is everything.
Putting fiber in the ground requires a skilled workforce - surveyors, trenching crews, splicers, and technicians who live in the communities they connect. These are jobs in your states: trenching crews in Nebraska, splicers in New Mexico, technicians in Minnesota, Illinois, Michigan, Wisconsin, Montana, and Ohio. Demand already outstrips supply. Industry analyses already project a shortfall approaching 180,000 skilled workers through the early 2030s./2
Not every technology now competing for funding creates work of this kind, in our communities.
The same is true at the community level. As our nation works to bring the production of chips, routers, microprocessors and optical transceivers back to the United States, we know advanced manufacturing does not locate where the network does not reach. Reindustrialization and broadband deployment are the same project, and the rural communities competing for those facilities know it.
II. Making Sure Every American Can Participate
AI adoption is not just a "big tech" story. It is a Main Street story. The JPMorgan Chase Institute finds that 17.7 percent of small businesses had purchased an AI service in 2025, more than triple the share two years earlier. This is almost certainly an undercount, since it captures only direct payments, not the free or embedded tools many use today./3
The U.S. Chamber puts generative AI use among small businesses at 58 percent, up from 23 percent in 2023./4
The work is routine and practical - whether it's drafting the estimate or the marketing email, answering the customer who calls after hours, keeping the books, or flagging equipment that may be in need of maintenance./5
Nearly two-thirds of foundations and nonprofits report using AI./6
And 80 percent of small businesses say AI enhances their workforce, with 82 percent of those using it having grown their headcount over the past year./7
The opportunities created by these advances extend beyond the private sector. In New Mexico, a public university now uses AI across its admissions office - answering prospective students around the clock, in multiple languages, and freeing staff for person-to-person counseling work.
What unites the companies USTelecom represents is a commitment to connect every community to the best technology we can get to them. That is why we are such large investors ourselves in the nation's broadband infrastructure, and why we have worked with this body on programs like BEAD that can bridge the cost gap where private capital alone cannot reach remote areas. The communities with the most to gain from AI are very often the ones where the economics are toughest. Which is the heart of the matter: the places where private capital struggles hardest are the places where your leverage is greatest. To those communities, a permit granted in 90 days instead of three years is worth more than any promise the rest of us can make them.
III. Broadband Companies Have Been Using AI in Our Networks for Decades
The Subcommittee asked how the communications sector is using AI tools to run networks more efficiently, anticipate outages, and defend against cyberattacks. The industry's experience here is not new. As far back as the 1980s, telephone companies deployed rule-based "expert systems" for fault management - flagging when something on the network failed, isolating the cause, and clearing it. Machine learning followed, for call routing, predictive monitoring, and fraud detection. Today our networks can increasingly diagnose and fix themselves, and some carriers now train AI models on their own networks, so the system learns the quirks of that particular network the way a seasoned engineer learns the one she has run for years - except across millions of readings at once, continuously, and without ever looking away. These are not hypotheticals: One major broadband provider reports making roughly 700,000 changes to its network every day with AI, using a foundation model built for its own network. Another now runs some 200 edge computing centers that automatically resolve more than three-quarters of network events before a customer is affected, locating faults with better than 99 percent precision./8
Network efficiency. Demand fluctuates enormously across a single day - the morning surge as a region logs on, the evening crest of video streaming, a stadium that fills on a Saturday. AI systems proactively watch the network in real time and steer traffic around congestion, dynamically allocating capacity where it is needed most. The same systems increasingly anticipate outages before they happen. This is the shift from reactive repair to predictive resilience. AI models can identify the small anomalies that precede a failure - a degraded optical signal, an irregular power draw, a component drifting out of alignment - so a provider can dispatch a technician or reroute traffic before customers are affected. The outage that never happens is invisible to the public. It is one of the most valuable things AI does in our networks.
The same logic scales up to disasters: Leading broadband providers use AI to model approaching hurricanes to predict which assets are most likely to be hit, so crews, generators, and portable capacity can be positioned before landfall.
Cybersecurity. Our broadband sector is keenly aware that America's adversaries are using this same technology to attack our infrastructure at greater speed and scale. That is why USTelecom is a contributor to the National Institute of Standards and Technology's work on AI as part of its Cybersecurity Framework. This is a dynamic, industry-informed approach where policy leaders and broadband experts roll up their sleeves together on the kinds of solutions these complex challenges call for./9
Network engineers now work alongside AI agents that read the flood of alarms coming off the network, pinpoint the real problem, open a ticket, propose a fix, and write the incident summary. These capabilities run in parts of our members' networks, not yet across all of them, and extending them takes expanding our fiber capacity, which again brings us back to permits.
IV. AI Is Changing the Shape of Network Traffic
As we look at the growth in network traffic attributable to AI, the volume is striking. Consumer network traffic is forecast to run roughly 6.6 times higher by 2035, with AI the dominant driver of growth. Agentic AI for enterprise is expected to bend the curve even more steeply, powering a 9-fold expansion in related traffic by 2035./10
Of course, ten years from now might as well be a century, given how quickly the landscape is evolving. The pace is already visible: this June, for the first time in the history of the internet, automated systems generated more search requests than human beings - 57.4 percent from bots and AI agents, against 42.6 percent from people./11
Experts can differ in good faith on the precise forecasts, but the more consequential change gets less attention: AI is not simply increasing the volume of network traffic. It is changing the shape of it. And the shape must determine what we build, and where.
* Traffic is reversing direction. Two terms are important to understand: upstream is everything traveling toward the computing - the prompt, the photograph, the sensor reading; downstream is everything coming back - the answer, the diagnosis, the instruction. For 30 years the internet has been engineered overwhelmingly for downstream. Picture a freeway built with eight lanes running into town and two running out. That is what was needed to meet the network demands of video streaming. But AI inverts the flow, pushing enormous volumes of data back upstream: rich prompts, video, sensor and lidar feeds that allow machines to register their surroundings. As an example, a single connected vehicle generates roughly 20 gigabytes of data per day./12
This is roughly 30 times what the average mobile customer consumes, and it flows in the direction most network technologies - copper, coaxial cable, wireless, satellite - were least designed to carry. Fiber is the exception: the same, highcapacity lanes in both directions.
* Traffic is becoming "bursty" and machine-driven. An AI agent does not browse the way a person does. A person loads a page, reads, thinks, clicks. An agent fires off dozens of requests in a second, goes quiet, then does it again - short, intense spikes rather than a steady stream, and a network sized for the steady stream is generally not sized for the spike. Cisco finds a 450 percent increase in traffic when a task is executed by an AI agent rather than a human./13
An AI chatbot says, "Here is an answer." An AI agent says, "Here is the plan, and I will carry it out step by step." Every one of those steps is traffic.
* Traffic is becoming intolerant of delay - and geographically distributed. This is the change that most directly implicates federal policy. There are machines acting in the physical world, on a deadline. A traffic camera using AI to prevent a collision cannot wait for a round trip to a server several states away. An autonomous system on a factory floor has no margin for a lagging signal. These applications require computing and connectivity close to where the work is happening. And that means more fiber, deeper into more places, to directly support communities and to support the wireless, satellite and other connectivity options that ride on it.
The defining infrastructure challenge of the AI era is not simply how much we build. It is where, with what, and how fast. Latency is a function of distance, and distance is a function of geography. You close it by building closer to the user - which means opening a trench, crossing a right-of-way, filing a permit. Thousands of times, in thousands of jurisdictions. Simply put, the physics of AI turns a computing challenge into a permitting challenge. And, that's a problem we can solve today.
V. The Broadband Community Is Doing Its Part
Consider the scale of what private capital has already built: $2.2 trillion invested by broadband companies since 1996, and $89.6 billion in 2024 alone/14 with 2025 expected to be higher still - sustained through recessions, higher interest rates, and a cautious capital environment. To put that in context, Congress committed $42.45 billion for BEAD, the largest federal broadband infrastructure program in U.S. history. It is designed to fill a critical gap: connecting the communities that cannot otherwise be reached, where the cost of building broadband networks exceeds any realistic private return. Roughly half of BEAD funds, about $21 billion, are going to deployment. This is about one percent of the private capital the industry has invested since 1996. America's digital infrastructure is overwhelmingly privately financed, and it was built because both parties, across many administrations, sustained a pro-investment posture: stable rules, predictable policy, and a settled preference for building.
VI. The Limiting Factor Is Government, Not Technology
Broadband providers are ready to build. What stands in the way is a permitting system designed for a different era, and a problem that is often misdiagnosed. Our difficulty is rarely an outright "no" from a permitting authority. Our primary difficulty is the absence of an answer. It is delay.
Running out the clock - through a construction season, through a grant deadline, through a community's patience - until the project is more expensive or abandoned. Among our members' experiences:
* Illinois. A provider may hold only one active permit at a time; each must be closed out before the next is released.
* Maryland. A city stalled a provider's effort to install a small fiber hut for nearly a year by requiring an extensive zoning review typically reserved for large commercial developments.
* Minnesota. A city demanded a $63,000 permit fee plus nearly $29,000 in per-foot charges for a single block of fiber. When it refused a compromise, the provider had to walk away.
* Hawaii. The legislature enacted a 60-day broadband shot clock nearly a decade ago. Not a single permit has ever been processed in that timeframe.
* Ohio. One town simply ignored a provider's application. The deployment was abandoned.
* Montana. A member received a federal grant in June 2023 and, two years later, had not turned a single shovel of dirt - waiting on permits.
* The Mountain West. One provider waited three years for a permit in an area with a sixweek annual construction window, narrowed by weather, terrain, and protected-species breeding seasons. By the time the permit arrived, the season had closed.
* Federal lands. Permitting can take up to four years. One member was made to conduct a fresh environmental review of a corridor that already held a telephone line, power and natural gas lines, and a county road - land previously disturbed and previously analyzed.
* Big Bend National Park. A member waited two years to bring fiber to homes and businesses inside the park, held up in DC by a required review of forest resources. The problem? Big Bend sits in the Chihuahuan Desert. There is no forest.
* Railroads. It is nearly impossible for a fiber deployment not to be stopped in its tracks by a railroad demanding fees that bear no relation to cost to bore or cross under a railroad.
Congress, and this committee, should pass the RAIL Act to put in place common-sense rules that preserve safety for railroads while limiting the delays that serve no safety purpose.
Permitting officials are doing important work, most of them under resourced. And when the rules are clear and followed, communities win. This leads to my most important point: We are not speculating about whether permitting reform works. We know that it does.
* In 2018, as part of the same legislative push that cleared the runway for 5G, Congress required federal agencies to grant or deny communications permits on federal land within 270 days./15
Where agencies kept reliable records, the Government Accountability Office found average processing time fell by 57 percent between 2018 and 2022. GAO's other finding is just as important: the Bureau of Land Management could not reliably determine its own processing time for 42 percent of applications./16
Congress set the deadline. Some agencies still cannot confirm they are meeting it. Both halves need fixing - and the CLOSE THE GAP Act would do just that.
* Also in 2018, the FCC adopted shot clocks and cost-based fee caps for small wireless facilities to more densely cover a neighborhood, a corridor, a stadium. Small-cell deployment then grew by 110 percent./17
This resulting densification is what turned 5G into the powerful network Americans now carry in their pockets. Importantly, the FCC today is working to advance a similar approach to streamline permitting for wired networks.
* More recently, state Broadband Ready Community certifications for towns and counties that commit to a single point of contact, a firm review deadline, and reasonable, cost-based fees have demonstrated something important: faster reviews and reasonable fees do not cost communities anything. They signal to providers that a community is open for business.
More than 280 towns, cities, and counties across Georgia, Indiana, Tennessee, and Wisconsin have adopted these certifications.
The Subcommittee will notice that some of these reforms date to 2018 - and that is no accident. They were built for wireless: for the small-cell densification that made 5G possible.
Nothing on the same scale has ever been done for wireline. The playbook exists. It worked once. It has simply never been applied to the infrastructure that matters most today.
Every delay is a constituent community that waits. And every delayed broadband build is a delay in strengthening America's AI infrastructure. Computation without connectivity reaches only those who can afford to build their own onramps. Our job, and yours, is to make sure it reaches everyone else, too.
VII. What We Ask of Congress
1. Pass the CLOSE THE GAP Act (S. 4561). Introduced by Senators Barrasso and Lummis, this bill goes directly at the federal-lands bottleneck: it streamlines agency permitting regulations for broadband and exempts previously permitted and previously analyzed land from duplicative review. The communities waiting on the far side of a four-year permit are found in New Mexico as surely as in Wyoming, Montana, and Nebraska. It is targeted, it is sensible, and it is ready./18
2. Pass the Broadband and Telecommunications RAIL Act (S. 3268). Introduced by Senators Blackburn and Lujan, this bill goes directly at the railroad-crossing bottleneck: it sets enforceable timelines for railroads to respond to and schedule crossing work, reins in the arbitrary fees that can run into the tens of thousands of dollars for a single crossing, narrows the grounds for denial to genuine questions of safety and railroad operations, and gives the FCC a role in resolving disputes. It does all of this while preserving the safety standards that rail carriers rightly require, since providers must still submit engineering plans and coordinate closely with the railroads. The bottleneck here is not safety. It is a permit that can sit unanswered for the better part of two years while the town on the far side of the tracks waits for service./19
3. Pass the Accelerating Broadband Permits Act (S. 4448). Introduced by Senators Thune, Lujan, and Barrasso, this bill goes directly at the accountability bottleneck. It cuts duplicative red tape in agency review and brings transparency to where an application sits in the process.
Congress set that deadline in 2018. GAO reported in 2024 that agencies were still missing it.
A deadline without a mechanism is only a suggestion, and the households waiting on an unanswered file are in South Dakota and New Mexico as surely as in Wyoming and Nebraska.
4. Reauthorize CISA 2015. Finally, to ensure the safety and security of the networks we build, it is imperative that Congress reauthorize CISA 2015 so that we can continue to safely, quickly, and effectively share information across our industry and with our government partners.
Conclusion
Chair Fischer, Ranking Member Lujan: at the end of all of this are families in small towns across the country who want to know what any of it means for them - for their quality of life, for their kids, for the cost of running their business. The honest answer is that everything we have described arrives in their lives only if there is a network at their door capable of delivering it.
America's broadband providers have built these networks across a generation, at a scale few industries can match, and we are ready to keep building. We are not asking Congress to build them for us. We are asking you to clear the path - and, where the economics will not reach, to keep the federal partnership strong enough to finish the job.
The gateway is government. The levers that remain are the ones you hold. Pull them, and the powerful modern networks through which America's future transits will be built faster than anyone expects - and every community will be on the other end of the opportunities they make possible.
Thank you. I look forward to your questions.
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1 U.S. Bureau of Labor Statistics, Current Employment Statistics: Wired and Wireless Telecommunications Carriers (Except Satellite), NAICS 517111 (2024). This figure reflects broadband providers' direct employees; it excludes the construction and contractor trades that build and maintain networks, as well as satellite carriers.
2 Patience Haggin, "High-Speed Internet Boom Hits Low-Tech Snag: A Labor Shortage," Wall Street Journal, February 1, 2026, https://www.wsj.com/business/telecom/high-speed-internet-boom-hits-low-tech-snag-a-laborshortage9c92b514
3 JPMorganChase Institute, Understanding AI Use by Small Businesses (2026), https://www.jpmorganchase.com/institute/all-topics/business-growth-and-entrepreneurship/understanding-ai-usebysmall-businesses.
4 U.S. Chamber of Commerce, Empowering Small Business: The Impact of Technology on U.S. Small Business (2025), https://www.uschamber.com/technology/empowering-small-business-the-impact-of-technology-on-u-s-smallbusiness.
5 U.S. Chamber of Commerce Foundation, AI in Action: Early Lessons from Small Businesses on the Front Lines of Adoption (2026), https://www.hiringourheroes.org/resources/ai-in-action-whitepaper/.
6 Center for Effective Philanthropy, AI With Purpose: How Foundations and Nonprofits Are Thinking About and Using Artificial Intelligence (2025), https://cep.org/report-backpacks/ai-with-purpose-how-foundations-andnonprofits-arethinking-about-and-using-artificial-intelligence/
7 Goldman Sachs small business survey, as reported in Fox Business (August 8, 2025), https://www.foxbusiness.com/economy/small-business-ai-adoption-jumps-68-owners-plan-significantworkforcegrowth-2025; U.S. Chamber of Commerce, Empowering Small Business: The Impact of Technology on U.S. Small Business, supra note 4.
8 Phil Harvey, "The AI-Enabled Network Is Here. The Pitch Is Stuck in Traffic," Light Reading (May 29, 2026), https://www.lightreading.com/ai-machine-learning/the-ai-enabled-network-is-here-the-pitch-is-stuck-in-traffic
9 See National Institute of Standards and Technology, Cybersecurity Framework Profile for Artificial Intelligence (Cyber AI Profile): NIST Community Profile, NIST IR 8596, initial preliminary draft (December 2025), https://csrc.nist.gov/pubs/ir/8596/iprd.
10 Cisco, AI Impact on Wide Area Networks (2026), https://www.cisco.com/c/dam/en/us/solutions/collateral/artificialintelligence/mass-scale-infrastructure/ainetwork-traffic-report.pdf.
11 Cloudflare data reported, NBC News, Bot Web Traffic Has Overtaken Human Web Traffic, Data Shows (June 3, 2026), https://www.nbcnews.com/tech/tech-news/bot-web-traffic-overtaken-human-web-traffic-data-showsrcna348522.
12 U.S. Department of Transportation, Federal Highway Administration, Maricopa Association of Governments Finds Value in Connected Car Data, Crowdsourcing for Operations Case Study, FHWA-HOP-22-047 (2022), https://www.fhwa.dot.gov/innovation/everydaycounts/edc_6/docs/crowdsourcing_mag_case_study.p df
13 Cisco, AI Impact on Wide Area Networks (2026), supra note 10.
14 USTelecom, 2024 Broadband Capex Report (October 21, 2025), https://ustelecom.org/research/2024broadbandcapex-report/.
15 Consolidated Appropriations Act, 2018, Pub. L. No. 115-141, div. P, tit. VI (MOBILE NOW Act), Sec. 606 (establishing timelines for federal agency action on communications facility siting applications on federal property).
16 U.S. Government Accountability Office, Broadband: Agencies Should Improve Data Reliability and Coordination for Permitting on Federal Lands, GAO-24-106157 (2024), https://www.gao.gov/products/gao-24-106157.
17 Accelerating Wireless Broadband Deployment by Removing Barriers to Infrastructure Investment, Declaratory Ruling and Third Report and Order, WT Docket No. 17-79, 33 FCC Rcd 9088 (2018); CTIA, 2025 Annual Survey Highlights at 7 (2025), https://api.ctia.org/wp-content/uploads/2025/09/2025-Annual-Survey-Highlights.pdf.
18 CLOSE THE GAP Act, S. 4561, 119th Cong. (2026) (introduced by Sen. Barrasso, cosponsored by Sen. Lummis), https://www.congress.gov/bill/119th-congress/senate-bill/4561.
19 Broadband and Telecommunications RAIL Act, S.3268, 119th Cong. (2026) (introduced by Sen. Blackburn, cosponsored by Sen. Lujan), https://www.congress.gov/bill/119th-congress/senate-bill/3268.
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Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/379d9950-dc84-b1bf-2c6a-ee1ebb42fbe2/Spalter_Written_Testimony_7eddc348-33d3-4f43-b974-3023b1205597.pdf
Senate Finance Committee Ranking Member Wyden Issues Opening Statement at Hearing on Trade Policy Agenda
WASHINGTON, Aug. 6 -- Sen. Ron Wyden, D-Oregon, ranking member of the Senate Finance Committee, released the following opening statement from a July 22, 2026, hearing entitled "The President's 2026 Trade Policy Agenda":
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Donald Trump's trade agenda is dominated by big promises. First, he promised lower costs. Second, he promised to be tough on China. Third, he promised to open up new markets for American farmers and small businesses.
Those promises haven't panned out. About a year and a half into Trump's second term, farmers are on the ropes. I just got back from six town hall meetings ... Show Full Article WASHINGTON, Aug. 6 -- Sen. Ron Wyden, D-Oregon, ranking member of the Senate Finance Committee, released the following opening statement from a July 22, 2026, hearing entitled "The President's 2026 Trade Policy Agenda": * * * Donald Trump's trade agenda is dominated by big promises. First, he promised lower costs. Second, he promised to be tough on China. Third, he promised to open up new markets for American farmers and small businesses. Those promises haven't panned out. About a year and a half into Trump's second term, farmers are on the ropes. I just got back from six town hall meetingsin Eastern Oregon over the state work period. Wheat farmers in Eastern Oregon told me they're barely hanging on to family farms. They're getting hammered by high prices for key inputs like fuel and equipment.
Small businesses, like a local toy shop in Portland, are closing their doors after high tariffs made their business impossible. Uncertainty is costing workers jobs, including at Portland Coffee Roasters. Supply chains have been disrupted and small businesses are delaying investments in new employees and manufacturing.
Like every other promise Trump has made to working people, the second he set foot in the White House, he did nothing to put American businesses and workers first. Instead, he embarked on a tariff spree that put the cost of living crisis on steroids.
The Supreme Court eventually overturned Trump's global tariffs but the higher costs are here to stay. In 2025, Trump's tariffs cost the average American family $1,700, with grocery and gas prices way up.
This week, Trump announced massive new tariffs on clothes, school supplies and other products from Canada, using a provision passed in the Smoot-Hawley Tariff Act of 1930. No one, not even Herbert Hoover, ever used this provision before, but Trump dug up a zombie law to make things even more expensive for Americans.
Trump's next trade scheme is ordering USTR to reconstruct his illegal global tariffs under the guise of addressing forced labor. As I said in a recent letter, if the Administration wants to get serious about forced labor, the first step is to look in the mirror at its own enforcement record.
Trump is failing to enforce the laws we have on the books and has cut government efforts dedicated to fighting forced labor around the world.
Ambassador Greer, at our hearing last year, you told us that nearly 50 countries had approached you clamoring for trade deals.
Instead of agreements, we have press releases and announcements about supposed "deals" that haven't been approved by Congress, haven't been enforced, haven't opened markets, and haven't reduced tariffs. There's no there there.
The one piece of the Trump administration's trade agenda that does seem to be progressing is the corrupt enrichment of Trump, his family, and his allies.
* Trump lowered tariffs on Switzerland after Swiss billionaires gifted Trump a gold bar worth more than $100,000 and a custom Rolex clock.
* A Korean company under investigation for trade cheating paid Trump's holding company $2 million, and
* The Administration waived export restrictions on the U-A-E after an Emirati company invested $500 million in the Trump family's cryptocurrency.
I could go on, but I want to respect my colleagues' time. The bottom line is American families are paying more in tariffs so Trump and his cronies can profit.
The way to stop this corruption and chaos is to take unfettered tariff power out of the president's hands. It's well past time to put Congress back in the driver's seat on trade.
That's why today I am introducing my Congressional Trade Powers Reform Act.
It would overhaul existing trade powers to ensure that no future presidential administration can abuse its authority on trade and thrust us into a cost of living crisis. It would require Congressional approval of any tariff proposed by the president.
And it would establish an expert advisory committee to ensure Congress can exercise its rightful oversight over tariffs and trade. It's time to restore consistency, reliability, and success to our country's trade agenda.
I look forward to today's Q&A.
Chairman Crapo.
A web version of this statement is here (https://www.finance.senate.gov/ranking-members-news/wydens-statement-at-hearing-on-trumps-trade-agenda).
* * *
Original text here: https://www.finance.senate.gov/imo/media/doc/07222026_wyden_opening_statement.pdf
* * *
Donald Trump's trade agenda is dominated by big promises. First, he promised lower costs. Second, he promised to be tough on China. Third, he promised to open up new markets for American farmers and small businesses.
Those promises haven't panned out. About a year and a half into Trump's second term, farmers are on the ropes. I just got back from six town hall meetings ... Show Full Article WASHINGTON, Aug. 6 -- Sen. Ron Wyden, D-Oregon, ranking member of the Senate Finance Committee, released the following opening statement from a July 22, 2026, hearing entitled "The President's 2026 Trade Policy Agenda": * * * Donald Trump's trade agenda is dominated by big promises. First, he promised lower costs. Second, he promised to be tough on China. Third, he promised to open up new markets for American farmers and small businesses. Those promises haven't panned out. About a year and a half into Trump's second term, farmers are on the ropes. I just got back from six town hall meetingsin Eastern Oregon over the state work period. Wheat farmers in Eastern Oregon told me they're barely hanging on to family farms. They're getting hammered by high prices for key inputs like fuel and equipment.
Small businesses, like a local toy shop in Portland, are closing their doors after high tariffs made their business impossible. Uncertainty is costing workers jobs, including at Portland Coffee Roasters. Supply chains have been disrupted and small businesses are delaying investments in new employees and manufacturing.
Like every other promise Trump has made to working people, the second he set foot in the White House, he did nothing to put American businesses and workers first. Instead, he embarked on a tariff spree that put the cost of living crisis on steroids.
The Supreme Court eventually overturned Trump's global tariffs but the higher costs are here to stay. In 2025, Trump's tariffs cost the average American family $1,700, with grocery and gas prices way up.
This week, Trump announced massive new tariffs on clothes, school supplies and other products from Canada, using a provision passed in the Smoot-Hawley Tariff Act of 1930. No one, not even Herbert Hoover, ever used this provision before, but Trump dug up a zombie law to make things even more expensive for Americans.
Trump's next trade scheme is ordering USTR to reconstruct his illegal global tariffs under the guise of addressing forced labor. As I said in a recent letter, if the Administration wants to get serious about forced labor, the first step is to look in the mirror at its own enforcement record.
Trump is failing to enforce the laws we have on the books and has cut government efforts dedicated to fighting forced labor around the world.
Ambassador Greer, at our hearing last year, you told us that nearly 50 countries had approached you clamoring for trade deals.
Instead of agreements, we have press releases and announcements about supposed "deals" that haven't been approved by Congress, haven't been enforced, haven't opened markets, and haven't reduced tariffs. There's no there there.
The one piece of the Trump administration's trade agenda that does seem to be progressing is the corrupt enrichment of Trump, his family, and his allies.
* Trump lowered tariffs on Switzerland after Swiss billionaires gifted Trump a gold bar worth more than $100,000 and a custom Rolex clock.
* A Korean company under investigation for trade cheating paid Trump's holding company $2 million, and
* The Administration waived export restrictions on the U-A-E after an Emirati company invested $500 million in the Trump family's cryptocurrency.
I could go on, but I want to respect my colleagues' time. The bottom line is American families are paying more in tariffs so Trump and his cronies can profit.
The way to stop this corruption and chaos is to take unfettered tariff power out of the president's hands. It's well past time to put Congress back in the driver's seat on trade.
That's why today I am introducing my Congressional Trade Powers Reform Act.
It would overhaul existing trade powers to ensure that no future presidential administration can abuse its authority on trade and thrust us into a cost of living crisis. It would require Congressional approval of any tariff proposed by the president.
And it would establish an expert advisory committee to ensure Congress can exercise its rightful oversight over tariffs and trade. It's time to restore consistency, reliability, and success to our country's trade agenda.
I look forward to today's Q&A.
Chairman Crapo.
A web version of this statement is here (https://www.finance.senate.gov/ranking-members-news/wydens-statement-at-hearing-on-trumps-trade-agenda).
* * *
Original text here: https://www.finance.senate.gov/imo/media/doc/07222026_wyden_opening_statement.pdf
Nebraska Public Service Commissioner Watermeier Testifies Before Senate Commerce, Science & Transportation Subcommittee
WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following testimony by Nebraska Public Service Commissioner Dan Watermeier from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications":
* * *
Good morning, Chair Fischer and members of the Committee. My name is Dan Watermeier, and I represent Nebraska's First District on the Nebraska Public Service Commission. I appreciate the invitation to speak to you today about the future of AI technology and the ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following testimony by Nebraska Public Service Commissioner Dan Watermeier from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications": * * * Good morning, Chair Fischer and members of the Committee. My name is Dan Watermeier, and I represent Nebraska's First District on the Nebraska Public Service Commission. I appreciate the invitation to speak to you today about the future of AI technology and theopportunities - and the challenges - it offers in Nebraska and on the national scale.
The Nebraska Public Service Commission is dedicated to the advancement of broadband funding. Since the early 2000s, we have administered the Nebraska Universal Service Fund, which now supports the development and maintenance of broadband-capable networks at speeds of at least 100/100 Mbps. We have also administered the Nebraska Broadband Bridge Program and the federally funded Capital Projects Fund, which over the course of just a few grant cycles funded broadband deployment projects and fueled competition to build new service to nearly 20,000 unserved and underserved locations across the state.
Over the past five years, we have been fortunate to receive federal and state support for this work. I'm excited to see how this enthusiasm will grow as we develop new AI tools. In Nebraska, AI could become a tremendous support for our agricultural producers. Precision agriculture tools are fantastic ways to conserve water, monitor livestock health, and target application of fertilizers and pesticides, saving our farmers money and protecting the environment at the same time. These AI-powered tools are the future of agriculture. But we need reliable, universally available broadband in order to use them.
As adoption of - and reliance on - AI technology increases, it will become even more important to ensure that all Americans have access to reliable, high-speed broadband networks. We need to ensure that rural Americans are not left behind as this technology moves forward. For the average citizen, AI technology is only useful if they have access to broadband connectivity. We must continue funding rural buildout, or this technology simply won't be available for all who need it.
I also want to mention the importance of fiber buildout for an AI future. While we recognize that many technologies may be able to deliver broadband to our citizens, our commission has focused on fiber deployments for a few reasons:
* First, scalability. Not every technology can easily meet the FCC's current 100/20 Mbps threshold defining broadband-capable speed. Meanwhile, fiber-optic networks can greatly exceed those speed thresholds, and are capable of meeting our needs into the foreseeable future. I want to point out that data centers aren't using wireless or satellite technology to move data - they are using fiber.
* Second, supporting development. Fiber is the backbone for basically all other technologies. We need fiber-based networks to support wireless, fixed wireless, and cable networks.
* Third, reliability. Buried fiber is much less susceptible to weather hazards and natural disasters. Fires, tornadoes, and ice storms in recent years in Nebraska have shown the need for the resiliency that fiber can provide.
I also want to mention that deploying broadband infrastructure will not be enough if these networks are not maintained. We have heard from industry providers that although the investment in fiber itself may last for 40 years, the circuitry required by fiber must be upgraded much more frequently to accommodate changing technology. This is to be expected as demands on the networks for download and upload capacity, as well as latency demands, continue to grow. The tasks AI is capable of today will be eclipsed by what it can do five years from now - so long as the network is upgraded to keep up. For that reason, I want to emphasize that new buildout must be as future-proof as possible in the face of developing technology.
Senator Fischer, I know that you recognize the importance of maintaining broadband networks. Hopefully it is clear that AI technology is dependent on these networks. I also think it is important to recognize that many of these networks are supported through federal and state universal service funds. I, and the Nebraskans I represent, are grateful for your work and the work of your colleagues on the Universal Service Fund Working Group. I look forward to hearing the group's recommendations for the future of the USF. I know there is discussion as to how the federal USF is funded. I would just point out that AI creates, and will create, significant demand on the network - a network that is supported today through surcharges on voice service.
Thank you for the invitation to speak with you today. I'm happy to answer any questions you may have.
* * *
Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/379d9950-dc84-b1bf-2c6a-ee1ebb42fbe2/Watermeier-Senate-Testimony-7.30.2026-Hearing-1.pdf
* * *
Good morning, Chair Fischer and members of the Committee. My name is Dan Watermeier, and I represent Nebraska's First District on the Nebraska Public Service Commission. I appreciate the invitation to speak to you today about the future of AI technology and the ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following testimony by Nebraska Public Service Commissioner Dan Watermeier from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications": * * * Good morning, Chair Fischer and members of the Committee. My name is Dan Watermeier, and I represent Nebraska's First District on the Nebraska Public Service Commission. I appreciate the invitation to speak to you today about the future of AI technology and theopportunities - and the challenges - it offers in Nebraska and on the national scale.
The Nebraska Public Service Commission is dedicated to the advancement of broadband funding. Since the early 2000s, we have administered the Nebraska Universal Service Fund, which now supports the development and maintenance of broadband-capable networks at speeds of at least 100/100 Mbps. We have also administered the Nebraska Broadband Bridge Program and the federally funded Capital Projects Fund, which over the course of just a few grant cycles funded broadband deployment projects and fueled competition to build new service to nearly 20,000 unserved and underserved locations across the state.
Over the past five years, we have been fortunate to receive federal and state support for this work. I'm excited to see how this enthusiasm will grow as we develop new AI tools. In Nebraska, AI could become a tremendous support for our agricultural producers. Precision agriculture tools are fantastic ways to conserve water, monitor livestock health, and target application of fertilizers and pesticides, saving our farmers money and protecting the environment at the same time. These AI-powered tools are the future of agriculture. But we need reliable, universally available broadband in order to use them.
As adoption of - and reliance on - AI technology increases, it will become even more important to ensure that all Americans have access to reliable, high-speed broadband networks. We need to ensure that rural Americans are not left behind as this technology moves forward. For the average citizen, AI technology is only useful if they have access to broadband connectivity. We must continue funding rural buildout, or this technology simply won't be available for all who need it.
I also want to mention the importance of fiber buildout for an AI future. While we recognize that many technologies may be able to deliver broadband to our citizens, our commission has focused on fiber deployments for a few reasons:
* First, scalability. Not every technology can easily meet the FCC's current 100/20 Mbps threshold defining broadband-capable speed. Meanwhile, fiber-optic networks can greatly exceed those speed thresholds, and are capable of meeting our needs into the foreseeable future. I want to point out that data centers aren't using wireless or satellite technology to move data - they are using fiber.
* Second, supporting development. Fiber is the backbone for basically all other technologies. We need fiber-based networks to support wireless, fixed wireless, and cable networks.
* Third, reliability. Buried fiber is much less susceptible to weather hazards and natural disasters. Fires, tornadoes, and ice storms in recent years in Nebraska have shown the need for the resiliency that fiber can provide.
I also want to mention that deploying broadband infrastructure will not be enough if these networks are not maintained. We have heard from industry providers that although the investment in fiber itself may last for 40 years, the circuitry required by fiber must be upgraded much more frequently to accommodate changing technology. This is to be expected as demands on the networks for download and upload capacity, as well as latency demands, continue to grow. The tasks AI is capable of today will be eclipsed by what it can do five years from now - so long as the network is upgraded to keep up. For that reason, I want to emphasize that new buildout must be as future-proof as possible in the face of developing technology.
Senator Fischer, I know that you recognize the importance of maintaining broadband networks. Hopefully it is clear that AI technology is dependent on these networks. I also think it is important to recognize that many of these networks are supported through federal and state universal service funds. I, and the Nebraskans I represent, are grateful for your work and the work of your colleagues on the Universal Service Fund Working Group. I look forward to hearing the group's recommendations for the future of the USF. I know there is discussion as to how the federal USF is funded. I would just point out that AI creates, and will create, significant demand on the network - a network that is supported today through surcharges on voice service.
Thank you for the invitation to speak with you today. I'm happy to answer any questions you may have.
* * *
Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/379d9950-dc84-b1bf-2c6a-ee1ebb42fbe2/Watermeier-Senate-Testimony-7.30.2026-Hearing-1.pdf
Cisco Chief Architect Everson Testifies Before Senate Commerce, Science & Transportation Subcommittee
WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following written testimony by Bob Everson, chief architect of provider mobility at Cisco, from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications":
* * *
1. Introduction.
Chairman Fischer, Ranking Member Lujan, Chairman Cruz, Ranking Member Cantwell, and distinguished Senators, thank you for the invitation to testify today. My name is Bob Everson, and I am a proud resident of Dallas, Texas. I have ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following written testimony by Bob Everson, chief architect of provider mobility at Cisco, from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications": * * * 1. Introduction. Chairman Fischer, Ranking Member Lujan, Chairman Cruz, Ranking Member Cantwell, and distinguished Senators, thank you for the invitation to testify today. My name is Bob Everson, and I am a proud resident of Dallas, Texas. I havespent more than 25 years at Cisco, where I currently serve as chief architect of our provider mobility team. In this role, I lead Cisco's platform definition and development, end-to-end architecture, ecosystem strategy, and strategic partnerships. My focus includes developing AI systems that operate across distributed environments and defining the mobile networking technologies, architectures, and products that advance enterprise mobility, IoT, and the platforms and ecosystems needed to bring these capabilities to scale.
For decades, Cisco has served as the foundational layer for the world's most critical networks, supporting a diverse customer base that spans service providers, global enterprises, government agencies, and small-to-medium businesses. Today, Cisco is building the critical infrastructure for the AI era. Our goal is to ensure the United States remains the global leader in fixed and wireless technology by building a secure, interoperable infrastructure ecosystem where licensed, unlicensed, and satellite networks work in concert and where networks are secure, observable, and highly scalable. While Cisco develops the infrastructure, our customers--whether fixed or mobile service providers, enterprises, governments, or hyperscalers--operate their own networks. For service providers, Cisco provides the core infrastructure behind the tower and the Radio Access Network (RAN).
Beyond hardware, we are integrating AI into the network itself. Cisco's deep expertise in network analytics and security makes it possible for us to automate operations, predict and prevent anomalies in real time, and enable customers to bring AI models into the enterprise with visibility and trust by leveraging the network. We recognize that AI workloads--characterized by massive data movement, high-density traffic, and some anticipated use cases requiring submillisecond latency--place unprecedented demands on the network. By combining our industryleading networking, security, and observability portfolios, Cisco is ensuring that as organizations scale their AI initiatives, they do so on a foundation of resilience, security, and performance.
I want to first thank this Committee, and Congress, for its tireless work to make 800 MHz of spectrum available for commercial use and for recognizing the importance of the 6 GHz band to enterprise networks powering schools, manufacturing facilities, hospitals, and campuses of all sizes. A balanced spectrum policy is critical to enhancing service for consumers and businesses in both rural and urban areas and across different modes and use cases of technology. I would also like to thank Federal Communications Commission (FCC) Chairman Brendan Carr and National Telecommunications and Information Administration (NTIA) Administrator Arielle Roth for their leadership in bringing these efforts to life. As I will explain, network traffic is only going to increase in volume and complexity, and these policies will help enable continued U.S. technological leadership.
At Cisco, our approach toward AI in communications networks can be distilled into two questions: How is AI reshaping our networks, and how can networks leverage the power of AI?
Today, I will address these questions and share how Cisco is building the infrastructure for more resilient, secure, and capable networks. My testimony draws on three recently released reports that draw from real-world traffic analysis, third-party data, Cisco-controlled lab tests with AI agents, and interviews with more than 6,000 organizations, all of which I have appended to my written testimony.
2. AI Usage Is Reshaping the Network.
AI traffic has led to increased demand--we expect a 209% increase in campus and branch traffic volume in the next three years. However, the more consequential shift is in the nature of that traffic. Today, I will highlight what drives that change and how networks and policy must evolve to support its deployment and adoption.1 AI workloads are not like traditional web traffic: they will change traffic shape, symmetry, duration, and criticality.2 AI workloads are also twice as long in duration and significantly more upstream-heavy due to large, context-rich prompts. The use of new agentic AI, AI inferencing, and applications built on the connectivity fabric that AI-native networks will make possible are largely driving these workloads.
Agentic AI: AI agents are supercharging demand on the network, generating up to 450% more traffic per task than humans in a Cisco-controlled test. Unlike consumer-driven traffic, which produces minor, episodic spikes throughout the day, AI agents are always on.3 Agents may continually perform automated network configuration checks, analyze telemetry data, or interact with other agents to perform business functions.
Additionally, survey data shows 67% of respondents reporting an increase in "east-west" traffic.4,5 Modern AI models are rarely processed by a single server; they are distributed across clusters of GPUs and compute nodes. These nodes must constantly communicate with one another to share model parameters, gradients, and training data, which creates a massive amount of lateral traffic within a network. Traditional planning assumptions (e.g., burstiness, downlink dominance, human-paced interactions) will need to adapt to the new reality of AI and agentic AI traffic that lasts longer, demands more upstream capacity, and operates at software speed.
Continuous measurement of AI-specific Key Performance Indicators (KPIs) and adaptive network architectures will be essential to sustain expectations for performance and user experience, particularly as network operators use agentic operations to assist with network management functions.
AI Inferencing: While AI inference traffic is currently negligible compared to dominant categories such as video streaming, observed growth rates are exceptional. Token-consumption data shows nearly 10x year-over-year growth, while some service provider measurements show around 4x growth in just the last eight months.6 Sustained growth at these rates signals that AI traffic will become a meaningful component of overall network traffic by 2035.7 AI inference paths will also become strategic network assets, requiring high levels of resilience, observability, and differentiated treatment (e.g., Quality of Service (QoS) and path security).8
* * *
1 "AI Impact on Wide Area Networks," Cisco. (June 2026). Available at: https://www.cisco.com/c/dam/en/us/solutions/collateral/artificial-intelligence/mass-scale-infrastructure/ai-networktraffic-report.pdf.
2 Id.
3 Supra, Note 1.
4 "East-West" traffic refers to the movement of data between servers, devices, or nodes within a network, rather than moving into or out of the network.
5 "No Time to Wait: The Accelerating Impact of AI on Campus and Branch Networks," Cisco. (June 2026). Available at: https://www.cisco.com/c/dam/m/en_us/solutions/networking/ai-impact-campus-branchnetworks/documents/the-accelerating-impact-of-ai-on-campus-and-branch-networks.pdf.
* * *
Enterprise Networks: In campus and branch networks--like the one that powers the Senate office building we are sitting in today--we have already seen customers report a 34% increase in traffic tied to AI workloads over the last 12 months, and they expect to see a 96% increase this coming year.9 Half of enterprise customers report that AI demand is concentrated on their Wi-Fi networks, and 73% of organizations already face or expect to face campus and branch capacity limitations within the next 24 months.10 This is largely because large majorities of organizations report increases in east-west traffic, latency-sensitive traffic, and continuous, automated AI traffic.11 While the large majority of AI to date has come from foundation models running on central infrastructure, we are seeing enterprises deploy more small language models, open-source models, and specialized models--such as vision and voice models--which can be distributed throughout the network. Each of these characteristics underscores the value of the FCC's forward-thinking decision in 2020 to authorize the full 6 GHz band for unlicensed Wi-Fi use. Indeed, wireless leaders deploying 6 GHz-capable Wi-Fi networks report that the band is solving their congestion issues, enabling high-bandwidth applications, and supporting AI workloads and applications. These developments are translating into concrete economic benefits: one study reports that the U.S. 6 GHz decision will generate $1.2 trillion in economic value per year by 2027./12
Infrastructure: AI is driving the shift toward edge computing. Service providers must also consider "AI-native" traffic profiles for several reasons, including technical considerations, cost, and data sovereignty and security issues.
* Technical: Physical AI use cases such as robotics, autonomous vehicles, and industrial automation could require sub-millisecond decision-making. If an autonomous robot sends data to a central cloud and has to wait for a response, the round-trip latency could be too high for safe, real-time operation.
* Cost: AI operations generate massive amounts of data. For example, high-definition video analytics for public safety can generate terabytes of data daily. Backhauling that data to a central cloud is prohibitively expensive and creates massive network congestion.
* Data Sovereignty and Security: Enterprises and governments are increasingly concerned about data sovereignty and security. Many customers have security or regulatory concerns about moving sensitive information across the public internet to a third-party cloud.
* * *
6 Supra, Note 1.
7 Id.
8 Id.
9 Supra, Note 5.
10 Id.
11 Id.
12 Telecom Advisory Services, "Assessing the Economic Value of Wi-Fi in the United States" (September 2024), Available at: https://www.teleadvs.com/assessing-the-economic-value-of-wi-fi-in-the-united-states/.
* * *
AI operations at the edge of the network address each of these challenges. Latency is dramatically reduced by cutting the distance data needs to travel. Processing data locally similarly allows for transmission of only the result to the cloud, reducing bandwidth and cloudrelated costs. And operating at the edge keeps sensitive data local, reducing the risk of interception or unauthorized access.
3. Networks Will Leverage AI for Increased Performance and Resiliency.
While AI workloads present several challenges for network operators seeking to ensure seamless performance, reliability, and security, there is a tremendous opportunity to leverage AI to deliver new applications and better performance, infuse security into the fabric of the network, and manage the increased complexity.
Agentic Operations: Agentic AI will change the nature of traffic on the network, but it will also provide network operators new tools to operate at machine speed and deliver greater performance, efficiency, and security. For service providers, whose networks are the backbone of the entire digital economy, AgenticOps--the shift from manual, human-led network management to an autonomous, AI-driven operational model overseen by humans--is not just a luxury; it is a necessity for managing the exponential growth in traffic and the stringent latency requirements of AI-native applications. Cisco is helping service providers navigate the transition to AgenticOps by providing the infrastructure, telemetry, and intelligence required to manage the massive complexity of modern networks. Cisco's products help service providers close "visibility gaps" in complex, distributed networks. Cisco's deep visibility tools allow AI agents to map the entire path of a data flow across public and private infrastructure to identify the root cause of latency or packet loss in seconds rather than hours.
AgenticOps also allows the network to act as a self-healing system. Cisco's AI-native tools enable the network to reroute traffic, adjust capacity, or reconfigure network nodes when the system detects performance degradation or an impending hardware failure.13 This dramatically increases uptime and reliability for mission-critical services. AI-driven networks can also help deliver greater energy efficiency by continuously optimizing network operations through intelligent automation using real-time telemetry.14 AgenticOps enables networks to dynamically adjust power usage, traffic routing, and resource allocation, allowing operators to power down underutilized components during low-demand periods and reduce energy consumption without compromising service quality.15
One of the most significant challenges for network operators is the talent gap in managing increasingly complex, software-defined networks. AgenticOps allows operators to automate repetitive, low-value tasks--such as ticket resolution, configuration updates, and routine maintenance. These tools also help close the workforce talent gap by lowering the barrier to entry and allowing more junior analysts to ramp up quickly. By automating these tasks, Cisco's AI-enabled platforms can free network engineers to focus on higher-level architectural strategy and innovation, and free cybersecurity analysts to dedicate more time to strategic threat hunting and detection engineering.
* * *
13 Masum Mir, "Leading the Next Era of Intelligent Connectivity," (Oct. 28, 2025). Available at: https://blogs.cisco.com/news/leading-the-next-era-of-intelligentconnectivity?dtid=osolie001456&utm_loc=ar&utm_lang=en.
14 "Energy Management in the AI Era: Cisco's Digital Transformation Advantage," Available at: https://blogs.cisco.com/industries/energy-management-in-the-ai-era-ciscos-digital-transformation-advantage
15 Id.
* * *
Resiliency and Security: In an AgenticOps environment, the network transitions from a passive transport layer to the primary cybersecurity enforcement point. Cisco empowers service providers to implement AI-native security that operates at machine speed--a critical requirement in today's dynamic threat landscape.16 By leveraging Cisco's purpose-built Deep Network Models, we analyze traffic patterns in real time to distinguish between legitimate AI-driven workloads and malicious anomalies.17 This predictive capability allows our customers to identify vulnerabilities before they escalate. Should a device or service be compromised, the network can autonomously isolate the threat, halt lateral movement and safeguard the broader service provider infrastructure against sophisticated cyberattacks.18
Moreover, AI-native architectures enable network operators to simulate and validate cybersecurity patches on a digital twin of the network.19 This capability eliminates the "patching paradox"--where the fear of downtime prevents critical security updates--by ensuring that every change is tested for stability and performance at machine speed. To maintain essential human oversight in an AgenticOps environment, we enable network operators to leverage a collaborative interface that allows security engineers to visualize, discuss, and refine complex policies with AI assistance.20 This human-in-the-loop approach ensures that while our network operations run at machine speed, strategic security decisions remain guided by human expertise and transparent, shared visibility.
AI-Enabled Network Applications: In addition to changes in traffic patterns, networks are moving toward AI-native platforms that will become the fabric of intelligent connectivity rather than a simple pipe. As network operators move compute toward the network edge--such as at a cell site where a tower sits--they will be able to run applications directly from the network.
One promising application is Integrated Sensing and Communication (ISAC), which combines wireless communications and radio-frequency sensing to "see" objects' position and path using radio waves that reflect off them. Unlike optical sensors, it can detect intrusion even in low-light conditions, through smoke, or around obstructions where traditional video analytics might fail. This technology has been prototyped and demonstrated already, and it holds great promise for autonomous systems and robotics, AI-driven smart facilities, and public safety.
* * *
16 "Shields Up: Cisco Live Protect Closes Vulnerability Gap with Compensating Controls," June, 2, 2026. Available at: https://blogs.cisco.com/news/shields-up-cisco-live-protect-closes-vulnerability-gap-with-compensating-controls
17"Cisco Hypershield - Our Vision to Combat Unknown Vulnerabilities," Available at: https://blogs.cisco.com/security/cisco-hypershield-our-vision-to-combat-unknown-vulnerabilities.
18 Id.
19 See, https://www.cisco.com/c/en/us/products/collateral/security/hypershield/hypershieldso.html#Solvingrealcustomerchallenges.
20 See, https://blogs.cisco.com/ai/ai-canvas-controlled-availability.
* * *
Robotics: For example, ISAC enables the network to provide collision avoidance and path optimization, offloading some of the computational burden from the robot's own onboard sensors and ensuring safer operations in complex industrial environments.
AI-Driven Smart Facilities: By sensing occupancy and environmental changes through ISAC, the network can dynamically adjust energy, HVAC, and lighting systems in enterprise campuses. This creates "self-aware" buildings that optimize for both human comfort and energy efficiency.
Public Safety: Because the network can act as a pervasive radar system to track objects in restricted areas, ISAC could support Counter-Unmanned Aircraft Systems missions leveraging commercial networks around a stadium or critical infrastructure.
Cisco and its partners are already exploring this technology, having demonstrated an early version at NVIDIA's GTC DC Conference last year and at Mobile World Congress Barcelona this spring.
The network will also enable 'Physical AI', which is the integration of digital intelligence with physical action that allows machines to operate independently in environments where conditions change continuously.21 While traditional AI is confined to software-based analysis, physical AI is designed to interact with the physical world. Physical AI applications--whether a warehouse robot rerouting around an unexpected obstacle, robotics in a manufacturing facility, or intelligent transportation infrastructure--will drive significant demand and constraints on the network while also opening new business monetization opportunities for network operators.
Demand is driven by several factors: 1) data collection from many types of sensors such as cameras for visual detail, lidar for depth, and radar for motion detection; 2) decision-making and reinforcement learning (where the model improves by attempting physical actions and receiving feedback to improve future action); and, 3) performing physical action through mechanical components like motors, conveyors, or robotic arms.22
Each of these applications represents how AI is changing the network design: they require compute at the edge. They require an AI-native network core capable of integrating terrestrial and non-terrestrial networks and they require additional capacity to manage east-west traffic as agents, devices, and networks work in concert. Ultimately, Cisco envisions a combination of interoperable technologies to ensure ubiquitous, seamless, secure, and reliable connectivity for all end users, regardless of whether communications travel on wired, licensed, unlicensed, or satellite networks.
4. Policy Considerations.
As this Committee considers the changing landscape, I offer three suggestions that will help enable continued U.S. leadership and better outcomes for American consumers and businesses.
* * *
21 See, https://www.cisco.com/site/us/en/learn/topics/artificial-intelligence/what-is-physical-ai.html.
22 Id.
* * *
First, we must work together to protect American leadership in AI and compute and remain focused on building an American AI-native tech stack for wireless networks. However, we cannot wait for 6G standards to begin deployment; and we aren't. Through the AI-WIN partnership, Cisco, NVIDIA, MITRE, Booz Allen Hamilton, ODC, and T-Mobile are collaborating to develop America's first AI-native wireless telecommunications stack, while also developing innovative technologies to support new use cases in 5G-Advanced networks. We seek to leverage American leadership in AI and compute to demonstrate technical success through real-world deployments, ultimately shaping international standards to meet American companies where the technology is moving. I encourage Congress to lean in on areas where the United States has a strategic leadership role, such as compute, core networking, and applications.
Second, as compute infrastructure becomes more distributed and moves closer to the network edge, service providers may face increased infrastructure demands. Policies that enable the responsible and efficient permitting and deployment of this infrastructure will be essential to helping service providers offer new services as the network becomes an AI-native platform.
Additionally, as Congress considers the future of the Universal Service Fund, we encourage you to consider how these changes in network traffic will affect the operational costs borne by rural health care facilities, service providers, and schools, regardless of whether they are rural or urban.
Finally, I encourage this Committee to continue its bipartisan approach to a balanced spectrum policy. As WRC-27 approaches, the United States needs to maintain its global leadership and expand its support for unlicensed operations in the 6 GHz band, including by authorizing higher-power operations and allowing expanded use in key enterprise environments.
Likewise, the 800 MHz of spectrum made available for mobile use will be essential to enabling the connectivity needed to power new applications on AI-native networks, including the volume of uplink traffic we expect to see.
5. Conclusion.
We are at an exciting inflection point in how AI is transforming our communications networks. As Cisco works with service providers, critical infrastructure operators, enterprises, and governments to deploy the foundational infrastructure of the AI era, we are proud to offer our expertise to this Committee and to Congress to help shape a technology future that connects consumers, businesses, and communities. Thank you for the invitation to testify, and I look forward to answering your questions.
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Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/379d9950-dc84-b1bf-2c6a-ee1ebb42fbe2/Everson_Testimony-7.30.26-Final_d1fc268a-ed9e-42a7-abbe-d52a960f49e1.pdf
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1. Introduction.
Chairman Fischer, Ranking Member Lujan, Chairman Cruz, Ranking Member Cantwell, and distinguished Senators, thank you for the invitation to testify today. My name is Bob Everson, and I am a proud resident of Dallas, Texas. I have ... Show Full Article WASHINGTON, Aug. 6 -- The Senate Commerce, Science and Transportation Subcommittee on Telecommunications and Media released the following written testimony by Bob Everson, chief architect of provider mobility at Cisco, from a July 30, 2026, hearing entitled "Intelligent Networks: Powering Artificial Intelligence and Transforming Communications": * * * 1. Introduction. Chairman Fischer, Ranking Member Lujan, Chairman Cruz, Ranking Member Cantwell, and distinguished Senators, thank you for the invitation to testify today. My name is Bob Everson, and I am a proud resident of Dallas, Texas. I havespent more than 25 years at Cisco, where I currently serve as chief architect of our provider mobility team. In this role, I lead Cisco's platform definition and development, end-to-end architecture, ecosystem strategy, and strategic partnerships. My focus includes developing AI systems that operate across distributed environments and defining the mobile networking technologies, architectures, and products that advance enterprise mobility, IoT, and the platforms and ecosystems needed to bring these capabilities to scale.
For decades, Cisco has served as the foundational layer for the world's most critical networks, supporting a diverse customer base that spans service providers, global enterprises, government agencies, and small-to-medium businesses. Today, Cisco is building the critical infrastructure for the AI era. Our goal is to ensure the United States remains the global leader in fixed and wireless technology by building a secure, interoperable infrastructure ecosystem where licensed, unlicensed, and satellite networks work in concert and where networks are secure, observable, and highly scalable. While Cisco develops the infrastructure, our customers--whether fixed or mobile service providers, enterprises, governments, or hyperscalers--operate their own networks. For service providers, Cisco provides the core infrastructure behind the tower and the Radio Access Network (RAN).
Beyond hardware, we are integrating AI into the network itself. Cisco's deep expertise in network analytics and security makes it possible for us to automate operations, predict and prevent anomalies in real time, and enable customers to bring AI models into the enterprise with visibility and trust by leveraging the network. We recognize that AI workloads--characterized by massive data movement, high-density traffic, and some anticipated use cases requiring submillisecond latency--place unprecedented demands on the network. By combining our industryleading networking, security, and observability portfolios, Cisco is ensuring that as organizations scale their AI initiatives, they do so on a foundation of resilience, security, and performance.
I want to first thank this Committee, and Congress, for its tireless work to make 800 MHz of spectrum available for commercial use and for recognizing the importance of the 6 GHz band to enterprise networks powering schools, manufacturing facilities, hospitals, and campuses of all sizes. A balanced spectrum policy is critical to enhancing service for consumers and businesses in both rural and urban areas and across different modes and use cases of technology. I would also like to thank Federal Communications Commission (FCC) Chairman Brendan Carr and National Telecommunications and Information Administration (NTIA) Administrator Arielle Roth for their leadership in bringing these efforts to life. As I will explain, network traffic is only going to increase in volume and complexity, and these policies will help enable continued U.S. technological leadership.
At Cisco, our approach toward AI in communications networks can be distilled into two questions: How is AI reshaping our networks, and how can networks leverage the power of AI?
Today, I will address these questions and share how Cisco is building the infrastructure for more resilient, secure, and capable networks. My testimony draws on three recently released reports that draw from real-world traffic analysis, third-party data, Cisco-controlled lab tests with AI agents, and interviews with more than 6,000 organizations, all of which I have appended to my written testimony.
2. AI Usage Is Reshaping the Network.
AI traffic has led to increased demand--we expect a 209% increase in campus and branch traffic volume in the next three years. However, the more consequential shift is in the nature of that traffic. Today, I will highlight what drives that change and how networks and policy must evolve to support its deployment and adoption.1 AI workloads are not like traditional web traffic: they will change traffic shape, symmetry, duration, and criticality.2 AI workloads are also twice as long in duration and significantly more upstream-heavy due to large, context-rich prompts. The use of new agentic AI, AI inferencing, and applications built on the connectivity fabric that AI-native networks will make possible are largely driving these workloads.
Agentic AI: AI agents are supercharging demand on the network, generating up to 450% more traffic per task than humans in a Cisco-controlled test. Unlike consumer-driven traffic, which produces minor, episodic spikes throughout the day, AI agents are always on.3 Agents may continually perform automated network configuration checks, analyze telemetry data, or interact with other agents to perform business functions.
Additionally, survey data shows 67% of respondents reporting an increase in "east-west" traffic.4,5 Modern AI models are rarely processed by a single server; they are distributed across clusters of GPUs and compute nodes. These nodes must constantly communicate with one another to share model parameters, gradients, and training data, which creates a massive amount of lateral traffic within a network. Traditional planning assumptions (e.g., burstiness, downlink dominance, human-paced interactions) will need to adapt to the new reality of AI and agentic AI traffic that lasts longer, demands more upstream capacity, and operates at software speed.
Continuous measurement of AI-specific Key Performance Indicators (KPIs) and adaptive network architectures will be essential to sustain expectations for performance and user experience, particularly as network operators use agentic operations to assist with network management functions.
AI Inferencing: While AI inference traffic is currently negligible compared to dominant categories such as video streaming, observed growth rates are exceptional. Token-consumption data shows nearly 10x year-over-year growth, while some service provider measurements show around 4x growth in just the last eight months.6 Sustained growth at these rates signals that AI traffic will become a meaningful component of overall network traffic by 2035.7 AI inference paths will also become strategic network assets, requiring high levels of resilience, observability, and differentiated treatment (e.g., Quality of Service (QoS) and path security).8
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1 "AI Impact on Wide Area Networks," Cisco. (June 2026). Available at: https://www.cisco.com/c/dam/en/us/solutions/collateral/artificial-intelligence/mass-scale-infrastructure/ai-networktraffic-report.pdf.
2 Id.
3 Supra, Note 1.
4 "East-West" traffic refers to the movement of data between servers, devices, or nodes within a network, rather than moving into or out of the network.
5 "No Time to Wait: The Accelerating Impact of AI on Campus and Branch Networks," Cisco. (June 2026). Available at: https://www.cisco.com/c/dam/m/en_us/solutions/networking/ai-impact-campus-branchnetworks/documents/the-accelerating-impact-of-ai-on-campus-and-branch-networks.pdf.
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Enterprise Networks: In campus and branch networks--like the one that powers the Senate office building we are sitting in today--we have already seen customers report a 34% increase in traffic tied to AI workloads over the last 12 months, and they expect to see a 96% increase this coming year.9 Half of enterprise customers report that AI demand is concentrated on their Wi-Fi networks, and 73% of organizations already face or expect to face campus and branch capacity limitations within the next 24 months.10 This is largely because large majorities of organizations report increases in east-west traffic, latency-sensitive traffic, and continuous, automated AI traffic.11 While the large majority of AI to date has come from foundation models running on central infrastructure, we are seeing enterprises deploy more small language models, open-source models, and specialized models--such as vision and voice models--which can be distributed throughout the network. Each of these characteristics underscores the value of the FCC's forward-thinking decision in 2020 to authorize the full 6 GHz band for unlicensed Wi-Fi use. Indeed, wireless leaders deploying 6 GHz-capable Wi-Fi networks report that the band is solving their congestion issues, enabling high-bandwidth applications, and supporting AI workloads and applications. These developments are translating into concrete economic benefits: one study reports that the U.S. 6 GHz decision will generate $1.2 trillion in economic value per year by 2027./12
Infrastructure: AI is driving the shift toward edge computing. Service providers must also consider "AI-native" traffic profiles for several reasons, including technical considerations, cost, and data sovereignty and security issues.
* Technical: Physical AI use cases such as robotics, autonomous vehicles, and industrial automation could require sub-millisecond decision-making. If an autonomous robot sends data to a central cloud and has to wait for a response, the round-trip latency could be too high for safe, real-time operation.
* Cost: AI operations generate massive amounts of data. For example, high-definition video analytics for public safety can generate terabytes of data daily. Backhauling that data to a central cloud is prohibitively expensive and creates massive network congestion.
* Data Sovereignty and Security: Enterprises and governments are increasingly concerned about data sovereignty and security. Many customers have security or regulatory concerns about moving sensitive information across the public internet to a third-party cloud.
* * *
6 Supra, Note 1.
7 Id.
8 Id.
9 Supra, Note 5.
10 Id.
11 Id.
12 Telecom Advisory Services, "Assessing the Economic Value of Wi-Fi in the United States" (September 2024), Available at: https://www.teleadvs.com/assessing-the-economic-value-of-wi-fi-in-the-united-states/.
* * *
AI operations at the edge of the network address each of these challenges. Latency is dramatically reduced by cutting the distance data needs to travel. Processing data locally similarly allows for transmission of only the result to the cloud, reducing bandwidth and cloudrelated costs. And operating at the edge keeps sensitive data local, reducing the risk of interception or unauthorized access.
3. Networks Will Leverage AI for Increased Performance and Resiliency.
While AI workloads present several challenges for network operators seeking to ensure seamless performance, reliability, and security, there is a tremendous opportunity to leverage AI to deliver new applications and better performance, infuse security into the fabric of the network, and manage the increased complexity.
Agentic Operations: Agentic AI will change the nature of traffic on the network, but it will also provide network operators new tools to operate at machine speed and deliver greater performance, efficiency, and security. For service providers, whose networks are the backbone of the entire digital economy, AgenticOps--the shift from manual, human-led network management to an autonomous, AI-driven operational model overseen by humans--is not just a luxury; it is a necessity for managing the exponential growth in traffic and the stringent latency requirements of AI-native applications. Cisco is helping service providers navigate the transition to AgenticOps by providing the infrastructure, telemetry, and intelligence required to manage the massive complexity of modern networks. Cisco's products help service providers close "visibility gaps" in complex, distributed networks. Cisco's deep visibility tools allow AI agents to map the entire path of a data flow across public and private infrastructure to identify the root cause of latency or packet loss in seconds rather than hours.
AgenticOps also allows the network to act as a self-healing system. Cisco's AI-native tools enable the network to reroute traffic, adjust capacity, or reconfigure network nodes when the system detects performance degradation or an impending hardware failure.13 This dramatically increases uptime and reliability for mission-critical services. AI-driven networks can also help deliver greater energy efficiency by continuously optimizing network operations through intelligent automation using real-time telemetry.14 AgenticOps enables networks to dynamically adjust power usage, traffic routing, and resource allocation, allowing operators to power down underutilized components during low-demand periods and reduce energy consumption without compromising service quality.15
One of the most significant challenges for network operators is the talent gap in managing increasingly complex, software-defined networks. AgenticOps allows operators to automate repetitive, low-value tasks--such as ticket resolution, configuration updates, and routine maintenance. These tools also help close the workforce talent gap by lowering the barrier to entry and allowing more junior analysts to ramp up quickly. By automating these tasks, Cisco's AI-enabled platforms can free network engineers to focus on higher-level architectural strategy and innovation, and free cybersecurity analysts to dedicate more time to strategic threat hunting and detection engineering.
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13 Masum Mir, "Leading the Next Era of Intelligent Connectivity," (Oct. 28, 2025). Available at: https://blogs.cisco.com/news/leading-the-next-era-of-intelligentconnectivity?dtid=osolie001456&utm_loc=ar&utm_lang=en.
14 "Energy Management in the AI Era: Cisco's Digital Transformation Advantage," Available at: https://blogs.cisco.com/industries/energy-management-in-the-ai-era-ciscos-digital-transformation-advantage
15 Id.
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Resiliency and Security: In an AgenticOps environment, the network transitions from a passive transport layer to the primary cybersecurity enforcement point. Cisco empowers service providers to implement AI-native security that operates at machine speed--a critical requirement in today's dynamic threat landscape.16 By leveraging Cisco's purpose-built Deep Network Models, we analyze traffic patterns in real time to distinguish between legitimate AI-driven workloads and malicious anomalies.17 This predictive capability allows our customers to identify vulnerabilities before they escalate. Should a device or service be compromised, the network can autonomously isolate the threat, halt lateral movement and safeguard the broader service provider infrastructure against sophisticated cyberattacks.18
Moreover, AI-native architectures enable network operators to simulate and validate cybersecurity patches on a digital twin of the network.19 This capability eliminates the "patching paradox"--where the fear of downtime prevents critical security updates--by ensuring that every change is tested for stability and performance at machine speed. To maintain essential human oversight in an AgenticOps environment, we enable network operators to leverage a collaborative interface that allows security engineers to visualize, discuss, and refine complex policies with AI assistance.20 This human-in-the-loop approach ensures that while our network operations run at machine speed, strategic security decisions remain guided by human expertise and transparent, shared visibility.
AI-Enabled Network Applications: In addition to changes in traffic patterns, networks are moving toward AI-native platforms that will become the fabric of intelligent connectivity rather than a simple pipe. As network operators move compute toward the network edge--such as at a cell site where a tower sits--they will be able to run applications directly from the network.
One promising application is Integrated Sensing and Communication (ISAC), which combines wireless communications and radio-frequency sensing to "see" objects' position and path using radio waves that reflect off them. Unlike optical sensors, it can detect intrusion even in low-light conditions, through smoke, or around obstructions where traditional video analytics might fail. This technology has been prototyped and demonstrated already, and it holds great promise for autonomous systems and robotics, AI-driven smart facilities, and public safety.
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16 "Shields Up: Cisco Live Protect Closes Vulnerability Gap with Compensating Controls," June, 2, 2026. Available at: https://blogs.cisco.com/news/shields-up-cisco-live-protect-closes-vulnerability-gap-with-compensating-controls
17"Cisco Hypershield - Our Vision to Combat Unknown Vulnerabilities," Available at: https://blogs.cisco.com/security/cisco-hypershield-our-vision-to-combat-unknown-vulnerabilities.
18 Id.
19 See, https://www.cisco.com/c/en/us/products/collateral/security/hypershield/hypershieldso.html#Solvingrealcustomerchallenges.
20 See, https://blogs.cisco.com/ai/ai-canvas-controlled-availability.
* * *
Robotics: For example, ISAC enables the network to provide collision avoidance and path optimization, offloading some of the computational burden from the robot's own onboard sensors and ensuring safer operations in complex industrial environments.
AI-Driven Smart Facilities: By sensing occupancy and environmental changes through ISAC, the network can dynamically adjust energy, HVAC, and lighting systems in enterprise campuses. This creates "self-aware" buildings that optimize for both human comfort and energy efficiency.
Public Safety: Because the network can act as a pervasive radar system to track objects in restricted areas, ISAC could support Counter-Unmanned Aircraft Systems missions leveraging commercial networks around a stadium or critical infrastructure.
Cisco and its partners are already exploring this technology, having demonstrated an early version at NVIDIA's GTC DC Conference last year and at Mobile World Congress Barcelona this spring.
The network will also enable 'Physical AI', which is the integration of digital intelligence with physical action that allows machines to operate independently in environments where conditions change continuously.21 While traditional AI is confined to software-based analysis, physical AI is designed to interact with the physical world. Physical AI applications--whether a warehouse robot rerouting around an unexpected obstacle, robotics in a manufacturing facility, or intelligent transportation infrastructure--will drive significant demand and constraints on the network while also opening new business monetization opportunities for network operators.
Demand is driven by several factors: 1) data collection from many types of sensors such as cameras for visual detail, lidar for depth, and radar for motion detection; 2) decision-making and reinforcement learning (where the model improves by attempting physical actions and receiving feedback to improve future action); and, 3) performing physical action through mechanical components like motors, conveyors, or robotic arms.22
Each of these applications represents how AI is changing the network design: they require compute at the edge. They require an AI-native network core capable of integrating terrestrial and non-terrestrial networks and they require additional capacity to manage east-west traffic as agents, devices, and networks work in concert. Ultimately, Cisco envisions a combination of interoperable technologies to ensure ubiquitous, seamless, secure, and reliable connectivity for all end users, regardless of whether communications travel on wired, licensed, unlicensed, or satellite networks.
4. Policy Considerations.
As this Committee considers the changing landscape, I offer three suggestions that will help enable continued U.S. leadership and better outcomes for American consumers and businesses.
* * *
21 See, https://www.cisco.com/site/us/en/learn/topics/artificial-intelligence/what-is-physical-ai.html.
22 Id.
* * *
First, we must work together to protect American leadership in AI and compute and remain focused on building an American AI-native tech stack for wireless networks. However, we cannot wait for 6G standards to begin deployment; and we aren't. Through the AI-WIN partnership, Cisco, NVIDIA, MITRE, Booz Allen Hamilton, ODC, and T-Mobile are collaborating to develop America's first AI-native wireless telecommunications stack, while also developing innovative technologies to support new use cases in 5G-Advanced networks. We seek to leverage American leadership in AI and compute to demonstrate technical success through real-world deployments, ultimately shaping international standards to meet American companies where the technology is moving. I encourage Congress to lean in on areas where the United States has a strategic leadership role, such as compute, core networking, and applications.
Second, as compute infrastructure becomes more distributed and moves closer to the network edge, service providers may face increased infrastructure demands. Policies that enable the responsible and efficient permitting and deployment of this infrastructure will be essential to helping service providers offer new services as the network becomes an AI-native platform.
Additionally, as Congress considers the future of the Universal Service Fund, we encourage you to consider how these changes in network traffic will affect the operational costs borne by rural health care facilities, service providers, and schools, regardless of whether they are rural or urban.
Finally, I encourage this Committee to continue its bipartisan approach to a balanced spectrum policy. As WRC-27 approaches, the United States needs to maintain its global leadership and expand its support for unlicensed operations in the 6 GHz band, including by authorizing higher-power operations and allowing expanded use in key enterprise environments.
Likewise, the 800 MHz of spectrum made available for mobile use will be essential to enabling the connectivity needed to power new applications on AI-native networks, including the volume of uplink traffic we expect to see.
5. Conclusion.
We are at an exciting inflection point in how AI is transforming our communications networks. As Cisco works with service providers, critical infrastructure operators, enterprises, and governments to deploy the foundational infrastructure of the AI era, we are proud to offer our expertise to this Committee and to Congress to help shape a technology future that connects consumers, businesses, and communities. Thank you for the invitation to testify, and I look forward to answering your questions.
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Original text here: https://www.commerce.senate.gov/wp-content/uploads/meetings/379d9950-dc84-b1bf-2c6a-ee1ebb42fbe2/Everson_Testimony-7.30.26-Final_d1fc268a-ed9e-42a7-abbe-d52a960f49e1.pdf
FERC Commissioner Rosner Testifies Before Senate Energy & Natural Resources Committee
WASHINGTON, Aug. 5 -- The Senate Energy and Natural Resources Committee released the following written testimony by Federal Energy Regulatory Commissioner David Rosner from a July 22, 2026, hearing on the oversight of the agency:
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Chairman Lee, Ranking Member Heinrich and Members of the Committee:
I am honored to appear before you today alongside my colleagues to discuss FERC's work to deliver affordable and reliable energy for all Americans and our commitment to upholding Congress's vision for a bipartisan, independent, resource-neutral regulator. It is an honor to serve the country in ... Show Full Article WASHINGTON, Aug. 5 -- The Senate Energy and Natural Resources Committee released the following written testimony by Federal Energy Regulatory Commissioner David Rosner from a July 22, 2026, hearing on the oversight of the agency: * * * Chairman Lee, Ranking Member Heinrich and Members of the Committee: I am honored to appear before you today alongside my colleagues to discuss FERC's work to deliver affordable and reliable energy for all Americans and our commitment to upholding Congress's vision for a bipartisan, independent, resource-neutral regulator. It is an honor to serve the country inthis capacity.
This mission entrusted to us, while simple to articulate, is increasingly complex to execute.
Energy demand continues to grow at a pace not seen in a generation, driven by growth in new industries, like artificial intelligence, by reshoring manufacturing, and by changing patterns of energy use in homes and businesses. Energy technologies are evolving, reshaping how energy infrastructure is planned, built, and used by all types of consumers. And we face these changes at a time when families and small businesses struggle with high prices for essential services-- including in their utility bills.
While meeting this moment presents challenges, it also creates opportunities for us to modernize our nation's energy infrastructure. Upgrading our energy system is necessary to ensure every American receives the reliable and affordable power on which they depend, and it is also essential to our country's economic competitiveness and national security.
With this in mind, I would like to highlight a few of the specific actions FERC is taking to confront the challenges and realize the opportunities before us:
First, we are accelerating the addition of new electric generation of all kinds: Building the energy resources needed to meet growing demand and reduce consumer costs is my top focus.
We implemented FERC's landmark Order No. 2023 generator interconnection reforms and fast-tracked more than 50 gigawatts of shovel-ready power plants. Looking forward, I am encouraging grid operators to deploy automation and artificial intelligence tools that have been shown to accelerate steps in the interconnection study process from years to weeks.
Second, we are establishing forward-looking grid planning, centered around reliability, affordability, and economic growth: This year, FERC will begin acting on electric transmission providers' proposals to implement Order No. 1920, which modernized long term transmission planning. When I arrived as a Commissioner, this rule was divided along party lines. Today, thanks to extensive state engagement and collaborative problem solving, I am proud it is bipartisan and unanimously supported.
Third, we are emphasizing predictability, speed, and legal durability for infrastructure permitting: Since January 2025, FERC has issued over 120 permits for hydropower and natural gas projects, moving from NEPA review to final permit faster than any other agency in the federal government and more than 30% faster than was typical during the last decade. Critically, we have done this is without compromising quality--FERC's permits have been routinely upheld in court in recent years. Looking to the future, permitting efficiency will become only more essential as demand growth accelerates.
Fourth, we are embracing innovative pathways to power data centers and other large loads while protecting consumers: One of the most significant developments in energy is the rapid growth of large loads. Over the last year the Commission has acted to ensure that demand growth leads to a more reliable, affordable, and sustainable grid--and that America can win the AI race without regular families paying for it.
At FERC, this is a top priority. In December and January, the Commission implemented programs in the PJM and SPP regions to pair new large loads with new generation. This pairing reduces how much these projects lean on the grid, minimizes the need to construct additional transmission upgrades, promotes flexible operations, and helps ensure that new loads pay their fair share.
In parallel, more than 30 states have adopted special retail rate structures for large loads--an important complement to FERC action and a reminder of the cooperative federalism needed to protect regular consumers.
Finally, building upon the progress and the record developed in response to Secretary Wright's ANOPR, in June the Commission issued a show cause order to each regional grid operator asking them to modernize their rules for interconnecting large loads. The Commission's orders are based upon four key pillars: protecting consumers, enhancing transparency, safeguarding reliability, and fostering innovation. Put simply, the goal is lowering costs, protecting grid reliability, and helping new customers and power plants get online faster.
Fifth, we continue to enhance grid security and reliability through modernization and flexibility: It is essential that we continue to deploy 21st-century reliability solutions, including advanced demand response, dynamic line ratings, predictive artificial intelligence, and emerging cybersecurity technologies. Last year, for example, FERC and NERC implemented enhanced power plant performance standards for extreme cold weather that have already proved essential in keeping the lights on during winter storms. And FERC's work now includes robust reliability standards for large loads.
Sixth, we are staying laser-focused on energy affordability: In addition to the examples I have already mentioned, over the last two years, FERC has approved expanded competitive electric markets in the West and in the Southeast, which will enable customers to access lower-cost power. And in PJM we approved a temporary price cap that saved consumers more than $25 billion. It is my priority to deliver more examples like this in 2026.
Finally, I will conclude by highlighting the value of our current Commission's consensusdriven mindset. I am immensely proud that the vast majority of orders I have voted on have been bipartisan and unanimous, including every single energy project permit. This consensus ensures regulatory predictability, legal durability, and strong outcomes for all Americans, informed by five distinct perspectives. I thank my colleagues for their collaboration, which I am confident will continue to deliver for our country.
Thank you again for the opportunity to testify today. I look forward to your questions.
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Original text here: https://www.energy.senate.gov/services/files/E8A70A65-EDDD-45AC-B378-B04DA73C6A6D
* * *
Chairman Lee, Ranking Member Heinrich and Members of the Committee:
I am honored to appear before you today alongside my colleagues to discuss FERC's work to deliver affordable and reliable energy for all Americans and our commitment to upholding Congress's vision for a bipartisan, independent, resource-neutral regulator. It is an honor to serve the country in ... Show Full Article WASHINGTON, Aug. 5 -- The Senate Energy and Natural Resources Committee released the following written testimony by Federal Energy Regulatory Commissioner David Rosner from a July 22, 2026, hearing on the oversight of the agency: * * * Chairman Lee, Ranking Member Heinrich and Members of the Committee: I am honored to appear before you today alongside my colleagues to discuss FERC's work to deliver affordable and reliable energy for all Americans and our commitment to upholding Congress's vision for a bipartisan, independent, resource-neutral regulator. It is an honor to serve the country inthis capacity.
This mission entrusted to us, while simple to articulate, is increasingly complex to execute.
Energy demand continues to grow at a pace not seen in a generation, driven by growth in new industries, like artificial intelligence, by reshoring manufacturing, and by changing patterns of energy use in homes and businesses. Energy technologies are evolving, reshaping how energy infrastructure is planned, built, and used by all types of consumers. And we face these changes at a time when families and small businesses struggle with high prices for essential services-- including in their utility bills.
While meeting this moment presents challenges, it also creates opportunities for us to modernize our nation's energy infrastructure. Upgrading our energy system is necessary to ensure every American receives the reliable and affordable power on which they depend, and it is also essential to our country's economic competitiveness and national security.
With this in mind, I would like to highlight a few of the specific actions FERC is taking to confront the challenges and realize the opportunities before us:
First, we are accelerating the addition of new electric generation of all kinds: Building the energy resources needed to meet growing demand and reduce consumer costs is my top focus.
We implemented FERC's landmark Order No. 2023 generator interconnection reforms and fast-tracked more than 50 gigawatts of shovel-ready power plants. Looking forward, I am encouraging grid operators to deploy automation and artificial intelligence tools that have been shown to accelerate steps in the interconnection study process from years to weeks.
Second, we are establishing forward-looking grid planning, centered around reliability, affordability, and economic growth: This year, FERC will begin acting on electric transmission providers' proposals to implement Order No. 1920, which modernized long term transmission planning. When I arrived as a Commissioner, this rule was divided along party lines. Today, thanks to extensive state engagement and collaborative problem solving, I am proud it is bipartisan and unanimously supported.
Third, we are emphasizing predictability, speed, and legal durability for infrastructure permitting: Since January 2025, FERC has issued over 120 permits for hydropower and natural gas projects, moving from NEPA review to final permit faster than any other agency in the federal government and more than 30% faster than was typical during the last decade. Critically, we have done this is without compromising quality--FERC's permits have been routinely upheld in court in recent years. Looking to the future, permitting efficiency will become only more essential as demand growth accelerates.
Fourth, we are embracing innovative pathways to power data centers and other large loads while protecting consumers: One of the most significant developments in energy is the rapid growth of large loads. Over the last year the Commission has acted to ensure that demand growth leads to a more reliable, affordable, and sustainable grid--and that America can win the AI race without regular families paying for it.
At FERC, this is a top priority. In December and January, the Commission implemented programs in the PJM and SPP regions to pair new large loads with new generation. This pairing reduces how much these projects lean on the grid, minimizes the need to construct additional transmission upgrades, promotes flexible operations, and helps ensure that new loads pay their fair share.
In parallel, more than 30 states have adopted special retail rate structures for large loads--an important complement to FERC action and a reminder of the cooperative federalism needed to protect regular consumers.
Finally, building upon the progress and the record developed in response to Secretary Wright's ANOPR, in June the Commission issued a show cause order to each regional grid operator asking them to modernize their rules for interconnecting large loads. The Commission's orders are based upon four key pillars: protecting consumers, enhancing transparency, safeguarding reliability, and fostering innovation. Put simply, the goal is lowering costs, protecting grid reliability, and helping new customers and power plants get online faster.
Fifth, we continue to enhance grid security and reliability through modernization and flexibility: It is essential that we continue to deploy 21st-century reliability solutions, including advanced demand response, dynamic line ratings, predictive artificial intelligence, and emerging cybersecurity technologies. Last year, for example, FERC and NERC implemented enhanced power plant performance standards for extreme cold weather that have already proved essential in keeping the lights on during winter storms. And FERC's work now includes robust reliability standards for large loads.
Sixth, we are staying laser-focused on energy affordability: In addition to the examples I have already mentioned, over the last two years, FERC has approved expanded competitive electric markets in the West and in the Southeast, which will enable customers to access lower-cost power. And in PJM we approved a temporary price cap that saved consumers more than $25 billion. It is my priority to deliver more examples like this in 2026.
Finally, I will conclude by highlighting the value of our current Commission's consensusdriven mindset. I am immensely proud that the vast majority of orders I have voted on have been bipartisan and unanimous, including every single energy project permit. This consensus ensures regulatory predictability, legal durability, and strong outcomes for all Americans, informed by five distinct perspectives. I thank my colleagues for their collaboration, which I am confident will continue to deliver for our country.
Thank you again for the opportunity to testify today. I look forward to your questions.
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Original text here: https://www.energy.senate.gov/services/files/E8A70A65-EDDD-45AC-B378-B04DA73C6A6D
Augusta University Professor Talbert Testifies Before House Education & Workforce Committee
AUGUSTA, Georgia, Aug. 5 -- The House Education and Workforce Committee released the following testimony by Jeffery Talbert, professor and department chair of AI and health at Augusta University, and an eminent scholar at the Georgia Research Alliance, from a July 24, 2026, field hearing entitled "Building an AI-Ready America: How AI Is Creating Opportunities Across America's Workforce":
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Artificial intelligence (AI) is rapidly reshaping discussions about the future of work. Public debate often focuses on automation, job displacement, and the possibility that machines will replace human ... Show Full Article AUGUSTA, Georgia, Aug. 5 -- The House Education and Workforce Committee released the following testimony by Jeffery Talbert, professor and department chair of AI and health at Augusta University, and an eminent scholar at the Georgia Research Alliance, from a July 24, 2026, field hearing entitled "Building an AI-Ready America: How AI Is Creating Opportunities Across America's Workforce": * * * Artificial intelligence (AI) is rapidly reshaping discussions about the future of work. Public debate often focuses on automation, job displacement, and the possibility that machines will replace humanworkers. The healthcare industry offers a different and increasingly evidence-based perspective. Rather than replacing clinicians, many of the most successful AI applications are helping healthcare professionals perform their jobs more effectively by reducing administrative workload, improving efficiency, increasing positive patient outcomes, and expanding workforce capacity.
Recent studies involving ambient AI scribes, digital documentation systems, and generative AI workflow tools demonstrate measurable improvements in clinician experience, reductions in burnout, decreases in documentation burden, and gains in workforce productivity. Collectively, these findings provide some of the strongest currently available real-world evidence that AI can create workforce opportunity through augmentation rather than replacement. At the same time, AI is likely to transform some administrative occupations, creating a need for workforce adaptation, reskilling, and lifelong learning as job responsibilities evolve. AI is also beginning to demonstrate measurable improvements in patient outcomes, including earlier detection of breast cancer, reductions in sepsis mortality, faster stroke treatment, expanded diabetic retinopathy screening, and accelerated drug discovery.
The central finding of this testimony is that the healthcare industry can now provide direct evidence that AI can strengthen workforce performance, improve worker experience, preserve expertise, expand workforce capacity, and create new career opportunities when implemented as a workforce-support technology. In a healthcare system facing projected shortages of tens of thousands of physicians and more than 100,000 nurses, this distinction has important implications for workforce policy and positions AI not as a threat to the industry but as a force multiplier for it.
Georgia is already investing in this future through Augusta University's Department of Artificial Intelligence and Health, one of the first academic departments in the nation dedicated to integrating AI, healthcare delivery, workforce development, education, and responsible implementation. Leadership in healthcare AI is increasingly becoming a matter of economic competitiveness. Nations that develop the workforce capable of building, deploying, and governing AI-enabled healthcare systems will be better positioned to lead in biomedical innovation, healthcare delivery, and life sciences research. (Dai et al., 2026; Lukac et al., 2025; Olson et al., 2025) Key Findings
A survey of current research provides several key points for policy makers' consideration:
1. Healthcare workforce research demonstrates that AI can strengthen workforce performance through augmentation rather than replacement.
2. Ambient AI and digital documentation systems significantly reduce documentation burden, cognitive workload, and burnout.
3. Reducing administrative burden increases effective workforce capacity without increasing workforce size.
4. Early healthcare AI implementation suggests that AI is creating new workforce
opportunities in governance, implementation, informatics, workforce training, quality assurance, AI operations, and digital transformation while simultaneously reshaping some administrative occupations.
5. AI knowledge, working with AI tools, and accountability structures are becoming essential workforce skills.
6. Successful adoption depends on governance, workforce engagement, workflow redesign, and ongoing workforce development.
7. AI is transforming job responsibilities rather than eliminating occupations in healthcare.(Atabeygi & Mitchell, 2026; Davidson et al., 2025; Lukac et al., 2025; Olson et al., 2025) The healthcare workforce's experience suggests that the most significant workforce impact of AI may not be workforce reduction, but workforce redesign, reducing routine administrative work while increasing demand for human judgment, oversight, communication, and education.
1. The Healthcare Workforce Challenge
The American healthcare system faces substantial workforce pressures that extend far beyond shortages of physicians and nurses. Increasingly, workforce researchers describe the problem as one of workforce sustainability. Healthcare organizations are confronting burnout, retention challenges, administrative burden, and the loss of experienced personnel whose expertise is difficult to replace.
National physician workforce data illustrate the seriousness of these challenges. Approximately 41.9 percent of physicians report symptoms of burnout, 42.9 percent report substantial job-related stress, and nearly one-third indicate some likelihood of leaving their organization within two years.
Physicians report average workweeks exceeding 58 hours. These findings suggest that retaining experienced professionals has become an urgent workforce priority. Clinicians consistently identify administrative and operational burdens, not patient care itself, as major contributors to dissatisfaction. Documentation requirements, electronic health record workflows, inbox management, insurance-related tasks, staffing shortages, and bureaucratic processes are frequently cited as major workplace stressors. These observations suggest that many workforce challenges arise from how work is organized rather than from the providers' core mission. (American Medical Association, 2025; Graunke et al., 2026)
These workforce pressures are expected to intensify substantially over the coming decade. The Association of American Medical Colleges projects a national physician shortage of up to 86,000 physicians by 2036, driven by population aging, increasing healthcare utilization, and physician retirements. Nursing shortages present similar challenges. Federal workforce projections indicate the United States could face a shortage of more than 100,000 registered nurses by 2038. The challenge is particularly acute in rural and underserved communities, where provider shortages already limit access to care and where workforce deficits may be felt most severely. These shortages are particularly concerning because demand for healthcare services continues to grow while large segments of the clinical workforce approach retirement age. AI is not a substitute for addressing workforce supply, but it may help organizations use scarce professional expertise more effectively and expand workforce effectiveness in the face of growing demand.
2. Administrative Burden and the 1.2-FTE Problem
The National Academy of Medicine's concept of the "1.2-FTE problem" provides a useful framework for understanding workforce strain. Clinicians are employed as 1.0 full-time equivalent (FTE) workers but perform the work of 1.2 FTEs because of documentation requirements, inbox management, prior authorizations, insurance communications, and compliance activities. This hidden workload often extends beyond scheduled clinical hours into evenings and weekends.
Over time, these obligations contribute directly to burnout, dissatisfaction, and workforce attrition.
Administrative burden also reduces effective organizational capacity. Every hour spent documenting, managing messages, or completing administrative requirements is an hour unavailable for patient care, teaching, leadership, mentoring, research, or quality improvement.
In the healthcare industry, AI experience is particularly important because many of the most successful AI applications directly target these burdens. (Atabeygi & Mitchell, 2026)
3. Evidence from Ambient AI and Digital Scribes
Among all healthcare AI applications currently available, ambient AI scribes and digital documentation systems have generated the strongest evidence regarding workforce outcomes.
A multisystem study involving 263 physicians and advanced practice clinicians across six health systems demonstrated substantial improvements in workforce measures. Burnout declined from 51.9 percent to 38.8 percent. Clinicians reported reductions in cognitive workload, decreased after-hours documentation, improved professional well-being, and greater ability to focus on patients. Similar findings emerged from Stanford Health Care's evaluation of ambient AI documentation systems, where researchers documented reductions in physician task load and burnout along with improvements in workflow efficiency and user satisfaction.
Evidence from pediatric healthcare settings further strengthens the case. A six-month implementation involving eighty-four pediatric providers demonstrated significant reductions in documentation burden and cognitive workload. Burnout rates fell from 54.9 percent to 33.3 percent, while providers collectively saved more than 2,100 hours of documentation time.
The consistency of these findings across multiple specialties and organizational settings suggests that workforce benefits are neither isolated nor specialty specific.
Several years before the widespread deployment of ambient AI systems, cardiologist and digitalhealth researcher Dr. Eric Topol argued that one of healthcare AI's greatest opportunities would be to "give clinicians back the gift of time." In his book Deep Medicine, Topol envisioned AI not primarily as a replacement for physicians, but as a means of reducing clerical burden and restoring the human relationship at the center of care. The emerging evidence from ambient AI documentation systems suggests that this vision is beginning to materialize in practice. (Olson et al., 2025; Pelletier et al., 2025; Shah et al., 2025; Topol, 2019)
4. Moving from Promise to Proof
Healthcare AI research has increasingly progressed beyond pilot projects and implementation studies to include randomized evaluations. A randomized trial involving 238 physicians across fourteen specialties evaluated ambient AI scribes against traditional documentation workflows.
Researchers found meaningful reductions in documentation burden, task load, and work exhaustion, along with improvements in physician experience. These findings reinforce a broader workforce principle: reducing administrative work can improve workforce well-being, organizational performance, and professional satisfaction. Rather than treating burnout primarily as an individual problem, the evidence suggests that improving how work is organized can address important systemic contributors to workforce strain. Collectively, implementation studies and randomized evidence indicate that AI's workforce benefits are measurable, reproducible, and increasingly supported by rigorous research. (Lukac et al., 2025; Olson et al., 2025)
5. Workforce Opportunity Through Augmentation
Perhaps the most important finding emerging from the healthcare literature is that AI's workforce value lies primarily in augmentation rather than replacement. Across virtually every major study, clinicians remain responsible for reviewing documentation, exercising professional judgment, making decisions, communicating with patients, and maintaining accountability. AI supports these activities by reducing clerical effort and simplifying routine administrative tasks.
This distinction is critical because it shifts the conversation away from labor substitution and toward expertise amplification. Rather than asking whether AI can replace workers, healthcare organizations are increasingly asking how AI can help professionals spend more time practicing at the top of their training. Activities such as communication, diagnosis, leadership, teaching, mentoring, and relationship-building continue to depend upon human capabilities. AI creates value by helping workers devote more time to these higher-value activities.
The implications for workforce policy are substantial. The operational capacity can be expanded without increasing the size of the staff if clerical responsibilities are reduced. Retention may improve when workplace conditions improve. Expertise may be preserved when experienced professionals remain engaged in practice longer. In this sense AI functions as a workforce multiplier that strengthens the productivity and sustainability of existing personnel rather than replacing them. This opportunity is especially important as projected physician and nursing shortages continue to grow nationally. In an environment where healthcare organizations may be unable to fully meet workforce demand through hiring alone, technologies that allow clinicians to devote more time to patient care can effectively increase capacity without requiring equivalent increases in staffing.
The workforce implications of AI extend beyond improving existing jobs. As healthcare organizations adopt AI-enabled documentation, workflow, governance, and decision-support systems, they are increasingly creating demand for new roles focused on implementation, oversight, quality assurance, workforce training, and organizational transformation. These emerging occupations suggest that AI may expand workforce opportunities not only by improving productivity but also by creating entirely new career pathways that combine domain expertise with AI-related competencies. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Topol, 2019)
6. AI Is Delivering Measurable Improvements in Health Outcomes
While workforce benefits are important, AI is also producing measurable improvements in patient outcomes, disease detection, and biomedical innovation. AI is also particularly valuable in rural and underserved communities where specialist shortages are most severe. Technologies that support screening, triage, documentation, and clinical decision-making may help extend the reach of limited healthcare workforces and improve access to expertise that is otherwise unavailable.
In breast cancer screening, the landmark Mammography Screening with Artificial Intelligence (MASAI) randomized trial demonstrated that AI-assisted screening increased breast cancer detection by approximately 29 percent while reducing radiologist workload. Researchers also observed improved detection of small, node-negative invasive cancers that are more likely to be treated successfully when identified early. (Hernstrom et al., 2025; Topol, 2026)
AI is also improving outcomes for patients with sepsis, one of the leading causes of hospital mortality in the United States. The Targeted Real-Time Early Warning System (TREWS), deployed across dozens of hospitals, has been associated with approximately an 18 percent reduction in sepsis mortality, earlier recognition of deteriorating patients, reduced intensive care utilization, and shorter hospital stays. (Adams et al., 2022)
Stroke care provides another example of AI-enabled improvements. Because neurological injury progresses rapidly during acute stroke, every minute saved can help preserve brain function. A randomized clinical trial found that AI-enabled large-vessel occlusion detection and automated team notification reduced time to thrombectomy initiation by approximately 11 minutes, a clinically meaningful improvement in a condition where outcomes are highly time dependent. (Martinez-Gutierrez et al., 2023)
AI has also become one of the first successful examples of autonomous disease screening in routine clinical practice. FDA-cleared AI systems for diabetic retinopathy can identify patients at risk of preventable blindness in primary care settings without requiring immediate specialist review. Studies have demonstrated sensitivities exceeding 87 percent and have shown that AIbased screening can expand access to eye care, particularly in underserved populations where specialist availability is limited. (Gulshan et al., 2016; Leong et al., 2026)
Cardiovascular medicine is beginning to show similar promise. Researchers at Mayo Clinic and collaborators developed FDA-cleared AI tools capable of identifying heart failure with preserved ejection fraction from standard echocardiographic images. In independent validation studies, these systems demonstrated strong diagnostic performance and may help identify patients whose condition is frequently underdiagnosed using conventional approaches. (Akerman et al., 2025) AI is also demonstrating potential to predict serious complications before they occur. Deep learning models developed using large-scale electronic health record data have been shown to predict acute kidney injury up to 48 hours before traditional clinical recognition and identified approximately 90 percent of the most severe cases requiring dialysis. Earlier identification provides clinicians with an opportunity to intervene before irreversible damage occurs. (Tomasev et al., 2019)
Finally, AI is beginning to reshape biomedical innovation as well. Rentosertib (INS018_055), an investigational treatment for idiopathic pulmonary fibrosis discovered and designed using artificial intelligence, advanced from target identification to human clinical testing in approximately 30 months--about half the time commonly required for traditional drug discovery programs. Early Phase II results have demonstrated encouraging improvements in lung function, providing one of the first clinical demonstrations that AI-assisted drug discovery can successfully generate novel therapeutic candidates. (Xu et al., 2025)
Collectively, these examples demonstrate that AI is not merely improving efficiency; it is improving patient outcomes, accelerating medical discovery, and creating demand for a workforce capable of deploying these tools safely and effectively. As these technologies continue to mature, healthcare organizations will require a growing workforce capable of implementing, evaluating, governing, and safely deploying AI-enabled systems.
7. Workforce Transformation and Job Displacement Risks
The healthcare sector's experience with AI suggests that workforce impacts will not be distributed evenly across occupations. While most clinical professionals are experiencing augmentation rather than replacement, some administrative functions are likely to undergo significant transformation as AI systems assume a growing share of routine information-processing tasks.
Emerging AI applications already support documentation, scheduling, records management, patient communication, revenue-cycle activities, coding assistance, prior-authorization workflows, claims processing, and other administrative functions. As these capabilities mature, organizations may require fewer workers in some highly structured clerical functions while increasing demand for workers who can manage, supervise, implement, and improve AI-enabled systems.
Healthcare leaders and workforce experts have identified administrative operations as an area particularly susceptible to AI-driven workflow transformation. Occupations potentially affected include medical scribes, scheduling personnel, administrative assistants, coding and billing personnel, and prior-authorization support teams. Workforce impacts are likely to occur through gradual role redesign, attrition, and changing hiring patterns rather than sudden large-scale replacement. Most healthcare administrative functions involve exception handling, coordination, communication, compliance oversight, and patient interaction that continue to benefit from human involvement. This now-proven pattern of administrative work reduction shows that AI is likely reducing tasks faster than it is eliminating jobs.
Unlike many industries, healthcare also faces persistent workforce shortages, increasing demand associated with population aging, and growing complexity of care delivery. As a result, productivity gains generated by AI may often be used to absorb unmet demand and expand capacity rather than solely reduce staffing levels. Nevertheless, policymakers should anticipate workforce transitions for some administrative occupations and support retraining pathways that help affected workers move into emerging AI-enabled roles. Longitudinal evidence on healthcare workforce displacement remains limited, and many workforce effects will likely emerge over several years as adoption expands across care settings. The healthcare industry's experience therefore provides a nuanced lesson regarding AI and employment. AI is likely to reduce demand for some routine administrative activities while simultaneously increasing demand for workers who can implement, evaluate, govern, supervise, and improve AI-enabled systems. The policy challenge is not solely preserving existing jobs but supporting successful transitions into higher-value roles that leverage uniquely human capabilities.
Although evidence regarding documentation burden, burnout reduction, and workforce productivity is now substantial, evidence regarding long-term workforce displacement remains limited. Continued monitoring will be necessary as AI adoption expands and organizations evaluate how productivity gains affect staffing patterns, workforce composition, and career pathways. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Teo et al., 2025)
8. Emerging Workforce Opportunities in an AI-Enabled Healthcare System
While much of the public discussion surrounding artificial intelligence focuses on automation, evidence suggests that AI is also creating demand for new categories of workers. As health systems expand their use of ambient documentation, workflow-support tools, predictive analytics, and generative AI, organizations increasingly require professionals capable of implementing, governing, evaluating, training, and monitoring these technologies. Rather than reducing demand for skilled workers, AI is creating new opportunities for professionals who can combine healthcare expertise with digital and analytical competencies.
Augusta University's Department of Artificial Intelligence and Health was established as a response to a challenge facing health systems across the nation: the need for a workforce that understands both healthcare and artificial intelligence. By integrating education, workforce development, implementation science, governance, research, and health system partnerships within a single academic unit, the department offers one model for how universities can help prepare the AI-ready workforce required for the future of healthcare delivery. Health systems need AI implementation specialists, AI governance leaders, clinical AI evaluators, and data scientists trained to ensure that AI is safe, effective, and trustworthy.
The skills associated with these new opportunities differ from those traditionally associated with healthcare technology. The Journal of the American Medical Association (JAMA) Summit on Artificial Intelligence concluded that healthcare professionals need more than training on specific AI tools. AI literacy is becoming a foundational workforce competency. Clinicians, administrators, and operational leaders must understand how AI systems function, their limitations, how outputs should be validated, and how responsibility should be shared between humans and AI-enabled systems. In addition to AI literacy, workers increasingly need competencies in human-AI collaboration, governance, privacy, cybersecurity, compliance, quality monitoring, risk management, and workflow redesign. Successful AI adoption depends as much on organizational integration as it does on technological capability.
Importantly, many of these emerging opportunities can be filled through targeted upskilling rather than wholesale career changes. Physicians, nurses, pharmacists, therapists, informaticians, administrators, quality-improvement professionals, and information technology specialists already possess expertise that remains highly valuable in AI-enabled environments. Additional preparation in AI competency, governance, analytics, implementation science, and human-AI collaboration can position these workers for expanding roles in AI deployment and oversight.
Emerging evidence suggests that AI may also transform workforce education itself. Scholars have begun describing the development of "AI-enabled precision education" systems that use digital data, performance metrics, simulation platforms, and AI-driven coaching tools to identify individual competency gaps and personalize learning pathways. Such systems may improve workforce readiness by helping workers acquire new competencies more efficiently and supporting continuous professional development throughout their careers. In an AI-enabled economy, lifelong learning may become increasingly important as workers regularly adapt to evolving technologies and changing job responsibilities. AI education itself is becoming an important workforce priority. National workforce discussions increasingly emphasize that healthcare workers require not only knowledge of specific tools but broader competencies in use of AI systems, data literacy, digital literacy, human-AI collaboration, and critical evaluation of AI outputs. These competencies are increasingly relevant across clinical, operational, administrative, and leadership roles.
These changes have important implications for workforce development. Community colleges, universities, health professions programs, and workforce-development organizations should increasingly incorporate practical understanding of AI, ethical AI, implementation science, governance, and working effectively with AI tools into existing curricula. Employers will similarly need to invest in continuing education and lifelong learning programs that help incumbent workers acquire new competencies throughout their careers. The emerging challenge is not a shortage of technology, but a shortage of workers prepared to effectively implement, govern, and leverage that technology.
Healthcare provides an important lesson for policymakers. AI is not only altering how work is performed; it is creating demand for a new generation of professionals responsible for managing AI-enabled systems, ensuring their safety and effectiveness, training the workforce, and helping organizations realize the benefits of technological innovation. The most durable workforce opportunities created by AI may ultimately arise not from automation itself, but from the growing need for human expertise that guides, governs, and improves AI-enabled work. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Davidson et al., 2025; Desai et al., 2026)
9. Lessons from Early Implementations
Evidence from early adopters provides several important lessons. Successful implementations begin with clearly defined workforce problems such as burnout, documentation burden, workflow inefficiencies, or retention concerns. Documentation support remains the most mature and consistently successful use case. Across organizations and specialties, documentation-focused AI applications repeatedly demonstrate workforce benefits. Implementation studies also emphasize the importance of workflow integration. Technology alone does not improve work.
Benefits depend on how effectively new tools fit into clinical workflows, align with organizational processes, and meet user needs. Frontline engagement, leadership support, usability, and training all play important roles in implementation success. The strongest outcomes occur when technology is combined with thoughtful workflow redesign.
These findings are consistent with lessons learned during earlier waves of healthcare digitization.
Dr. Robert Wachter, one of the nation's leading scholars of healthcare innovation, observed that transformative technologies succeed not because of the technology itself but because organizations redesign workflows, train staff, adapt culture, and rethink how work is performed.
In his books The Digital Doctor and later in A Giant Leap, Wachter argued that successful technology adoption is fundamentally an organizational and workforce challenge. Healthcare's early AI experience strongly reinforces that observation. Effective implementation depends as much on leadership, governance, and workforce engagement as it does on technical performance.
10. Governance, Trust, and Responsible AI
The workforce benefits associated with AI do not eliminate the need for governance and oversight.
The healthcare industry's experience demonstrates that trust is essential to adoption. Clinicians are more likely to embrace AI systems when they remain actively involved in reviewing outputs and maintaining accountability for decisions. Human oversight therefore remains a foundational principle of responsible AI implementation. Generative AI introduces additional governance requirements involving validation, transparency, quality monitoring, privacy protection, and accountability. Strong governance frameworks help ensure that technology serves workforce goals rather than creating new burdens. Building an AI-ready workforce therefore requires investments in governance and education alongside investments in technology.
Organizations should also monitor for unintended workforce consequences associated with AI adoption, including automation bias, overreliance on AI-generated outputs, and potential erosion of professional skills. Evidence suggests that effective human-AI collaboration requires maintaining core professional competencies while using AI as a support tool. Workforce development programs should therefore emphasize critical thinking, independent professional judgment, and appropriate oversight alongside foundational AI knowledge and technical skills. (Maddox et al., 2025; Teo et al., 2025; US Department of Health and Human Services, 2025)
11. Georgia's Leadership in Building the AI Healthcare Workforce
The need for workforce innovation is particularly acute in Georgia, but the challenge is national in scope. Health systems across the country increasingly require professionals who can implement, evaluate, govern, and improve AI-enabled technologies. Augusta University's Department of Artificial Intelligence and Health could be used as a model for how universities can address one of the most important workforce challenges of the next decade: preparing professionals to safely and effectively work alongside AI-enabled technologies. Rather than treating AI as a purely technical discipline, the department integrates workforce development, implementation science, healthcare delivery, governance, education, and translational research. This interdisciplinary approach reflects the reality that successful AI adoption depends not only on technology, but on people who understand how to implement, evaluate, govern, and improve these systems in realworld settings. Augusta University is helping build this workforce through education, research, and partnerships. These efforts reflect a broader national need for professionals who understand both healthcare and artificial intelligence. Building an AI-ready America will require not only technological innovation but also investments in the educational infrastructure needed to develop the workforce that can responsibly govern and deploy these tools. America does not simply have an AI opportunity. It has a workforce necessity. AI matters because healthcare demand is growing faster than the available workforce, especially in states like Georgia, and healthcare needs tools that help clinicians spend more time caring for patients and less time on administrative work. (GlobalData Plc, 2024; HRSA, 2025; Olson et al., 2025; Shah et al., 2025; Wachter, 2026)
12. Policy Considerations
The healthcare industry's AI experience to date suggests several priorities for policymakers:
* Administrative burden reduction should be recognized as both a workforce-development and workforce-retention strategy. Simplifying documentation requirements, improving interoperability, reducing redundant reporting, and responsibly deploying AI-enabled workflow tools can expand workforce capacity, improve professional satisfaction, preserve expertise, and strengthen workforce sustainability.
* Invest in AI workforce development, AI literacy, and lifelong learning. Educational pathways, stackable credentials, employer-based training programs, continuing education opportunities, and community-college partnerships can help current workers adapt to changing responsibilities while preparing future workers for emerging AI-enabled occupations.
* Support university initiatives focused on workforce innovation. Institutions such as Augusta University are developing interdisciplinary models that combine healthcare, data science, implementation science, and AI governance to prepare workers for emerging occupations created by AI-enabled healthcare delivery.
* Support workers whose occupations may experience substantial task automation. Retraining and upskilling programs should create pathways into AI operations, healthcare informatics, digital workflow management, implementation support, quality assurance, patient navigation, and AI governance roles.
* Invest in digital infrastructure and rural workforce capacity. AI-enabled tools can help expand access to care in underserved communities, but their benefits depend on interoperable systems, workforce training, and reliable digital infrastructure.
* Measure workforce outcomes directly. Success should be evaluated not only through technology adoption but also through changes in burnout, documentation burden, workforce satisfaction, retention, productivity, patient access, improved patient outcomes, and organizational capacity. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Davidson et al., 2025; Graunke et al., 2026)
Conclusion
The healthcare sector provides one of the clearest examples of how artificial intelligence can create workforce opportunity. Across multisystem studies, academic medical centers, and randomized clinical trials, AI-enabled documentation and workflow-support tools have demonstrated measurable reductions in administrative burden, cognitive workload, after-hours work, and burnout. These improvements strengthen productivity, support retention, preserve expertise, and expand effective workforce capacity.
The evidence also suggests that AI's workforce impact will involve transformation as well as opportunity. While some routine administrative activities may become increasingly automated, the healthcare experience shows that AI is reducing tasks. Administrative processing, documentation, scheduling, and information-management tasks are increasingly supported by AI, while demand grows for oversight, implementation, governance, patient engagement, workforce education, and human-centered services. As seen in the healthcare experience, organizations that successfully combine AI-enabled productivity with workforce development are likely to achieve the greatest long-term benefits. AI is not removing the need for human professionals. Instead, it is helping workers spend less time on clerical activity and more time applying expertise, judgment, and human relationships.
The healthcare industry also demonstrates that AI is creating demand for new categories of workers focused on implementation, education, oversight, quality monitoring, and organizational transformation. The emergence of these occupations underscores the importance of workforce development, lifelong learning, and AI competency as national priorities. These lessons extend beyond healthcare. As AI adoption expands across the U.S. economy, organizations that successfully combine human expertise with AI-enabled productivity gains will be better positioned to strengthen workforce resilience, improve competitiveness, and support long-term economic growth.
The greatest workforce opportunity created by AI is not automation for its own sake. It is the creation of work environments in which professionals can focus more fully on the activities that generate the greatest value for organizations, communities, and the citizens they serve. This objective becomes increasingly important as healthcare grows more complex. As surgeon and public health researcher Dr. Atul Gawande observed, "The volume and complexity of what we know has exceeded our individual ability to deliver its benefits correctly, safely, or reliably." In many respects, early experience with healthcare AI demonstrates the value of technologies that help professionals manage complexity while preserving the uniquely human skills of judgment, communication, empathy, and trust that remain central to high-quality care.
When implemented responsibly, AI can become a powerful tool for workforce sustainability, expertise amplification, and long-term economic competitiveness. Emerging evidence suggests that the most promising future for AI is not healthcare workforce replacement, but workforce enhancement--helping people spend more time doing the work that only people can do. The workforce challenge posed by AI is not primarily one of replacement, but one of preparation. The question is no longer whether AI will become part of the healthcare workforce. The question is whether the United States will invest now in the people, education, and oversight needed to ensure that workforce is prepared to lead rather than follow. (Adams et al., 2022; Gawande, 2010; Lukac et al., 2025; Olson et al., 2025; Topol, 2019; Wachter, 2026; Xu et al., 2025)
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I acknowledge use of Copilot for assistance with editing this document. Following the use of this tool, I reviewed and edited the content to ensure accuracy and tone.
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* Hernstrom, V., Josefsson, V., Sartor, H., Schmidt, D., Larsson, A. M., Hofvind, S., Andersson, I., Rosso, A., Hagberg, O., & Lang, K. (2025). Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): a randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study. Lancet Digit Health, 7(3), e175-e183. https://doi.org/10.1016/S2589-7500(24)00267-X
* HRSA. (2025). National Center for Health Workforce Analysis Projections.
* Leong, A., Wolf, R. M., Channa, R., Wang, J., Lehmann, H., Abramoff, M. D., & Liu, T. Y. A. (2026). Autonomous AI-assisted diabetic retinopathy screening at primary care is associated with increased presentation to eye care by at risk patients. NPJ Digit Med, 9(1). https://doi.org/10.1038/s41746-026-02460-5
* Lukac, P. J., Turner, W., Vangala, S., Chin, A. T., Khalili, J., Shih, Y. T., Sarkisian, C., Cheng, E.
* M., & Mafi, J. N. (2025). Ambient AI Scribes in Clinical Practice: A Randomized Trial. NEJM AI, 2(12). https://doi.org/10.1056/aioa2501000
* Maddox, T. M., Embi, P., Gerhart, J., Goldsack, J., Parikh, R. B., & Sarich, T. C. (2025). Generative AI in Medicine - Evaluating Progress and Challenges. N Engl J Med, 392(24), 2479-2483. https://doi.org/10.1056/NEJMsb2503956
* Martinez-Gutierrez, J. C., Kim, Y., Salazar-Marioni, S., Tariq, M. B., Abdelkhaleq, R., Niktabe, A., Ballekere, A. N., Iyyangar, A. S., Le, M., Azeem, H., Miller, C. C., Tyson, J. E., Shaw, S., Smith, P., Cowan, M., Gonzales, I., McCullough, L. D., Barreto, A. D., Giancardo, L., & Sheth, S. A. (2023). Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times: A Cluster Randomized Clinical Trial. JAMA Neurol, 80(11), 1182-1190. https://doi.org/10.1001/jamaneurol.2023.3206
* Olson, K. D., Meeker, D., Troup, M., Barker, T. D., Nguyen, V. H., Manders, J. B., Stults, C. D., Jones, V. G., Shah, S. D., Shah, T., & Schwamm, L. H. (2025). Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Netw Open, 8(10), e2534976. https://doi.org/10.1001/jamanetworkopen.2025.34976
* Pelletier, J. H., Watson, K., Michel, J., McGregor, R., & Rush, S. Z. (2025). Effect of a generative artificial intelligence digital scribe on pediatric provider documentation time, cognitive burden, and burnout. JAMIA Open, 8(4), ooaf068. https://doi.org/10.1093/jamiaopen/ooaf068
* Shah, S. J., Devon-Sand, A., Ma, S. P., Jeong, Y., Crowell, T., Smith, M., Liang, A. S., Delahaie, C., Hsia, C., Shanafelt, T., Pfeffer, M. A., Sharp, C., Lin, S., & Garcia, P. (2025). Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden. J Am Med Inform Assoc, 32(2), 375-380. https://doi.org/10.1093/jamia/ocae295
* Teo, Z. L., Thirunavukarasu, A. J., Elangovan, K., Cheng, H., Moova, P., Soetikno, B., Nielsen, C., Pollreisz, A., Ting, D. S. J., Morris, R. J. T., Shah, N. H., Langlotz, C. P., & Ting, D. S. W. (2025). Generative artificial intelligence in medicine. Nat Med, 31(10), 3270-3282. https://doi.org/10.1038/s41591-025-03983-2
* Tomasev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., Connell, A., Hughes, C. O., Karthikesalingam, A., Cornebise, J., Montgomery, H., Rees, G., Laing, C., Baker, C. R., Peterson, K.,...Mohamed, S. (2019). A clinically applicable approach to continuous prediction of future acute kidney injury. Nature, 572(7767), 116-119. https://doi.org/10.1038/s41586-019-1390-1
* Topol, E. J. (2019). Deep medicine : how artificial intelligence can make healthcare human again (First edition. ed.). Basic Books.
* Topol, E. J. (2026). Mammography should include artificial intelligence support. Lancet, 407(10537), 1415. https://doi.org/10.1016/S0140-6736(26)00659-8
* US Department of Health and Human Services. (2025). Artificial Intelligence Strategy.
* Wachter, R. M. (2026). A giant leap : how AI is transforming healthcare and what that means for our future. Portfolio / Penguin.
* Xu, Z., Ren, F., Wang, P., Cao, J., Tan, C., Ma, D., Zhao, L., Dai, J., Ding, Y., Fang, H., Li, H., Liu, H., Luo, F., Meng, Y., Pan, P., Xiang, P., Xiao, Z., Rao, S., Satler, C.,...Zhavoronkov, A. (2025). A generative AI-discovered TNIK inhibitor for idiopathic pulmonary fibrosis: a randomized phase 2a trial. Nat Med, 31(8), 2602-2610. https://doi.org/10.1038/s41591025-03743-2
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Original text here: https://edworkforce.house.gov/uploadedfiles/talbert_testimony.pdf
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Artificial intelligence (AI) is rapidly reshaping discussions about the future of work. Public debate often focuses on automation, job displacement, and the possibility that machines will replace human ... Show Full Article AUGUSTA, Georgia, Aug. 5 -- The House Education and Workforce Committee released the following testimony by Jeffery Talbert, professor and department chair of AI and health at Augusta University, and an eminent scholar at the Georgia Research Alliance, from a July 24, 2026, field hearing entitled "Building an AI-Ready America: How AI Is Creating Opportunities Across America's Workforce": * * * Artificial intelligence (AI) is rapidly reshaping discussions about the future of work. Public debate often focuses on automation, job displacement, and the possibility that machines will replace humanworkers. The healthcare industry offers a different and increasingly evidence-based perspective. Rather than replacing clinicians, many of the most successful AI applications are helping healthcare professionals perform their jobs more effectively by reducing administrative workload, improving efficiency, increasing positive patient outcomes, and expanding workforce capacity.
Recent studies involving ambient AI scribes, digital documentation systems, and generative AI workflow tools demonstrate measurable improvements in clinician experience, reductions in burnout, decreases in documentation burden, and gains in workforce productivity. Collectively, these findings provide some of the strongest currently available real-world evidence that AI can create workforce opportunity through augmentation rather than replacement. At the same time, AI is likely to transform some administrative occupations, creating a need for workforce adaptation, reskilling, and lifelong learning as job responsibilities evolve. AI is also beginning to demonstrate measurable improvements in patient outcomes, including earlier detection of breast cancer, reductions in sepsis mortality, faster stroke treatment, expanded diabetic retinopathy screening, and accelerated drug discovery.
The central finding of this testimony is that the healthcare industry can now provide direct evidence that AI can strengthen workforce performance, improve worker experience, preserve expertise, expand workforce capacity, and create new career opportunities when implemented as a workforce-support technology. In a healthcare system facing projected shortages of tens of thousands of physicians and more than 100,000 nurses, this distinction has important implications for workforce policy and positions AI not as a threat to the industry but as a force multiplier for it.
Georgia is already investing in this future through Augusta University's Department of Artificial Intelligence and Health, one of the first academic departments in the nation dedicated to integrating AI, healthcare delivery, workforce development, education, and responsible implementation. Leadership in healthcare AI is increasingly becoming a matter of economic competitiveness. Nations that develop the workforce capable of building, deploying, and governing AI-enabled healthcare systems will be better positioned to lead in biomedical innovation, healthcare delivery, and life sciences research. (Dai et al., 2026; Lukac et al., 2025; Olson et al., 2025) Key Findings
A survey of current research provides several key points for policy makers' consideration:
1. Healthcare workforce research demonstrates that AI can strengthen workforce performance through augmentation rather than replacement.
2. Ambient AI and digital documentation systems significantly reduce documentation burden, cognitive workload, and burnout.
3. Reducing administrative burden increases effective workforce capacity without increasing workforce size.
4. Early healthcare AI implementation suggests that AI is creating new workforce
opportunities in governance, implementation, informatics, workforce training, quality assurance, AI operations, and digital transformation while simultaneously reshaping some administrative occupations.
5. AI knowledge, working with AI tools, and accountability structures are becoming essential workforce skills.
6. Successful adoption depends on governance, workforce engagement, workflow redesign, and ongoing workforce development.
7. AI is transforming job responsibilities rather than eliminating occupations in healthcare.(Atabeygi & Mitchell, 2026; Davidson et al., 2025; Lukac et al., 2025; Olson et al., 2025) The healthcare workforce's experience suggests that the most significant workforce impact of AI may not be workforce reduction, but workforce redesign, reducing routine administrative work while increasing demand for human judgment, oversight, communication, and education.
1. The Healthcare Workforce Challenge
The American healthcare system faces substantial workforce pressures that extend far beyond shortages of physicians and nurses. Increasingly, workforce researchers describe the problem as one of workforce sustainability. Healthcare organizations are confronting burnout, retention challenges, administrative burden, and the loss of experienced personnel whose expertise is difficult to replace.
National physician workforce data illustrate the seriousness of these challenges. Approximately 41.9 percent of physicians report symptoms of burnout, 42.9 percent report substantial job-related stress, and nearly one-third indicate some likelihood of leaving their organization within two years.
Physicians report average workweeks exceeding 58 hours. These findings suggest that retaining experienced professionals has become an urgent workforce priority. Clinicians consistently identify administrative and operational burdens, not patient care itself, as major contributors to dissatisfaction. Documentation requirements, electronic health record workflows, inbox management, insurance-related tasks, staffing shortages, and bureaucratic processes are frequently cited as major workplace stressors. These observations suggest that many workforce challenges arise from how work is organized rather than from the providers' core mission. (American Medical Association, 2025; Graunke et al., 2026)
These workforce pressures are expected to intensify substantially over the coming decade. The Association of American Medical Colleges projects a national physician shortage of up to 86,000 physicians by 2036, driven by population aging, increasing healthcare utilization, and physician retirements. Nursing shortages present similar challenges. Federal workforce projections indicate the United States could face a shortage of more than 100,000 registered nurses by 2038. The challenge is particularly acute in rural and underserved communities, where provider shortages already limit access to care and where workforce deficits may be felt most severely. These shortages are particularly concerning because demand for healthcare services continues to grow while large segments of the clinical workforce approach retirement age. AI is not a substitute for addressing workforce supply, but it may help organizations use scarce professional expertise more effectively and expand workforce effectiveness in the face of growing demand.
2. Administrative Burden and the 1.2-FTE Problem
The National Academy of Medicine's concept of the "1.2-FTE problem" provides a useful framework for understanding workforce strain. Clinicians are employed as 1.0 full-time equivalent (FTE) workers but perform the work of 1.2 FTEs because of documentation requirements, inbox management, prior authorizations, insurance communications, and compliance activities. This hidden workload often extends beyond scheduled clinical hours into evenings and weekends.
Over time, these obligations contribute directly to burnout, dissatisfaction, and workforce attrition.
Administrative burden also reduces effective organizational capacity. Every hour spent documenting, managing messages, or completing administrative requirements is an hour unavailable for patient care, teaching, leadership, mentoring, research, or quality improvement.
In the healthcare industry, AI experience is particularly important because many of the most successful AI applications directly target these burdens. (Atabeygi & Mitchell, 2026)
3. Evidence from Ambient AI and Digital Scribes
Among all healthcare AI applications currently available, ambient AI scribes and digital documentation systems have generated the strongest evidence regarding workforce outcomes.
A multisystem study involving 263 physicians and advanced practice clinicians across six health systems demonstrated substantial improvements in workforce measures. Burnout declined from 51.9 percent to 38.8 percent. Clinicians reported reductions in cognitive workload, decreased after-hours documentation, improved professional well-being, and greater ability to focus on patients. Similar findings emerged from Stanford Health Care's evaluation of ambient AI documentation systems, where researchers documented reductions in physician task load and burnout along with improvements in workflow efficiency and user satisfaction.
Evidence from pediatric healthcare settings further strengthens the case. A six-month implementation involving eighty-four pediatric providers demonstrated significant reductions in documentation burden and cognitive workload. Burnout rates fell from 54.9 percent to 33.3 percent, while providers collectively saved more than 2,100 hours of documentation time.
The consistency of these findings across multiple specialties and organizational settings suggests that workforce benefits are neither isolated nor specialty specific.
Several years before the widespread deployment of ambient AI systems, cardiologist and digitalhealth researcher Dr. Eric Topol argued that one of healthcare AI's greatest opportunities would be to "give clinicians back the gift of time." In his book Deep Medicine, Topol envisioned AI not primarily as a replacement for physicians, but as a means of reducing clerical burden and restoring the human relationship at the center of care. The emerging evidence from ambient AI documentation systems suggests that this vision is beginning to materialize in practice. (Olson et al., 2025; Pelletier et al., 2025; Shah et al., 2025; Topol, 2019)
4. Moving from Promise to Proof
Healthcare AI research has increasingly progressed beyond pilot projects and implementation studies to include randomized evaluations. A randomized trial involving 238 physicians across fourteen specialties evaluated ambient AI scribes against traditional documentation workflows.
Researchers found meaningful reductions in documentation burden, task load, and work exhaustion, along with improvements in physician experience. These findings reinforce a broader workforce principle: reducing administrative work can improve workforce well-being, organizational performance, and professional satisfaction. Rather than treating burnout primarily as an individual problem, the evidence suggests that improving how work is organized can address important systemic contributors to workforce strain. Collectively, implementation studies and randomized evidence indicate that AI's workforce benefits are measurable, reproducible, and increasingly supported by rigorous research. (Lukac et al., 2025; Olson et al., 2025)
5. Workforce Opportunity Through Augmentation
Perhaps the most important finding emerging from the healthcare literature is that AI's workforce value lies primarily in augmentation rather than replacement. Across virtually every major study, clinicians remain responsible for reviewing documentation, exercising professional judgment, making decisions, communicating with patients, and maintaining accountability. AI supports these activities by reducing clerical effort and simplifying routine administrative tasks.
This distinction is critical because it shifts the conversation away from labor substitution and toward expertise amplification. Rather than asking whether AI can replace workers, healthcare organizations are increasingly asking how AI can help professionals spend more time practicing at the top of their training. Activities such as communication, diagnosis, leadership, teaching, mentoring, and relationship-building continue to depend upon human capabilities. AI creates value by helping workers devote more time to these higher-value activities.
The implications for workforce policy are substantial. The operational capacity can be expanded without increasing the size of the staff if clerical responsibilities are reduced. Retention may improve when workplace conditions improve. Expertise may be preserved when experienced professionals remain engaged in practice longer. In this sense AI functions as a workforce multiplier that strengthens the productivity and sustainability of existing personnel rather than replacing them. This opportunity is especially important as projected physician and nursing shortages continue to grow nationally. In an environment where healthcare organizations may be unable to fully meet workforce demand through hiring alone, technologies that allow clinicians to devote more time to patient care can effectively increase capacity without requiring equivalent increases in staffing.
The workforce implications of AI extend beyond improving existing jobs. As healthcare organizations adopt AI-enabled documentation, workflow, governance, and decision-support systems, they are increasingly creating demand for new roles focused on implementation, oversight, quality assurance, workforce training, and organizational transformation. These emerging occupations suggest that AI may expand workforce opportunities not only by improving productivity but also by creating entirely new career pathways that combine domain expertise with AI-related competencies. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Topol, 2019)
6. AI Is Delivering Measurable Improvements in Health Outcomes
While workforce benefits are important, AI is also producing measurable improvements in patient outcomes, disease detection, and biomedical innovation. AI is also particularly valuable in rural and underserved communities where specialist shortages are most severe. Technologies that support screening, triage, documentation, and clinical decision-making may help extend the reach of limited healthcare workforces and improve access to expertise that is otherwise unavailable.
In breast cancer screening, the landmark Mammography Screening with Artificial Intelligence (MASAI) randomized trial demonstrated that AI-assisted screening increased breast cancer detection by approximately 29 percent while reducing radiologist workload. Researchers also observed improved detection of small, node-negative invasive cancers that are more likely to be treated successfully when identified early. (Hernstrom et al., 2025; Topol, 2026)
AI is also improving outcomes for patients with sepsis, one of the leading causes of hospital mortality in the United States. The Targeted Real-Time Early Warning System (TREWS), deployed across dozens of hospitals, has been associated with approximately an 18 percent reduction in sepsis mortality, earlier recognition of deteriorating patients, reduced intensive care utilization, and shorter hospital stays. (Adams et al., 2022)
Stroke care provides another example of AI-enabled improvements. Because neurological injury progresses rapidly during acute stroke, every minute saved can help preserve brain function. A randomized clinical trial found that AI-enabled large-vessel occlusion detection and automated team notification reduced time to thrombectomy initiation by approximately 11 minutes, a clinically meaningful improvement in a condition where outcomes are highly time dependent. (Martinez-Gutierrez et al., 2023)
AI has also become one of the first successful examples of autonomous disease screening in routine clinical practice. FDA-cleared AI systems for diabetic retinopathy can identify patients at risk of preventable blindness in primary care settings without requiring immediate specialist review. Studies have demonstrated sensitivities exceeding 87 percent and have shown that AIbased screening can expand access to eye care, particularly in underserved populations where specialist availability is limited. (Gulshan et al., 2016; Leong et al., 2026)
Cardiovascular medicine is beginning to show similar promise. Researchers at Mayo Clinic and collaborators developed FDA-cleared AI tools capable of identifying heart failure with preserved ejection fraction from standard echocardiographic images. In independent validation studies, these systems demonstrated strong diagnostic performance and may help identify patients whose condition is frequently underdiagnosed using conventional approaches. (Akerman et al., 2025) AI is also demonstrating potential to predict serious complications before they occur. Deep learning models developed using large-scale electronic health record data have been shown to predict acute kidney injury up to 48 hours before traditional clinical recognition and identified approximately 90 percent of the most severe cases requiring dialysis. Earlier identification provides clinicians with an opportunity to intervene before irreversible damage occurs. (Tomasev et al., 2019)
Finally, AI is beginning to reshape biomedical innovation as well. Rentosertib (INS018_055), an investigational treatment for idiopathic pulmonary fibrosis discovered and designed using artificial intelligence, advanced from target identification to human clinical testing in approximately 30 months--about half the time commonly required for traditional drug discovery programs. Early Phase II results have demonstrated encouraging improvements in lung function, providing one of the first clinical demonstrations that AI-assisted drug discovery can successfully generate novel therapeutic candidates. (Xu et al., 2025)
Collectively, these examples demonstrate that AI is not merely improving efficiency; it is improving patient outcomes, accelerating medical discovery, and creating demand for a workforce capable of deploying these tools safely and effectively. As these technologies continue to mature, healthcare organizations will require a growing workforce capable of implementing, evaluating, governing, and safely deploying AI-enabled systems.
7. Workforce Transformation and Job Displacement Risks
The healthcare sector's experience with AI suggests that workforce impacts will not be distributed evenly across occupations. While most clinical professionals are experiencing augmentation rather than replacement, some administrative functions are likely to undergo significant transformation as AI systems assume a growing share of routine information-processing tasks.
Emerging AI applications already support documentation, scheduling, records management, patient communication, revenue-cycle activities, coding assistance, prior-authorization workflows, claims processing, and other administrative functions. As these capabilities mature, organizations may require fewer workers in some highly structured clerical functions while increasing demand for workers who can manage, supervise, implement, and improve AI-enabled systems.
Healthcare leaders and workforce experts have identified administrative operations as an area particularly susceptible to AI-driven workflow transformation. Occupations potentially affected include medical scribes, scheduling personnel, administrative assistants, coding and billing personnel, and prior-authorization support teams. Workforce impacts are likely to occur through gradual role redesign, attrition, and changing hiring patterns rather than sudden large-scale replacement. Most healthcare administrative functions involve exception handling, coordination, communication, compliance oversight, and patient interaction that continue to benefit from human involvement. This now-proven pattern of administrative work reduction shows that AI is likely reducing tasks faster than it is eliminating jobs.
Unlike many industries, healthcare also faces persistent workforce shortages, increasing demand associated with population aging, and growing complexity of care delivery. As a result, productivity gains generated by AI may often be used to absorb unmet demand and expand capacity rather than solely reduce staffing levels. Nevertheless, policymakers should anticipate workforce transitions for some administrative occupations and support retraining pathways that help affected workers move into emerging AI-enabled roles. Longitudinal evidence on healthcare workforce displacement remains limited, and many workforce effects will likely emerge over several years as adoption expands across care settings. The healthcare industry's experience therefore provides a nuanced lesson regarding AI and employment. AI is likely to reduce demand for some routine administrative activities while simultaneously increasing demand for workers who can implement, evaluate, govern, supervise, and improve AI-enabled systems. The policy challenge is not solely preserving existing jobs but supporting successful transitions into higher-value roles that leverage uniquely human capabilities.
Although evidence regarding documentation burden, burnout reduction, and workforce productivity is now substantial, evidence regarding long-term workforce displacement remains limited. Continued monitoring will be necessary as AI adoption expands and organizations evaluate how productivity gains affect staffing patterns, workforce composition, and career pathways. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Teo et al., 2025)
8. Emerging Workforce Opportunities in an AI-Enabled Healthcare System
While much of the public discussion surrounding artificial intelligence focuses on automation, evidence suggests that AI is also creating demand for new categories of workers. As health systems expand their use of ambient documentation, workflow-support tools, predictive analytics, and generative AI, organizations increasingly require professionals capable of implementing, governing, evaluating, training, and monitoring these technologies. Rather than reducing demand for skilled workers, AI is creating new opportunities for professionals who can combine healthcare expertise with digital and analytical competencies.
Augusta University's Department of Artificial Intelligence and Health was established as a response to a challenge facing health systems across the nation: the need for a workforce that understands both healthcare and artificial intelligence. By integrating education, workforce development, implementation science, governance, research, and health system partnerships within a single academic unit, the department offers one model for how universities can help prepare the AI-ready workforce required for the future of healthcare delivery. Health systems need AI implementation specialists, AI governance leaders, clinical AI evaluators, and data scientists trained to ensure that AI is safe, effective, and trustworthy.
The skills associated with these new opportunities differ from those traditionally associated with healthcare technology. The Journal of the American Medical Association (JAMA) Summit on Artificial Intelligence concluded that healthcare professionals need more than training on specific AI tools. AI literacy is becoming a foundational workforce competency. Clinicians, administrators, and operational leaders must understand how AI systems function, their limitations, how outputs should be validated, and how responsibility should be shared between humans and AI-enabled systems. In addition to AI literacy, workers increasingly need competencies in human-AI collaboration, governance, privacy, cybersecurity, compliance, quality monitoring, risk management, and workflow redesign. Successful AI adoption depends as much on organizational integration as it does on technological capability.
Importantly, many of these emerging opportunities can be filled through targeted upskilling rather than wholesale career changes. Physicians, nurses, pharmacists, therapists, informaticians, administrators, quality-improvement professionals, and information technology specialists already possess expertise that remains highly valuable in AI-enabled environments. Additional preparation in AI competency, governance, analytics, implementation science, and human-AI collaboration can position these workers for expanding roles in AI deployment and oversight.
Emerging evidence suggests that AI may also transform workforce education itself. Scholars have begun describing the development of "AI-enabled precision education" systems that use digital data, performance metrics, simulation platforms, and AI-driven coaching tools to identify individual competency gaps and personalize learning pathways. Such systems may improve workforce readiness by helping workers acquire new competencies more efficiently and supporting continuous professional development throughout their careers. In an AI-enabled economy, lifelong learning may become increasingly important as workers regularly adapt to evolving technologies and changing job responsibilities. AI education itself is becoming an important workforce priority. National workforce discussions increasingly emphasize that healthcare workers require not only knowledge of specific tools but broader competencies in use of AI systems, data literacy, digital literacy, human-AI collaboration, and critical evaluation of AI outputs. These competencies are increasingly relevant across clinical, operational, administrative, and leadership roles.
These changes have important implications for workforce development. Community colleges, universities, health professions programs, and workforce-development organizations should increasingly incorporate practical understanding of AI, ethical AI, implementation science, governance, and working effectively with AI tools into existing curricula. Employers will similarly need to invest in continuing education and lifelong learning programs that help incumbent workers acquire new competencies throughout their careers. The emerging challenge is not a shortage of technology, but a shortage of workers prepared to effectively implement, govern, and leverage that technology.
Healthcare provides an important lesson for policymakers. AI is not only altering how work is performed; it is creating demand for a new generation of professionals responsible for managing AI-enabled systems, ensuring their safety and effectiveness, training the workforce, and helping organizations realize the benefits of technological innovation. The most durable workforce opportunities created by AI may ultimately arise not from automation itself, but from the growing need for human expertise that guides, governs, and improves AI-enabled work. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Davidson et al., 2025; Desai et al., 2026)
9. Lessons from Early Implementations
Evidence from early adopters provides several important lessons. Successful implementations begin with clearly defined workforce problems such as burnout, documentation burden, workflow inefficiencies, or retention concerns. Documentation support remains the most mature and consistently successful use case. Across organizations and specialties, documentation-focused AI applications repeatedly demonstrate workforce benefits. Implementation studies also emphasize the importance of workflow integration. Technology alone does not improve work.
Benefits depend on how effectively new tools fit into clinical workflows, align with organizational processes, and meet user needs. Frontline engagement, leadership support, usability, and training all play important roles in implementation success. The strongest outcomes occur when technology is combined with thoughtful workflow redesign.
These findings are consistent with lessons learned during earlier waves of healthcare digitization.
Dr. Robert Wachter, one of the nation's leading scholars of healthcare innovation, observed that transformative technologies succeed not because of the technology itself but because organizations redesign workflows, train staff, adapt culture, and rethink how work is performed.
In his books The Digital Doctor and later in A Giant Leap, Wachter argued that successful technology adoption is fundamentally an organizational and workforce challenge. Healthcare's early AI experience strongly reinforces that observation. Effective implementation depends as much on leadership, governance, and workforce engagement as it does on technical performance.
10. Governance, Trust, and Responsible AI
The workforce benefits associated with AI do not eliminate the need for governance and oversight.
The healthcare industry's experience demonstrates that trust is essential to adoption. Clinicians are more likely to embrace AI systems when they remain actively involved in reviewing outputs and maintaining accountability for decisions. Human oversight therefore remains a foundational principle of responsible AI implementation. Generative AI introduces additional governance requirements involving validation, transparency, quality monitoring, privacy protection, and accountability. Strong governance frameworks help ensure that technology serves workforce goals rather than creating new burdens. Building an AI-ready workforce therefore requires investments in governance and education alongside investments in technology.
Organizations should also monitor for unintended workforce consequences associated with AI adoption, including automation bias, overreliance on AI-generated outputs, and potential erosion of professional skills. Evidence suggests that effective human-AI collaboration requires maintaining core professional competencies while using AI as a support tool. Workforce development programs should therefore emphasize critical thinking, independent professional judgment, and appropriate oversight alongside foundational AI knowledge and technical skills. (Maddox et al., 2025; Teo et al., 2025; US Department of Health and Human Services, 2025)
11. Georgia's Leadership in Building the AI Healthcare Workforce
The need for workforce innovation is particularly acute in Georgia, but the challenge is national in scope. Health systems across the country increasingly require professionals who can implement, evaluate, govern, and improve AI-enabled technologies. Augusta University's Department of Artificial Intelligence and Health could be used as a model for how universities can address one of the most important workforce challenges of the next decade: preparing professionals to safely and effectively work alongside AI-enabled technologies. Rather than treating AI as a purely technical discipline, the department integrates workforce development, implementation science, healthcare delivery, governance, education, and translational research. This interdisciplinary approach reflects the reality that successful AI adoption depends not only on technology, but on people who understand how to implement, evaluate, govern, and improve these systems in realworld settings. Augusta University is helping build this workforce through education, research, and partnerships. These efforts reflect a broader national need for professionals who understand both healthcare and artificial intelligence. Building an AI-ready America will require not only technological innovation but also investments in the educational infrastructure needed to develop the workforce that can responsibly govern and deploy these tools. America does not simply have an AI opportunity. It has a workforce necessity. AI matters because healthcare demand is growing faster than the available workforce, especially in states like Georgia, and healthcare needs tools that help clinicians spend more time caring for patients and less time on administrative work. (GlobalData Plc, 2024; HRSA, 2025; Olson et al., 2025; Shah et al., 2025; Wachter, 2026)
12. Policy Considerations
The healthcare industry's AI experience to date suggests several priorities for policymakers:
* Administrative burden reduction should be recognized as both a workforce-development and workforce-retention strategy. Simplifying documentation requirements, improving interoperability, reducing redundant reporting, and responsibly deploying AI-enabled workflow tools can expand workforce capacity, improve professional satisfaction, preserve expertise, and strengthen workforce sustainability.
* Invest in AI workforce development, AI literacy, and lifelong learning. Educational pathways, stackable credentials, employer-based training programs, continuing education opportunities, and community-college partnerships can help current workers adapt to changing responsibilities while preparing future workers for emerging AI-enabled occupations.
* Support university initiatives focused on workforce innovation. Institutions such as Augusta University are developing interdisciplinary models that combine healthcare, data science, implementation science, and AI governance to prepare workers for emerging occupations created by AI-enabled healthcare delivery.
* Support workers whose occupations may experience substantial task automation. Retraining and upskilling programs should create pathways into AI operations, healthcare informatics, digital workflow management, implementation support, quality assurance, patient navigation, and AI governance roles.
* Invest in digital infrastructure and rural workforce capacity. AI-enabled tools can help expand access to care in underserved communities, but their benefits depend on interoperable systems, workforce training, and reliable digital infrastructure.
* Measure workforce outcomes directly. Success should be evaluated not only through technology adoption but also through changes in burnout, documentation burden, workforce satisfaction, retention, productivity, patient access, improved patient outcomes, and organizational capacity. (Atabeygi & Mitchell, 2026; Dai et al., 2026; Davidson et al., 2025; Graunke et al., 2026)
Conclusion
The healthcare sector provides one of the clearest examples of how artificial intelligence can create workforce opportunity. Across multisystem studies, academic medical centers, and randomized clinical trials, AI-enabled documentation and workflow-support tools have demonstrated measurable reductions in administrative burden, cognitive workload, after-hours work, and burnout. These improvements strengthen productivity, support retention, preserve expertise, and expand effective workforce capacity.
The evidence also suggests that AI's workforce impact will involve transformation as well as opportunity. While some routine administrative activities may become increasingly automated, the healthcare experience shows that AI is reducing tasks. Administrative processing, documentation, scheduling, and information-management tasks are increasingly supported by AI, while demand grows for oversight, implementation, governance, patient engagement, workforce education, and human-centered services. As seen in the healthcare experience, organizations that successfully combine AI-enabled productivity with workforce development are likely to achieve the greatest long-term benefits. AI is not removing the need for human professionals. Instead, it is helping workers spend less time on clerical activity and more time applying expertise, judgment, and human relationships.
The healthcare industry also demonstrates that AI is creating demand for new categories of workers focused on implementation, education, oversight, quality monitoring, and organizational transformation. The emergence of these occupations underscores the importance of workforce development, lifelong learning, and AI competency as national priorities. These lessons extend beyond healthcare. As AI adoption expands across the U.S. economy, organizations that successfully combine human expertise with AI-enabled productivity gains will be better positioned to strengthen workforce resilience, improve competitiveness, and support long-term economic growth.
The greatest workforce opportunity created by AI is not automation for its own sake. It is the creation of work environments in which professionals can focus more fully on the activities that generate the greatest value for organizations, communities, and the citizens they serve. This objective becomes increasingly important as healthcare grows more complex. As surgeon and public health researcher Dr. Atul Gawande observed, "The volume and complexity of what we know has exceeded our individual ability to deliver its benefits correctly, safely, or reliably." In many respects, early experience with healthcare AI demonstrates the value of technologies that help professionals manage complexity while preserving the uniquely human skills of judgment, communication, empathy, and trust that remain central to high-quality care.
When implemented responsibly, AI can become a powerful tool for workforce sustainability, expertise amplification, and long-term economic competitiveness. Emerging evidence suggests that the most promising future for AI is not healthcare workforce replacement, but workforce enhancement--helping people spend more time doing the work that only people can do. The workforce challenge posed by AI is not primarily one of replacement, but one of preparation. The question is no longer whether AI will become part of the healthcare workforce. The question is whether the United States will invest now in the people, education, and oversight needed to ensure that workforce is prepared to lead rather than follow. (Adams et al., 2022; Gawande, 2010; Lukac et al., 2025; Olson et al., 2025; Topol, 2019; Wachter, 2026; Xu et al., 2025)
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I acknowledge use of Copilot for assistance with editing this document. Following the use of this tool, I reviewed and edited the content to ensure accuracy and tone.
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Original text here: https://edworkforce.house.gov/uploadedfiles/talbert_testimony.pdf