    [Senate Hearing 119-107]
    [From the U.S. Government Publishing Office]

                                                            S. Hrg. 119-107

                    HARNESSING ARTIFICIAL INTELLIGENCE CYBER
                                    CAPABILITIES

    =======================================================================

                                    HEARING

                                  BEFORE THE

                                SUBCOMMITTEE ON
                                 CYBERSECURITY

                                     OF THE

                          COMMITTEE ON ARMED SERVICES
                              UNITED STATES SENATE

                        ONE HUNDRED NINETEENTH CONGRESS

                                 FIRST SESSION

                                   __________

                                 MARCH 25, 2025

                                   __________

             Printed for the use of the Committee on Armed Services

    [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                     Available via: http://www.govinfo.gov

                                    __________

                       U.S. GOVERNMENT PUBLISHING OFFICE
    60-836 PDF                  WASHINGTON : 2025

    -----------------------------------------------------------------------------------

                          COMMITTEE ON ARMED SERVICES

            	ROGER F. WICKER, Mississippi, Chairman

    DEB FISCHER, Nebraska			JACK REED, Rhode Island
    TOM COTTON, Arkansas			JEANNE SHAHEEN, New Hampshire
    MIKE ROUNDS, South Dakota		KIRSTEN E. GILLIBRAND, New York
    JONI ERNST, Iowa			RICHARD BLUMENTHAL, Connecticut
    DAN SULLIVAN, Alaska			MAZIE K. HIRONO, Hawaii
    KEVIN CRAMER, North Dakota		TIM KAINE, Virginia
    RICK SCOTT, Florida			ANGUS S. KING, Jr., Maine
    TOMMY TUBERVILLE, Alabama		ELIZABETH WARREN, Massachusetts
    MARKWAYNE MULLIN, Oklahoma	        GARY C. PETERS, Michigan
    TED BUDD, North Carolina		TAMMY DUCKWORTH, Illinois
    ERIC SCHMITT, Missouri			JACKY ROSEN, Nevada
    JIM BANKS, INDIANA			MARK KELLY, Arizona
    TIM SHEEHY, MONTANA                  	ELISSA SLOTKIN, MICHIGAN

    		   John P. Keast, Staff Director
    		Elizabeth L. King, Minority Staff Director

    _________________________________________________________________

                         Subcommittee on Cybersecurity

        MIKE ROUNDS, South Dakota,
                 Chairman
    TOM COTTON, Arkansas			JACKY ROSEN, Nevada
    JONI K. ERNST, Iowa			KIRSTEN E. GILLIBRAND, New York
    TED BUDD, North Carolina		GARY C. PETERS, Michigan
    ERIC SCHMITT, Missouri           	ELISSA SLOTKIN, Michigan

                                  (ii)

                             C O N T E N T S

    _________________________________________________________________

                                 march 25, 2025

                                                                       Page

    Harnessing Artificial Intelligence Cyber Capabilities............     1

                               Members Statements

    Statement of Senator Mike Rounds.................................     1

    Statement of Senator Jacky Rosen.................................     2

                               Witness Statements

    Mitre, Mr. Jim, Vice President and Director, Rand Global and          3
      Emerging Risks.

    Tadross, Mr. Dan, Head of Public Sector, Scale AI................    10

    Ferris, Mr. David, Global Head of Public Sector, Cohere..........    19

                                     (iii)

             HARNESSING ARTIFICIAL INTELLIGENCE CYBER CAPABILITIES

                                  ----------

                            TUESDAY, MARCH 25, 2025

                          United States Senate,
                         Subcommittee on Cybersecurity,
                                   Committee on Armed Services,
                                                        Washington, DC.
        The Committee met, pursuant to notice, at 3:31 p.m. in room
    SR-232A, Russell Senate Office Building, Senator Mike Rounds
    (Chairman of the Subcommittee) presiding.
        Committee Members present: Senators Rounds, and Rosen.

                OPENING STATEMENT OF SENATOR MIKE ROUNDS

        Senator Rounds. Good afternoon, and I'd like to thank our
    witnesses for appearing today to discuss how artificial
    intelligence can be utilized to enhance the Department of
    Defense's (DOD) cyber capabilities. We have just heard from
    experts in our closed session from the U.S. Cyber Command, the
    Defense Advanced Research Projects Agency (DARPA), and the
    DOD's Chief Digital and Artificial Intelligence Office. These
    organizations all play a crucial role in making sure the
    Department is postured to carry out its national security
    mission in cyber space.
        Recent cyberattacks against U.S. critical infrastructure
    are a stark reminder of the growing sophistication and
    persistence of cyber threat actors. To outpace our adversaries
    in the cyber domain, the Department must rapidly harness the
    advances of AI [Artificial Intelligence] technologies. This
    means that the Department of Defense needs capable partners
    outside of the Pentagon who are moving at breakneck speed to
    solve our national security challenges.
        This brings us to our hearing topic today; how the
    Department can leverage AI-enabled capabilities to field
    exquisite, offensive, and defensive cyber tools, enhance our
    ability to detect cyber threats, and automate threat mitigation
    to gain an enduring advantage in cyberspace.
        I also look forward to hearing from the witnesses about how
    the Department can be better equipped to counter enemy AI-
    enabled cyber capabilities, and leverage AI to enhance our
    overall war fighting ability in the cyber domain. Our
    innovators and tech companies are one of our asymmetric
    advantages in the cyber fight, but the gap is steadily closing.
        At the tip of the spear is artificial intelligence.
    Unfortunately, the Chinese Communist Party understands this all
    too well. Xi Jinping has spoken about the importance of AI.
    With the release of DeepSeek earlier this year, it is clear
    unless we act decisively and soon, China will not be playing
    catch up. We will.
        U.S. advancements in this critical technology are
    impressive, and we are fortunate to have some of the best
    innovators in the world. As Silicon Valley and other leading
    technology developers continue their research and development
    of AI at the bleeding edge, our job must be to integrate those
    tools in a secure, but rapid fashion into our cyber
    capabilities.
        I look forward to hearing from our witnesses who all bring
    unique and firsthand experience about how the Department can
    speed up its use of AI in the cyber domain. Again, thank you to
    our witnesses for coming here today.
        Before I introduce them, I'll now recognize Ranking Member
    Senator Rosen.

                    STATEMENT OF SENATOR JACKY ROSEN

        Senator Rosen. Well, thank you, Chairman Rounds, and I'd
    like to begin by welcoming our panel, and thanking you all for
    joining us. This topic has profound implications for our
    national security, I would say, for our personal security, for
    everything in our world to come.
        But this is actually my first hearing as Ranking Member of
    this Subcommittee, and I am really honored to work alongside
    Chairman Rounds, our colleagues, and each of you on how we can
    responsibly integrate innovation and the increasing pace of
    technology including artificial intelligence into our national
    defense strategy and into the hands of our service members to
    enhance their speed, their capabilities, and their operating
    picture. Well, of course, all the time we have to balance the
    risks and rewards concerns of AI and what it teaches us.
        So, with great promise comes great responsibility. We know
    that our adversaries are developing new AI tools and have the
    potential to fundamentally shift the nature of warfare. We've
    began to see how new uses of AI can help our own service
    members counter such threats and take proactive offensive
    actions in the moment as well.
        However, the rapid pace of AI innovation also raises really
    important questions about its ethical implications, its
    governance, and the security risks it poses as well. We're
    operating in a new world without guardrails and we need to
    tread carefully, balancing such caution with the need to create
    an environment that allows for innovation and agility.
        There are also challenges we must overcome in order to both
    mitigate the risks of AI and make the most of the opportunities
    that I know it presents. In particular, we need to further
    invest in and expand the AI workforce, both at DOD, and across
    the Government, across the private sector. We have to increase
    it everywhere to harness our full potential. I truly believe
    this.
        As a former computer programmer, systems analyst, myself, I
    can say from firsthand experience that AI has vastly changed
    the technology landscape since I began my career. Many of the
    coding and the programming skills that people like me brought
    to the table, which form the backbone of what CYBERCOM
    personnel do every day, in both offensive and defensive
    operations, can now be supplemented by AI.
        I know it doesn't replace us, that's for sure. But however,
    this does pose its own set of risks. It creates a deep need for
    us to invest in that new kind of cyber workforce that is
    centered around understanding these AI skills, and we continue
    to have a cyber and AI skills gap.
        Until we meet that challenge of bridging it, understanding
    it, being able to see its potential, and at the same time
    understand how it improves our own potential as human beings,
    we're going to continue to be at the risk of our adversaries
    having the upper hand.
        So, I look forward to discussing such challenges today and
    over the course of this Congress. I thank our panel once again
    for your expertise and contributions to that effort, and I
    thank you again, Mr. Chairman.
        Senator Rounds. Thank you, and it is a pleasure to have you
    here on the team with us. This is one of those subcommittees in
    which it is very bipartisan, and we have focused on this since
    the creation of this by Senator McCain back in 2017, I believe.
    The path forward, I think, has been made better because of the
    work that we've done in the past on a bipartisan basis to keep
    everything on the straight and narrow.
        I want to thank all of you once again for coming in and
    participating in this open session, and we have with us, today,
    all three of you here. Beginning with Mr. Jim Mitre, Vice
    President and Director of RAND Global and Emerging risks. Mr.
    Mitre, welcome. Mr. David Ferris, Global Head of Public Sector,
    Cohere. Welcome, and Mr. Dan Tadros, Head of Public Sector,
    Scale AI.
        I understand that the agreement has been made that Mr.
    Mitre, you will begin today. So, we welcome you for your
    opening statement, sir.

     STATEMENT OF MR. JIM MITRE, VICE PRESIDENT AND DIRECTOR, RAND
                       GLOBAL AND EMERGING RISKS

        Mr. Mitre. Terrific. Chairman Rounds, Ranking Member Rosen,
    thank you so much for the opportunity to testify today on the
    national security implications posed by the potential emergence
    of advanced artificial intelligence, or artificial general
    intelligence, AGI.
        Leading AI companies in the United States, China, and the
    rest of the world, are in hot pursuit of AGI, which would
    possess human level or potentially even superhuman level
    intelligence across a wide variety of cognitive tasks. The pace
    and potential progress of AGI's emergence, as well as the
    composition of a post-AGI future, are uncertain and hotly
    debated. Yet the emergence of AGI is plausible and the
    consequences so profound that the U.S. national security
    community should take it seriously and plan for it.
        Consider the following. What would the U.S. Government do
    if in the next few years, a leading AI company announced that
    its forthcoming model had the ability to produce the equivalent
    of 1 million computer programmers as capable as the top 1
    percent of human programmers at the touch of a button. The
    national security implications are substantial and could cause
    a significant disruption of the current cyber offense defense
    balance.
        At RAND, we are planning for it. Our work has revealed that
    AGI presents five hard national security problems. First, AGI
    might enable a significant first-mover advantage via the sudden
    emergence of a decisive wonder weapon. For example, a
    capability so proficient at identifying and exploiting
    vulnerabilities in enemy cyber defenses, that it provides what
    might be called a splendid first cyber strike, that completely
    disables a retaliatory cyber strike. Such a first mover
    advantage could disrupt the military balance of power in key
    theaters, create a host of proliferation risks, and accelerate
    technological race dynamics.
        Second, AGI might cause a systemic shift in the instruments
    of national power that alters the balance of global power. The
    history of military innovation suggests that being able to
    adopt a new technology is more consequential than being the
    first to achieve a specific scientific or technological
    breakthrough.
        As the U.S. allied and rival militaries establish access to
    AGI and adopted it at scale, it could upend military balances
    by affecting key building blocks of military competition such
    as hiders versus finders, precision versus mass, or centralized
    versus decentralized command and control. States that are
    better postured to capitalize on and manage systemic shifts
    caused by AGI could have greatly expanded influence.
        Third, AGI might serve as a malicious mentor that explains
    and contextualizes the specific steps that non-experts can take
    to develop dangerous weapons such as violent cyber malware,
    widening the pool of people capable of creating such threats.
        Fourth, AGI might achieve enough autonomy and behave with
    enough agency to be considered an independent actor on the
    global stage. Consider an AGI with advanced computer
    programming abilities that is able to break out of the box and
    engage with the world across cyberspace. It could possess
    agency beyond human control, operate autonomously, and make
    decisions with far reaching consequences.
        Fifth, the pursuit of AGI could foster a period of
    instability as nations and corporations race to achieve
    dominance in this transformative technology. This competition
    might lead to heightened tensions reminiscent of the nuclear
    arms race, such that the quest for superiority risks triggering
    rather than deterring conflict. Misinterpretations or
    miscalculations could precipitate preemptive strategies or arms
    buildups that destabilize global security.
        As the U.S. Department of Defense embarks on developing the
    National Defense Strategy, it will have to grapple with how
    advanced AI will affect cyber along with all other domains. The
    five hard problems that AGI presents to national security can
    serve as a rubric to evaluate how the strategy addresses the
    potential emergence of AGI.
        Thank you for the opportunity to testify. I welcome your
    questions.
        [The prepared statement of Mr. Jim Mitre follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Senator Rounds. I thank you. Mr. Tadross, unless you folks
    have agreed on a different. Mr. Tadross.

     STATEMENT OF MR. DAN TADROSS, HEAD OF PUBLIC SECTOR, SCALE AI

        Mr. Tadross. Chairman Rounds, Ranking Member Rosen, Members
    of the Subcommittee, thank you for the opportunity to be here
    today.
        My name is Dan Tadross. I lead Scale AI's public sector
    business. Every day, my team is singularly focused on how to
    bring best-in-class AI into the DOD and other agencies. Scale
    was founded in 2016, and since that time, has powered nearly
    every AI innovation. Our role in this critical ecosystem
    provides us a unique opportunity to understand how to build
    high quality AI systems powered by the world's best data.
        Our work is deeply personal to me as I have worked nearly
    my entire career at the intersection of AI and the Government.
    During my time as an Active Duty marine, I had the privilege of
    helping to stand up the Joint Artificial Intelligence Center,
    which enabled me to see firsthand the challenges and struggles
    associated with the DOD's implementation of AI.
        This hearing comes at a critical time for the future of AI
    leadership, and before we discuss what the United States must
    do to win, it's important to analyze where things stand today.
        AI is made up of three main pillars; compute, data, and
    algorithms. More than 1 year ago, the United States was clearly
    ahead on all three. However, today, that is no longer the case.
    Advancements from China have shown that they've closed the gap.
    Today, China is leading on data. We're tied on algorithms, but
    the United States remains ahead on compute. It's clear that the
    race is neck and neck.
        In order to compete more aggressively, the CCP [Chinese
    Communist Party] has implemented a whole-of-country approach to
    accelerating its pursuit of becoming a global standard for AI
    from an investment standpoint. For the first time in history,
    China is benchmarking AI investment off the leading tech
    companies and not the United States Government.
        Last year, China spent at least $1.2 billion on data
    labeling alone compared to our under $100 million by the United
    States. As part of China's AI Plus initiative, the Government
    established seven data labeling centers around the country to
    mainly support public sector application.
        Beyond data, while the U.S. has been stuck in a research
    and pilot mindset, the CCP has rapidly increased their
    investment in fielding AI capabilities. In the first half of
    2024 alone, the PLA [People's Liberation Party] issued 81
    contracts with large language model companies to rapidly grow
    their capability. To win, the U.S. needs to unleash our
    technology to the warfighter at an unprecedented pace.
        When it comes to adopting and implementing AI, the DOD has
    not launched a new AI program in nearly a decade. For the past
    4 years, DOD leadership spent countless hours developing
    potential use cases for AI, researching and piloting AI
    systems, and even putting out guidance to stop users from
    utilizing AI.
        We still have time, but the window is closing. If we want
    to win, we must not only buy into a vision, but it also takes
    three clear and decisive actions. Number one, is put the right
    AI foundation in place. To start, the DOD lacks the foundation
    piece, the foundational pieces necessary to build, scale, and
    implement widespread AI solutions. This needs to change, and we
    must put in place the elements necessary to expand the use of
    AI programs, and this starts with data.
        To truly prioritize and execute the strategy, it requires
    two main aspects; AI-ready data requirements, and enterprise-
    wide AI data infrastructure. The U.S. Government is the world's
    leading producer of both quantity and diverseness of data. But
    nearly all that data is going unused. If the U.S. wants to turn
    our data into an advantage, this must change.
        In multiple NDAAs [National Defense Authorization Acts],
    his committee has directed, suggested, and tried to require the
    DOD to prioritize AI-ready data requirements, but it's clear
    that more must be done. In parallel to implementing the
    requirement, the Department should also set up enterprise-wide
    AI data infrastructure.
        This commercial best practice ensures that AI programs are
    developed in the most efficient and cost-effective manner, and
    leading tech companies have long realized this requirement for
    effectiveness. For that reason, China is mirroring this same
    approach.
        Number two, is to shift our mindset to be an
    implementation-first. If the U.S. is going to win, we must
    shift into an implementation-first mindset. In order for this
    to occur, Scale believes that the DOD must set must first set a
    North Star related to robust AI implementation in no more than
    5 years.
        This should focus on agentic applications such as agentic
    warfare, and would provide an ambitious vision and enable
    tangible multi-year plan to reach it. Scale is actively working
    on deploying the first instance of this in INDOPACOM [United
    States Indo-Pacific Command] and EUCOM [United States European
    Command] through DIU's [Defense Innovation Unit] Thunderforge
    effort.
        Number three, is to ensure our acquisition system no longer
    slows us down. AI is unique in that it is software, but needs
    to be maintained like hardware, which presents challenges for
    the DOD given that it doesn't neatly fit into a legacy
    acquisition system. Congress took a strong first step by
    requiring the DOD to break out AI elements of programs in the
    future budgets, and it is critical that Congress continues to
    provide oversight to push the DOD to do so quickly as possible.
        In addition to proposals like the FoRGED Act, Scale also
    believes that we need to continue to look at finding ways to
    break through the challenges of multi-year budgeting, which is
    clearly still holding back the DOD's implementation of AI. With
    these three decisive actions, the DOD will be better positioned
    to adopt and effectively implement AI solutions.
        Thank you again for the opportunity to be here, and I look
    forward to your questions.
        [The prepared statement of Mr. Dan Tadross follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Senator Rounds. Thank you very much, sir. Mr. Ferris.

     STATEMENT OF MR. DAVID FERRIS, GLOBAL HEAD OF PUBLIC SECTOR,
                                 COHERE

        Mr. Ferris. Chairman Rounds, Ranking Member Rosen,
    distinguished Members of the Subcommittee, thank you for the
    opportunity to testify today.
        My name is Dave Ferris, and I'm the Head of Global Public
    Sector at Cohere. I previously served nearly 17 years in the
    Canadian Armed Forces, including deployments to Afghanistan and
    Ukraine, and spent the last 2 years of my career on the U.S.
    Joint Staff in the Pentagon.
        Cohere is a leader in building AI systems designed
    exclusively for government and enterprise use, prioritizing
    privacy, security, multilingual capability, and verifiability.
    Our expertise spans from building foundational AI models, to
    developing AgentX systems. We focus on operationalizing AI,
    integrating it into real missions, under real world
    constraints. We partner with allied governments, agencies, and
    leading global companies.
        Our primary goal is seamless integration, deep
    customization, and accessible solutions that deliver immediate
    practical value and confidence. We specialize in private
    deployments, even air gapped environments where we do not see
    our customer's data.
        Today, I would like to highlight four key topics of focus
    gleaned from having worked with high security cyber defense
    government organizations. The first key topic is how AI can
    significantly enhance the Department of Defense's mission,
    particularly in cybersecurity and intelligence.
        AI systems can dramatically improve pattern recognition and
    anomaly detection across vast data sets. They can be invaluable
    for sorting through and synthesizing huge volumes of multi-
    source information, and they can help automate a number of
    crucial tasks to provide early warnings and free humans to
    focus on making strategic decisions.
        Similarly, effective AI adoption requires integrating
    technology thoughtfully with existing workflows. Human AI
    teaming is crucial in ensuring AI tools have user-friendly
    interfaces. It helps build trust and maximizes operational
    value.
        A second key topic is to consider how AI can help fight
    back against competitor nations and malicious actors that are
    already employing AI-enabled cyber capabilities. Reports have
    shown these countries are automating their intrusion attempts
    using AI to generate deceptive deep fakes, develop more
    convincing phishing lures, and create information warfare.
        To stay ahead of these AI augmented threats, DOD must
    likewise incorporate AI across its offensive and defensive
    cyber operations. Large language models provide a unique
    ability beyond traditional, rule-based machine learning systems
    for language understanding and reasoning capabilities that
    allows for dynamic identification, analysis, and generation of
    conclusions across a wide range of use cases.
        The third key topic is to understand how technical
    considerations are critical to successful AI deployments in
    defense. Models should be right-sized for their specific
    mission. Specialized efficient AI models can often outperform
    larger general-purpose systems. This enables deployment even on
    limited hardware such as edge devices like laptops or
    classified data centers.
        Flexible secure deployment architecture is critical. AI
    systems must be deployable across multiple secure environments
    and ensure AI sovereignty. Similarly, ensuring models are
    hardware agnostic and interoperable, so there is no lock into
    one cloud or one chip provider, is essential to ensuring supply
    chain and operational security.
        Collaborative development through public-private
    partnerships allows for rapid customization of or creation of
    new AI models to meet specific operational context while
    protecting sensitive information. The DOD does not need to
    undertake the costly, time-consuming task of developing every
    AI model from scratch.
        The final key point is to highlight that Congress can take
    immediate action to accelerate responsible AI adoption.
    Congress should modernize procurement processes to allow
    innovative AI startups easier entry. Procurement should reward
    innovation, agility, and performance, not just size or past
    contracts. New legislation should promote interoperability, and
    open standards to prevent vendor-locking and enable diverse AI
    solutions to seamlessly integrate into defense ecosystems.
        Finally, Congress should support robust internal
    benchmarking and testing specific defense applications rather
    than the use of generic academic benchmarks. This would ensure
    AI reliability and trustworthiness in critical missions.
        In conclusion, Cohere is committed to partnering with DOD
    in Congress ensuring AI tools are secure, effective, and
    mission-ready. Thank you, and I look forward to your questions.
        [The prepared statement of Mr. David Ferris follows:]

        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

        Senator Rounds. First of all, thank you to all of you, and
    I appreciated your opening comments. We'll pass this back and
    forth a little bit with regard to questions and so forth, but
    we'll try to get to as many as we can in a short period of
    time.
        I wanted to begin, Mr. Mitre. The artificial intelligence
    is here to stay. It's not going away. You gave us some warning
    signs out there, but I wanted to hear from you. We can't slow
    down on the development of AI, or we know that our competitors
    will clearly outpace us.
        Give me your rendition of how we do this without losing
    facts or losing sight of the facts that there can also be some
    dangers involved. You've identified a number of the possible
    dangers, but how are we going to do this and still keep that in
    mind?
        Mr. Mitre. That's a great question, and I welcome it. I
    wholeheartedly agree that it's in America's interest to stay at
    the forefront of the development of generative AI and AI
    technologies more broadly.
        So, the way in which we can address this issue is, first,
    it's helpful for the U.S. Government to really understand what
    the current State of the technology is, and make sure that
    folks within the Government, particularly those that are
    working in the national security community, really understand
    what's happening with the technology.
        Because one of the challenges with this technology is that
    it's not being developed by Government, it's being developed by
    the private sector. So, just understanding what the current
    State is critical so there aren't technological surprises that
    come out that shock people in the national security community.
        The second thing that Government should be doing here is
    really looking for applications in the national security
    context. What are the specific use cases that it can be
    applied? What are potential pathways to wonder weapon or ways
    in which it could be highly advantageous in a military
    competition that's critical to do, and that means having the AI
    in an environment where you've got sufficient compute, where
    you've got the right networks, et cetera. You can actively
    experiment with it, and get the technology in the hands of the
    operators to play around with it.
        The third thing is preparing for contingencies. There's a
    wide range of possible things that could happen. A loss of
    control scenario, for example, areas where there is
    technological surprise and the Chinese get ahead. What would
    the U.S. Government do in such contingencies? We should think
    that through in advance and have plans ready to address it.
        Senator Rounds. Thank you. Mr. Tadross, this works right
    into some of the comments that you had made, and I want to
    just, number one, I think it would be a statement we would all
    agree on that continuing resolutions are absolutely not the
    long-term plan that we need.
        If we're going to be able to move forward with the
    investment in AI that we need, that may very well save a lot of
    lives in the battlefield. So, I would recognize that up front,
    and I think you were rather suggesting that a little bit in
    terms of our failure to keep up with the demands of how quickly
    AI is developing elsewhere.
        You also said something else, though, and I wanted to touch
    on two items. Number one, you talked about the fact that we
    have data, which is unused. I want you to explain that a little
    bit, and then, second, of all feeding into to what Mr. Mitre
    talked about, you talked about agentic warfare.
        Can you talk a little bit about what that really means for
    the--I mean, we've got a lot of folks out here that this may be
    their first introduction to the coordination of different
    applications that are directly involved in warfare versus the
    application of AI in general. So, first of all, data unused,
    and second of all, agentic warfare.
        Mr. Tadross. Of course, Senator, and thank you for the
    question. So, in terms of data being unused, the approach that
    I was kind of looking at there is the aspect that, right, now
    an enormous amount of information is being collected day to
    day. But to take kind of a quote from one of the previous
    Secretaries of the Air Force, ``We treat data like exhaust as
    opposed to something that's really critical to use.''
        So, as a result, every time that we run an exercise, run a
    command post exercise in terms of large amounts of chat data is
    being developed, large amounts of chat data is being traced
    back and forth, what's happening is at the end of that
    exercise, all of those hard drives are just being purged or
    being neglected and goes into storage.
        So, those are instances where the interactions between
    participants of a staff, for example, should be getting
    captured, and we should be using that to help develop training
    data to using it to help develop benchmarks against how these
    algorithms should operate. Then by doing so, are eventual
    development of agentic solutions can be more in line with what
    is required by those end users, which I think then brings us
    into the idea of like agentic warfare.
        Really what that means, my interpretation of this, is we're
    trying to move humans, move to a position from humans are the
    loop to humans on the loop. So, right now, if a staff at
    INDOPACOM, or at EUCOM, or any other combatant command needs to
    make a decision, the process at which they do that hasn't
    really changed since the advent of the Napoleonic staff
    structure. We take the problem, we divide it up, and then
    what's required is that the commander at the last minute has to
    synthesize all of those things together and then make an
    informed decision.
        The effort of agentic warfare is to move to the point where
    much of that low-level staff work can be done by these AI
    agents through automated methods with human oversight and
    supervision of the process. It's important to maintain some
    human oversight of the entire process to ensure that human-
    context judgment, and the competitive advantage of the U.S.
    military, which is the fact that we have the most well-trained,
    well-versed staff and NCOs on the globe.
        Senator Rounds. Thank you. Mr. Ferris, I've got some
    questions for you as well, but my first 5 minutes is up. We
    will do a second round, but at this point, I'll come back to
    Senator Rosen.
        Senator Rosen. Thank you. You know, I want to talk a little
    about guardrails and benchmarks. Both, I believe they go hand
    in hand. Over the last year, discussions between Congress,
    prior administrations, they've always centered around trying to
    come up with guardrails to promote responsible AI. You all know
    what I'm talking about; nobody wants it to become an unchecked
    technology.
        The current administration has raised concerns that
    guardrails might inhibit innovation. I believe we need both
    effective guardrails and benchmarks because the benchmarks,
    just as if your child goes to school, they're the test to show
    if they're learning and going in the direction that you're
    expecting them to go. That's what's going to keep that circle
    in check.
        So, I'm going to have questions for all three of you, but
    I'll start they're similar, but I'm going to start with you,
    Mr. Mitre. How should we develop guidelines, or the guardrails,
    and benchmarks in ways that mitigate risk without stifling
    innovation?
        I might also add, I'm actually going to ask all three of
    you this. How do we develop, for those of us sitting in this
    seat with all of you, a common policy language that is both
    nimble, but provides the availability for us to do effective
    oversight?
        Mr. Mitre. Thank you, Senator. So, I wholeheartedly agree
    that it's important for us to understand what these models are
    capable of doing, right? They're developed, and they're
    released into the world with no user manual. It's not entirely
    clear what applications they'll be able to perform or how
    capable they'll be at doing that.
        So, benchmarks are crucial, particularly in a national
    security context. It's helpful to understand what might the
    latest generation model be able to do in terms of offensive
    cyber defensive, cyber capabilities in terms of potentially
    informing non-experts on how they go about designing a
    bioweapon that could be highly transmissible and lethal, et
    cetera. So, the real focus that is warranted is on developing
    benchmarks to really just evaluate and understand what the
    risks are.
        Separate question in terms of what should Government do
    about those risks if they emerge, and should regulations or
    something along those lines be appropriate in that regard? I
    defer to Government for specific thoughts on that. What we're
    trying to do is just understand at first pass what are some of
    the risks here and make sure that people are well informed on
    that point.
        Senator Rosen. Thank you, and I'm going to just go down.
    Mr. Tadross, the same thing. Developing the guardrails. The
    benchmarks tell us one thing, the guardrails tell us another. I
    guess I'll make it all the same question. We are going to
    struggle. We have to put this down in some way on paper that
    allows us to be nimble and provide that ability to do the
    oversight we need to.
        So, if you have thoughts about how we develop this common
    language that we can all speak from or start from, I think is
    really critical, so.
        Mr. Tadross. Absolutely. So, the way that our company kind
    of looks at this, at least as it relates to guardrails in the
    implementation of AI in the Department of Defense, is to really
    look at it from a perspective of people, process, and
    technology. That while the technology needs to have guardrails
    by itself in terms of like its responses when it will trigger a
    refusal, or when it may not, there still needs to be the other
    two portions of this triangle.
        So, people need to be trained on how to best leverage the
    capability. Then, the process needs to be adapted. Because if
    we just bolt AI onto an existing process, then the advantages
    are somewhat lost. So, the doctrine and training of the
    individuals needs to adapt at the same time as the technology
    has fielded.
        This goes back to my position about implementation. The
    only way to do this is to experiment in low-risk environments
    and to iterate very quickly. short of that, I'm afraid that the
    concern about trying to write out the full answer at the
    beginning of the test is probably unlikely. So, you need to be
    able to learn from doing and be able to build off of that.
        As it relates to benchmarks, this is an area where our
    company's done quite a bit of interesting work. So, we have a
    paper that we've published showing that most of these large
    language models and AI systems will essentially cheat off of
    existing benchmarks. They've seen them, they understand the
    rules of the test, and as a result, they will score abnormally
    high.
        The approach that we've taken in partnership with
    organizations like CSIS [Center for Strategic and International
    Studies] and the CDAO [Chief Digital and Artificial
    Intelligence Office] is to build custom benchmarks that are
    focused on the domain at which it actually matters to test. So,
    we've built these custom benchmarks. The algorithms have never
    seen them, they've never been incorporated in their training
    data. As a result, you can have a little bit more faith in the
    performance of those algorithms.
        Senator Rosen. Thank you. Mr. Ferris?
        Mr. Ferris. Thank you, Senator. I echo the sentiment of my
    colleague on the panel here. I think public benchmarks can
    often be gamed. I'll start from the perspective of benchmarks
    because I think it's relevant to what my colleague was saying.
    They don't typically show the performance in real-world
    context. So, we would----
        Senator Rosen. Is using the word ``audit'' better than
    benchmark?
        Mr. Ferris. Well, no, I think we would say creating custom
    benchmarks.
        Senator Rosen. Just like right-sizing your model.
        Mr. Ferris. Yes, exactly. Okay, and, you know, to kind of
    take that down one step further, we work very closely with our
    customers from beginning to end in order to ensure that we're
    right-sizing that model, developing the benchmarks. But that
    also includes some human evaluations because that human AI
    interface is obviously imperative as we're moving down this.
        With respect to guardrails, you know, there's this healthy
    tension between accountability and agility, I would say, in
    this environment. So right now, we obviously would suggest that
    we want to lean into the agility. We want to take an adoption
    mindset, but can't, you know, sacrifice really the security
    reliability and verifiability.
        So, you know, ensuring that you have clear visualization
    into the data lineage, ensuring that you have a good
    understanding of how those safety measures have been built into
    the model during its development and deployment, I think, is
    imperative.
        Senator Rosen. Well, I think because you say you want to
    lean in to--oops, I'm going over my time. I'm sorry. Can I
    finish the thought? Lean into the agility, but if you don't
    keep humans, if you don't keep someone else in the loop,
    people's lives are on the line. It's still a computer just
    analyzing data, and so, at that execution point, you have to
    consider leaning into agility. But at what execution points do
    we allow for a better decision? I'll let it go to my--maybe
    that's a philosophical question.
        Senator Rounds. Well, look here, and I'm going to lead into
    this a little bit, too. I'm going to start with Mr. Ferris. We
    talked about right-sizing systems, and kind of along the same
    line here, I'm going to compare that because I'm not sure if
    I'm thinking the same thing that you're proposing.
        But loitering, munitions as an example, we have clear
    evidence that in the Nagorno-Karabakh War between Azerbaijan
    and Armenia, loitering munitions were used. They were able to,
    as you know, basically unmanned aerial vehicles, they moved
    into a particular kill box, identified targets that were there.
    Then without a human in the loop, they were able to identify
    the types of systems that were there, whether it was a tank and
    an armored personnel carrier, a command center, a radar station
    aircraft, and so forth.
        But because they had that capability, they could then
    choose which weapon system based upon which drone was there in
    the area and at an appropriate time attack each of them. Is
    that the type of--can you talk about, is that what you mean
    when you say right-sizing in terms of having the capability for
    that particular mission set? Or share with me what you mean by
    that.
        Mr. Ferris. Yes, thank you, Senator. In that context, I
    think when we talk about right-sizing the model, we're talking
    about making sure we're bringing the appropriate solution to
    the use case. So, to use your example, we would be looking at,
    you know, how the models are used to analyze all that multi-
    source information that's coming into the system and from
    various sources, but also potentially from different sensors
    and systems.
        I think what's important is that we would suggest that by
    analyzing, using artificial intelligence to analyze all of that
    data, it allows you to elevate the level at which a human can
    make that decision. We would still suggest that the human AI
    interface is important, and that should be maintained during
    these types of operations. But really what AI allows you to do
    is to elevate that decision and make it closer to when it needs
    to be taken, potentially.
        Senator Rounds. I'm going to--you're following right into
    what my next question was going to be, and that is with regard
    to--and I'm going to run this all the way down the line again,
    but I want to talk a little bit about humans on the loop, and
    humans over the loop, and defining each of them, if you would,
    in terms of where we're at today and where we're going to be
    tomorrow.
        I'm going to talk about it in both offensive and defensive
    capabilities. The example that I would use that if you could
    buildupon, is we have systems right now that for defensive
    capabilities, we arm them, but once they've been armed, they
    can automate to protect our platforms.
        That means if you have incoming missiles, particularly if
    you're talking, you know, less than a minute to respond, to be
    able to identify a missile incoming, such as what we've seen in
    the Red Sea region with regard to Houthis attacking our
    systems.
        But to be able to identify it, identify the type of weapon
    system necessary to take it out, and then to be able to execute
    and then to have backups along with it, how far along are we,
    and what will AI do with regard to having that whether there's
    a human directly in the loop of making that decision, or on the
    loop having armed it, or over the top of the loop, not engage
    at all.
        I'd like your thoughts, then I'm going to ask our other two
    members here as well for their thoughts.
        Mr. Ferris. Yes. Thank you, Senator. So, obviously, I would
    say that, currently, we're supporting or we're seeing AI
    deployed in an environment with humans in the loop, as you
    described, and on the loop where there's some oversight. But
    certainly, I don't think we're yet at that over the loop where
    they're elevated outside of the analysis and execution of the
    mission set, if you will. But, certainly, as agentic AI becomes
    more advanced, and the models improve, and become more precise,
    and relevant, which is happening at an incredible pace, I would
    say we'd be able to see some of that.
        But again, our position at Cohere would be that we want to
    work--we would develop--because we deploy models, you know,
    with our customers in their environments, we would suggest that
    that integration on the front end with the customer and with
    our partners having that partnership in development,
    deployment, and then, you know, ultimately the decisions in how
    those guardrails are put in place. I think that's important on
    the front end of really understanding where in that loop it's
    necessary to have the human placed.
        Senator Rounds. Mr. Tadross?
        Mr. Tadross. The way that I would kind of look at this is
    for human in the loop. What you're sacrificing is speed over
    the oversight required to ensure that you're rendering it. In
    those cases, I think in, on, or over the loop, it really comes
    down to the use case and the speed at which you have to make
    the decision.
        So, if the use case is such in a defensive manner, similar
    to like a CIWS [close-in weapon system] or an Aegis Cruiser,
    which if certain triggers are hit, you default to the machine's
    knowledge because the speed at which things are changing is so
    great that you can no longer support the decisionmaking
    process.
        I think what it comes down to with that's a heuristic-based
    system where it's like very clear triggers to be able to
    implement that same type of approach with AI would require a
    certain amount of evaluation of those systems.
        So, going back to the benchmarking question from earlier,
    it would also require having a data infrastructure layer in
    place to be able to retrain those models effectively when the
    environment changes significantly. As a result of doing that,
    you can ensure that this rapid iteration of retraining, and
    testing, and evaluation can occur that would still provide the
    commander the opportunity to make that informed decision about
    if the staff needs to be in on or over the loop.
        Senator Rounds. Thank you. Mr. Mitre? I apologize, am I
    saying your name correctly? Is it Mitter?
        Mr. Mitre. Mitre.
        Senator Rounds. Mitre.
        Mr. Mitre. Mitter is fine, too, though. We get it all the
    time. Not a problem.
        Senator Rounds. Thank you.
        Mr. Mitre. Yes, no worries, Senator. On this point, I think
    fundamentally what the Department of Defense is looking for are
    weapons systems and military systems more broadly that are
    effective. So, the question is, what is effective in a
    particular use case in particular context?
        Now, certainly as the technology progresses, there are more
    opportunities to use it in different ways, and along with that
    can come greater dependence on the technology. With greater
    dependence, you potentially open up new vulnerabilities and new
    risks associated with that. So, it's incredibly important to
    understand what are ways in which it could go sideways.
        What are some of the vulnerabilities there? When you're
    integrating in a broader weapon system where it might act in
    ways that are inconsistent with human intentions, and do you
    have the right safeguards put in place to guard against those
    cases? Are there kill switches that might be necessary? Are
    there ways in which you're dealing with a model that's breaking
    out of the box and engaging more with the cyber world? Are you
    able to cut it off from certain applications if you need to?
        I think it's helpful for the Department to think through
    the wide range of potential applications here, and then make
    sure that it's thought through how you ensure effectiveness
    despite different ways in which the model could react in a
    particular context.
        Senator Rounds. Thank you. Senator Rosen.
        Senator Rosen. I want to talk about energy limitations, but
    I'm not going to ask this as a question. I'm just going to make
    this as a general statement, philosophically. Because if we
    move to no humans in the loop, why not just create a grand
    video game and save lives? Because at the end of the day, if
    it's the AI making the choice, there's still people on the
    ground. All of us. Not just men and women in the military, but
    the rest of us that live in the world that the computer may or
    may not really care too much about.
        So, it's a bigger philosophical question as we move
    forward. Not expecting it to be answered here, but in a way, we
    have to be sure that we think about that because for every
    action these computers might take to each other, theirs versus
    ours, the fallout happens to us living here on earth. That's
    all I'm going to say. But we got to speak about living here on
    earth.
        We got AI energy limitations. You know, a lot of data
    centers in Nevada. Let me tell you, there's an increasing
    demand for energy. They just gobble it up, and it's a hardware
    problem, software problem. It's largely based of course, on the
    current architectures that we have.
        Like I said, Nevada's dry weather and our vast open spaces
    that we have really become a national leader in data storage
    centers. Our companies are constantly innovating, but we know
    that the growing use of all this is going to create great
    energy burdens on our commercial, our Government Data Centers.
        So, I guess we'll go this way. We'll start with Mr. Ferris.
    How do we address this challenge? Do you see it as a barrier to
    more widespread DOD and Government adoption? What research,
    what should we be investing in to try to maybe reduce that that
    great energy suck as it's going to take everything it can,
    right?
        Mr. Ferris. Yes. Thank you, Senator. So, Cohere, this is
    actually fundamental to our company. We build custom models
    designed to be efficient and deployable in the environment that
    our clients and customers are working in. So, in pursuit of
    that efficiency, a couple of things. One, we're chip agnostic
    and cloud agnostic. So, that means we've had to focus on
    building our models in somewhat of a resource-constrained
    environment. So, we've built----
        Senator Rosen. What if you put it on tanks? You've got
    heat, you have to be sure that they adapt in heat environments
    and they're going to generate energy, right?
        Mr. Ferris. Absolutely, Senators. But we've built some of
    these models to be deployed on as small as two GPUs [graphics
    processing unit] or even, you know, we're pushing toward edge
    deployments in laptops. So, being able to bring down that
    energy cost, but also the infrastructure as a whole. Then, even
    it has implications, broadly speaking, into the supply chain as
    well.
        Senator Rosen. Thermodynamics. Thank you. What can we do
    about all the energy we need to do all of this and then make it
    portable?
        Mr. Tadross. Yes, ma'am. So, the way that I kind of look at
    this is as these technologies start to be fielded, there's
    always an interest in the Department of Defense in order to be
    able to operate in a disconnected environment.
        So, what that requirement's going to come along with is
    fine tune smaller models that can interact together, which is
    similar to the approach that we're taking with INDOPACOM and
    EUCOM for agentic warfare. So, what this really results in is a
    lower power requirement because back at home station, while
    we've been doing the development and training, we're able to
    tune these models. You've been using very specific data sets.
    So, individual models are very good at a specific thing.
    They've been tested and evaluated, and then the interaction
    between those models is what can be fielded at the edge. So,
    that minimizes the energy requirements as these things begin to
    get fielded and proliferated.
        Senator Rosen. Thank you. Mr. Mitre?
        Mr. Mitre. The only thing I'll add is that it's important
    to think about the entire tech stack to include power. Not just
    the data layer and compute layer, and then, the models itself
    and certain applications.
        So, you're right to think holistically. The power is a big
    part of that, and certainly, there are ways to find smaller,
    more efficient models that you could deploy abroad along the
    lines of what the other panelists said. It's worth the
    Department looking at that aggressively.
        Senator Rosen. Thank you.
        Senator Rounds. Same question for all of you now. You all
    work with the Department of Defense probably in different ways,
    but my question is, what can the Department of Defense do with
    regard to either policy acquisition policies the way that they
    treat contractors? What can they do to enhance their ability to
    take advantage of the private sector's capabilities that
    they're not doing today? Mr. Ferris.
        Mr. Ferris. Thank you, Senator. The first thing we'd say is
    we believe that the Department needs to have an adoption
    mindset. We've seen a really good shift. You know, the software
    acquisition pathway and the use of other transaction
    authorities from an acquisition perspective. There are some
    really great strides in acquisition.
        I would offer using existing mechanisms. I'm an advocate
    for the simple acquisition threshold being, you know, either a
    provision similar to what we have currently. The simple
    acquisition threshold is $250,000 for, you know, contracting
    officer can buy anything under that without a competitive
    process.
        There's a provision for contingency operations or cyber
    defense and CBRN [chemical, biological, radiological, and
    nuclear] defense, where that simple acquisition threshold is
    raised because of urgent operational requirements. I think
    similarly, we could have an approach in procurement where for
    artificial intelligence, urgent operational requirements,
    perhaps the simple acquisition threshold could be a provision
    for that.
        What that would do is it would shift the burden away from,
    you know, the DIUs, and DARPAs, and organizations like that
    that are well versed in using OTAs [other transaction
    agreements] and allow contracting officers and project managers
    at like much lower levels in the department to execute and
    acquire these types of capabilities.
        Senator Rounds. Mr. Tadross?
        Mr. Tadross. Thank you, Senator. So, when I think about
    making it easier to acquire this technology, I tend to actually
    go back to the AI infrastructure standpoint. The reason for
    that is it actually opens the barrier, reduces the barrier of
    entry of companies to come in. If they're able to operate off
    of a central data repository, then that that company's pathway
    to being able to create relevant technology for the Department
    of Defense is considerably easier than one of the legacies that
    have been in that space for a while and may have troves of data
    that they've saved over 20 years of conflict.
        Senator Rounds. Thank you. Mr. Mitre?
        Mr. Mitre. I agree with the panelists on everything that
    relates to narrow AI or AI that exists today. What I think is
    principally lacking from the Department's approach to the issue
    is anticipating where AI might be in a couple of years' time,
    and really working closely with the technologists that are at
    the forefront of developing generative AI and frontier AI
    models to get their head around what that world might look
    like.
        So, there's a lot of attention, rightfully put toward
    maintaining our lead in the development of technology itself to
    better promote its development, to better protect our lead
    through expert controls, and AI security, and things of that
    nature. But how well does the Department really understand what
    capabilities it may unearth in the next 2, 3, 4, 5 years, I
    don't know, and what that means for the future character of
    warfare. That's crucially important, especially as the
    Department now embarks on developing a new defense strategy.
        Senator Rounds. One last question for all of you, and you
    don't have to spend a lot of time on this. But is there a place
    somewhere, a safe space, so to speak, where industry and DOD
    can actually interface and ask questions of one another, offer
    ideas, offer products, and so forth that is ongoing? Or is it a
    case-by-case basis?
        In other words, if industry has a particular product that
    they think would be great in its application within DOD, do
    they know where to go to get it? DOD on the other hand, do they
    have a place where they can go and ask the questions about what
    do you have that can help us fix this problem? Does that exist
    today? Don't everybody speak at once?
        [Laughter.]
        Mr. Mitre. Not in a structured and systematic way, right? I
    think it happens in ad hoc cases here and there, but not in a
    coherent approach to really have a tight public-private
    partnership, if you will, to really understand where are we in
    the development of AI technologies relative to key competitors,
    like the Chinese, in particular, what are things that we need
    to be doing to make sure that America maintains that lead.
    DeepSeek is a great example here where surprises like that can
    come out and people wonder, well, what does that mean in terms
    of where we are?
        I don't think we have that kind of environment to enable
    that constant flow of communication, especially when a cleared
    environment where you can have more sensitive conversations
    with key experts in terms of what's happening with this
    technology and what the U.S. Government needs to be doing in
    partnership with the private sector to maintain America's lead.
        Senator Rounds. Thank you. Any other thoughts?
        Mr. Tadross. Yes, Senator. So, I think the closest that
    I've seen of that existing is Project Maven where the efforts
    behind that was to bring technology into the Department of
    Defense in a very aggressive manner. Because they took that
    approach and because you had a single program that was well-
    funded, well organized, and manned by the right individuals,
    what you end up with was a situation in which they were seeking
    to find as many technology experts as they could bring them and
    figure out ways to get them into the Department to satisfy a
    mission requirement that was set forth.
        Senator Rounds. Thank you. Mr. Ferris, anything?
        Mr. Ferris. I'll just add that, you know, echo that it is
    very ad hoc and unstructured. However, I think that's precisely
    why actually, you know, people like us end up staying in these
    types of companies and working in them for as long as we do
    because it's important to know those pathways, know those
    venues in which these conversations do unfold, and how to get
    after, you know, getting in front of the Government customer as
    quickly and rapidly as possible, especially when you do think
    you have something that can support the mission. So, it's a
    little bit at this point, it's experience for some of us where
    we can find that opening and get in front of the Department.
        Senator Rounds. Thank you. Senator Rosen.
        Senator Rosen. I have one last question. I think for those
    of you who don't know, Maven means ``know it all'' in Yiddish,
    I should say. We should have the Maven marketplace. How about
    that? There you go. That maybe that solves what you need.
        What I want to talk about and just finish up with, we can't
    do any of this without building our AI workforce. That is
    something that Congress can help invest and promote, and we can
    only go as far as we are willing to invest in all of that. It's
    just so very important.
        So, for all of you, as we just finish up in our last few
    minutes, the workforce issues that you see in adoption of AI,
    what do we need to do to grow? Well, coders, engineers? All of
    the things that we have to do to build out this robust
    workforce? Because these are the kinds of things that Congress
    does work on and does fund. What advice would you give to us?
        No one starts in the center. We started on the ends. We'll
    start with you, and I think it's a good way that's something
    that is in our wheelhouse and work on that Maven marketplace.
    Will you? There you go. I'm going to trademark that name. You
    heard it here first.
        Mr. Tadross. Absolutely, Senator. So, I can say that I'm
    actually very, very proud of the work that we're doing in St.
    Louis. So, in this case, what we're doing is we're taking
    individuals that would normally not participate in the national
    defense and give them an opportunity to support data
    development and AI development in the St. Louis community.
        So, in some cases, what we've done is taken individuals off
    the fry line, train them on how to look at electro optical
    imagery, gotten them to the point, through training, that they
    are then able to look at synthetic aperture radar, get them to
    the point where they have a clearance, and then even elevate
    them even further so that they're able to pass certain imagery
    tests.
        Senator Rosen. So, like community college certificate
    programs to bring people just into the workforce, or would you
    say even things like that, right?
        Mr. Tadross. Yes, ma'am, and give them an opportunity to
    kind of participate in that national defense. This is an area
    where like Scale believes very strongly in. Kind of elevating
    this workforce in order to support the needs of the national
    defense in this space.
        Senator Rosen. Yes. Perfect. Mr. Ferris?
        Mr. Ferris. Thank you, Senator. I agree. I mean, I think
    what we would say, we try to partner with, you know, it's a
    public private partnership. That's extremely important.
    Workforce development is critical as part of the body of work
    that the Department and really the Government needs to
    undertake to achieve the advancement in AI that we're hoping
    for.
        But at within the company, we do partner with educational
    institutions and within the community, and we're searching for
    ways to continue to grow that workforce. I do think it's a
    collaborative process that we need to take with the Government
    and work in concert on it because, from a Cohere perspective,
    we want to be, in terms of our deployment and how we work with
    our customers, it's really early on. So, we want to make sure
    that we're contributing to the workforce development in a way
    that's meaningful for the Department as time goes on.
        Senator Rosen. Mr. Mitre?
        Mr. Mitre. This is not exactly my area of expertise, but in
    my experience, there's no more compelling reason to go work in
    Government than for the mission. So, emphasizing that is the
    key ability to attract top technical talent, I think is
    crucial, as is giving them opportunities to develop their
    skills.
        That requires actually having the right compute
    infrastructure and networking analytic tools available so that
    they can grow and develop their skillset while in Government.
    That's often a challenge to bring together, but there's a
    broader point than just the technical talent, the AI talent
    skillset here as well.
        Given advances in AI, it's going to impact all elements of
    the workforce. What we're seeing in the private sector right
    now, by way of analogy, is those companies that are better
    leveraging AI or outcompeting companies that don't have it.
        I think that's likely what we could see in the military
    context, do those militaries that are fully embracing and
    applying it across a range of applications are going to be at a
    significant advantage relative to those militaries that aren't.
    So, I would think a little bit more holistically on the
    workforce dynamics here.
        Senator Rosen. Thank you. Appreciate it.
        Senator Rounds. Well, with that, let me take the
    opportunity to thank all three of our presenters here today;
    Mr. Jim Mitre, Vice-President and Director, RAND Global and
    Emerging Risks. Mr. David Ferris, Global Head of Public Sector,
    Cohere, and Mr. Dan Tadross, Head of Public Sector, Scale AI.
    We thank you for participating in this open discussion today
    that's been very, very helpful.
        My thanks also to my Vice-Chair, Senator Rosen, for
    participating today as well. We appreciate that, and unless you
    have any closing comments, I thank you for being here. Thank
    you for your work, and look forward to continuing to work with
    you and the ideas you have.
        With that, this Subcommittee hearing of the Cybersecurity
    Subcommittee is now closed.
        [Whereupon, at 4:29 p.m., the Subcommittee adjourned.]

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