Max Fenkell

Artificial intelligence is no longer a subject of theoretical policy debate; it is now a test of governance. Since the launch of ChatGPT in November 2022, the United States has moved rapidly from studying AI&rsquo;s capabilities and risks to enabling its growth. During this period, early restraint helped avoid premature regulation and preserve U.S. leadership.
Over the past year, Congress and the Trump Administration have begun using the full range of policy tools available to them&mdash;accelerating data center permitting, directing investment through the Departments of Energy and War, and enabling access to the American AI technology stack abroad. These actions mark a clear shift from experimentation to execution. But they do not yet amount to a governing strategy capable of sustaining U.S. leadership in AI.
The central question facing policymakers is no longer whether AI will reshape the economy and national security. It is whether the federal government can govern, deploy, operationalize, and scale AI systems to maintain leadership.
If 2023 and 2024 were about understanding AI, and 2025 was about enabling growth, then 2026 must be the year of governance and implementation. Securing U.S. leadership in the AI era will require progress on three priorities.
AI Governance Should Modernize Existing Law, Not Replace It
Challenge: Effective AI governance must be grounded in how AI systems are used, not in the technology itself. Use-based regulation is the only approach that preserves innovation while placing guardrails where real risks arise. Treating all AI systems as interchangeable regardless of context, function, or impact creates regulatory uncertainty without improving reliability, accountability, or outcomes.
In practice, AI is deployed in two fundamentally different ways: the broad release of general-purpose models, and the use-case-specific application of AI systems within defined operational and regulatory environments. Both are essential to the AI ecosystem, but they present materially different risk profiles and therefore require different forms of oversight.
For use-case-specific AI applications, the appropriate starting point is the existing regulatory system. U.S. law already governs outcomes and conduct across sectors such as financial services, housing, healthcare, and consumer protection. In many cases, the law itself does not need to change. What is missing is clear, authoritative guidance on how existing requirements apply, and be complied with, when AI systems are involved.
That guidance should modernize compliance rather than invent new regulatory regimes. In practice, this may mean replacing static practices, such as generalized employee training or paper-based controls, with obligations to work with third parties to test, evaluate, and red-team AI systems against real-world risks. The underlying legal standards remain the same; what changes is how you demonstrate compliance in an AI-enabled environment.
In parallel, governance of the broad deployment of general-purpose models must be clearly scoped and disciplined. Overly expansive, technology-based regulation at the model level risks slowing adoption and investment without addressing downstream harms, which overwhelmingly arise at the point of use.
Recommendation: Congress and the Administration should direct federal agencies to adopt a use-based approach to AI governance and issue clear, authoritative guidance clarifying how existing laws apply to AI-enabled systems and how companies can comply with them via third party testing. Agencies should distinguish between areas where current frameworks are sufficient, where guidance is needed, and where regulatory gaps exist.
U.S. AI Leadership Depends on Government-Wide Implementation
Challenge: Much of the federal AI agenda has focused on governance frameworks and access to technology. That work is necessary but insufficient. Winning on AI requires adoption, integration, and sustained operationalizing across government departments and agencies, and the federal government is not yet positioned to do so at scale.
Recent efforts to make large language models available to government users are a positive first step. These tools can improve back-office productivity and day-to-day efficiency. However, access alone will not resolve the government&rsquo;s most persistent operational bottlenecks. The hardest problems&mdash;permitting delays, acquisition backlogs, compliance workflows, and benefits administration&mdash;require use-case-specific AI systems designed to operate within complex, regulated processes.
Permitting reform illustrates the challenge. Despite wide recognition of the need to modernize the permitting process, progress has been incremental. Historically, agencies faced a trade-off between speed and completeness. Today, that is no longer the case: meaningful improvement will require AI applications that support the entire permitting workflow from intake and review to interagency coordination and decision-making. Even then, deployment remains difficult.
Legacy policies, fragmented data systems, and unclear operational ownership continue to block implementation across agencies. Data remains siloed, infrastructure uneven, and responsibility for AI deployment diffused. As a result, promising pilots stall and successful proofs of concept fail to scale.
True AI implementation in government will require a different foundation: routine data sharing across agencies, government-wide AI-ready data and infrastructure, and clear accountability for outcomes. Today, those foundations do not exist.
Recommendation: Congress should pass legislation or the Administration should take action to establish a Chief AI Officer interagency working group, led by the White House, with responsibility for removing&mdash;not merely identifying&mdash;barriers to AI implementation across the federal government. The group should drive actions on data sharing, AI-ready infrastructure, and operational ownership for priority use cases, with defined deliverables and timelines.
Advancing U.S. Leadership through AI Exports and Standards
Challenge: We are entering a decisive period. Over the next year, foundational standards governing how AI systems are built, evaluated, and deployed worldwide will take shape. If the United States does not lead that process, others will. China has already begun to export not only AI technologies, but also the standards and governance models that accompany them. The strategic consequences of losing that contest would extend far beyond the technology sector.
This risk is not theoretical. In the race to shape global 5G standards, China moved quickly, aligned industrial policy with standards bodies, and embedded its technology across global markets. The United States responded too slowly and without sufficient coordination&mdash;and continues to bear the consequences.
The United States must establish itself as the global standard-setter for artificial intelligence. Domestic adoption and sound governance are necessary, but insufficient. Global leadership will be determined by which country&rsquo;s technology, standards, and operating norms are adopted by allies and partners at scale.
The Trump Administration&rsquo;s <a href="https://www.whitehouse.gov/presidential-actions/2025/07/promoting-the-export-of-the-american-ai-technology-stack/">Promoting the Export of the American AI Technology Stack Executive Order</a> reflects a necessary shift in approach. It recognizes that we must assert leadership, not assume it is ours for the taking. To secure durable leadership, we must go beyond exporting the technology stack: U.S. technical standards must become the global default.
The Department of Commerce&rsquo;s Center for AI Standards and Innovation (CAISI) is central to this effort. Its ability to coordinate across industry, government, and a growing network of international AI safety and security institutions positions it as a critical node in the global standards ecosystem. But without sufficient resourcing and empowerment, the CAISI will not be able to deliver.
Recommendation: Congress and the Administration should align trade policy, export promotion, and international standards engagement to ensure that American AI technologies and technical standards become the default among U.S. allies. Failure to coordinate these efforts risks ceding long-term leadership to competitors that move faster and not aligned with our values.
Conclusion
The United States now faces a different kind of AI race. The defining question is no longer who can build the most powerful models. It is who can deploy AI into real government workflows, measure its performance, and make trusted standards the global default. Preserving leadership will require governing AI through existing law, deploying it effectively across federal institutions, and ensuring that American technologies and standards shape global adoption. These priorities are mutually reinforcing: governance without implementation will stall, implementation without standards will fragment, and standards without credibility will fail to scale. In the AI era, leadership will belong to the countries that can move from policy to practice, and the United States must prove it can do both.
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What it will take to move from AI experimentation to real governance, federal deployment, and lasting global leadership.

The Next Phase of U.S. AI Policy: Governance and Leadership

The Next Phase of U.S. AI Policy: Governance, Implementation, and Global Leadership

AI promises to be the most important innovation of our time and it is already making our lives better. Today, AI is powering groundbreaking research, improving efficiency in workplaces, and strengthening our nation&rsquo;s Department of Defense (DOD). Beyond the technology itself, AI presents trillions of dollars of potential economic output and is on track to create millions of job opportunities for Americans.
In response to the White House&rsquo;s Request for Information on an AI Action Plan, <a href="https://static.scale.com/uploads/6019a18f03a4ae003acb1113/Scale%20AI%20Comment%20in%20Response%20to%20the%20AI%20Action%20Plan%20RFI%20(3_15_2025).pdf">Scale submitted recommendations</a> to the Office of Science and Technology Policy (OSTP). To ensure America maintains a leadership position on AI, the United States must take bold steps. Scale believes the path forward must include decisive action on these four main pillars &ndash; Protect, Promote, Unleash, and Innovate.
<ol>
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Protect: When it comes to AI, it&rsquo;s clear that the United States still has the world&rsquo;s best technology companies, working on the world&rsquo;s best technology which is being developed by the world&rsquo;s best talent. These innovations have set the global standard for what best-in-class means and because of that, our adversaries are fully committed to gaining access to it. Scale recommends that prior to putting in place any new export controls, the Trump Administration does a comprehensive review of the USG&rsquo;s existing controls to better understand where new ones are required. Additionally, the Administration should place new emphasis and focus on the enforcement of our existing export controls to better protect our best-in-class technology from ending up in the hands of adversaries.
</li>
<li dir="ltr" aria-level="1">
Promote: Effective tech diplomacy heavily relies on the access and adoption of U.S. technology by our allies. Over the past few years, the United States has simply not prioritized the promote aspect nearly as much as the protect aspect. But if our nation is to fulfill President Trump&rsquo;s vision of United States technological dominance this must change. In the coming year, countries around the world may be forced to effectively choose between whether they want to use Western technology or Chinese technology. We have already seen examples of this choice and, in Scale&rsquo;s experience, it&rsquo;s clear that countries want to leverage the United States technology. However, when asking countries to make this choice, we believe, the United States must also provide those countries full access to U.S. technology to ensure effective alignment.&nbsp;
</li>
<li dir="ltr" aria-level="1">
Unleash: In order to dominate, the United States needs to unleash our technology to the world, and the first step happens right here at home. As China has correctly identified, the best way to leverage AI is to start using it. While that may seem obvious, and despite the uneven playing field, the United States Government has fallen behind China when it comes to government use of AI. If we want to win, we must not only buy into a vision, but also start to take clear and decisive actions on it.
</li>
<li dir="ltr" aria-level="1">
Innovate: Scale has long been proponents of the idea that the right regulatory framework is one that maximizes innovation, but still creates proper guardrails. For the United States to continue to be the most attractive place for leading tech companies to develop the next generation of cutting edge AI systems, it relies on ensuring the best environment for them to do so. Additionally,the U.S. private sector and government must start to look at how we can all support and build the workforce of the future in the United States. AI is still relatively new, and much like coding in the 1990s, acquiring the skills necessary to train, fine tune, and evaluate AI systems will be vital in the future. These skillsets did not exist a decade ago, but are already proving to be large drivers of economic activity.&nbsp;
</li>
</ol>
In the coming months, Scale looks forward to working with Congress and the Trump Administration to put in place the policies that will not only fulfill President Trump&rsquo;s mandate for U.S. AI dominance but cement that position for decades to come.&nbsp;

The future of AI leadership around the world has never been clearer

Scale AI submitted our response to the AI Action Plan

Scale AI’s Proposal for the U.S. AI Action Plan

It is no secret that the world is heavily investing in Artificial Intelligence (AI) because of the economic and societal benefits that it has the potential to deliver. Last year, President Biden proposed roughly $5.5 billion in government spending and his most recent budget proposal once again prioritized AI investment to ensure the United States&rsquo; global leadership in the government&rsquo;s adoption of AI. Congress has also done its part by funding critical elements, such as the National Institute of Standards and Technology&rsquo;s (NIST) recently announced <a href="https://www.nist.gov/artificial-intelligence/artificial-intelligence-safety-institute">AI Safety Institute Consortium</a> that will bring industry together to develop essential AI safety benchmarks, frameworks and techniques.&nbsp;
Despite the attention that AI is receiving, much of the funding still prioritizes early-stage research, incremental improvements for existing programs, or short-term pilot projects. While these efforts are important, Congress must also fund new, potentially transformative AI initiatives that will unlock the potential economic and efficiency gains.
We cannot afford to wait to invest sufficiently in AI. As Congress begins to debate the FY25 Authorizations and Appropriations, Scale recommends addressing three key items to ensure that the United States remains the global leader in the government&rsquo;s adoption of AI and to yield the maximum value of its investments for American taxpayers.&nbsp;
<h3 dir="ltr" role="presentation">&nbsp;</h3>
<h3 dir="ltr" role="presentation">Recommendation 1: The government must start to adequately plan for AI costs to truly implement AI.&nbsp;</h3>
If the United States wants to lead the world in the development and deployment of AI across the federal government, then it must start by properly planning for AI program costs, such as training data, in the same way that it plans for more traditional costs like hardware and maintenance.
AI has the potential to improve the efficiency of nearly every government use case. In a matter of years, it is likely to be used in every program in the U.S. government, from building more high-quality algorithms for Department of Defense autonomy programs to accelerating regulatory actions, such as speeding up the rulemaking process. To achieve this vision, however, the President and Congress must include the ongoing sustainment costs for AI in the budgets. AI systems require constant high-quality data to train, maintain, and improve foundational models so the technology performs reliably for its intended use case. For this reason, the commercial industry has long invested in &ldquo;AI-ready data&rdquo; to consistently train and maintain its AI models. While the federal government has long considered AI-ready data to be a &ldquo;national asset,&rdquo; the acquisition system, which was set up around maintaining platforms like ships and airplanes, is not well suited to fund a different type of technology that requires ongoing refinement for high-quality outputs.
To truly harness the power of AI, Congress and the White House must create and fund dedicated AI lines in each program that will create, maintain, and enhance effective AI capabilities. &nbsp;
<h3 dir="ltr" role="presentation">Recommendation 2: The government must prioritize implementing and fielding AI, in addition to research.</h3>
There is no doubt that some fundamental research gaps exist, particularly around AI safety, and we must adequately fund agencies, such as NIST, to do this crucial work. However, If the U.S. government is serious about deriving economic value and efficiency gains from AI, then it must invest in putting AI capabilities into users&rsquo; hands so its true benefits can be realized.
Historically, keeping up with the pace of emerging technologies, like AI, has been a challenge for the U.S government, primarily because funding cycles require budgets to be crafted, in some cases, 18 months before they are approved. Beyond the budget cycle, acquisition programs were designed decades ago to procure legacy systems. For example, the funding levels contained in the FY24 budget, which Congress recently passed in March, 2024, were largely set before ChatGPT thrust the idea of generative AI into the national consciousness. In reviewing FY24 spending, and as more details related to the FY25 budget become available, it is clear that there is a strong, continued, emphasis on funding AI research and development and short-term pilot programs. While this can be valuable, as history has shown, this strategy often fails at getting AI into the hands of government users and warfighters in a meaningful way.&nbsp; In the past five years, only one AI effort has fully transitioned from a pilot program to a productive program of record.
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<h3 dir="ltr" role="presentation">Recommendation 3: More targeted investment is needed for AI programs, instead of using AI funds on legacy software programs.&nbsp;</h3>
Despite the at least $5 billion per year in reported topline AI funding, the amount of funding available for AI programs is less than it seems as the majority of that money typically is used to fund legacy software and hardware, which may only include a small use of AI. For example, if a program is receiving $350 million for its entire annual budget, but only $10 million of that funding is geared towards AI development and deployment, all $350 million is reported as AI spend.&nbsp;
There is no shortage of newly launched AI initiatives that have been established across the government. However, the programs are underfunded to achieve the intended outcome. To adequately fund these programs, it is vital that the reported AI funding is going to carry out actual, dedicated AI programs. To do so, Scale recommends that the U.S. government ensures that the entirety of its AI investment truly works to fund AI programs and efforts.
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Essential Elements to Ensure Effective AI Adoption in the U.S. Government 

As we step into 2024, it is clear that artificial intelligence (AI) will continue to dominate global government discussions. Last year saw over 100 new requirements for the federal governments from the Executive Order, OMB implementation memo, and NDAA. Specifically they recognized that AI-ready data is a national asset, initiated AI standards development work through the launch of the US AI Safety Institute, and established provisions that require external Test and Evaluation (T&amp;E) before the government procures AI. Scale strongly supports these developments because they are critical steps to ensure that AI is safe, secure, and trustworthy for its intended use cases.
Government officials are now focused on implementation and legislation, aiming to build on the robust foundation established in 2023 and sustain the momentum.&nbsp;
<h3 dir="ltr">Agencies</h3>
More than 100 new requirements must be implemented over the next year or so and these requirements are in addition to the existing ones from the two previous Executive Orders and bipartisan legislation. This has provided agencies no shortage of items to prioritize, positions to create, and work products to kick off. As they work through prioritization, the following three items should be top of mind because they underpin the adoption of responsible AI.
<ul>
<li dir="ltr" aria-level="1">
Test and Evaluation.&nbsp;The Executive Order, accompanying OMB Agency Implementation Memo, and NDAA all included provisions requiring external T&amp;E prior to government procurement of AI. The federal agencies must now determine how to best implement comprehensive AI T&amp;E by August 1, 2024 to ensure that AI is safe to be deployed on government networks.&nbsp;
</li>
<li dir="ltr" aria-level="1">
Standards. Initial work establishing AI best practices and frameworks must be transitioned into Standards Development Organizations. The newly announced U.S. AI Safety Institute, which will be housed within the Department of Commerce, will be critical to build the frameworks necessary to advance AI trustworthiness and will underpin safety techniques such as red teaming and T&amp;E.&nbsp;
</li>
<li dir="ltr" aria-level="1">
Chief AI Officers. The Executive Order establishes the positions of Chief AI Officers at every agency. This is an important step forward to ensuring the efficient adoption of AI at all federal agencies and that the over 700 identified use cases can be carried out. However, on day one, it will be critical that the newly appointed person correctly prioritizes items like AI-ready data strategies to lay the foundation for successful AI adoption.&nbsp;
</li>
</ul>
<h3 dir="ltr">Congress</h3>
One of the biggest questions is what Congress will do this year related to AI governance. The Executive Order and accompanying OMB Implementation Memo established a strong foundation for the United State&rsquo;s approach to AI governance, but gaps still exist around critical topics like commercial AI safety. Key pieces of the EO must be funded and codified to be fully implemented. Thanks to the leadership of key Members of Congress, AI has remained a bipartisan issue that everyone recognizes that the United States must lead on. To maintain American leadership in AI, it will be critical that Congress works on three key topics this year:
<ul>
<li dir="ltr" aria-level="1">
Shifting from Learning to Legislating. Since Senate Leader Chuck Schumer&rsquo;s announced that AI is a key issue in April 2023, Congress has prioritized learning about the complexities of AI. This involved hearings, roundtables, hands-on demonstrations, and Insight Forums involving many of the leading technologists and visionaries in the field. As a longtime builder and expert in the AI spaces, Scale supported these educational objectives through building the testing &amp; evaluation platform for the generative red team efforts at DEF CON31, providing expert testimony to Congress, sharing our expertise at the Insight Forums, and providing Congressional members and their staff hands-on red teaming experience for generative AI models. It is critical for Congress to leverage these learnings and take action to craft a legislative package that moves U.S. leadership in AI forward.
</li>
</ul>
<ul>
<li dir="ltr" aria-level="1">
Maintaining a Pro-innovation Approach to AI Safety. The Administration&rsquo;s actions took these steps for government procured AI systems by establishing sector specific risk-based external test and evaluation requirements. However, due to the limits of Administrative actions, it was unable to cover commercial and enterprise AI use cases. Congress must fill this gap and establish an effective approach to safety for all AI use cases.
</li>
<li dir="ltr" aria-level="1">
Funding Key Elements of Government AI Use. Recently federal agencies submitted over 700 different potential use cases for AI. However, despite the clear signal of interest, federal agencies are not funded to take advantage of AI and lack the AI-ready data foundation to do so. It is critical that Congress prioritizes AI funding in the FY25 appropriation process for agencies to help them build the right data infrastructure and begin funding some of the first use cases for agencies to employ AI.&nbsp;
</li>
</ul>
<h3>Moving Forward</h3>
Countries globally are accelerating their development of AI, and the United States must harness the full strength of its innovation ecosystem to maintain American AI leadership. Scale looks forward to continuing our work across the industry and federal spaces to help our nation adopt safe, secure, and trustworthy AI.
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Government officials are now focused on implementation and legislation, aiming to build on the robust foundation established in 2023 and sustain the momentum. 

2024: The Year of AI Implementation and Legislation

This past month has perhaps been the most consequential for AI governance to date. President Biden unveiled his Administration&rsquo;s long awaited Executive Order (EO), the Office of Management and Budget (OMB) released their Federal Agency AI Implementation Memo, the G7 released their AI Guiding Principles, and global leaders attended the first ever Global AI Safety Summit in the UK. Scale is pleased to support all of these, which build on two previous Executive Orders and bipartisan pieces of legislation, months of Congressional hearings, Insight Forums, and other events that have started to shape the United States&rsquo; approach to AI governance.&nbsp;
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As Vice President Kamala Harris said during her remarks at the EO event on October 30, &ldquo;Let us be clear: When it comes to AI, America is a global leader. It is American companies that lead the world in AI innovation. It is America that can catalyze global action and build global consensus in a way that no other country can.&rdquo; This vision can only be realized if the United States&rsquo; governance framework maximizes innovation while putting in place the proper guardrails. The EO and OMB Implementation Memo are foundational steps that advance that vision in four key ways:
&nbsp;
<ul>
<li>First, these efforts recognize the critical role that thorough red-teaming and use-case specific test and evaluation will play in safely deploying AI. Scale has long supported and has worked across industry to pioneer the techniques for red teaming and test and evaluation. Recently we released our <a style="color: rgb(17, 85, 204);" href="https://static.scale.com/uploads/6019a18f03a4ae003acb1113/test-and-evaluation.pdf" target="_blank" rel="noopener noreferrer">methodology</a> that includes red teaming to expose unintended vulnerabilities, followed by a risk-based approach to test and evaluation to ensure AI that is safe to deploy.&nbsp;</li>
</ul>
&nbsp;
<ul>
<li>A risk-based approach aligns the level of risk associated with the anticipated use and an appropriately rigorous test and evaluation process. As the OMB Memo points out, certain uses like auto correction of text pose a very different risk than AI being used for medical diagnosis, and their safety assessments should be treated as such. Paired together, these two techniques provide the confidence necessary to rely on AI for specific tasks.&nbsp;</li>
</ul>
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<ul>
<li>The EO highlights the necessity of consensus-based standards to put in place a repeatable and robust process for test and evaluation. Specifically, the National Institute for Standards and Technology (NIST) is tasked with launching new working groups, including industry experts, to write them. Once completed, these standards will be critical to reference in future rulemakings, legislation, and ultimately underpin the deployment of safe AI systems.&nbsp;&nbsp;</li>
</ul>
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<ul>
<li>Second, the EO and OMB Memo recognize that data is a critical asset for the government and directs agencies to prioritize AI-ready data. This is important because AI-ready data is the key to unlocking the power of AI. The government recently released a report with more than 700 different government AI use cases, but the data infrastructure to support them must be in place for them to be successful. AI is only as good as the data that powers it, and without the proper data infrastructure in place, the government&rsquo;s ability to efficiently adopt AI will be limited. The Department of Defense has long recognized the value of AI-ready data and has called for it within their own data strategy, and the OMB memo calls it a &ldquo;National Asset.&rdquo;&nbsp;</li>
</ul>
&nbsp;
It is critical that agencies and Congress prioritize both AI-ready data strategies and devote adequate resources to carrying out those strategies.&nbsp;
&nbsp;
<ul>
<li>Third, the EO and OMB memo encourage agencies to use generative AI in ways that are safe and responsible for their day-to-day use. Generative AI applications like ChatGPT have taken the world by storm over the past year, and there is no shortage of use cases that would enhance the government&rsquo;s daily work. For example, a large language model at the Department of Energy would drastically speed up the time it takes to understand the electrical grid for next generation transportation technologies. Encouraging agencies to embrace the technology responsibly is a step in the right direction, and when paired with test and evaluation, will help the government address data overload&ndash;one of the most pressing challenges that the government faces.&nbsp;</li>
</ul>
&nbsp;
<ul>
<li>Fourth, the EO establishes &ldquo;Chief AI Officers&rdquo; at every agency. As the use case inventory referenced above notes, there are no shortage of potential AI use cases at each agency, and this new position would enable a leader responsible for ensuring that each agency truly embraces each one of them. Beyond identifying use cases, this person will also have the critical task of leading the efforts on AI-ready data, building out a team that works throughout the agency to train staff on the benefits of AI, and also understanding the funding necessary to carry out these actions.</li>
</ul>
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This is a critical time for the future of U.S. leadership in AI, and the release of these documents is just the starting point. Scale calls on the Administration and Congress to continue prioritizing AI over the coming months because there will be no shortage of work needed across the U.S. government. This includes implementing these new requirements, kicking off rulemakings, and developing the underlying standards to serve as the foundation for them.&nbsp;
&nbsp;
Lastly, Congress still has an important role to play. Scale looks forward to continuing to work with bipartisan leadership in the House and Senate to ensure that we craft legislation that codifies provisions from these documents, fund them where necessary, and fill the gaps that still exist.

Last month was significant for AI governance: Biden's EO, OMB's Memo, G7's Principles, and the Global AI Safety Summit. Scale supports these efforts.

Scale AI Policy Framework: Key Elements to Ensure American Leadership in AI 

As policymakers return to Washington from the annual August recess, AI regulation will dominate political discussions. Senate Majority Leader Chuck Schumer has announced multiple &ldquo;AI Insight Forums&rdquo; beginning September 13, the White House has announced plans to issue an Executive Order, and the world is getting ready for a first-of-its-kind global AI Safety Summit in the United Kingdom this November.
Since our earliest days as a company, Scale has worked across industries to help accelerate the development of AI. Today, we work with the leading frontier model developers to fine tune, red team, and test and evaluate their large language models. This work also provides us a unique vantage point to understand, create, and implement industry best practices and understand what we believe is the right balance to strike on a regulatory approach that maximizes innovation, while putting in place the proper guardrails.
In addition to our work with the leading tech and automotive companies, Scale has worked with the U.S. government since 2020, helping to ensure the United States leads the world in AI adoption. The U.S. Department of Defense (DoD) has led the deployment of AI in the federal government to date, and there are no shortage of use cases for every agency to better enhance their day-to-day operations through AI. For example, a large language model at the U.S. Department of Energy would put information at people&rsquo;s fingertips more quickly on complex research topics like grid or hydrogen capacity for electric vehicles in major cities.&nbsp;
Our collective years of working with leading companies and the DoD has uniquely positioned Scale to understand what works and provide recommendations for the federal government to efficiently and fully embrace AI.&nbsp;
This blog is the first in our series titled, &ldquo;Scale AI Policy Framework,&rdquo; and will focus on our recommendations for successful government AI adoption based on our takeaways from our work with the DoD and broader national security community. Scale strongly believes that three things are necessary for the U.S. government to successfully adopt AI:&nbsp;
<ol>
<li>Robust AI-ready data infrastructure at each Agency&nbsp;</li>
<li>Implementation of a risk-based Test and Evaluation framework prior to AI deployment&nbsp;</li>
<li>Innovative and expedited paths to AI acquisition&nbsp;</li>
</ol>
To read the full white paper click <a href="https://pub-9ba39fa3c4764cd0bb3a9579562941d6.r2.dev/gov-ai.pdf" target="_blank" rel="noopener">here.</a>