Testimony for hearing, “Artificial Intelligence and its impact on the American workforce and education system”

SUBMITTED STATEMENT OF
KEVIN FRAZIER
AI INNOVATION AND LAW FELLOW
THE UNIVERSITY OF TEXAS SCHOOL OF LAW

SENIOR FELLOW
THE ABUNDANCE INSTITUTE

BEFORE THE
HOUSE COMMITTEE ON EDUCATION AND THE WORKFORCE
U.S. HOUSE OF REPRESENTATIVES

HEARING ON
“ARTIFICIAL INTELLIGENCE AND ITS IMPACT ON THE AMERICAN WORKFORCE AND
EDUCATION SYSTEM”

Chairman Walberg, Ranking Member Scott, and distinguished members of the Committee, thank you for the opportunity to testify.

My name is Kevin Frazier. I’m the AI Innovation and Law Fellow at the University of Texas School of Law, a Senior Editor at Lawfare, and a Senior Fellow with the Abundance Institute.

The economic normal in the Age of AI is and will be marked by flexibility. Future generations of Americans won’t associate traditional work with 9-to-5 employment. The career ladder will be replaced by a career flywheel, in which individuals succeed due to their capacity to adapt and willingness to learn. In short, we’ll soon see more and more Americans participating in what I call the Portfolio Economy: workers will maintain an array of skills that they can offer to a range of employers on a project-by-project basis. This transition will put pressure on New Deal labor and employment laws, such as the Fair Labor Standards Act.

My written testimony makes three points: the transition to the Portfolio Economy must be data-driven, worker-focused, and flexible.

Absent more data about how the economy is evolving, Congress may lack the information necessary to assess whether existing laws are functioning as intended. Many of the current sources of labor market data are infrequent, imprecise, or inaccurate. The Contingent Worker Survey, for example, is issued on an irregular basis and does not accurately capture the number of Americans in non-traditional jobs.

It’s also unlikely that Congress has up to date and comprehensive information on AI adoption. According to recent polls, just ten percent of workers use AI daily and a mere nine percent of small firms have picked up AI. This information, though, is akin to learning LeBron James played 47 minutes in a game—it’s better than nothing but it’s missing what’s really important like how many points he scored or, to return to AI, whether its use is increasing productivity and altering hiring decisions.

Updating and expanding the sources of information on how AI progress will become all the more important as the technology continues to evolve. While most experts agree we should expect ongoing advances in AI, they diverge when it comes to pinpointing the specifics. The so-called jagged frontier of AI will cause firms to preserve the option of automating certain tasks and roles and, consequently, to prioritize finding workers with specific skills for finite projects. In turn, workers will need to be ready and willing to learn new skills and fast.

With a better understanding of AI advances and adoption, I recommend Congress analyze laws such as the Fair Labor Standards Act with a focus on two aspects:

1. To what extent does the law rely on frameworks and definitions that clash with the Portfolio Economy?
2. Does the law incentivize workers to engage in the career flywheel–to study, to shadow via apprenticeships, and to work in non-traditional arrangements?

Thank you again for this opportunity. I look forward to your questions.

1. Introduction: The Portfolio Economy

Artificial intelligence (AI) will inevitably and permanently alter the nature of work. Where, how, and to what extent is unknown and, critically, unknowable. Economists do not have a definitive test to determine which jobs are most likely to be disrupted nor when such disruption will occur. They also lack the means to reliably predict which corporations and industries will successfully integrate AI and which may struggle to do so. This explains why, depending on the day, the public may come across headlines anticipating the rapid elimination of entire professions due to AI or reports touting how AI development is creating jobs and leading to entire new fields of work. 

Technologists are similarly in the dark. They cannot precisely forecast the capabilities of future AI models. They vary in their expectations about how and when AI will achieve “superintelligence” or achieve “AGI.” Their differences do not end there. Some contest whether those are definable concepts or concepts worth defining in the first place! Technologists even struggle to pin down the exact capabilities of existing models. These numerous and vast gaps in knowledge will persist for the foreseeable future. America’s world-leading AI labs are exploring new training methods that will result in AI models that create even more capable and diverse AI tools. 

Despite the litany of known unknowns and unknown unknowns in AI development and diffusion, it is generally agreed upon that AI will accelerate workforce trends that were already underway before ChatGPT. Work has been and will be increasingly skill-based, short-term, and independent. The future of work looks far more like the gig economy than a 30-year career with a single firm. It will soon be the norm, rather than the exception, that Americans are simultaneously performing work for several firms under a range of different employment arrangements. 

Put differently, we have entered the first innings of a Portfolio Economy. Workers will strive to maintain a range of valuable skills and a stable of clients; they will have to regularly update both as AI continues to advance and the nature of human-AI collaboration shifts. This economic reality is the product of how AI seems likely to develop and diffuse. AI does not progress at the same rate across all tasks and domains; its capacity to handle a specific job function is highly variable. AI experts commonly refer to this as the technology’s “jagged frontier.” 

Whether AI will augment how a human performs a specific function, take over that function, or have no ability to augment or automate that function is a guessing game. While some tasks have been and will be delegated to AI, others will remain the exclusive domain of humans or involve some human-AI collaboration. This is precisely why those who warned that radiologists would soon be out of work have had to walk back their statements. Across the spectrum of tasks performed by radiologists, only some are suitable to entirely delegate to AI. For operational and legal reasons, many of the remaining radiological tasks must and will be performed by humans. 

Learning from the case study of radiologists, assessments of the future value of any one task or profession must consider the substantial technical limitations of AI as well as broader legal and institutional inertia. While technological hurdles and regulatory barriers may eventually be cleared, many jobs with even a high rate of “exposure” to AI—meaning that AI tools seem capable of taking on many of that job’s tasks—will remain to be human-held positions. In some cases, AI augmenting or automating tasks may actually increase demand for the profession in question. A majority of firms with fewer than twenty employees expect that AI will cause them to hire more employees. AI as a job creator makes intuitive sense in many contexts. Consider the vast shortage of mental health professionals, for instance. As AI allows therapists to take more accurate notes and handle administrative tasks—thereby reducing the cost of treatment, more members of the public may seek out mental health support. More generally, AI can lower the costs of things like customer service that may have previously caused a customer to prefer larger firms to a smaller one.

Of course, in other domains, the productivity gains induced by AI will cause some employers to demand fewer workers in that field. It is fairly clear that there will be fewer court reporters in the future, for example. There are only so many trials in so many courts, so as AI makes key tasks of that role more cost effective, court systems will simply hire fewer reporters. Individuals in these sorts of fields will be formal members of the Portfolio Economy. They may spend a fraction of their time in their old, traditional W-2 role but will otherwise need to develop additional skills to market to other firms. 

In this near-future, professional stability and economic security will look like having the means and opportunity to study new skills through private or public educational and vocational programs, train under mentors through apprenticeships, and work for a variety of firms around the world. Whether Americans thrive in the Portfolio Economy rests on whether labor and employment laws evolve to permit and encourage flexibility or maintain their current rigidity.

Policymakers seeking to navigate this challenge by developing the flexible, adaptive laws required in the Age of AI should adhere to a few best practices. First, seek to understand the underlying technology. A foundational knowledge of the flaws and likely capabilities of AI models in the near- and medium-term is essential to sorting through conflicting and even contradictory reports of how AI will alter the economy and society, more generally. Policymakers should also have a strong grasp of how and when AI can complement and augment humans rather than automate roles. Technological literacy will go a long way toward sorting through sensationalistic AI claims that tend to dominate the headlines.

Second, gather more information from the private sector about AI adoption plans and workforce needs. Information on how small-, medium-, and large firms plan to integrate AI can inform both immediate retraining and upskilling initiatives as well as more long-term reforms to our educational and workforce development programs. This data will similarly help dispel hyperbolic claims about the imminent demise of entire industries and professions. 

Third, develop and test policies crafted in response to a thorough understanding of AI and reliable data on its adoption. What it means to succeed in the Portfolio Economy is unclear and contingent on variable factors—including but not limited to the pace and nature of AI advances and the level of AI adoption by firms and laborers alike. Laws and regulations crafted to today’s AI or based on the current use of AI by firms and laborers will rapidly become technologically obsolete. Legislative tools such as sunrise clauses, retrospective review, and regulatory sandboxes are indispensable as lawmakers strive to make sure the United States is first to the future rather than the last to move on from the past. 

The remainder of this testimony provides initial guidance on each of those practices. This guidance is far from comprehensive and is soon to be out of date. In the same way that workers in the Portfolio Economy will have to continually update their menu of skills and services, policymakers will have to serially seek out new information on AI capabilities, AI adoption, and the regulatory tools most responsive to technological progress and its diffusion. 

2. Understanding the Technology: The Technical Reasons Why AI Will Transform the Nature of Work

Study of prior general purpose technologies, such as the steam engine and electricity, indicates a two-stage process to the transformation of the economy. In the first phase, the technology is applied to existing processes—often with little or marginal effects. In the second phase, systemic changes take place as entire institutions and processes develop around the specific attributes of the emerging technology. 

A historical case study helps illustrate the difference between task-based adoption of technology and systemic reorientation around new technology. A large percentage of people may think of the steam engine as being invented in the 1800s. Yet, Thomas Newcomen developed such a system in 1710. The reason for the wide discrepancy? Significant technical limitations meant that the Newcomen engine wasn’t of much use outside of pumping water out of flooded mines. Firms found it cheaper to stick with coal than to upend their workflows around this early iteration of the steam engine. So while the Newcomen engine may have displaced a few miners who were no longer needed for the one-off task of addressing flooded mines, it fell far short of transforming mining or any other industry. When technological adoption is in this first stage, it’s best to assess its societal and economic impacts on a more granular basis. It will never be the case that a new technology achieves its full potential in the days and months following its initial introduction. Cultural, economic, legal, and political factors all shape and slow technological diffusion. 

AI is in many ways in its Newcomen stage. The vast majority of firms have yet to adopt AI. Barely more than twelve percent of large firms are using AI. Smaller firms, those under 250 employees, report even less use—below ten percent. Across the U.S. workforce, just one in ten employees regularly engage with AI; the majority of workers sparingly turn to AI for assistance. Many workers—about one in four—are unsure of whether their company has an AI policy or strategy.

Even among the firms that have formally adopted AI, it’s likely that they are generally doing so to handle or augment discrete tasks; systemic redesign seems years (and millions of dollars) away. Small and large firms that use AI tend to do so for just two specific tasks, such as developing marketing materials. Critically, these more AI-forward firms have yet to even attempt to reorient their entire operations around AI. Among small firms that have adopted AI, half have made no substantive investments in staff training, consultants, or operational updates. Only slightly more large firms have made AI-related investments. This dearth of investment suggests that it will be quite some time before AI causes systemic changes to the nature of work. Technologists expect that for every one dollar spent by a firm on AI they will have to invest nine more on intangible human capital. Firms have clearly yet to follow that ratio. While some may excuse underinvestment as a strategy to save costs, economists expect that firms willing to invest in AI and related institutional changes will experience greater productivity gains from AI sooner.

Cultural factors may also be slowing the workplace effects of AI. Reports of so-called AI stigma—a sense that colleagues may look down on co-workers for using AI—is pervasive. An unwillingness to use AI among a firm’s employees may reduce the usefulness of even highly reliable AI tools and delay any potential productivity gains. Stigmatization may also cause workers to engage in riskier uses of AI because of a hesitancy to seek out information on how to properly use AI. When I travel the country talking to lawyers about AI, for instance, many attendees tell me after the fact that they rarely share how they use AI with colleagues because so many lawyers fear that they will become the subject of the next story detailing a lawyer submitting a brief with a hallucinated citation. Lawyers aren’t alone in feeling as though they have to hide their AI use. So-called secret cyborgs—employees clandestinely using AI—exist in many companies.

Technical limitations additionally explain why AI adoption has generally been confined to taking over or assisting with discrete tasks. Evaluations of the extent to which AI tools have “economically relevant capabilities” show that AI has a long way to go before outpacing workers on each of their tasks. OpenAI’s GDPval, which assesses the performance of AI tools across 1,320 specialized tasks relevant to 44 occupations, indicates that leading AI tools demonstrate near expert-level performance on about 48 percent of key tasks. Certain tasks—though involving “know-what” or judgement, wisdom, and intuition–will likely remain beyond the capabilities of AI tools for quite some time. That said, today’s AI is the worst AI we will ever use. 

Several likely technical advances in the short- to medium-term may hasten the ability of AI to augment or automate a broader suite of tasks as well as to assist in the redesign of entire processes. Agentic AI systems—tools capable of autonomously performing any tasks someone could do on their computer–loom on the horizon. In short, whereas most AI tools today require the user to continually prompt or instruct the tool, AI agents can pursue goals set by the user with little to no intervention. While early agentic systems are already available, they tend to struggle on especially complex or long-lasting tasks. AI developers expect that these shortcomings can and will be addressed in the near future–heralding the second phase of AI-driven transformation of the economy.

AI agents will allow for a new kind of business—companies designed entirely around AI rather than simply turning to AI to aid humans with current obligations. AI-native firms will differ from today’s firms in meaningful ways. First, they will require fewer humans relative to competitors that refrain from altering their processes. Second, AI-native firms will operate in a nimbler fashion. AI agents do not tire; they work 24/7/365. AI agents can also quickly be re-tasked at minimal expense, whereas humans may need time and training to become productive in a new line of work. Third, these firms can easily move in and out of different markets, so long as regulatory and technical systems facilitate such cross-border activity. As an aside, progress in robotics will allow for greater use of AI agents in sectors such as manufacturing where AI use is less common today relative to the knowledge sector, for instance. It is likely that developments in world models—a new set of AI tools that “predict what will happen next in the world, modeling how things move, collide, fall, interact and persist over time” —will accelerate this progress. As the sophistication of world models improves, robots will be able to take on a greater range of tasks with lower error rates and for longer periods of time. For these reasons and more, there will be a strong incentive for firms in many sectors to become more and more oriented around AI agents. 

Yet not all sectors are amenable to a systemic overhaul around AI. The most common AI tools have significant limitations in certain domains due to their inherent technical features. As described by John Pavlus, today’s AI tools learn “scores of disconnected rules of thumb that can approximate responses to specific scenarios, but don’t cohere into a consistent whole.” In other words, the usefulness of today’s AI is highly context dependent. If AI has not been trained on relevant, up to date data, then it will struggle in that domain. 

This is due to the probabilistic nature of generative AI tools. In a very simplified sense, today’s AI tools predict the next best word in response to a user’s prompt based on their training data, the AI developer’s instructions for how to prioritize certain information or responses over others, and safeguards that the AI developer may have imposed to limit the generation of illegal or harmful outputs. Fields lacking data for AI to train on—think everything from the massage industry to crisis response management—will likely not experience a systemic reorientation around AI. 

The unpredictability of how AI will advance means that there is no definitive timeline for how these stages will play out in different sectors. The best path forward is to develop agile and adaptive frameworks that facilitate two tasks: first, gathering information about how and to what extent (e.g. for augmentation, automation, or systemic redesign) AI is being adopted in different sectors; and, second, based on that information, updating labor laws as necessary to permit workers to meaningfully contribute to existing and new tasks and sectors.

3. Measuring Adoption: The Key Information Necessary to Determine How AI is Actually Changing the Economy

Congress cannot help American workers thrive in the Age of AI if it is operating with outdated, incomplete, or inaccurate data about the aforementioned phases of AI adoption into the economy. Yet, the Federal Government’s current approach to learning about private sector use of novel technology and complex scientific and technological matters in general is highly reactionary and fragmented. Notably, these issues predated the current AI policy conversation. “Congress is science-poor,” concluded Martha Kinsella & Maya Kornberg in 2023. They continued, “The lack of scientific understanding and expertise cramps policymaking, with terrible effects on the country. Congress can fill this gap itself, and it must.” Absent changes, Congress will lack the information necessary to properly evaluate and, if necessary, respond to economy-wide trends emerging from AI. 

In theory, a lawmaker focused on identifying the tasks and roles their constituents should seek out in the Portfolio Economy could gather information from the following sources: tax returns that may provide indirect evidence of the intensity of corporate investment in AI development and adoption; SEC disclosures that refer to corporate AI strategies; notices of layoffs that may have been driven by AI as compelled by the Worker Adjustment and Retraining Notification (WARN) Act; and, responses to AI-related Census and other recurring survey questions. In practice, that lawmaker will find themself woefully uninformed about the nature of AI adoption. 

These information sources are either too narrow, too broad, or too infrequent to provide Congress with an accurate picture of AI capabilities and the extent to which those capabilities are being adopted by private actors. For instance, the WARN Act was enacted to provide state and federal officials with more information about large-scale factory closures, which differ from the timing and nature of AI-induced layoffs. More generally, the aforementioned sources generally do not require explicit and ongoing reporting about AI use by the private sector. Even if several agencies attempted to collect such AI-related metrics, the resulting information would still be of limited value. There is no standard agreement among these various agencies nor within the applicable statutes as to how to define AI, AI adoption, and related terms that would be of interest to the lawmaker in question. Reporting requirements may also elicit too much as well as too little information. On the one hand, not all firms of interest are captured by these disparate collection mechanisms and not all firms may invest the same level of resources to accurately respond to such inquiries; on the other, firms may opt to flood agencies with information to reduce the odds of the meaningful kernels being identified. Congress and receiving agencies may also lack the capacity to meaningfully analyze what may be troves of data on AI development, diffusion, and adoption. 

Absent significantly more accurate and timely data, it is highly likely that Congress will be tempted to legislate in response to anecdotes rather than based on evidence. That’s a solvable problem. Rather than rush to regulate AI and hope that the chosen statutory response will work as intended, Congress needs to thoroughly examine and improve how the Federal Government learns about AI use across the economy. Notably, this will mark an improvement upon how the government has previously responded to information gaps related to emerging technology—consider that there was a twelve year gap (2005 to 2017) between formal reports on the state of the contingent and alternative work arrangements, well after the rise of this key part of the economy. As late as 2024, such reports did not even include specific analysis of app-based work arrangements. Assuming that Congress corrects for this lag in the context of AI, a few key principles should guide any information gathering proposal. 

First, collected AI-related information should generally be anonymized when submitted and aggregated when shared so that companies are incentivized to provide the most accurate and comprehensive data possible. If companies are coerced into making their AI adoption plans fully known to the public, they may face popular scrutiny for merely attempting to adjust to the Age of AI. This will have the pervasive effect of slowing AI adoption, resulting in U.S. firms being technological laggards and, consequently, slower to create the products, services, and jobs of the future. Lawmakers seeking to develop educational and workforce development programs for the Portfolio Economy can do so with broader measures of AI adoption rates by firm size and industry type, for instance.

Second, information sharing processes should be as automatable as possible to reduce the costs and operational burdens associated with compliance, an especially key concern for small businesses. The costs to comply with even straightforward regulations are disproportionately high for small and medium-sized businesses. Some may accordingly call for businesses under a certain threshold being omitted from any mandatory AI adoption information scheme. However, the omission of smaller companies will deprive Congress of critical information when it comes to preparing for the Portfolio Economy. 

Startups and small businesses are often on the vanguard of creating and offering new products and services. They also are engines of economic opportunity and dynamism—facilitating the sort of churn that will allow workers to build out a larger portfolio of client companies. Congress must have a strong grasp of the state of AI across firms of all sizes. To accomplish this goal, policymakers should explore the use of AI to gather this information from private stakeholders and should mandate that agencies collecting any relevant data use standard forms and definitions.

Third, companies that make a good faith effort to comply with any such reporting requirements should be given the opportunity to cure any incorrect or late disclosures. This regulatory safe harbor will have the dual benefit of increasing the odds of companies submitting information in the first place and, therefore, providing Congress with a more complete picture of the AI landscape and state of the Portfolio Economy. 

Fourth, any information collection schemes should be subject to a sunset clause. Congress should have to regularly reexamine whether it still requires certain information. This will reduce the odds of America’s companies being saddled by increasingly onerous, duplicitous, or antiquated information reporting requirements. Additionally, this recurring investigation of the need for specific information will force Congress to clearly think through why certain information may or may not be necessary for its regulatory goals. It’s highly likely that the metrics that matter most for informing AI policy will shift as the technology and its inputs evolve. By way of example, demands for information on the training data used by AI labs may be less legally important if labs begin to instead train on synthetic data—data generated by another AI. 

Adherence to these principles will put Congress and the entire Federal Government in a much stronger position to see how the Portfolio Economy is emerging in real-time. In turn, policymakers can develop responsive policies that help Americans navigate this new economic reality. That said, Congress should not wait to begin to study how to proactively set Americans up for success in a more dynamic and fluid labor market. 

4. Planning for the Portfolio Economy 

As previously mentioned, AI is compounding several trends that were already straining labor laws better suited to technologies and market forces in place in the 1920s than the 2020s. A few such trends are especially relevant to the Portfolio Economy. For one, it’s far from a new phenomenon that more Americans are working in a continent or alternative work arrangement. Somewhere between ten and thirty percent of US workers derive their primary income from a nontraditional work arrangement. Despite that vast span, it is evident that such arrangements have become more common in the 2000s. Numerous signals suggest this trend will not abate. 

The jagged frontier of AI means that which tasks are in demand will vary in a rapid fashion. New tools will be deployed with minimal notice and innovators will devise creative ways for humans to leverage AI. The net effect of these two facts is a shifting menu of highly sought after skills. Firms, especially following recent overhiring, are rightfully cautious of hiring too many people in fields that may soon be eliminated or altered; increased uncertainty as to the value of different skills will only further entrench their preference for alternative work arrangements over traditional W-2 agreements.

Workers, too, increasingly seek out flexible work arrangements. Following the pandemic, businesses that tolerate a wider range of hours, schedules, and work locations have seen an uptick in interest by applicants and retention among employees. The next generations of workers may place an even higher premium on bespoke work arrangements. Members of Gen Z have signaled a strong demand for anything other than 9-5 work. Forecasters expect Gen Alpha will seek out similarly flexible job opportunities.

In this fluid, shifting, and skill-specific labor market, there’s also a strong mutual interest among employers and workers alike for efficacious upskilling and retraining programs. All else equal, employers stand to benefit from a deeper labor pool–both in terms of the absolute number of qualified workers and the range of skills held by the average worker. Workers with more skills or a proven ability to quickly pick up skills will allow firms to easily shift between AI, humans, and human-AI teams as the technology, culture, and regulations evolve. 

Relatedly, workers have an obvious interest in maintaining and, when possible, increasing their skill portfolio. In an economy that turns quickly to reward certain skills, the workers with a wide range of skills and the capacity to apply them in different contexts will fare better. Employers may soon look for evidence that workers are capable of adding immediate value to small and large businesses as well as to businesses operating in different sectors and even in different countries; in other words, the capacity to adapt and to problem solve will likely become even more valuable as the economy and technology continue to evolve.

Crucially, the Federal Government also has an interest and role to play in a skills-based economy. In an international market for skills, employers may turn to workers in other countries to tackle specific short-term efforts if they cannot find domestic talent. When companies come to rely more and more on foreign talent, the domestic economy will struggle, which has obvious negative ramifications on the government. Rather than attempt to interfere in dictating the specific skills workers ought to learn and the precise means to do so, the Federal Government can instead ensure the proper market and legal structures exist that achieve the following: first, make it as easy as possible for workers to accurately signal their skills to employers; second, ensure workers and employers alike have plenty of opportunities to learn new skills and to provide ongoing training opportunities, respectively; and, third, design labor laws such that workers can easily shift between different employers and projects and participate in lifelong learning and apprenticeship opportunities. 

This is an ambitious but necessary agenda. In the same way that the successful business of the future will find ways to reorient their processes around AI rather than merely improve existing systems, success on this agenda will turn on the extent to which policymakers are willing to reinvent the wheel. The recommendations below are presented at a high level to facilitate this sort of bold thinking—the goal is to prevent the sort of piecemeal, fragmented approach that may take place through one-off amendments to current frameworks. 

A. Skill Signaling Reform

The transition to a Portfolio Economy will falter if workers lack credible, low-cost ways to signal what they can do—and if employers lack reliable tools to identify that talent. Today’s dominant signals of worker competence—grades, formal educational degrees, and static certifications—are increasingly ill-suited to a labor market defined by rapid skill turnover, short-term engagements, and shifting patterns of human-AI collaboration. Grade inflation has eroded the informational value of transcripts. Degree requirements frequently function as blunt proxies for aptitude rather than evidence of job-relevant skills. At the same time, a proliferation of fast-moving credentials and training programs has added noise rather than clarity to the labor-matching process.

In the Portfolio Economy, skill signaling systems should communicate competence as well as encourage and shape future investment. Workers are more likely to pursue retraining when they can credibly document and monetize new skills. Employers are more likely to fund training when those investments are applicable across projects and teams for varying periods of time. Absent more accurate and dynamic signals of skills, both sides of the labor market will underinvest in upskilling, slowing adaptation at a time when economic dynamism is of extreme importance.

For these reasons, Congress should prioritize reforms that modernize how skills are documented, verified, and shared. While traditional credentialing mechanisms may continue, they should no longer occupy favored status in terms of federal funding and value in the labor market. A revised, skills-based, standardized skills signaling system can supplement—and over time improve upon—traditional credentials with more precise, continuously updated, and trustworthy signals that better align with the realities of portfolio-based work.

1. Initiate a study and pilot program for a Cryptographic Curriculum Vitae.

Congress should direct the Department of Labor, in coordination with the Department of Education and the National Institute of Standards and Technology, to study the feasibility of providing each American worker with a cryptographic curriculum vitae (C-CV)—a secure, portable, and serially updated record of verified skills, competencies, and work experiences. A C-CV would allow workers to document what skills they possess and both how and where those skills were acquired and applied, including through formal education, apprenticeships, short-term contracts, and on-the-job learning. 

For employers operating in the Portfolio Economy, C-CVs would significantly reduce the transaction costs associated with identifying the right talent for discrete tasks or time-limited projects. Rather than relying on coarse proxies such as degrees or job titles, firms could examine more reliable sources of information, such as whether a worker has demonstrated proficiency in specific tools, methods, or workflows. For workers, C-CVs would lower barriers to entry across firms and sectors, enabling them to market discrete skills to multiple employers simultaneously and to update their profiles as their capabilities evolve.

At a minimum, the study should address:

• How to evaluate and record skill proficiency across K–12 education, higher education, apprenticeships, and professional settings in ways that are comparable without being rigid or exclusionary.
• How to incorporate evidence of real-world application—such as project outcomes, employer attestations, or peer validation—while protecting sensitive or proprietary information.
• Which governance structures are best suited to oversee a C-CV Exchange—where employers and workers can post opportunities and share their skills, including clear standards for privacy, cybersecurity, and antidiscrimination compliance.
• A phased rollout strategy, including pilot programs in select states, regions, or industries, to assess adoption, usability, and labor-market effects.
• Whether, and under what conditions, recipients of federal education or workforce development funds should be required to participate in the C-CV Exchange to ensure interoperability and broad access.

2. Convene competitive, employer-validated skill assessment frameworks.

Federal legislators and regulators should authorize and fund convenings that bring together employers, educators, workforce development organizations, and technologists to design multiple, competing skill evaluation tools. Rather than imposing a national standard, the Federal Government should facilitate experimentation and then collect data on which assessments employers find most predictive of job performance and most accurate in terms of worker expertise across various domains. These convenings ought not crowd out the work that is already being done in this space but rather fuel it. The Markle Group has collaborated with others to create a “Job Posting Skillitizer.” Hiring managers can use this tool to craft skills-based job descriptions that lend themself to a skills-based approach to labor matching. While it’s a useful product, it could likely benefit from ongoing scrutiny by labor market participants—that’s where such convenings could come in handy. Iterative assessment of this kind could occur across all aspects of the labor market.

This competitive, information-driven process would allow ineffective or noisy assessments to fall out of use while rewarding those that accurately capture job-relevant skills. Over time, this feedback loop would improve the quality of skill signals available to both workers and firms, ensuring that educational and training programs evolve in response to actual labor-market demand rather than static credentialing norms.

3. Align federal education and workforce funding with improved skill transparency.

Even if Congress were to follow the first two recommendations, there will be an ongoing need to create and share skill evaluation tools. If Congress determines that the private sector is not sufficiently developing and adopting such evaluations, then it ought to commission an analysis of the extent to which traditional grading and credentialing systems fall short of employers’ and workers’ needs in a skills-based labor market. This rigorous examination is pivotal to deterring students and workers from chasing credentials with little to no economic return on investment. Of the hundreds of thousands, if not millions of badges, certificates, and other credentials, many fail short of qualifying as “credentials of value,” or credentials that “equip recipients for strong career trajectories, improve their earnings opportunities, align with high-demand jobs offered by . . . employers,” and propel recipients to “earn enough within 10 years to pay for the cost of their education[.]” That analysis should inform the imposition of conditions on federal education and workforce development funds and grants, with the aim of encouraging—though not abruptly mandating—the adoption of more granular, skills-based reporting mechanisms.

By tying federal support to improved skill transparency rather than to particular credentials, Congress can help institutions orient toward outcomes that matter in the Portfolio Economy without dictating curricular content or instructional methods. To be blunt, ongoing direct and indirect support of educational and vocational programs that do little to help workers show their capabilities and employers find the best workers represents a poor allocation of federal funds given superior alternatives.

4. Establish a legal safe harbor for skills-based hiring and signaling.

Congress should enact a statutory safe harbor clarifying that employers who hire based on the C-CV Exchange and otherwise rely in good faith on validated, skills-based signals—rather than degree requirements or pedigree-based proxies—will not face heightened liability under federal employment or civil rights laws, provided those tools are demonstrably job-related and nondiscriminatory in design.

This reform would remove a significant legal disincentive to modernizing hiring practices. Many employers may default to degree requirements when hiring because they are familiar and legally safe more so than due to the informational value of degrees. A clear safe harbor would accelerate the shift toward skills-first hiring, expanding opportunity for workers whose competencies were acquired outside traditional pathways and improving labor-market matching efficiency.

Application of these reforms would strengthen the informational infrastructure of the labor market. Workers would be better positioned to invest in new skills with confidence that those investments can be credibly signaled and rewarded. Employers would gain faster, cheaper, and more accurate access to talent. And policymakers would support a labor market that rewards adaptability, continuous learning, and demonstrated competence—the core attributes required to thrive in a Portfolio Economy shaped by rapid technological change.

B. Increasing Skill Development Opportunities

The Portfolio Economy will reward workers who can repeatedly acquire, apply, and redeploy skills across shifting tasks, firms, and industries. Yet much of federal labor and education policy still reflects a linear model of work: education occurs upfront, retraining is episodic and reactive, and employer-provided training is treated as a discretionary benefit rather than an operational necessity. Those assumptions create avoidable friction. They raise the cost of entering learning-oriented roles, make it harder to finance mid-career pivots, and discourage both workers and firms from investing in skills that will be valuable precisely because they are adaptable and portable.

Congress need not and should not attempt to forecast which skills will matter most. The jagged frontier of AI makes such predictions unreliable. A more durable federal role is to remove legal and financial barriers that prevent workers and employers from responding to shifting skill demand in real time, while building in mechanisms for learning and course correction. The proposals below are intended to expand the range of lawful, practical pathways through which Americans can build skills through formal education, vocational programs, and on-the-job learning—without imposing unnecessary federal mandates or rigid national standards.

1. Ease the creation of more trainee opportunities for young Americans.

Young Americans have found the current economic churn particularly hard to navigate. The combination of a shifting labor market and inadequate incentives for firms to gamble on entry-level workers with unknown capabilities has resulted in a troublingly high rate of unemployment among recent graduates and other young Americans. Failure to help these Americans find their economic footing may have dire long-term consequences. The first few years of a worker’s career go a long way toward shaping their future professional path. If they find themselves underemployed for prolonged periods, then they may never find their way to more appropriate and remunerative forms of employment. 

That’s why it is necessary to amend the 90 calendar-day youth minimum wage exception under the Fair Labor Standards Act (FLSA) to 180 days, subject to a two-year sunset clause. The labor-market logic behind this proposal is simple: many entry-level, learning-oriented positions require more than a brief introductory period before a worker becomes productive; yet firms face an uncertain return on investment in expending resources on training opportunities. Extending the youth minimum window reduces the marginal cost to employers of offering longer trainee roles and increases the likelihood that youth work experiences generate durable, transferable skills rather than short-term, low-skill churn. Moreover, this longer term can foster more meaningful work-trial periods over which the worker and employer can determine if there’s a good fit for a longer-term, more permanent role. 

Because the risks of abuse of this extension are real—particularly substitution of lower-cost youth labor for adult labor or the creation of extended low-wage roles with little skill accumulation—Congress should require retrospective evaluation of such programs by the Department of Labor during the two-year pilot period. That evaluation should focus on measurable outcomes rather than compliance formalities: rates of participation, wage progression after the exception ends, duration of employment, transition into apprenticeships or higher-wage roles, and any displacement effects. This sunset clause will force Congress to revisit the policy in light of evidence and to narrow, expand, or terminate it accordingly.

Relatedly, a minor, yet strategic adjustment to the Trump Account (TA) framework by Congress to allow employers who hire student trainees under 18 to contribute directly to those trainees’ accounts can contribute to ongoing flexibility and training. Specifically, pursuant to the TA, employers should be permitted to reallocate the existing $2,500 credit available to dependents of employees to eligible student trainees—so long as the employee does not claim it or affirmatively waives it. This would strengthen early skill development while avoiding the creation of a new entitlement program or a large new administrative apparatus. Though the potential benefits of this policy change will not be realized for some time, it’s nevertheless a wise strategic investment in a more flexible labor force down the road. 

Finally, the Department of Labor should launch a pilot program that encourages firms to launch apprenticeship roles with an income-sharing agreement (ISAs) as the primary basis for covering the cost of the training. Fear of runaway apprentices—trainees benefiting from the time and expertise of a mentor, then fleeing for another role—understandably chills development of apprenticeships. ISAs curtail that concern by allowing firms to recoup apprenticeship costs and, depending on the ISA terms, even profit from running particularly effective programs. This initial pilot of two or three years should then inform agency guidance to firms looking to offer such programs. 

2. Create a narrowly tailored FLSA trainee pathway for students aged 17 and older in hazardous occupations, with strict safety constraints and time-limited authorization.

The FLSA should be amended to allow students aged 17 and older to work in hazardous occupations in bona fide trainee roles. Many of the most economically valuable, labor-constrained, and AI-resistant skills—advanced manufacturing, energy infrastructure, logistics, and skilled trades—cannot be developed solely through classroom learning. A categorical prohibition reduces exposure to high-demand fields and delays skill accumulation in sectors where experience is itself a credential.

This reform should be deliberately narrow. Congress can initiate this reform by authorizing a time-limited pilot that applies only to clearly defined trainee roles and requires safeguards that are practical, yet not overly burdensome: supervision requirements; restrictions on the most dangerous task categories; mandatory safety instruction consistent with industry norms; and reporting of serious incidents and program completion rates. The aim is to expand opportunity in the fields of the future, albeit taking the requisite steps to guard against the exception becoming a loophole that places youth in unsafe settings. After the pilot period, Congress should evaluate whether the pathway increased entry into apprenticeships and high-demand skilled roles without adverse safety outcomes and, if so, how best to make such pathways more available and permanent.

3. Continue expanding Pell Grant flexibility to cover high-quality vocational and short-term training, while hardening guardrails against low-value programs. 

Following the recent expansion of permissible Pell Grant uses, Congress should further broaden eligible Pell Grant recipients to vocational and short-term training opportunities, including bootcamps and training programs. The Portfolio Economy requires training that is modular, stackable, and accessible in short intervals—particularly for workers who cannot afford to exit the labor market for multi-year programs. It is likely that entities other than higher education institutions—currently off the table for Pell recipients—are best suited to provide such training. Pell Grants recipients should not be unduly forced to spend their funds on less efficacious opportunities.

At the same time, Congress should recognize that expanding financing without quality safeguards risks subsidizing credential mills and further degrading skill signals. Any expansion should therefore be coupled with outcome-focused guardrails that are administrable and technology-neutral, such as transparent completion rates, job placement measures where appropriate, and earnings or advancement indicators. This preserves flexibility while ensuring federal dollars support programs that plausibly improve a worker’s labor-market prospects.

4. Modernize overtime rules to permit voluntary conversion of overtime pay into training comp time.

Rigid approaches to how overtime is calculated and rewarded deprive workers of more autonomy over their preferred means of compensation. Here, again, the FLSA requires amendment. Congress should update how employers may alter overtime policies to allow workers, by voluntary agreement, to convert overtime compensation into compensatory time that can be used for qualifying skill development opportunities. This is especially well-suited to the Portfolio Economy because it acknowledges time—rather than only money—as a significant constraint on retraining. Many workers can finance a course but cannot attend one without sacrificing wages or risking job loss. 

To prevent abuse, Congress can task the Department of Labor with spelling out rules that facilitate worker opt-in, prohibit coercion by employers, and, if deemed necessary, set reasonable caps on total comp time accrual. Congress may also direct Labor to study whether comp time should be portable or usable across multiple employers within a defined period, recognizing that workers increasingly move between short-term engagements. 

5. Permit a vocational education exception to early withdrawal penalties from Trump Accounts.

Disparate access to training opportunities among workers has long hindered America’s total productive capacity. Though AI and other technologies such as virtual reality and artificial reality will eliminate some of the geographic and quality barriers that have contributed to access gaps, many Americans may still face financial hurdles to enrolling in the best training opportunities. This marks a key spot for congressional action, such as permitting TA holders to access their funds early without penalty for qualifying vocational and workforce training. In a labor market defined by workers experiencing numerous career and employer transitions, restricting savings vehicles to narrow categories of “traditional” education or penalizing mid-career use undermines the very flexibility the Portfolio Economy requires.

This exception should be structured to reduce fraud and misuse by tying eligibility to clearly defined categories of qualifying programs (including apprenticeships and accredited vocational programs) and by requiring basic documentation of expenditures. Congress should authorize a review period to evaluate whether the exception increases training participation and whether it is used primarily for bona fide skill acquisition.

6. Revise the Internal Revenue Code to expand write-offs for worker training and increase per-worker caps.

Employer-based training opportunities can serve as a win-win: firms benefit directly from a workforce that can integrate new tools—especially AI-enabled systems—and can move between tasks as workflows change. Yet training is often underprovided due to free-rider dynamics, accounting constraints, and legal uncertainty. Federal policy can address these structural barriers by making training easier to finance, easier to offer, and less legally fraught—without dictating training content or imposing rigid national standards.

Congress should revise the Internal Revenue Code to allow firms to more fully write off expenses for training workers for new roles and to increase caps on training-related deductions where they limit participation. Present policy effectively discourages employers from supporting workers who want to upskill by participating in a program of study that will qualify the employee for a new trade or business. In short, the tax code limits employer support to the bare minimum of education that is required for a worker to perform their current role. What’s more, deductible employer support is capped at around $5,000, which may not meaningfully assist with more substantive training opportunities.

Updates to these policies make sense even if the Portfolio Economy is slower to emerge than expected. Employers have tremendous incentives to upskill their existing workforce rather than search for workers in the existing market. HR specialists estimate that recruiting and onboarding a new employee may involve about $7,000 to $28,000 in costs, as well as lost productivity associated with the myriad tasks associated with bringing a new person onto the team.

This reform recognizes a basic reality: in an AI-influenced economy, training is not a perk but a recurring input into productivity. To ensure the policy is usable beyond large enterprises, Congress should prioritize administrative simplicity—clear eligibility standards, straightforward documentation, and minimal compliance burdens for small and medium-sized firms. Congress should also require the Treasury to report, in aggregate, on uptake by firm size and sector, enabling future refinement.

7. Fund Skill Development Opportunity Centers through competitive grants modeled on P-TECH, with local flexibility and measurable outcomes.

An economy that demands more agile workers would benefit from more agile educational and vocational institutions. Thankfully, Congress need not look far to find promising examples of educational institutions of the future. The P-TECH model in place in New York is a particularly compelling model. These programs permit students of varied academic backgrounds to seek out a hybrid model of education–a mix of classroom learning and on-the-job training–that culminates in students earning both their high school diploma and an associate degree.

Yet, most Americans have few to no odds of enrolling in any similar program. Congress can make such institutions far more common by launching a grant program for local “Skill Development Opportunity Centers” in which employers, community colleges, and high schools collaborate to offer six-year pathways combining classroom learning with paid, work-based training. As with the P-TECH schools, successful participants should earn both a high school diploma and an associate degree. This structure directly serves the Portfolio Economy by embedding skill development within real work settings and by producing graduates with both credentials and demonstrated competence.

Critically, rather than forcing uniform design, recipients of any such federal funds should be encouraged to adapt to local labor-market conditions. Grant selection should prioritize employer engagement, evidence of regional skill demand, and credible placement pathways. The agency tasked with overseeing this grant program—presumably the Department of Education—should require periodic evaluation of outcomes—completion, placement, wage progression—and should retain the ability to reallocate funding toward the most effective education models over time.

C. Easing the Transition to the Portfolio Economy 

As work becomes more task-based, short-term, and distributed across firms, the challenge facing policymakers is no longer simply how workers acquire skills, but how they use those skills across multiple jobs without incurring unnecessary legal, financial, or administrative penalties. For many Americans, participating in the Portfolio Economy will mean holding multiple roles at once—combining W-2 employment with contract work, pursuing training while actively working, or serving clients across state lines. Yet core elements of federal labor, benefits, and tax law remain structured around exclusivity: one employer, one job, one benefits bundle, one jurisdiction. 

That mismatch imposes real costs. Workers delay taking on additional clients for fear of losing benefits or triggering tax complexity. Employers refrain from offering support—such as benefits contributions or flexible arrangements—out of concern that doing so will alter worker classification. States erect licensing barriers that frustrate geographic and professional mobility. The net effect is to slow labor-market adjustment at precisely the moment when adaptability is most valuable.

The recommendations below focus on easing these transition costs. They are not intended to privilege portfolio work over traditional employment, nor to mandate new employment structures. Instead, they seek to ensure that federal law does not penalize workers and firms that operate across multiple engagements and that it provides clear, predictable rules for doing so.

1. Establish a portable benefits framework centered on worker-controlled Opportunity Accounts.

Bipartisan members of Congress have long called for a portable benefits framework suited to today’s economy and the Portfolio Economy of the future. Now is the time for Congress to act on that bipartisan consensus by authorizing a portable benefits program. Pursuant to this program, employers—whether engaging workers as employees or independent contractors—may contribute to worker-controlled “Opportunity Accounts.” These accounts would travel with the worker across jobs and clients and could be used for a defined set of purposes closely tied to portfolio work: qualifying education and vocational training; physical and mental health expenses for the worker and dependents; and relocation or travel costs associated with pursuing work in regions showing strong demand for the worker’s skills.

The core advantage of this approach is structural neutrality. Opportunity Accounts would decouple benefits from long-term attachment to a single firm, while preserving flexibility in contribution levels and participation. Congress should design the program to be voluntary, administratively lightweight, and accessible to small firms, while requiring periodic evaluation to assess uptake, usage patterns, and effects on worker mobility and retention.

2. Adopt a single, clear federal standard for worker classification and specify that benefit provision is classification-neutral.

Legislators must also act on seemingly extensive congressional agreement when it comes to ongoing and unnecessary confusion as to worker classification. Congress should replace the current patchwork of federal worker-classification tests with a single, clear standard. At the same time, it should make explicit that the voluntary provision of benefits—whether through Opportunity Accounts or other portable mechanisms—does not weigh in favor of employee classification. This would align with efforts already underway at the state level, such as in Utah, and signal congressional dedication to identifying and improving ambiguous policies. 

Classification uncertainty remains one of the most significant barriers to portfolio work. Firms frequently avoid offering benefits, training, or flexibility not because they oppose worker support, but because such actions risk reclassification and retroactive liability. Clarifying that benefits are neutral with respect to classification would reduce that chilling effect, expand access to support, and allow firms to compete on worker experience without fear of legal exposure. As with other recommendations, this reform should include an opportunity for Congress or the applicable regulator to revisit the standard after an initial implementation period to assess its effects on misclassification disputes and labor-market participation.

3. Direct Treasury to study simplified tax compliance for workers with multiple income streams.

As outlined from the start of this testimony, workers should not face disparate legal treatment or regulatory burdens simply because they opt for non-traditional work arrangements. Taxes represent one of the most obvious gulfs between workers that opt for standard employment arrangements and those who carve a different path. Congress can remedy this issue by directing the Department of the Treasury to examine options for simplifying tax compliance for workers earning income through multiple arrangements—such as combinations of W-2 employment, 1099 contracting, and short-term project work. Workers that lean into the Portfolio Economy frequently face higher compliance costs, uneven withholding, and greater risk of error, all of which discourage participation in flexible work.

The study should evaluate mechanisms such as standardized withholding across income types, simplified quarterly payment systems, and consolidated reporting tools. Any reforms should be piloted and assessed before broad adoption, with particular attention to impacts on compliance rates and ease of filing (or lack thereof).

Conclusion: Entrepreneurial Liberty in the Portfolio Economy

The Portfolio Economy is unavoidable. It’s an inevitable product of how AI develops, diffuses, and interacts with human work. Countries that attempt to steer around it will eventually run aground—and likely sooner than later. Countries that instead adapt to the economy of the near-future can thrive. The key to success is acknowledging and responding to a labor market in which individuals build durable economic security by cultivating skills, assembling projects, and moving fluidly across firms, sectors, and geographies. In such an economy, stability no longer comes from a single job title or employer, but from agency—the capacity to learn, adapt, and apply one’s talents where they are most valued.

The central policy question, then, is not how to freeze work in a familiar form, nor how to preordain which jobs should exist and who may perform them. It is whether our laws expand or constrain the freedom Americans need to navigate constant change. 

History counsels restraint. When lawmakers attempt to lock in outcomes amid technological uncertainty, they tend to protect incumbents, entrench inefficiency, and narrow opportunity. When they instead focus on enabling individual initiative—lowering barriers to learning, mobility, and experimentation—they create the conditions for broad-based prosperity.

This testimony has advanced a simple organizing principle: the Age of AI demands a renewed commitment to entrepreneurial liberty. That means protecting the freedom to study—by making skills legible, portable, and worth investing in. It means protecting the freedom to shadow—by expanding apprenticeships, trainee pathways, and real-world learning that lower the cost of entry into new fields. And it means protecting the freedom to work—by ensuring labor, tax, and benefits laws do not punish those who move between roles, clients, or places in pursuit of opportunity.

If Congress gets this right, Americans will not merely endure the transition to an AI-shaped economy; they will shape it themselves. The surest path to a future of work that is innovative, inclusive, and resilient is not to manage outcomes from the center, but to trust individuals with the tools, signals, and legal freedom to get ahead. That is how the American Dream has always been renewed—and how it can endure in the Portfolio Economy.