Faulty reasoning signals

• Confirmation Bias - 5.4% (225 hits)
• Anchoring Bias - 0.1% (6 hits)
• Availability Heuristic - 3.7% (151 hits)
• Representativeness Heuristic - 1.5% (64 hits)
• Hindsight Bias - 1.4% (56 hits)
• Overconfidence Bias - 7.2% (296 hits)
• Framing Effect - 5.1% (210 hits)
• Loss Aversion - 0.9% (37 hits)
• Status Quo Bias - 0%
• Sunk Cost Effect - 1% (43 hits)
• Optimism Bias - 11.4% (470 hits)
• Pessimism Bias - 1.1% (46 hits)

Article text

Getting to all-of-the-above: A framework of solutions for AI’s coming impacts on work and workers

Introduction: From no policy ideas to many

On June 2, The Washington Post ran a <a href="https://www.washingtonpost.com/technology/2026/06/02/if-ai-robots-kill-all-jobs-can-these-5-policy-ideas-help/">story</a> titled “Five ideas for how we survive the possible AI jobs apocalypse.”

The headline underscored the public’s anxiety about AI’s implications for jobs and the apparent lack of a plan, at any level of government, for addressing that.

This was not the typical story about how AI could destroy jobs or, conversely, how hard it still is to discern exactly what the impacts of AI on work and workers could be.

That challenge, along with the nation’s remarkably buoyant economy and low overall unemployment rate, helps explain why shaping AI’s economic impacts was barely on the policy agenda, especially at the federal level, until this year.

The Post article signals an important inflection in the national conversation about AI.

It is among the first of its kind by a major news source to highlight the wide range of economic “solutions” that have garnered some attention and prominent supporters.

While we still lack a plan, we suddenly have a kind of “ideas soup” that reflects many influences: different political agendas; hope versus pessimism about government’s ability to govern AI (and as part of that, help shape a positive future of work); wide-ranging expectations about what disruptions will be most harmful as AI transforms the economy; and, as the public worries most, the business interests of a tiny handful of Big Tech companies that <a href="https://www.forbes.com/lists/ai50/">currently dominate</a> the development of AI and its infrastructure, including Anthropic, OpenAI, Google, Amazon, Apple, Meta, Microsoft, SpaceX, and chipmaker Nvidia.

The ideas highlighted in the Post article—some of which are championed by sitting elected officials or candidates for office in this year’s elections, or by influential scholars and business innovators—range from modernizing unemployment insurance and making it more generous (e.g., to replace the comparatively higher incomes earned by white collar workers whose jobs are increasingly vulnerable to AI’s advanced capabilities) to proposals to make sure the tax code does not inadvertently serve as an accelerant for automation.

It also includes proposals to directly tax companies’ AI use (the <a href="https://www.computerworld.com/article/4175277/the-world-of-ai-tokens-and-why-they-matter.html">tokens</a> that indicate usage), and creating public wealth funds or broad-based stock ownership (i.e., direct “gain sharing” in AI-produced wealth by the public).

This month, Anthropic became the first leading AI developer to publish an <a href="https://www-cdn.anthropic.com/files/4zrzovbb/website/9ea607a5dd67c168093829b701f3a0a6d21156d5.pdf">economic policy framework</a>, organized around economic impact scenarios and multiple policy tools such as those above.

As news headlines about AI-related job threats constantly remind us, prediction has a utility all its own, especially for grabbing public attention.

But it has well-documented limits too.

When uncertainty and complexity are both significant, building scenarios and asking what-if questions can help decisionmakers consider everything from early “no-regret bets” to medium- and longer-term contingency plans.

In that vein, between extremes of doomerism and optimism, several core premises about AI, work, and economic security are gaining credibility:

1.

**AI’s potential for large-scale job disruption is real and broad, given the demonstrated range and sophistication of its capabilities and the fact that they continue to advance.

** For now, that disruption has been mostly <a href="https://www.brookings.edu/articles/generative-ai-the-american-worker-and-the-future-of-work/">held in check</a> by the organizational “friction” seen in earlier waves of technology adoption—not by intentional pro-worker design features of the AI tools, the direct costs of acquiring the tech, formal labor regulation, or worker bargaining power.

The public sector is an important outlier, given how differently talent decisions work and what constrains them, but not a total exception.

As Anthropic emphasizes in its recent <a href="https://www.anthropic.com/institute/recursive-self-improvement">report</a> about the latest step change in AI capabilities, societies need viable ways to buy time.

2.

**Employers in every sector have a broad, not narrow, spectrum of choices about how to adopt and use AI in workflows and about the role and voice of workers in shaping those changes.

** This “effects-are-not-predetermined” premise reflects the discretion available to managers, owners, and shareholders (in the case of business and traded companies, specifically); the range of the technology’s capabilities, with a capacity to surprise even its creators; and a well-documented, global history of <a href="https://mitsloan.mit.edu/centers-initiatives/institute-work-and-employment-research/bringing-worker-voice-generative-ai">worker-informed, iterative technology adoption</a>, especially in manufacturing but in services too—a practice that Thomas Kochan of the Massachusetts Institute of Technology (MIT) Sloan School of Management termed “giving wisdom to the machine.”

Likewise, in a report on <a href="https://www.brookings.edu/articles/building-pro-worker-ai/">building pro-worker AI</a> for The Hamilton Project at Brookings, MIT economists Daron Acemoglu, David Autor, and Simon Johnson made the case for a range of rules and incentives, from procurement and taxation to mobilizing public and private investment, to directly encourage (steer) employers toward pro-worker “innovation,” as Amodei defined it.

So in addition to brakes, societies need to find ways to constructively steer the choices that both workers and employers can make.

3.

**Job disruptions and related effects are likely to unfold unevenly across geographies, occupations, and industry sectors, and across shorter and longer time horizons.

** Many so-called “general purpose” technologies, such as electric power, took decades to diffuse through the economy.

AI is already moving much faster than that but affecting AI adopters and uses in very different ways, in part because of what Wharton School professor Ethan Mollick and collaborators termed the “jagged edge” or <a href="https://www.penguinrandomhouse.com/books/741805/co-intelligence-by-ethan-mollick/">“jagged frontier”</a> of the technology’s capabilities.

This has many potential implications; for example, as previewed above, that economic regions, not just workers and organizations, could benefit from “AI readiness” planning and other support.

The already-significant inequality between U.S. regions has been sharply amplified in recent decades by technological change.

In addition, when it comes to economic security adjacent to work, the U.S. may need means of supporting worker transitions that are much more effective than similarly motivated programs launched decades ago as part of <a href="https://equitablegrowth.org/research-paper/lessons-from-past-trade-adjustment-policies-to-support-displaced-workers-in-the-era-of-artificial-intelligence/">trade adjustment assistance</a> to aid “displaced workers.”

To address displacement, such buffers need to be more robust, flexible, and effectively implemented.

4.

**Livelihood risks—together with the potential for AI-powered benefits—are significant and sweeping enough to warrant a rethink of the role and footprint of human labor, wealth or “gain” sharing, and the social contract.

** This includes, but need not be limited to, everything from the length of the work week and the nature of employment relationships—for now, a long-established and rather basic binary in federal labor law, which separates salaried from contracted workers—to safety net policies broader than displacement assistance, innovative forms of public sector or publicly <a href="https://www.newamerica.org/insights/juliet-schor-makes-the-case-for-a-four-day-work-week/">guaranteed employment</a>, <a href="https://www.theatlantic.com/membership/archive/2018/08/a-moral-case-for-giving-people-money/568207/">guaranteed income</a>, public wealth funds, and more.

Four-day work <a href="https://www.newamerica.org/insights/juliet-schor-makes-the-case-for-a-four-day-work-week/">week demonstration studies</a>, for example, have been gaining steam and <a href="https://www.newamerica.org/insights/juliet-schor-makes-the-case-for-a-four-day-work-week/">attention from business</a> in several advanced economies for over a decade now.

But the lessons are almost never connected to discussions of our economic future with AI.

These kinds of deeper shifts—which go beyond brakes, steers, and buffers—are, at best, at the margins of the current public conversation in the U.S.

But in the current political climate, such proposals could move quickly toward the mainstream of discussable policy ideas.

While government action alone is likely inadequate to meet any of these work and economic security challenges tied to the diffusion of AI, government has multiple, indispensable roles to play—making greater and more adaptive <a href="https://www.niskanencenter.org/wp-content/uploads/2024/12/Niskanen-State-Capacity-Paper_-Jen-Pahlka-and-Andrew-Greenway-2.pdf">“state capacity”</a> (the conditions for government effectiveness) essential.

Government is implicated via myriad roles, including: regulator and standard setter; research and development investor; large-scale buyer and user of tech and related services; keeper of a social contract and funder of a safety net to help workers, employers, and communities manage financial risks, especially income loss; insurer, lender, and financial guarantor for lenders, operating companies large and small, college students and trainees, and others; critical source of data on industries, employment, and more; and attention-focusing “bully pulpit.”

In spite of the persistent unknowns about AI’s full range of uses and impacts, it’s becoming clear that we’ll need solutions that complement each other and have distinct objectives and potential.

Grounded in the premises outlined above, what follows is a framework of such solutions, organized into four main types: brakes, steers, buffers, and shifts.

<strong>Brakes</strong> on automation mainly buy us time to adjust to disruption without undermining technology development itself and without necessarily discouraging AI adoption.

That distinction is important now, especially for getting past the false choice between innovation and broadly shared gains.

<strong>Steers for workers,</strong> on the other hand, could nudge large numbers of workers, including but not limited to young people starting their careers, toward less vulnerable occupations.

This includes well-paid ones, such as skilled nursing, which are nonetheless already being transformed by AI.

Steers for employers would encourage AI uses that improve work for workers and help them create more value for employers, customers, and society.

<strong>Buffers</strong> will seek to reduce harms and hardship, in ways that unemployment insurance and other tools were designed to do for the 20th century industrial economy—but potentially multiple times over the course of a given worker’s career and retirement.

Meanwhile, more deeply transformative <strong>shifts</strong> could restructure our relationship to work and economic value—and even to each other, for example by creating public goods through AI-fueled profits and intellectual property, which we can access regardless of occupation or specific experiences of job displacement.

Reimagined public wealth, a shorter work week, and a different kind of safety net could fall into this category.

The framework underscores why popular, even intuitive responses, such as encouraging broad-based AI literacy building in education and training, are at once very important and quite limited.

They proceed from familiar models and fields, such as workforce training and income replacement, or—less visible and broadly supported—how trade unions think about and bargain over the use of new technologies in the workplace.

But as an example of the shortcomings of familiar tools, training or retraining alone does not produce good jobs, at least not directly and readily.

Deindustrialization highlighted that lesson, but so did the more recent rise of heavily indebted <a href="https://socialfinance.org/insight/is-talent-finance-right-for-me/">trainees</a> without a job.

To date, the most familiar tools have also generated little dialogue, let alone coalition building to drive action on public policy.

Nor are they fueling realism about what particular solutions or levers can be reasonably expected to achieve in the face of something so transformative and complex.

It’s time to stop lamenting that so much of our workforce investing system operates on <a href="https://nationalskillscoalition.org/blog/news/wioa-reauthorization-is-on-the-horizon-heres-a-refresher-on-americas-primary-workforce-program/#:~:text=NSC's%20network%20had%20successfully%20advocated,jobs%20that%20did%20not%20exist.">“train-and-pray”</a> wishfulness—the phrase former Labor Secretary <a href="https://obamawhitehouse.archives.gov/realitycheck/the-press-office/2014/01/30/remarks-president-opportunity-all-and-skills-americas-workers">Tom Perez</a> helped popularize.

In a recent New York Times <a href="https://www.nytimes.com/2026/03/06/opinion/ai-labor-unemployment.html">op-ed</a>, former Rhode Island Governor and Commerce Secretary Gina Raimondo focused on this aspect of the AI challenge, and what stepping up boldly must include if the U.S. economy is to generate broad-based opportunity and the dignity that work confers in the decades ahead.

It is an argument joining the competitiveness of firms and the nation to worker success and a much more outcome-driven role for government and public spending.

The compelling potential and the heavy lifts in that all center on a “buffer” response that already receives far more attention and support (including bipartisan political energy, as skilling is the centerpiece of the Trump administration’s policy agenda for an AI-affected job market) than contentious responses that could be much more transformative over time, whether initially as brakes, steers, or deeper shifts.

Examples include:

* Expanding union membership and securing collective bargaining agreements that meaningfully govern AI automation, in addition to applying <a href="https://aflcio.org/reports/workers-first-ai">“worker-first” principles for AI use</a> beyond unionized workplaces.

As leading researchers of business innovation have underscored, the broader premise is that skilling and rapid AI awareness building should be complemented by steps to <a href="https://mitsloan.mit.edu/centers-initiatives/institute-work-and-employment-research/bringing-worker-voice-generative-ai">give workers more voice and leverage</a> in shaping the use of AI in work.

* Building on <a href="https://laborcenter.berkeley.edu/tech-and-work-policy-guide/">human-in-command</a> rules, for now state by state and mainly as safety-driven technology policy that doubles as a brake on job automation, to advance “pro-worker AI” steers more broadly and ambitiously.

* Using the immense buying power of government to encourage business models and practices enabled by <a href="https://www.brookings.edu/articles/building-pro-worker-ai/">pro-worker AI</a>, both protecting access to livelihoods over time and enhancing the experience and value of work.

Such policies would complement growing efforts by nonprofit, business-performance groups, such as <a href="https://justcapital.com/?

gad_source=1&amp;gad_campaignid=23631392590&amp;gbraid=0AAAAADAzIrk6bOXvP7c014FHZDxsgErrC&amp;gclid=CjwKCAjwuanRBhBSEiwAY5y6V2mVp1rIUiY9-sDvV6W1Va7kZ46rQu0cwILlKYz30uTp-pOyuFA9DhoC0pQQAvD_BwE">JUST Capital</a>, <a href="https://cecp.co/cecp-insights-blog/press-release/cecp-and-partners-lead-cross-sector-collaboration-empowering-companies-to-build-ai-ready-workforce/">CECP</a>, <a href="https://www.inclusivecapitalism.com/news-insights/how-pre-distribution-can-shape-a-fairer-ai-economy/">Council for Inclusive Capitalism</a>, and the <a href="https://goodjobsinstitute.org/about/">Good Jobs Institute</a>, that are tackling AI use and its impacts on both profitability and workers.

* Mobilizing and resourcing the coordinated public and private commitments required to recruit and equip millions of workers into well-paid though not <a href="https://www.brookings.edu/articles/the-ai-durability-of-built-environment-careers/">“future-proof” careers</a>—such as skilled nursing and the building trades—that face chronic labor shortages and are less vulnerable to AI automation.

We should not underestimate what it will take to meet these big talent pipeline challenges.

For one, young people have long been taught to stigmatize work in the trades and associate good careers with white collar professional jobs—some of the very jobs that AI is poised to disrupt most—and to assume, inaccurately, that college degrees are a minimum requirement for those jobs.

But based on <a href="https://www.hrdive.com/news/60-of-generation-zers-say-they-will-pursue-skilled-trade-work-this-year/812074/">recent surveys</a> of Gen Z, growing anxiety about AI and frustrating job searches may be shifting those views.

* Rewiring our safety net and skilling system, perhaps inspired by Denmark’s widely admired “Golden Triangle” <a href="https://faos.ku.dk/pdf/iirakongres2010/track3/78.pdf/">flexicurity</a> model, which combines labor market flexibility (relatively easy hiring and firing), generous unemployment assistance, and active labor market policies.

In this vein, Anthropic has just proposed some form of wage insurance.

* As previewed earlier, rethinking the length and demands of the work week, drawing on <a href="https://www.newamerica.org/insights/juliet-schor-makes-the-case-for-a-four-day-work-week/">encouraging experiments</a> in a variety of national settings, to enhance productivity as well as worker satisfaction and well-being.

* As OpenAI described in a <a href="https://openai.com/index/industrial-policy-for-the-intelligence-age/">white paper</a> that did not endorse specific proposals, creating some form of wealth fund to offer the mass public an equity stake in AI companies.

Anthropic’s new <a href="https://www-cdn.anthropic.com/files/4zrzovbb/website/9ea607a5dd67c168093829b701f3a0a6d21156d5.pdf">framework</a> makes the case for “universal pre-distributive capital accounts” for every American.

* Closer to long-standing debates about tax fairness and growth impacts, reforming the tax code to put the value of labor for business on a more level playing field with that of invested capital, as economists Acemoglu, Autor, and Johnson have advocated as an additional lever for <a href="https://www.brookings.edu/articles/building-pro-worker-ai/">“building pro-worker AI.”</a> As Sarita Gupta of the Ford Foundation put it in a recent Time magazine <a href="https://time.com/article/2026/06/02/to-save-democracy-we-need-to-reimagine-the-economy/">essay</a>, “Our tax code shouldn&#8217;t favor buying software over hiring people.”

Having a broad repertoire—designed to include complementary elements—can help policymakers, their constituents, advocates, and government’s delivery partners understand where the bigger versus easier “lifts” may lie and what part each player would need to play.

The public debate lacks any such shared vocabulary, let alone tested repertoire, for now.

But the OpenAI white paper and Anthropic economic policy framework begin to signal a shift—an inflection point.

Big Tech has become much more eager to be associated with solutions.

Encouraging signs in state policymaking

With political Washington mostly at an impasse over what to do about AI’s implications for jobs, our laboratories of democracy are at work: At the state level, policymakers are taking action well beyond AI-related skilling.

As Mishal Khan and Annette Bernhardt, researchers at the University of California, Berkeley’s Labor Center, documented in a 2025 <a href="https://laborcenter.berkeley.edu/tech-and-work-policy-guide/">landscape analysis of work and tech policy</a>, state governments have enacted a limited number of what this report has termed “brakes,” primarily in the labor-intensive sectors of health care, education, and creative production.

To re-emphasize, “brakes” encompasses strategies that aim primarily to slow and scrutinize automation, not AI adoption generally.

Some of these guardrail policies are clearly motivated by consumer safety, not just concern for workers.

For example, in 2025, Illinois enacted laws prohibiting the use of AI in place of community college faculty and mental health professionals, and replica bills have been introduced in Florida, New York, and Pennsylvania.

Concerned about transparency for consumers, California has outlawed “advertising products using terms reserved for licensed health care professionals,” while Oregon did so for nurses.

California has also defined “community college faculty,” in law, as referring only to humans.

Similar, pending bills in New York, Maine, Texas, Connecticut, and other states would prohibit AI use in place of a range of human professionals, from media workers and teachers to commercial truck drivers.

More striking is the fact that a politically diverse range of states has enacted some form of the landmark “human-in-the-loop” law that California passed in 2024.

These laws require human judgment where algorithms are used to shape sensitive decisions, such as determining a health insurance benefit or law enforcement finding.

Proposed legislative protections tied to public rights and public benefit determinations by public agencies have expanded rapidly as well.

In principle, such protective brakes might evolve into more ambitious steers; i.e., to design and implement broadly pro-worker, AI-powered workflows.

Some of the building blocks for doing so are available but not yet widely known; for example, the <a href="https://mitsloan.mit.edu/ideas-made-to-matter/these-human-capabilities-complement-ais-shortcomings">EPOCH framework</a> created by Isabella Loaiza and Roberto Rigobon of the MIT Sloan School of Management to more rigorously specify human capabilities that complement AI’s shortcomings.

In other examples of brakes, a range of state bills would require employers to do automation impact assessments prior to deploying new digital technologies and changing core job functions or prohibit employers from requiring workers to train AI on certain worker-produced work products, such as likeness, voice, art, and music.

Such policies buttress the few collective bargaining agreements, most famously that won by <a href="https://www.brookings.edu/articles/hollywood-writers-went-on-strike-to-protect-their-livelihoods-from-generative-ai-their-remarkable-victory-matters-for-all-workers/">Hollywood screenwriters</a> after a lengthy strike in 2023, that protect creative human output in the face of expected growth in AI use by media and entertainment companies.

As nascent “steers” to influence work design and employment choices by employers, workers, or both, New York lawmakers have introduced several bills that would either tax or withhold subsidies from companies that opt to replace human workers.

In a range of states (and at the federal level in the bipartisan <a href="https://www.congress.gov/bill/119th-congress/senate-bill/3108/text#:~:text=Introduced%2520in%2520Senate%2520(11/05,the%2520United%2520States)%252C%2520including%25E2%2580%2594">AI-Related Job Impacts Clarity Act</a>), other bills focus on required disclosure of AI-related layoffs.

These policy proposals typically seek to modernize laws already on the books, but they underscore something fundamentally important: It is hard to shape something, such as AI-related job effects, that we cannot track in credible ways.

And all of our available data sources—traditional employer and worker surveys, closely watched <a href="https://aiweekly.co/alerts/challenger-ai-behind-40-of-may-us-job-cuts">layoff data</a> and corporate statements about them, and the AI usage data made public by OpenAI, <a href="https://www.anthropic.com/economic-index">Anthropic</a>, and other AI product developers—have major limitations.

Stanford University’s recently announced <a href="https://digitaleconomy.stanford.edu/project/indicators/">AI Economic Indicators</a> project is one notable effort to do better, using a set of “economic dashboards.”

But for now, all such trackers rely on indirect indicators such as layoffs in AI-exposed occupations—not direct evidence that AI was a primary cause.

New Jersey, meanwhile, is showing itself a leader in “right-to-retraining” (a form of “buffer” strategy), at least by the measure of bills introduced.

A federal version, though not currently advancing in Congress, was introduced before the pandemic.

Beyond issue-by-issue policymaking, more holistic planning—and making space to consider alternate futures with generative scenario thinking—is sorely needed.

In this vein, New York Governor Kathy Hochul recently announced the <a href="https://www.governor.ny.gov/news/governor-hochul-announces-membership-futureworks-commission">FutureWorks Commission</a>, made up of business and labor leaders and policy experts, and is charged with “developing recommendations on ways New York can protect the economic security of workers while harnessing the economic benefits of AI.”

Other states should launch similar efforts.

With 36 gubernatorial races this year, many will be able to do so after the elections in November.

Philanthropy and the broader nonprofit sector are preparing to play a larger role in shaping the economic future.

For example, <a href="https://windfalltrust.org/">The Windfall Trust</a>, a global nonprofit, has begun to help decisionmakers in government employ <a href="https://www.wsj.com/tech/ai/inside-the-room-where-americas-brightest-game-out-how-to-avoid-an-ai-apocalypse-9e5e8526">scenario planning</a> to think about how best to respond and just how varied and bold our responses may need to be in the face of AI opportunities and disruptions.

Local leaders are also beginning to focus more on shaping the future with AI.

Bloomberg Philanthropies and the Bloomberg Center for Government Excellence at Johns Hopkins University recently announced a first-of-its-kind international <a href="https://hub.jhu.edu/2026/04/28/bloomberg-philanthropies-johns-hopkins-mayors-ai-forum/">Mayors AI Forum</a>, to debate and demonstrate an array of responsible AI uses with broad public benefits, including economic ones.

Likewise, a group of philanthropic donors has launched <a href="https://humanityai.ai/">Humanity AI</a>, a coalition emphasizing that “our future with AI can and will be what we make it,” with priorities that range from the quality and experience of work to the effectiveness of education and health care, progress of scientific research, and more.

These encouraging efforts can benefit from the range-expanding, clarifying, and more holistic framework of action outlined in this report.

Leveling up on the job and related impacts of AI

The U.S. has lacked a proactive strategy for shaping AI’s impact on work and workers and making ourselves more ready for disruptions that lie ahead—or even a framework for such a strategy.

As recently as the 2024 presidential election, candidates for higher office had little to say about it.

AI’s likely economic impacts—as distinct from product safety, privacy, and other concerns—were essentially nowhere on the national policy agenda.

But over the past year and a half, encouragingly, policymakers at the state level have shown a willingness to debate new approaches and also to legislate—imposing AI-focused guardrails and new requirements for employers.

In many cases, safety and transparency to the public have been the main policy goals, but sometimes basic digital rights and the livelihoods of workers have also been explicit goals.

These enacted and proposed laws hint at the range of building blocks already available and worth expanding and learning from.

The rapid evolution of machine intelligence poses more than one kind of economic challenge, and relevant predictions—for example, specifically about career ladders and new occupations, let alone new industries—come with huge uncertainties.

State and local policymakers are acting where their federal counterparts will not for now.

Taking a more holistic, adaptive approach has benefits beyond government and its voter and taxpayer constituents.

Such an approach has much to offer the market players as well, given the large investments by Big Tech and the many industries keen to use AI.

It offers a path away from extreme stances that do little to inform the public or catalyze public problem-solving: cavalier denial of economic security risks on one hand and warnings of imminent job apocalypse on the other.

But to date, elected officials and their constituents, along with regulated industries and public agencies, have engaged with only a fraction of the most immediate and sensitive AI uses that threaten human work, especially in recently enacted, judgment-focused human-in-the-loop laws.

Beyond healthy caution and focus, policymakers, interest groups and the public at-large need a basic vocabulary for judging what job-relevant policy strategies and employer practices can and cannot hope to achieve, especially when it comes to ensuring human livelihoods on a large scale, as distinct from safeguarding high-stakes judgments—for example, about access to public benefits, child welfare, or parole—which have always been made by relatively few humans.

Mindsets matter, and so do imagination and clarity.

We will need a wide-ranging, all-of-the-above mindset to understand and shape AI’s economic impacts, especially on the future of work.

And given one, we should be honest with ourselves about what each kind of “solution” can realistically hope to achieve.

As a starting point, this report has offered a solutions framework and vocabulary that prioritize buying time to adapt, equipping (and not just informing) people so they can be more adaptive, steering both workers and employers in promising directions that reduce vulnerability (if only for a time), and opening up broader conversations about the role of work and ownership of wealth, including the variety of ways we might generate both meaning and economic security in changing societies, through and beyond paid work.

Notably, almost none of the response strategies mentioned to date, even the biggest “shifts”—from the four-day work week to some form of job or income guarantee—is a new idea.

But as the long evolution and international variety of labor and safety net institutions suggest, such ideas might be repurposed and garner new support, or inspire better ideas, in a new and uncertain period of economic transformation.

A close look at the politics of these options is beyond the scope of this framework report, but they offer myriad opportunities to overcome positional and partisan impasse.

For now, the caricatured version of AI’s economic stakes offers a false and dangerous binary choice between innovation and an inclusive future.

Finally (and key to acting on those lessons), there is the central, underlying issue of state capacity, in the broad sense of effectiveness at all levels of government.

Its shortcomings show in the delivery of many relevant legacy policies, such as workforce development and providing a safety net (let alone doing so in new ways), but also in detecting and deterring AI-powered employment discrimination, investing in novel work practices and standards, and other functions that government can uniquely perform or scale up.