70 years on - where would the world be without Hirohito

 Project updates

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Asia Monetary Fund - basically west coast us and japan korea taiwan hk singapore would be best home for monetary funds- now far larger than eg rest of usa - all 3 million fold tech multipliers since 1965 have involved trade of west coast and this far east coast line  

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jobs across generations everywhere - latest networks 

https://www.scsp.ai/wp-content/uploads/2026/05/Preliminary-Findings-Task-Force-Report-.pdf includes jensen hunag 5 layer ai model - as well as potentially 10 different maps depending which biggest nvidia brain cluster you start with - usa saudi taiwan japan korea germany france uk UAE ...

Task Force Leadership Senator Mike Rounds (R-SD), Co-Chair Senator Mark Warner (D-VA), Co-Chair Chris Malachowsky, Co-Founder of NVIDIA, Co-Chair Ylli Bajraktari, President of SCSP, Co-Chair Task Force Members Dr. Erik Brynjolfsson, Stanford University Dr. France Cordova, President Science Philanthropy Alliance, External Faculty Professor, Santa Fe Institute Dr. José-Marie Griffiths, President Dakota State University Eric Holcomb, Former Governor of Indiana Dr. Tom Mitchell, Carnegie Mellon University Chan Park, Head of U.S. and Canada Policy and Partnerships, OpenAI Gina Raimondo, Former Secretary of Commerce

8. Traditional Educational Pathways and Trainings May Fall Short in Meeting the AI Moment The current education, credentialing, and training systems remain largely built around frontloaded learning in the lead up to a degree and career, leaving them misaligned with rapidly changing skill demands. To keep pace, these systems must be reimagined to leverage AI as a training mechanism and to produce a balanced mix of foundational capabilities and adaptable domain-specific skills. 

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world bank research of last couple of year is either on youth jobs or ai - i am trying to connect about 5 reports world bank has issued but this extract from ajay banga helps unite whats needed

rough notes on developing 1.2 bn jobs -view with ajay banga world bank

Banga (Youth) Jobs 1.2 Bn in dev country over next decade - world bank north star

Does not treat ai as end in itself rather (job) design tool. Does not specifically ytaslk of Huang 5th layer ai but congriuent with that map. Banga 

Applications (End-Use Tools, Sector-Specific Solutions): Strongest and most distinctive focus—“Small AI.”
Banga champions practical, edge-deployed AI applications on basic phones for real-world constraints:

• Agriculture: Tools for crop disease identification (voice/image), fertilizer/seeds/markets advice for smallholders/illiterate farmers (e.g., Google partnership in Uttar Pradesh).
• Healthcare & Education: Improving primary care, skills, and delivery.
• Small business/finance: Formalization, productivity, and inclusion.
  These raise productivity, create/augment jobs in the five priority sectors, and support inclusive growth without requiring massive infrastructure. He sees this as a force multiplier for the demographic dividend.
• 1.2 billion jobs breakdown:
• South Asia 280 million nearly 200 mn India' E Asia 280 million about 70 million China
• About 170 Million MENA (or 85 million ESWCA - UN Rania Al-Mashwat appointed from Egypt (ESWCA hq lebanon); about 330 million sub sahara

MONETARY FUND ISSUE _It has been argued that if eg Nigeria had been dlolar economy- would now be thriving instead of loan corruption every time local currency devalues (naira at independence uk pound now worth about 1/1000 of 1970) -maybe we dont just need asian monetary fund but eg cancer ai monetary fund as example of worldwide data collaboration map- but what fund do countries need that havent grown the way they coudl since 1960 like nigeria 

Extract Banga Youtube I'll just tell you as a person, as an individual,

53:48

my first belief is that you have to give people in society the right building blocks to be productive. And by that, I mean education. And I don't mean higher education. I mean just get them through the right quality of schooling. Give them good schooling. It changes everything. Good primary and secondary education is foundational to everyone's future. If I had to redraw myself, I'd say education would be terrific,

54:16

but I would say some basic human tenets of healthcare and electricity and water are kind of important, right? 

And then, the third part of this which I'd like to put to work is that talent is everywhere, but opportunity is not.-Right. -Because capital is not everywhere. So if there was a way to facilitate the flow of capital to be closer to where opportunity is,then I think you create a much better chance for those people to win. Hence that idea about the entrepreneurial centres and the venture capital.

55:00

Long back, when I was... early days in Mastercard, I was at a townhall once, and I told my colleagues that, "Look when I was young, what mattered for you to succeed was your IQ, your intelligence quotient. And, you know, people felt that was really important. By the time I sort of went to business school, this concept of EQ was being taught, emotional quotient. You couldn't choose your boss or your colleagues, but you had to get along and find ways to navigate difficult things with the equanimity and the like." And I said, "I think in our time, what will be the determining factor will be your DQ. And that's your decency quotient." Are you seen as somebody who people want to follow because you give them a fair chance, who people want to be with because your hand is on their back? You're still pushing people, but you're pushing them forward. It's not in their face, holding them back.

55:55

This idea of fairness, of decency, of openness, to me, DQ EQ IQ. I don't mean this in a traditional liberal order versus conservative order. That's not what I mean. But I do mean it in terms of being fair to people and being open and honest with them and giving them a chance to win,this idea of DQ to me is really important.

Inyervieweer  I was reading somewhere, someone had said that because the definition of intelligence is changing so quickly in our AI world, the alpha of being able to regurgitate information is no longer as high.People were saying that the intelligence of tomorrow might be metacognition, in a way, where you can look at yourself from the third person's lens and kind of like step out of the world, which is so cluttered with the noise of today.

56:52

Banga -That's an interesting way to think of it. -It is. It is interesting. I think the different way of discussing that same point is that...Are you able to take very complex things, even today, in today's world, and convert them into the few simple things that need to get done? You know, people think simplicity-- You'll get lectures in corporate life that the devil is in the details. Yeah. But it is the devil. And you need to think about whether the details will drag you into the devil's world or are you able to find the simple things in those details that are actually the game-changers that lead you to a world that is away from the devil? And I think that's just worth keeping in mind. Simplicity is your single biggest weapon in changing things. And my answer to you when you asked me, "What does the bank do?" And I said, "I want to create jobs. I want to kill poverty."

57:45

Because I could describe our mission and vision in long words, and will I take you with me or not? I don't know. But I know that if I focus all of these smart people and the capital they have into a simple mission of trying to find ways to create jobs for people, the complexity is inside that. There's a lot of complexity in that What we just discussed, what you said, "Can you think of three or four things?" That's a very complicated topic. -Right. -But in my own simple way of thinking, I'm trying to focus on the ones that I think would be the big game-changers.

And that's kind of not different to what you're saying. It's also one of the topics to do with what AI may enable or not enable.

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

bonus banga conversation

So when you spoke about electrification, Ajay...

58:33

-Yes. -...so, I've been an investor, I've been in the stock markets all my life, broking, asset management, things like that.

58:39

I spent a big part of the last two years investing in green energy,

58:45

energy transition, storage, everything which is a part of that overall grid,

58:51

including electric vehicles. Now, the country we sit in,

58:58

when the narrative seems to have become anti-green, in a way, I mean, I don't know how far whoever is saying it means it,

59:06

but they are saying it a lot, where do you think the opportunity lies in here from a capitalistic lens?

59:16

Yeah. Look, I think that you shouldn't think of it as green or-- Because green makes it sound like the other stuff is bad.

59:23

The value judgements are what you gotta stay away from. And you come to the underlying opportunity here.

59:29

Look at today, with a crisis in the Middle East in the Strait of Hormuz. Let's assume for a minute that this is fixable in a relatively short time,

59:38

whatever your timeframes are, as compared to a relatively longer time.

59:45

If it's a relatively longer time, and there is infrastructural damage at high levels,

59:50

then there's a whole other problem to think about. But if it's a relatively shorter time,

59:55

then the issue is of volatility and instability for a while, which eventually allows you to go back somewhere.

1:00:01

The question is what lesson did we learn from this? And the lesson to me is twofold.

1:00:07

One is, fossil fuels aren't going away in a hurry because the mix of energy you need and the quantum of energy you need,

1:00:14

in fact, with AI, even more so, you're not going to get that easily only with renewables because of various aspects to do with the cycle

1:00:23

as well as the storage capacity and the like. You do need a base load power from either fossil fuels or nuclear or hydroelectric,

1:00:32

which is also a problem in many countries these days, or geothermal. That's your sources.

1:00:37

The question is, shouldn't you diversify your sources of those various elements

1:00:42

you're coming from? Why would you be 70% reliant on three countries in one place where, to get things out of the water,

1:00:50

you need to go through a 25-kilometre wide stretch? That's one issue. The second is, would you not also be thinking about diversifying

1:00:58

the types of energy you possess? And the more you can do without importing, the more energy secure you are.

1:01:05

And, therefore, is energy security a part of your national security framework? This is another lesson that most people will come to

1:01:13

at the end of this conversation. Where would I allocate money to make the most alpha in the energy grid?

1:01:18

So then a private sector guy will play into that thinking. That's the point. Where would it be? If I were to ask you for... So I think there are three aspects in energy, right?

1:01:25

There's generation, there's transmission, and then there's the actual distribution. And the problem in figuring this game out is the following.

1:01:35

The actual distribution, if it's not privatised, and if it's controlled by government,

1:01:40

and pricing is controlled, and utilities are controlled, you end up with unpredictable revenue streams

1:01:45

for both transmission and generation. India has actually really tried to solve for that by privatising distribution in a lot of places.

1:01:53

And I think that was, in retrospect, a big game-changer in the electrification market.

1:01:59

And that has made a lot of sense. If that works well, then generation is where you'll get a lot of opportunity.

1:02:04

Generation, transmission, storage. That space. And that's where a lot of the green opportunities are as well.

1:02:11

-Do you think it still makes sense? -Oh, yeah, for sure. Not going after the base load, but the top and the bottom has enough alpha--

1:02:19

The base load tends to be dominated by large institutional organisations

1:02:24

and companies and countries. And, therefore, for a private entrepreneur to enter into oil and gas,

1:02:30

or to enter into nuclear, or to enter into geothermal, is interesting, but hard.

1:02:39

Nuclear, for example, if you believe that small modular reactors could be a real opportunity in the coming decade or two, then I think that's a real...

1:02:49

I know people, private entrepreneurs, who are going into it quite deep. Is it coming? So, the issue is there are anywhere between six and eight technologies,

1:02:56

depending on who you speak to today. But nobody's pragmatically running it right now. Unless you get to scale. How are you going to get to scale?

Energy security and nuclear opportunities

1:03:02

And the only way you'll get-- It's like, you know, in the old days, this tells you how old I am, -but VHS and Betamax? -Yeah.

1:03:08

If one hadn't won, the cost of tapes would never have come down. -Right. -You have to find scale.

1:03:14

If you end up with eight technologies, you're not going to get to scale. And so, figuring out a way to get to scale and do a, you know, focus on a few,

1:03:23

and then invest in those so you can create the cost synergies that come with it, is going to be key to SMRs.

1:03:30

And I think that may happen over the coming years. And the World Bank has gone back into nuclear energy after 40 years.

1:03:37

And we're starting with refinancing existing nuclear fleet, whether it's in countries like India or Brazil or Romania

1:03:46

or somebody who already has nuclear plants, by helping to extend their life, which is a cheaper way to do it.

1:03:51

Or through SMRs, that's what we think we can help with. Or, of course, our knowledge to help create the right regulatory policies

1:03:58

and safety and so on around nuclear. And you speak a lot about climate change, Ajay.

1:04:04

Do you think we are one adversity in a rich country away from taking climate more seriously?

1:04:10

I think the issue is of two parts. Climate is a word that has become politicised.

1:04:17

So, if you step away from the politics of climate, and you start thinking about what's going on in people's lives,

1:04:22

and that's where we are having this conversation come from. The concept of resiliency, of adaptation,

=

from us affordability crisis - source cato affordability playbook

broken systems that caught our eye most

6 infrastructure Jones Law -what may have been sensible 1920 has now messed with us infrastructure fir 100 years - shipping around us coast must be built and operated by americans; today  power, city, trains, roads, places as well as shipping all messed up by jones law making ai infrastructure unequal -how can public servants justify not reforming this law that less than 1% gain from and nation may lose its shirt around

americans pay twice as much for health as just about anywhere and system is collapsing with life expectancy going down- among many problems is over certifuication particularly on nursing tasks which ai could increasing help with - we asked x to write this up applying is terminolgy and refgerences 

US healthcare system has well-documented issues with over-professionalization, overly restrictive scope-of-practice laws, and resulting workforce shortages — especially for mid-level providers who could handle routine care. This creates bottlenecks for basic medicines, elderly care, pediatric care, and primary access, unlike more flexible systems in the UK (where pharmacists handle more independent prescribing for minor ailments) or other countries.Key Problems in American Terminology

• Scope-of-practice restrictions: State laws limit what Nurse Practitioners (NPs), Physician Assistants (PAs), and other advanced practice providers can do independently (e.g., diagnose, treat, prescribe). Many states require physician supervision or collaborative agreements, even when evidence shows NPs deliver safe, high-quality care.
• Over-professionalization: Excessive training/credentialing requirements and barriers raise costs and limit supply. This contributes to shortages of primary care providers and those serving elderly (geriatrics) and young children (pediatrics).
• Shortages: The US faces significant primary care physician shortages (projections range from tens of thousands to over 70,000 FTE by the 2030s). NPs and PAs are seen as key to filling gaps, but restrictions slow this. Your "100,000 quasi-nurses" figure aligns roughly with combined gaps in NPs/PAs and related roles for underserved populations.

States with full practice authority for NPs tend to have better access, outcomes, and provider density.Notable Reports Using US TerminologySeveral strong reports and analyses address this exact "mess":

• National Academies / Institute of Medicine (2010/2016 updates): "The Future of Nursing" report strongly recommends removing scope-of-practice barriers so APRNs (Advanced Practice Registered Nurses, including NPs) can practice to the full extent of their education and training. This is a foundational document.
• Brookings Institution: Multiple pieces on improving healthcare through occupational licensing reform, emphasizing loosening restrictions on NPs/PAs.
• Hamilton Project (Brookings): Morris Kleiner’s work on reforming occupational licensing, including healthcare.
• Federal reports: HHS, HRSA, and the 2019 Trump-era "Reforming America's Healthcare System Through Choice and Competition" explicitly called out scope restrictions as anti-competitive.
• Cato Institute and state-level think tanks often critique this in the context of affordability (tying into the handbook you mentioned).

These use precise US terms like "scope of practice," "full practice authority," "APRNs/NPs," "occupational licensing reform," and "practice to the top of their license."AI and Amazon as Potential BreakthroughsAI can help significantly by supporting triage, diagnostics, monitoring (especially chronic care for elderly), and decision support — allowing mid-level providers (or even pharmacists) to handle more volume safely. This aligns with Layer 5 (Applications) in Huang’s framework: practical tools for real-world care delivery.Amazon is already moving in this direction:

• Amazon One Medical and On-Demand Care (pay-per-visit telehealth, often starting at $29) let patients get virtual consultations with clinicians (doctors or NPs) for common conditions, with prescriptions sent to pharmacies (including Amazon Pharmacy).
• They offer services for adults and pediatrics (e.g., rashes, pink eye, asthma renewals, UTIs). Prescriptions can be delivered quickly.
• This bypasses some traditional barriers via convenience and direct-to-consumer models, though still subject to state licensing rules and clinician oversight.

Amazon could scale this further (e.g., AI-augmented triage + NP-led care + pharmacy integration), putting pressure on the system for reform. However, full transformation still requires policy changes on who can prescribe independently.Would you like me to pull quotes from a specific report, compare UK vs. US pharmacy prescribing, or map this to the broader Jones Act / infrastructure / AI connectivity discussion from earlier? Or draft a comment for the Cato handbook context?

==============from scsp draft workforce commission

Traditional Educational Pathways and Trainings May Fall Short in Meeting the AI Moment The modern education and workforce development system is structured around several core pathways: K–12 education, career and technical education, higher education, workforce training programs, and work-based learning.86 Together, these systems are designed to prepare individuals for long careers across a wide range of occupations, from corporate roles to skilled trades. Each pathway plays a distinct role, from building foundational skills to supporting specialization and reskilling. Work-based learning, in particular, has historically enabled workers to adapt over time by accumulating experience and transitioning into adjacent or more advanced roles. However, as the structure of work shifts in response to artificial intelligence, the effectiveness of these pathways is increasingly in question. As discussed in Section 4, certain early-career roles that historically functioned as training grounds have become less available, weakening traditional transitions from education into employment. This section focuses on a related issue: whether existing educational pathways are sufficient to prepare workers for an AI-driven labor market, how they may need to evolve, and whether new pathways are required. At a fundamental level, this challenge reflects a growing mismatch between how education systems are structured and how skills are now developed, deployed, and updated in the labor market. Educational systems are designed to reduce skills gaps by equipping workers with competencies aligned to employer demand, but this function depends on relatively stable skill requirements. Technological change has always reshaped skill demand, but AI may accelerate both the pace and scope of this process, requiring more continuous adaptation.87 The Limits of Existing Pathways Current education systems assume that early-life learning provides stable, long-lasting skills for a career before entering the workforce, and rely on those same competencies over time. This model is breaking down as skills evolve at rapid speed.88 Underlying skills requirements shift even within some of the most stable occupations, making continuous learning a more critical necessity.89 86 Emily Musil, et al., The Computing Imperative: Building America’s Talent Engine in the Age of AI, Milken Institute (2025). 87 David Autor, Why Are There Still So Many Jobs? The History and Future of Workplace Automation, Journal of Economic Perspectives at 3–30 (2015). 88 The Future of Jobs Report 2025, World Economic Forum (2025). 89 Daron Acemoglu & Pascual Restrepo, Automation and New Tasks: How Technology Displaces and Reinstates Labor, Journal of Economic Perspectives at 3–30 (2019). Page 30 At the same time, traditional credentials are becoming less reliable signals of capability in certain areas. Their value depends on the alignment with labor market demands, but fragmented and outdated data systems make it difficult to track outcomes and the infrastructure to verify this alignment cannot capture real-time transitions across occupations. This has contributed to the creation of the “invisible graduate flow,” in which it is difficult to determine how individuals, particularly those from generalist backgrounds, transition into emerging roles.90 These limitations are compounded by broader structural challenges. In certain areas, educational institutions, workforce programs, and employers may work separately from each other, resulting in a disconnection between curriculum design and evolving job requirements. The inability to keep pace with the speed of AI development and adoption contribute to the persistent gaps between training and labor market demand.91 Finally, the growing disparity between the number of graduates and available high-skill roles contributes to underemployment and credential inflation, further weakening degrees as signals of job readiness while overlooking workers with relevant skills but nontraditional backgrounds. Expanding and Adapting Existing Pathways One response is to modify existing pathways to better align with evolving skill demands. This includes further embedding AI literacy across education systems, strengthening connections between education providers and employers, and integrating more applied, work-based learning into curricula. There is growing consensus that AI literacy is becoming a baseline requirement across occupations, analogous to digital literacy in earlier technological transitions.92 Efforts to integrate AI concepts into K–12 education and career and technical programs reflect this shift, with the goal of ensuring that workers enter the labor market with a foundational ability to interact with AI systems.93 At the same time, there is increasing emphasis on aligning curricula with labor market needs through closer employer engagement. Sector-based training programs, apprenticeships, and community college partnerships are frequently cited as mechanisms for improving this 90 Briefing to the Task Force (March 27, 2026). 91 Gad Levanon, et. al., No Country for Young Grads: The Structural Forces That are Reshaping EntryLevel Employment, The Burning Glass Institute (2025). 92 Emily Musil, et al., The Computing Imperative: Building America’s Talent Engine in the Age of AI, Milken Institute (2025). 93 Briefing to the Task Force. (March 27, 2026). Page 31 alignment.94 However, scaling these approaches remains challenging, particularly given institutional constraints and uneven incentives across stakeholders. International models provide useful comparisons. The German vocational system integrates education and work-based training earlier in the lifecycle, enabling students to develop occupation-specific skills before entering the labor market.95 While effective in aligning training with employer demand, it has been criticized for requiring early specialization, potentially limiting flexibility. This highlights a broader tension between early alignment and long-term adaptability. The Emergence of Alternative Pathways Beyond modifying existing systems, new pathways are emerging that operate alongside or outside traditional education structures. We highlight four approaches that reflect different ways of acquiring and signaling skills in a more dynamic labor market. One category includes modular and stackable credentials, such as professional certifications and micro-credentials, that allow workers to acquire targeted skills over time. These credentials can serve as alternative signals of competency, particularly for mid-career reskilling; however, their value depends on consistent employer recognition.96 A second category includes employer-led training and work-based learning. Firms are increasingly investing in internal programs that embed learning within workflows, blurring the boundaries between education and work.97 However, these programs can be resourceintensive, limiting employer uptake, and may produce skills that are not easily transferable across employers. A third category includes online platform-based learning ecosystems, which provide scalable access to rapidly evolving technical skills. Enrollment data suggest strong demand for foundational AI skills: Coursera alone has recorded over 16.8 million global enrollments in generative AI courses, with rapid growth in recent years.98 At the same time, learners are prioritizing short, applied courses that can be integrated into existing work schedules, reflecting 94 Harry J. Holzer, Understanding the Impact of Automation on Workers, Jobs, and Wages, Brookings Institution (2022). 95 A Guide to the German School & Education System, CBS University of Applied Sciences (2026). 96 Alex Swartsel, et al., The AI-Ready Workforce: How Leaders and Workers Can Prepare for a Reshaped Future of Work, Jobs for the Future (JFF) and Intel (2023). 97 Cisco AI Workforce Consortium, AI Workforce Consortium Full Report, Cisco Systems (2025). 98 Briefing to the Task Force, (March 27, 2026). Page 32 a shift toward continuous, career-driven learning. Still, cost barriers and uneven course quality and returns for credentials remain great challenges.99 Finally, AI itself is emerging as a lower-cost training mechanism, providing a new style of more personalized and conversational instruction, offering real-time feedback and accelerating skill development particularly for less experienced workers.100 Coordination, Credibility, and System-Level Implications The expansion of alternative pathways has increased the number of ways workers can acquire skills, but not the system’s ability to translate those skills into clear labor market signals. The core challenge is not a lack of pathways, but the absence of a coherent system that connects them. Disconnection between credentials, training programs, and learning platforms result in inconsistent skill interpretation, weakening the value of credentials and perpetuating reliance on traditional degrees, particularly among smaller firms. Simultaneously, limited outcome data hinders the assessment of effective pathways, leaving workers and institutions with incomplete information. These fragmentations come to impede participation due to costly, time-intensive, and difficult-to-evaluate navigation. This suggests the future of education pathways will require integrating multiple training programs into a continuous, nonlinear learning system embedded within the labor market itself. In practice, this shift implies several areas of focus. First, improving the applicability of credentials will be critical to restoring their ability to signal job-readiness. Second, establishing responsive data infrastructure will allow for tracking skill acquisition and outcomes, enabling better informed decision-making by institutions. Third, stronger coordination throughout the workforce pipeline is necessary to align training with evolving demands in real time. At the same time, expanding access to these pathways will require reducing barriers related to cost, time, and information. This includes not only increasing the availability of training, but also ensuring that workers can engage with it without significant disruption to employment or income. Ultimately, the challenge is not simply to expand education pathways, but to make them function as a system—one that is legible to employers, accessible to workers, and capable of adapting alongside technological change. 99Matt Sigelman, et al., Holding New Credentials Accountable for Outcomes: We Need Evidence- Based Funding Models, Burning Glass Institute 100 Erik Brynjolfsson, et al., Generative AI at Work, The Quarterly Journal of Economics at 1–48 (2025). Page 33 9. The Distribution of AI’s Gains Need Not Be Concentrated, and Can Be Shaped by Implementation Choices The relationship between AI and economic inequality is not predetermined. AI could reduce wage inequality by extending high-productivity tools to those who previously lacked access, or it could accelerate the concentration of gains among those already well-positioned. Inequality in the modern world largely results from the uneven dissemination and adoption of technologies, a process that is dictated by whether a society's institutions are inclusive or extractive.101 Democratizing access to AI will break down barriers to help ensure these tools are accessible to all rather than limited to a few. Ultimately, which outcome prevails will depend less on the technology itself than on the choices made by firms, policymakers, and workers in the years immediately ahead. The optimistic case is grounded in real evidence. Research on earlier information technologies suggests that tools that augment cognitive tasks can raise the productivity and, over time, the wages, of workers who might otherwise be left behind by automation.102 When AI assists a midcareer worker in drafting documents, analyzing data, or navigating complex regulatory environments, it can reduce the skill premium that previously made those tasks the exclusive domain of highly credentialed professionals. Several recent studies examining the deployment of AI-assisted tools in customer service, legal research, and clinical settings have found that less-experienced workers often benefit disproportionately from AI assistance, narrowing within-firm performance gaps rather than widening them.103 If these patterns hold at scale, AI could function as a genuine equalizer; not by eliminating skill distinctions, but by lowering the cost of developing competence. The mechanism described above cuts both ways—AI-driven shifts in skill demand also could lead to reduced demand for labor in certain occupations, displacing workers in the process. Reabsorption into the workforce is not instantaneous: even where AI lowers barriers to entry in new or adjacent occupations, workers require time to retrain, relocate, and signal new competencies to employers. The empirical record on prior transitions—most rigorously documented in studies of trade-induced displacement—suggests this adjustment period spans years, and that the burden falls unevenly, concentrated among workers with less geographic 101 Daron Acemoglu & James A. Robinson, Why Nations Fail: The Origins of Power, Prosperity, and Poverty, Crown Currency at 53 (2012). 102 David H. Autor, Why Are There Still So Many Jobs? The History and Future of Workplace Automation, Journal of Economic Perspectives (2015). 103 Erik Brynjolfsson, et al., Generative AI at Work, The Quarterly Journal of Economics at 1-48 (2025). Page 34 mobility, fewer financial resources to weather unemployment, and less access to retraining infrastructure. 104 Further, AI could more fundamentally reshape the value of labor in general. Since the Industrial Revolution, labor has been the main bottleneck in production. Its relative scarcity is what made it valuable—capital is reproducible, labor is not, and that asymmetry underpinned a century of broadly rising wages. AI disrupts this asymmetry by enabling capital to substitute for the cognitive labor input itself.105 If AI absorbs more work than it generates, the result is an economy that grows without broadly sharing that growth—then labor's share of economic output contracts. The economy could grow significantly while the gains flow predominantly to those who own the capital doing the work. That outcome is not inevitable. The outcome in which AI raises living standards broadly while compressing rather than widening inequality is achievable, but it is not necessarily the default, and it may not arrive on its own. Ensuring this outcome will require getting a number of things right—deliberately and soon—on access to AI tools, on how firms structure and allocate human-AI workflows, on investment in education and training, and on the institutional frameworks governing how productivity gains are distributed. Even at the firm level, adoption creates winners and losers. Firms that adopt AI fastest tend to be larger and better-capitalized. Workers in those firms are more likely to receive training, to have job functions that lend themselves to human-AI collaboration, and to capture a share of the productivity gains through wages or advancement. Workers in smaller firms or in occupations with thinner margins and less institutional capacity for technology adoption may find themselves on the wrong side of an AI productivity gap, not because their work is being automated away, but because they are not gaining access to the augmentation benefits their peers elsewhere enjoy.106 This advantage gap also has a significant geographic component—firms adopting AI tend to be concentrated in metropolitan areas with existing talent advantages; similarly, AI development occurs in a small number of technology hubs. Workers in rural or economically distressed areas are less likely to receive the skills training firms adopting AI provide. Advantages accruing to dominant regions are therefore self-reinforcing. Concentration draws in further business investment, talent, and infrastructure, creating a gap that is increasingly difficult to close.107 The communities most exposed to displacement risk from AI, by contrast, tend to be those with 104 David H. Autor, et al., The China Shock: Learning from Labor Market Adjustment to Large Changes in Trade, Annual Review of Economics (2016). 105 Briefing to the Task Force, (March 27, 2026). 106 Stefan Koopman, et al., The Economic Impact of AI: Four Scenarios, Rabobank (2024). 107 Sandrine Kergroach & Julien Héritier, Emerging Divides in the Transition to Artificial Intelligence, OECD Publishing (2025). Page 35 higher concentrations of routine-task employment, limited access to retraining infrastructure, and fewer institutional anchors capable of managing a significant labor market transition. History offers cautionary precedent here: the gains from prior technological transitions were real, but they were not broadly shared in time or place, and the adjustment costs fell heavily on specific workers, families, regions, and institutions that lacked the resources to adapt. None of this argues for slowing AI adoption. The productivity gains AI makes possible are among the most promising mechanisms available for raising living standards broadly, and forgoing them would impose its own costs, particularly in the context of national competitiveness. But productivity gains that flow narrowly will not serve the nation's long-term interests, economic or otherwise. A workforce in which a significant fraction of workers feels that technological progress is something that happens to them rather than for them is not a foundation for durable competitiveness or social cohesion. The implication is that inequality must be treated as a first-order design problem for AI policy, not an afterthought to be addressed once the technology has matured. Decisions made now about access to AI tools in education and training, how firms structure and distribute human-AI workflows, which communities receive investment in digital infrastructure, and how the gains of AI-enabled productivity are shared will shape the distributional trajectory of this technology for a generation. The window for making those decisions well is open, but it is not unlimited.

10. The Need for Coordinated, Whole-of-Nation Action The evidence assembled in these findings leads to a single, unavoidable conclusion: the United States should not rely on any one sector, institution, or level of government to navigate this transition alone. The scale of AI's impact on work—its speed, its unevenness, and its capacity to either broadly distribute or narrowly concentrate economic gains—demands a response that is equally broad. What is required is a coordinated, whole-of-nation strategy that aligns the interests of workers, employers, educators, and policymakers at each critical inflection point along the path from AI capability to labor market outcome. The decisions made in the next several years will determine whether that strategy exists, and whether it arrives in time. The effects of AI on the future of work will ultimately reflect the cumulative actions and decisions of individuals and institutions across the country. That is both a warning and an opportunity. Where interests are misaligned, the transition will be painful and uneven. Employers who face no incentive to reskill, workers who lack access to retraining, and educators preparing students for a labor market that no longer exists are not edge cases; they are the default outcome in the absence of coordinated action. Where those interests are deliberately aligned, the outcome can be different. The whole-of-nation approach this Task Force recommends is not an abstract aspiration; it is the practical recognition that mitigating uneven impacts across individuals, firms, industries, and regions requires coordination that no single actor can provide alone. At the task level, the main dependencies revolve around the interplay between the capabilities and adoption of AI tools. AI capabilities and the infrastructure to support them will be key factors in the scope of automation in work tasks.108 As these capabilities continue to change, they will also determine which human skills remain complementary to AI-based work.109 However, the capabilities alone will not influence work tasks without adoption. Worker-driven and employer-driven AI adoption pathways will be key determinants of whether tasks are more augmented or automated, and the timeframe over which workflows substantially shift.110 108 Pascual Restrepo, We Won’t Be Missed: Work and Growth in the AGI World, National Bureau of Economic Research (2025). 109 Erik Brynjolfsson & Tom Mitchell, What can machine learning do? Workforce implications, Science (2017). 110 The AI Ready Workforce, Jobs for the Future (2023). Page 37 As task changes expand out to the workforce level, the economics of AI adoption will start to play a larger role. The interaction between tech, labor, employers, and educators will be key to understanding the extent to which hiring and investment decisions change as task-level adoption of AI changes. Worker replacement becomes more likely if AI is cheaper than equivalent human labor, and if that human labor is not reinvested and reskilled into new valueadd roles.111 Finally, macroeconomic effects will depend on the breadth of changes and how policies adapt. Workforce disruption that is narrow in scope or confined to certain industries would have limited macroeconomic fallout if workers are able to shift to other growing or emerging fields.112 Responses must harness the upside potential of AI in order to stay globally competitive relative to other countries.113 The United States has navigated technological transitions before, and it has risen to meet challenges that once seemed insurmountable. This moment is no different in kind, but it may be greater in scale and speed than anything that has come before. The country enters this transition with something it lacked in prior ones: advance warning and a clearer picture of where the risks are concentrated than has ever been available at this stage of a major technological shift. That is cause for confidence, not complacency. The United States has the tools, the talent, and the institutional capacity to lead this transition. 111 Erik Brynjolfsson & Tom Mitchell, What can machine learning do? Workforce implications, Science (2017). 112 Daron Acemoglu & Pascual Restrepo, Automation and New Tasks: How Technology Displaces and Reinstates Labor, Journal of Economic Perspectives (2019). 113 Erik Brynjolfsson, et al., A Research Agenda for the Economics of Transformative AI, National Bureau of Economic Research (2025). Page 38 Appendix 1. Task Force Staff and Contributors SCSP Task Force Staff Dr. Ryan Carpenter Veronica Jijon Dr. Nandita Balakrishnan Carina Ritcheson Caroline Armstrong James Ryseff Brock Dodds Sara Huta Ylber Bajraktari NVIDIA Task Force Staff David Shahoulian Meg King Faiza Khan Sarah Weinstein Sophie Goguichvili Advisors to the Task Force Dr. Harry Holzer Clara Kaluderovic Alex Kotran Maria Flynn Dr. Diana Gelhaus