Executive Summary

This AI Action Plan, America 2.0: Mission T.47, outlines a comprehensive approach to build a robust AI ecosystem through talent development, leadership renewal in key industries, strategic public-private investments, accelerated commercialization, and partnerships across sectors and borders. Key recommendations include:

Building a Collaborative AI Ecosystem: Establish Collaborative Innovation Hubs (CIHs) that unite stakeholders for innovation and strategic partnerships, ensuring AI solutions address national challenges in security, infrastructure, and the economy.

Driving AI-Based Economic Transformation: Leverage AI to re-industrialize America, transforming manufacturing, optimizing supply chains, and modernizing infrastructure. Specialized centers within CIHs will focus on financial optimization, operational excellence, sustainable innovation, R&D, and commercialization to ensure AI innovations translate into broad economic benefits.

Facilitating Generational Leadership Transition: Address the imminent leadership gap as baby-boomer business owners retire in national security, critical industries. Implement mentorship programs, leadership training, and innovative ownership transfer models to preserve intellectual capital and smoothly transition enterprises to a new generation of operators (Small business survival in the wake of the silver tsunami).

Developing the AI Workforce: Launch a national AI workforce development initiative to equip American workers. This includes AI education at all levels, industry-recognized certification programs, incentives for employer-led training, and targeted re-skilling programs (especially for veterans and displaced workers). AI investment is surging to $550 billion while an estimated 50% talent gap in AI looms (AI Skills Gap | IBM).

Mobilizing Public-Private Investment: Create an AI-focused public-private investment arm that blends government resources with private sector agility. Aligned incentives and innovative financing (e.g. venture capital, private equity, and infrastructure funds) will accelerate scaling of AI solutions in defense, energy, and other critical sectors. All stakeholders, government, industry, academia, should have ownership stakes in outcomes, ensuring shared commitment to success.

Forging Strategic Partnerships: Strengthen partnerships both domestically and internationally. Domestically, foster cross-sector collaborations and consortia to break down silos between government labs, universities, and companies. Internationally, coordinate with allies on AI research, standards, and commercialization.

Government, policymakers and industry leaders must act decisively on these recommendations. It must “invest substantially more resources in AI innovation to protect its security, promote its prosperity and safeguard the future of democracy” (NSCAI Report: US Can Gain Leading Edge on AI With This Plan | GovCIO Media & Research). The following sections provide detailed proposals and actions under each strategic theme.

Introduction and Context

The Imperative for an AI Action Plan: Artificial Intelligence has started to revolutionize and threaten industries, national security, and societies. The U.S. federal government and private sector have launched numerous AI initiatives, but efforts remain fragmented across agencies and regions. To maintain U.S. leadership a coordinated strategy must unite stakeholders, focus investments, and guide AI development in line with national priorities.

America 2.0, Mission T.47: This AI Action Plan presents a comprehensive strategy for AI-driven innovation, national security, and economic transformation. The plan centers on leveraging existing government infrastructure, such as the National Institute of Standards and Technology (NIST) Manufacturing Innovation Institutes, Manufacturing Extension Partnerships (MEPs), and Economic Development Administration (EDA) networks, to create a collaborative AI ecosystem. 

Objectives: This proposal aligns with the White House Office of Science and Technology Policy (OSTP) vision for a National AI Strategy. It aims to:

1.Integrate AI Technologies Nationwide: Promote integrating AI solutions in national security operations, economic infrastructure, and workforce to enhance productivity and resilience.

2.Foster Public-Private Collaboration: Build sustainable public-private partnerships and ecosystems that keep the U.S. at the forefront of AI innovation. This includes co-investment models and sharing of expertise between government, industry, and capital/investors.

3.Bridge the Generational Leadership Gap: Tackle the urgent challenge of a retiring generation of business owners by preserving their expertise and pairing them with emerging leaders. This ensures continuity of operations and knowledge in industries vital to national security and economic stability.

4.Empower Ethical and Secure AI Adoption: Encourage AI deployment in ways that uphold ethical standards, protect privacy, and ensure security. Considerations of safety and democratic values should accompany every innovation.

The following sections articulate five key themes of the plan, each corresponding to a strategic pillar of action. These themes echo national priorities and mirror elements highlighted in the OSTP’s AI Action Plan development process.

A. Creating a Collaborative AI Ecosystem and Strategic Partnerships

Collaborative Innovation Hubs (CIHs): We propose establishing Collaborative Innovation Hubs as regional focal points of AI activity. CIHs will unite government agencies, private industry, academic institutions, and investors into consortia focused on AI innovation.

Each hub addresses regional challenges (e.g., a Rust Belt CIH focusing on manufacturing and a Silicon Prairie CIH on agriculture) while contributing to national AI objectives. By leveraging and evolving existing entities like EDA offices, MEP centers, and NIST institutes, the CIHs will transform these organizations into active enablers of AI-driven ecosystems. 

Key Functions of CIHs: Each Collaborative Innovation Hub will perform critical functions to build the AI ecosystem:

1. Research and Development (R&D): Provide collaboration to identify common problems in industrial and infrastructure sectors to enable knowledge transfer, R&D, and co-innovation. Federally funded research can be paired with industry-led development to accelerate progress from concept to prototype.

2. Public-Private Partnerships (Cross-Sector Collaboration): Serve as a nexus for partnerships among government, private companies (from startups to large firms), universities, and non-profits. These partnerships will be structured to align commercialization and its financing. This reduces the “valley of death” for promising AI innovations and speeds up the time from R&D to deployment.  (This strategy echoes the National AI R&D Strategic Plan’s call to “expand public- private partnerships to accelerate advances in AI”.)

3. Commercialization Support: Create pathways to transition AI technologies from labs to the marketplace. Each hub will have partnerships to help innovators navigate technical validation, regulatory compliance, intellectual property (IP) protection, and other hurdles to bring AI products to market. 

4. Workforce Development: Partner with educational institutions and employers to build regional AI talent pipelines and hands-on training leadership programs. By upskilling and reskilling the local workforce, hubs pair the human capital needed to maintain these companies and implement technologies.

Strategic Partnerships Beyond CIHs: In addition to the regional hubs, a broader framework of partnerships is crucial. This includes:

- Interstate and Interagency Collaboration: CIHs in different states will coordinate with each other and with federal agencies (e.g. Department of Defense, Department of Energy) to share best practices and avoid duplication. 

- Industry Consortia: Encourage the formation of industry-specific AI consortia (for example, an AI in healthcare consortium or an AI in agriculture consortium) that bring competitors together pre-competitively to set standards and pool resources. 

- International Allies and Partnerships: The U.S. should deepen AI cooperation with allies and partners globally. This includes collaborative research initiatives, talent exchange programs, common standards for trustworthy AI, and commercialization.

By weaving together regional hubs, national consortia, and international collaborations, the United States can create a dense web of strategic partnerships. 

B. AI-Driven Economic Transformation and Commercialization

We propose targeted initiatives for economic transformation and commercialization of AI innovations for re-industrializing America through specialized centers and programs integrated within the Collaborative Innovation Hubs.Centers for Economic Transformation will provide expertise and resources to help industrial companies improve their efficiency and strength for growth and innovation. The proposed centers include:

I. Financial Optimization Center (FOC): Helps businesses strengthen their financial resilience and leverage all available incentives for AI adoption. This center will guide companies in utilizing grants, tax credits, and innovative financing to fund AI projects. It also offers advisory on managing intellectual capital and evaluating M&A or investment opportunities. The FOC fills a gap not covered by existing programs, ensuring companies can afford AI transformation and measure the full value (including intangible assets) that AI brings.

II. Operational Excellence Center (OEC): Drives efficiency improvements through AI-driven automation, predictive maintenance, and process optimization. The OEC assists firms to deploy AI solutions that reduce downtime, save energy, minimize waste, and improve output quality. AI tools can significantly enhance productivity where traditional consulting falls short.

III. Sustainable Innovation Center (SIC): Focuses on technology and intellectual capital and intellectual property solutions for sustainability and deep tech innovation. It supports companies in reducing environmental impact (e.g. optimizing energy consumption, cutting waste) while capitalizing on sustainable finance opportunities. The SIC will help firms navigate environmental regulations, adopt clean technologies, and tap into green subsidies or markets. By aligning innovation with sustainable development goals, this center ensures economic transformation is environmentally responsible and future-proof.

IV. Research and Development Center (RDC): Concentrates on problem-driven R&D by identifying pressing industry challenges and matching them with AI innovators (startups, inventors, research labs). The RDC’s process is: (1) Work with industry partners to pinpoint high-impact problems impeding economic, operational, financing, or security progress. (2) Scout or incubate AI solutions by partnering with entrepreneurs and researchers. (3) Support the development process with technical expertise, mentorship, and feasibility assessments. The RDC ensures that R&D is closely aligned with market needs and that promising ideas have a viable path to commercialization through early-stage funding and access to testbeds.

V. Commercialization and Market Expansion Center (CMEC): Dedicated to bringing AI solutions to market at scale. The CMEC works on go-to-market strategy, helping AI innovations developed in the CIH (from the RDC or elsewhere) reach real customers and achieve sustainable growth. 

By establishing these targeted centers from financial planning to technical R&D to commercialization, businesses, especially small and mid-sized ones, will have access to end-to-end support to identify areas of opportunity and address them for a solid focundation to then implement the entire value chain: innovation, validation, production, and diffusion of AI technology.

C. Generational Leadership Transition in Critical Industries

A less-discussed but urgent challenge in maintaining America’s industrial and technological leadership is the coming wave of retirements among business owners and experts of the baby boomer generation. In the next decade, a large proportion of owners of small and medium-sized businesses (SMBs), many in sectors crucial to national security and economic stability, intend to retire.

Yet, the majority lack concrete succession plans (NSCAI Report: US Can Gain Leading Edge on AI With This Plan | GovCIO Media & Research) (Small business survival in the wake of the silver tsunami).

This looming “silver tsunami” of retiring leadership puts at risk vast amounts of intellectual capital (IC), the knowledge, skills, and networks that these leaders carry. If their businesses collapse or are sold off without continuity, it could erode the industrial base in manufacturing, defense contracting, energy, and other areas vital to national interests.

The Urgency of Bridging the Leadership Gap: Over 10,000 baby boomers reach retirement age every day (Small business survival in the wake of the silver tsunami). By 2030, the entire boomer generation will be at or past retirement age, and 40% of U.S. small business owners are in this cohort (Small business survival in the wake of the silver tsunami). Reports estimate less than one-third of small business owners have a formal exit or succession plan (Small business survival in the wake of the silver tsunami). I

n other words, tens of thousands of firms, representing millions of jobs and significant economic output, may face leadership crises. In national security-related sectors (like precision manufacturing for defense, or critical infrastructure services), the stakes are even higher. If these businesses fail to transition, the U.S. could lose domestic capacity in areas where it cannot afford to be dependent on foreign supply.

Therefore, strategic intervention is needed to preserve these businesses, retain their know-how, and mentor new leadership to take the helm.Strategies for Generational Transition: This Action

Plan proposes several strategies to facilitate successful leadership transitions in critical industries:

1. Mentorship and Advisory Roles for Retiring Owners: Establish formal knowledge and industry relationships transferring programs. Apprenticeship programs pairing retiring business owners with the next generation of leaders.

Actionable Step: Create a National Apprenticeship Network for succession, possibly funded or incentivized by the Small Business Administration or EDA. Retiring leaders can receive stipends or tax benefits for participating, recognizing their role in sustaining the industry's intellectual capital.

2. Turnaround Experts and Leadership Certification: Develop a certification for turnaround experts, individuals trained to take over and revitalize established businesses.

Actionable Step: Launch a Transitioning Leadership Academy with tracks for financial planners, consultants, business owners, and executive employers offering certifications in business restructuring, financial optimization, and operations.

3. Gradual Ownership Transition Models: Promote equity-sharing and phased transfer models to avoid abrupt leadership handovers. Structures such as employee stock ownership plans (ESOPs) or management buyouts with mentorship can be utilized. Actionable Step: Encourage Seller Financing and Earn-outs, where retiring owners remain financially invested during the transition. The government can facilitate this by providing guarantees or tax incentives for installment sales of businesses to approved successors.

Additionally, craft template partnership agreements that outline shared decision-making between the outgoing and incoming owners during a transition phase (e.g., 3-5 years), with clear milestones for the successor to take over entirely. Similar to the standardized SAFE note for Venture Capitalists.

4. Platforms to Match Owners and Successors: Often, a retiring owner’s biggest challenge is finding a trustworthy, capable person to take over. We recommend creating online platforms and networks that connect retiring owners with certified (by our recommended turnaround and leadership certifications) aspiring business leaders and advisors interested in acquiring or running a company. These platforms can function like marketplaces for business succession, but with vetting and support mechanisms. Retiring owners could even invest alongside the new owners, preserving some equity and incentive to help the business succeed post-transition.

Actionable Step: The Department of Commerce or SBA could sponsor a Business Succession Exchange. In this secure portal, owners list opportunities and qualified individuals (or teams) apply to take on those businesses. The platform could integrate with mentorship and financing programs.

5. Leveraging Veteran Leadership and Skills: Many military veterans have technical skills, leadership experience, and mission-focused discipline that would make them excellent leaders in industrial and tech companies. We propose targeting veterans as a talent pool to fill critical industry leadership gaps.Actionable Step: Implement Sponsored programs to certified and placed veterans in the apprenticeship programs with retiring business owners. Partnerships with veteran organizations and the Department of Defense’s SkillBridge program could facilitate internships or apprenticeships where veterans work alongside an outgoing business owner before fully taking over a role.

Preserving and Developing Intellectual Capital: Underpinning all these strategies is the goal of preserving the invaluable intellectual capital in these businesses. Intellectual capital (IC) includes proprietary knowledge, trade secrets, skilled teams, and processes honed over decades. If not intentionally transferred or retained, IC can dissipate when an owner retires or a company is sold to an outsider who fails to appreciate its value.

The U.S. cannot afford to lose the expertise in domains like defense manufacturing, aerospace engineering, or even local infrastructure services, as these are the engines of innovation and readiness.However, they are often underutilized even when programs exist to help with succession or knowledge transfer.

Common challenges include: lack of awareness of assistance programs, bureaucracy and red tape that deter participation, financial constraints for new owners, misalignment of programs with industry needs, fragmentation of resources, and a focus by many firms on short-term survival over long-term planning.

To address these barriers:

- Improve Awareness and Access: Many SMB owners simply do not know about available succession planning resources or find them too complex to navigate. Simplified communication is needed. Solution: The IRS must deliver awareness and guidance to all accountants and tax professionals about the incentives to do financial planning and a transition plan. 

- Financial Support and Risk Mitigation: Transitioning a business can be expensive (legal costs, training a successor, potential downtime) and risky. Solution: Provide financial incentives such as low-interest loans, loan guarantees, or grants to facilitate critical sector leadership transitions. For instance, a Business Transition Loan Program could help fund a successor’s purchase of shares or investment in modernizing the firm during the handover. Coupling this with tax breaks or assurance of government contract continuity can reduce perceived risk.

- Foster Collaborative Networks: Often, owners don’t transition because they operate in silos without opportunities to partner or merge with others. 

By implementing these measures, the U.S. can provide the mechanisms that facilitate the generational transition in key industries and avoid a significant loss of capacity and know-how. Instead, retiring pioneers will become mentors and investors in the new generation, and incoming leaders will be empowered to rejuvenate legacy companies with fresh ideas and AI-driven improvements.

 D. AI Workforce Development

Preparing the American workforce for an AI-driven economy is a cornerstone of this Action Plan. Without a skilled and adaptable workforce, even the most advanced AI innovations will fail to gain traction, and the benefits of AI could accrue to only a few while many are left behind. To prevent this, we need a strategic, multifaceted AI workforce development initiative that spans education, training, and policies to incentivize continuous learning.

National AI Workforce Initiative: We recommend the launch of a National AI Workforce Initiative, a coordinated effort across government agencies (Department of Education, Department of Labor, NSF, etc.), industry partners, and educational institutions, to rapidly expand AI-related skills at all levels. This initiative would serve as an umbrella for programs and funding targeting AI skill gaps, similar in spirit to past national efforts in STEM education but focusing on AI and data literacy. Key components of this initiative include:

- Curriculum Integration: Integrate AI fundamentals into K-12 education and post-secondary programs. This means introducing students to concepts of algorithms, data analysis, and ethical implications of AI early on. High schools could offer introductory AI and coding classes, while community colleges and universities create or expand programs in data science, machine learning engineering, and AI ethics. The aim is to produce a steady flow of graduates with AI competencies and raise the general technological literacy of all students.

- Industry-Recognized Certifications: Develop AI certification and training programs for current workers, ensuring they are tailored to industry-specific needs. Not every job requires a PhD in machine learning; many require a practical understanding of using AI tools. By collaborating with industry, training providers can create credentials in areas like “AI in Manufacturing Operations” or “AI for IT Service Management”. These certifications would standardize the skill sets and reassure employers of a candidate’s proficiency. The government can encourage this by supporting standard-setting organizations or grants to educational tech companies to develop courseware.

- Broad Accessibility: Ensure training programs are accessible at all skill levels, from basic AI literacy for non-technical workers to advanced AI specialization for tech professionals. This could involve online courses, bootcamps, apprenticeships, and evening or part-time programs for working adults. Affordability is crucial: use public funding or public-private partnerships to subsidize tuition, especially for underrepresented groups. The goal is to democratize AI knowledge so that workers in any region or demographic can upgrade their skills.

Incentives for Employer-Led Training:

Employers should be key partners in workforce development. We propose strong incentives for companies to invest in upskilling their employees:

- Tax Incentives: Offer payroll tax reductions or credits to firms that provide approved AI training to their staff. For instance, if a manufacturing company retrains its assembly line workers to operate AI-augmented machinery or to perform data analysis, it could receive a tax credit offsetting part of the training cost. Additionally, expenses on employee AI education should be explicitly eligible for the R&D tax credit. Currently, U.S. tax law (Section 41 and related) credits R&D activities; expanding the definition to include workforce training for technology adoption would encourage more investment in human capital.

- Grants and Public-Private Programs: The government can co-fund training programs in critical sectors. For example, through the Department of Labor or NSF, create grant programs that match employer contributions to training programs dollar-for-dollar in cybersecurity, AI-driven manufacturing, or healthcare AI. This de-risks the cost for companies, especially small businesses.

- Recognition and Procurement Preferences: The government can reward companies that actively upskill workers by factoring it into federal contract awards. A contractor with a robust workforce development plan (including AI training) could be given preference or additional points in proposal evaluations, similar to how workforce diversity or past performance is valued. This leverages the federal government’s buying power to drive training investments.

Alignment with Tax Policy (Section 174 Fix): Recent changes to U.S. tax code require R&D expenses to be amortized over years instead of deducted immediately, which can be a disincentive for innovation spending (Section 174: Understanding Research & Development expenditures). We support efforts to simplify Section 174 of the IRS Code to allow Small and Medium-sized Enterprises (SMEs) to deduct AI-related R&D and training expenses immediately. Immediate expensing lowers the upfront cost of investing in new technology and skills. Policymakers should prioritize reversing or adjusting any provisions that unintentionally discourage companies from making R&D and training investments. (Notably, experts have observed that the shift to amortization in 2022 has correlated with a slowdown in R&D spending (R&D Expert: Capitalization, Amortization Requirement Hurts Smaller). Swift legislative action on this front will remove a financial barrier to AI innovation.)

Re-skilling and Inclusion Programs: As AI changes the nature of many jobs, workers whose tasks are automated or augmented need pathways to new roles. Targeted re-skilling initiatives should be launched for occupations most at risk of AI-driven disruption and for communities that might be left behind:

-Veterans Transition to AI Careers: Building on the idea from the leadership section, many veterans have transferable skills for AI and tech (e.g., experience with advanced systems, leadership). Expand programs (like DoD’s SkillBridge or VA training programs) to include specific tracks for AI, cybersecurity, and data analysis. Veterans could be fast-tracked into apprenticeships with industrial companies, combining their discipline with new technical training.

-Dislocated Worker Programs: For workers displaced from industries undergoing automation (e.g., specific manufacturing or clerical jobs), provide re-skilling scholarships and living stipends to enroll in AI or IT training courses. The Economic Development Administration can work with states to use existing dislocated worker funds or TAA (Trade Adjustment Assistance) for AI-related retraining, acknowledging AI as a factor in job displacement similar to trade.

- Inclusive Workforce Development: Ensure underrepresented groups (women, minorities, rural communities) have equitable access to AI education. Support organizations and community colleges in underserved areas to run AI training bootcamps. Leverage libraries and public institutions to offer basic AI literacy classes. This broadens the talent pool and helps reduce bias in AI systems by involving a diverse range of people in their development and use.

Urgency and Current Gaps: The push for AI workforce development comes at a critical time. So far, only 14% of frontline employees have had any AI-related upskilling to date (Employers Train Employees to Close the AI Skills Gap). Meanwhile, demand for AI talent is skyrocketing: companies plan to spend billions on AI, but struggle to find qualified staff (AI Skills Gap | IBM). By aggressively implementing the above measures, we can close the talent gap and ensure American workers are prepared for the jobs of the future.

The National Security Commission on AI (National Security Commission on Artificial Intelligence (NSCAI) recommendations aling with this AI Action Plan’s workforce recommendations to build a strong pipeline of AI-proficient professionals.

The next section turns to the equally important task of funding and investment mechanisms to support all these initiatives.

E. Public-Private Investment and Financing for AI Innovation

Achieving the ambitious goals of this AI Action Plan will require substantial investment. Traditional government grants and private venture capital alone are insufficient, we must leverage public-private investment vehicles that combine the strengths of both sectors. By aligning incentives and co-investing in strategic areas, we can mobilize far more resources for AI deployment than either sector could alone, and ensure that results are rapidly scaled for national impact.

Establish a Public-Private AI Investment Arm: We recommend the creation of an investment entity or program dedicated to funding AI technologies and infrastructure through public-private partnerships (PPPs). This could be a new government-sponsored enterprise, a joint fund, or an interagency program that coordinates with private investors. The core idea is to blend “the resources, speed, and innovation of the private sector with the strategic direction, funding, and regulatory support of the government.” For example, consider a scenario where the government identifies a critical AI technology (say, AI for supply chain security), instead of only issuing grants, the PPP investment arm might create a fund where government money is matched with venture capital. Projects are selected jointly by public and private experts. This ensures both funding and private sector discipline (due diligence, speed) and public interest (security, broad benefit) are represented in projects.

Key features of this investment arm would include:

- Aligned Ownership and Incentives: All stakeholders (government agencies, private investors, corporate partners, even universities) can hold equity or ownership stakes in the ventures supported. This is a departure from typical grantmaking, here, the government might take an equity stake or royalties in a company it supports, aligning its interest with the product's success. Likewise, a private investor co-invests knowing the government is a partner that will help reduce regulatory barriers or act as a lead customer for the AI solution. When everyone is a co-owner, each party is motivated to see the AI solution succeed in the market, not just reach a prototype.

- Strategic Guidance and Coordination: The government’s role is to provide funds, target the investment to national priorities, and convene the right partners. The investment arm would likely focus on areas with high national importance but where market failures exist (e.g., AI for critical infrastructure protection, where pure commercial ROI might be uncertain or longer-term). By declaring priority areas, the program can attract relevant private partners. It can also streamline regulatory processes for its projects (fast-track approvals, provide testing sandboxes) and use government procurement as a demand signal. Essentially, government de-risks and validates the technology, making private investors more willing to commit capital.

- Diverse Funding Sources: This model leverages alternative financing beyond venture capital alone to build a sustainable investment ecosystem. Private equity, infrastructure funds, corporate investment arms, and even philanthropic funds can participate, each bringing different time horizons and risk appetites. For instance, a private equity firm might invest in scaling up manufacturing for an AI hardware startup, while a venture fund focuses on earlier-stage AI software innovations. Combining these, the investment arm covers the entire spectrum from research to deployment. Hedge funds or private credit could provide debt financing to AI infrastructure projects (like data centers or broadband expansion needed for AI services). A mix of funding ensures that a promising AI solution can find support at every stage of its lifecycle, reducing the chances it falls into a capital “valley of death.”

Deployment and Global Collaboration: In deploying public-private investment, the U.S. should harness international partnerships and alliances to amplify impact. The U.S. government should use its diplomatic and trade channels to encourage cross-border co-investment in AI initiatives, particularly cybersecurity and defense where allies face common threats.

Specific actions to structure these investments include:

- Domestic Investment Networks: Form Strategic Investor Alliances at the national level. For example, create a consortium of U.S. venture capital and private equity firms that commit to evaluating and potentially funding AI projects sourced through the PPP program. This alliance could meet regularly with OSTP or a designated AI authority to review a pipeline of opportunities (from CIHs or federal labs) and fast-track the matchmaking of projects with capital. The government can sweeten deals by offering matching funds or first-loss capital (where the government absorbs initial losses, protecting private investors).

- International Co-Funding Agreements: Negotiate agreements with allied governments to co-fund AI research and startups. For instance, the U.S. and Japan could each put $50 million into a joint fund focusing on AI for semiconductor manufacturing, an area critical to both economies sharing costs and resulting benefits. These partnerships tap into global expertise and markets, ensuring U.S. innovations can scale globally with friendly markets from the start.

- Infrastructure for Scaling: Ensure that funding is available not just for software or algorithms but also for the infrastructure and hardware needed to deploy AI at scale. This includes semiconductor fabrication, cloud computing infrastructure, test ranges for autonomous systems, and more. Public-private financing models (like those used for traditional infrastructure) can be applied, for example, using public funds to guarantee loans for building a cutting-edge AI supercomputing facility that multiple companies and agencies can use.

- Regulatory and Policy Support: The investment arm should work with regulators to create a conducive environment. Essentially, the government’s non-monetary assets, convening power, regulatory flexibility, and role as a customer are as important as the dollars invested, and should be systematically applied to maximize the success of investments.

Through aggregated public-private efforts and allied cooperation, we can achieve a competitive scale of investment while staying true to our market-driven and values-driven approach. As noted earlier, the NSCAI urged substantial investment to keep the U.S. ahead in the global AI race (NSCAI Report: US Can Gain Leading Edge on AI With This Plan | GovCIO Media & Research), public-private partnerships are a force multiplier to achieve that.

Importantly, aligning incentives through co-ownership also guards against pitfalls: it discourages pure short-term profit motives from sacrificing national interest (since the government has a seat at the table) and ensures government-funded projects maintain commercial viability (since private investors demand it). In essence, it marries mission with market, it aligns purpose and capital.

Conclusion and Next Steps

This AI Action Plan provides a structured blueprint for the United States to strengthen its leadership in artificial intelligence while safeguarding economic prosperity and national security. Through clear thematic strategies, building collaborative ecosystems, transforming industries with AI, managing leadership transitions, developing the workforce, and innovating in investment, the plan addresses the challenge from multiple angles. To move forward, the following immediate next steps are recommended for policymakers and industry leaders:

- Public Communication and Stakeholder Buy-In: Communicate the vision of America 2.0 - Mission T.47 an AI-powered economic revival, to the public to build support. Emphasize not just the competitive necessity, but how these actions will create jobs, improve services, and secure the nation. Transparency and inclusion are essential; invite feedback from communities, civil society (to address ethical concerns), and the private sector. This could be done through public-private forums unified in the USA Economic Forum.

- Form a National AI Action Task Force to begin implementation. This task force, perhaps under OSTP or a joint interagency council, would prioritize the plan’s recommendations, assign roles to various agencies (DoD, DoE, DoC, NSF, etc.), and set timelines. Including representation from industry, academia, and state governments will help maintain the collaborative spirit.

- Secure Funding and Legislative Support: Work with Congress to authorize and appropriate funding for key initiatives like the Collaborative Innovation Hubs network and the public-private investment arm. Legislative action may be needed to provide tax incentives (e.g., training credits, Section 174 fix) and to establish new programs (such as the AI Leadership Academy or veteran transition programs). Early engagement with lawmakers can ensure these ideas are translated into policy.

- Pilot Programs: Launch pilot versions of CIHs in a few regions, focusing on different themes (for example, defense tech in a Midwestern state, clean energy AI in a Western state) to demonstrate the model. Similarly, pilot the mentorship and succession platform in one or two industries with high retirement rates. 

- Alignment with Ethical and Security Frameworks: As implementation begins, integrate the latest guidelines for responsible AI (such as the AI Bill of Rights and DoD’s ethical AI principles) into each action. For instance, CIHs should have committees on ethics to ensure that the development of technology is fair and secure. Workforce programs should include training on AI ethics. By design, our plan has highlighted ethics and privacy as considerations; concretizing that in execution will maintain public trust and prevent unintended harm.

The United States stands at a pivotal moment. If we act decisively and collaboratively, we can usher in a new era of American innovation, an America 2.0 where AI technologies bolster our economy, reinforce our national security, and uplift all citizens' living standards.

Conversely, inaction or disjointed efforts risk ceding leadership to adversaries and exacerbating domestic divides. In closing, the message from experts and commissions is clear: the U.S. must act now (NSCAI Report: US Can Gain Leading Edge on AI With This Plan | GovCIO Media & Research).

By implementing the strategies in this document, decision-makers can ensure the country not only keeps pace with the AI revolution, but leads it in a direction that aligns with American values and interests. We can launch America into its next great chapter of technological advancement and societal prosperity with commitment and cooperation.

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