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U.S. National AI Research Institutes

529 Collaborative Institutions

The U.S. National AI Research Institutes, led by the U.S. National Science Foundation (NSF), are strategic investments Behind AI Foundational Science and its use in Critical Sectors of Economy.

They consist of 29 institutes that connect over 500 funded and collaborative institutions across the U.S. and around the world.

Launched in 2020 and funded at about $20 million each over five years, these institutes represent one of the biggest public-private investments to date in AI research and development.

• Artificial Intelligence (AI) is built upon several foundational scientific principles, including logic, computation, and the study of the human mind. 

• Ancient philosophical inquiries into reasoning and knowledge laid the groundwork, while advancements in computer science, particularly in Machine Learning and Deep Learning, have driven its modern evolution.

• AI aims to create systems that can mimic human intelligence, enabling them to learn, reason, and solve problems.  

Artificial Intelligence (AI) is profoundly influencing Global Economies; driving Innovation; and Productivity while posing challenges like Skill Gap; Job Displacement; Socioeconomic Inequality and Regional Disparity.

Global AI Economy

2025 = US $ 294.16 Billion

2030 = US $ 19.9 Trillion

Compound Annual Growth Rate (CAGR) = 29.2% 

(Source: Fortune Business Insights, 2024)

AI Global Impact

• Geopolitical Competition
  The race for AI dominance has Economic and Strategic Implications. China aims to lead global AI by 2030, challenging the U.S.A. 

  • This competition is reshaping Trade, Supply Chains, and Innovation leadership worldwide.

• Automation and Job Displacement
  AI is automating routine tasks in industries such as manufacturing, logistics, and customer service. A McKinsey report estimates that by 2030, up to 375 million workers worldwide may need to switch occupations due to automation. At the same time, AI is creating new jobs in areas like technology development, data science, and AI system maintenance.

• Economic Growth and Productivity
  By optimizing processes, improving decision-making, and fostering innovation, AI boosts productivity. 

• The World Economic Forum predicts AI could contribute $15.7 trillion to the global economy by 2030, with the largest gains in countries leading AI adoption, such as the U.S. and China.

• Inequality and Skills Gap
  AI risks widening income inequality as high-skill workers benefit from new opportunities while low-skill workers face job losses. Regions with advanced tech ecosystems (e.g., Silicon Valley USA, Shenzhen China) may prosper, while others fall behind. Addressing this divide requires investment in education and retraining.

Economics of Transformative AI

Multiple research organizations predicts that business spending to adopt Artificial Intelligence (AI) will have a Cumulative Global Economic Impact of $19.9 trillion through 2030 and drive 3.5% of Global GDP in 2030. As a result, AI will affect jobs across every region of the world. This includes direct, indirect, and induced effects from AI adoption.

AI Economic Impact on U.S. Economy

As of April 2025, according to U.S. Congressional Budget Office:

👉...“General attribute of AI is its ability to identify patterns and relationships and to respond to queries that arise in complex scenarios for which the precise computational algorithm that is needed cannot be specified in advance”...

👉Research into the performance of businesses that have implemented AI is still in its early stages.

👉Surveys show that only 5 percent of businesses in the United States currently rely on AI to produce goods and services.

:👉Department of Defense's 2024 budget request dedicated to AI is less than 1%, signaling a cautious approach, fiscal year 2025 only $1.8 billion. 

AI Adoption

AI adoption is the use of Artificial Intelligence (AI) in businesses and organizations to improve efficiency and productivity. AI is software that mimics the human brain to solve problems. 

How is AI used? 

Customer service: AI is used to answer questions and provide recommendations

Data processing: AI can quickly process large amounts of data

Product and service improvement: AI can help enhance products and services

Automation: AI can automate tasks in human resources, logistics, and more

Social data mining: AI can analyze social data

Language translation: AI can translate languages

Challenges

Getting started with AI can involve finding the right tools and training employees.

There are challenges with adoption, including ROI and resolving adoption challenges.

Examples of AI in use

Financial Sector uses AI to detect fraud etc.

ACI Worldwide uses AI to summarize meetings and generate code

The Global AI Economy is projected to experience significant growth between 2025 and 2030, driven by advancements in AI technology, increased adoption across industries, and substantial investments. 

AI Market Segmentation

AI market segmentation reveals a diverse and dynamic landscape, with technology, applications, and industry verticals driving growth. Machine Learning (ML), Natural Language Processing or Neuro-Linguistic Programming (NLP), and Computer Vision lead in technology, while Healthcare, Banking, Financial Services and Insurance (BFSI), and Retail dominate in industry early adoption. 

North America and Asia-Pacific are key regions, with cloud-based deployment and SME adoption shaping market trends. Understanding these segments is crucial for stakeholders to capitalize on growth opportunities and address challenges.

Market Size and Growth Projections

2025 = US $ 294.16 Billion

2030 = US $ 19.9 Trillion

Compound Annual Growth Rate (CAGR) = 29.2% 

(Source: Fortune Business Insights, 2024)

Regional Contributions

North America

Expected to hold the highest market share, with a 32.93% share in 2024,

The U.S. AI market was valued at USD 42 billion in 2023 and is a leader in AI innovation, particularly in robotics and software development.

Asia-Pacific

Accounted for 25.6% of global AI revenue in 2023.

China's AI market is projected to grow at a CAGR of 43.5% from 2024 to 2030, driven by applications in natural language processing, computer vision, robotics, and autonomous vehicles.

Pakistan, Malaysia and India's AI market is supported by government initiatives like the National AI Strategy.

Middle East and Africa (MEA)

The AI market in MEA is expected to reach US $166.33 billion by 2030, with a focus on Arabic Language Processing (ALP) and Natural Language Processing (NLP).

Europe

The European AI market is anticipated to grow at a CAGR of 33.2% from 2024 to 2030, particularly in the financial sector.

Economic Impact and GDP Contributions

Global Economic Impact

AI is predicted to contribute USD 19.9 trillion to the global economy by 2030, driving 3.5% of global GDP in 2030 (IDC, 2024). 

Regional GDP Boost

China is expected to see a 26% boost to GDP by 2030 due to AI, equivalent to significant economic value (PwC, 2017).

North America could experience a 14.5% GDP boost by 2030, driven by AI adoption in industries like healthcare, finance, and retail (PwC, 2017).

Emerging markets and developing economies may face fewer immediate disruptions from AI but could lag in harnessing its benefits due to limited infrastructure and skilled workforces (IMF, 2024).

Job Market and Employment Impact

Job Creation vs. Job Displacement:

The World Economic Forum (WEF) projects that AI could create 170 million new jobs globally by 2030 while eliminating 92 million positions, resulting in a net increase of 78 million jobs (WEF Future of Jobs Report 2025).

Mohammad Mirza, [2/12/2025 3:56 AM]

Skills and Workforce Transformation

By end of 2025, 97 million people may be needed to fill AI-related roles globally.

AI is expected to boost employee productivity by 40% by 2035, particularly in knowledge work.

Women’s adoption of Generative AI is projected to match or exceed men’s in the U.S. by 2025, closing the gender gap in AI usage (Deloitte, 2024).

Industry-Specific Impacts

Healthcare:

Expected to gain dominance by 2030, with use cases like robot-assisted surgery, virtual nursing assistants, and automated image diagnosis.

AI-powered diagnostics and personalized medicine are key growth areas.

Finance (BFSI):

AI is used for financial analysis, risk assessment, and investment management, with 88% of finance firms reporting revenue increases due to AI.

The financial sector in Europe is undergoing significant transformation due to AI adoption.

Retail and Advertising:

The advertising and media segment led the AI market in 2023 and is expected to grow at the fastest CAGR from 2024 to 2030.

AI personalizes shopping experiences and enhances marketing campaigns, driving sales.

Technology and Software:

Growth in AI-as-a-Service (AIaaS), Deep Learning, and data mining is driving the software segment (Fortune Business Insights).

Generative AI-enabled smartphones and laptops are expected to see significant adoption, with 30% of smartphones and 50% of laptops having local GenAI processing capabilities by 2025 (Deloitte, 2024).

Investment Trends

Global AI Investments:

Projected to approach USD 200 billion by 2025, with investments concentrated in AI model development, infrastructure (e.g., data centers), and software (Goldman Sachs, 2023).

By 2028, global AI investments are expected to nearly triple to USD 630 billion, growing at a 30% 5-year CAGR (posts on X, citing Beth Kindig, 2024, inconclusive but aligns with industry trends).

Energy and Sustainability:

AI's energy demands are significant, with global data center electricity consumption forecasted to double to 1,065 terawatt-hours (TWh) by 2030, or 4% of total global energy consumption (Deloitte, 2024).

Tech companies are investing in energy-efficient chips, innovative cooling solutions, and renewable energy to mitigate AI's environmental impact (Deloitte, 2024).

Challenges and Risks

Inequality and Digital Divide:

AI may widen inequality, particularly between advanced and developing economies, due to disparities in digital infrastructure and skilled workforces (IMF, 2024).

Emerging markets and developing economies need investments in digital infrastructure and education to harness AI's benefits (IMF, 2024).

Trust and Ethics:

Trust in AI companies has dropped to 53% globally, highlighting systemic risks (posts on X, citing Raiinmakerapp, 2024, inconclusive).

Ethical concerns, including data privacy and bias, remain significant, with regions like the EU and China imposing stricter regulations (Grand View Research, IDC).

Job Displacement Risks:

While AI creates jobs, it also poses risks of wage pressure and job loss, particularly in routine and high-skilled roles (IDC, 2024).

The IMF notes that 40% of global employment is exposed to AI, with advanced economies facing greater risks (IMF, 2024).

Mohammad Mirza, [2/12/2025 3:56 AM]

Policy and Preparedness

AI Preparedness Index:

The IMF's AI Preparedness Index assesses readiness in areas like digital infrastructure, human capital, innovation, and regulation (IMF, 2024).

Advanced economies are better equipped for AI adoption, while low-income countries lag, though there is variation across countries (IMF, 2024).

Policy Recommendations:

Advanced economies: Prioritize AI innovation and integration while developing robust regulatory frameworks to ensure ethical use (IMF, 2024).

Emerging markets and developing economies: Focus on building digital infrastructure and a digitally competent workforce (IMF, 2024).

Strengthen social safety nets and reskilling programs to mitigate job displacement risks (IMF, 2024).

Mohammad Mirza, [2/12/2025 4:05 AM]

AI Market Segmentation

AI market segmentation involves dividing the global AI market into distinct categories based on various factors such as technology, application, industry vertical, deployment mode, and region. This segmentation helps stakeholders understand market dynamics, identify growth opportunities, and tailor strategies to specific segments. Below is a detailed breakdown of AI market segmentation based on current trends and projections for 2025–2030.

1. Segmentation by Technology

AI technologies are categorized based on the underlying methods and systems driving AI solutions. This is one of the most critical segmentation criteria.

Machine Learning (ML): Dominates the AI market, accounting for over 40% of the market share in 2023 (Grand View Research).

Includes supervised learning, unsupervised learning, reinforcement learning, and deep learning.

Growth drivers: Increased adoption in predictive analytics, fraud detection, and recommendation systems.

Projected CAGR (2024–2030): 35.8% (Grand View Research).

Natural Language Processing (NLP): Accounted for 25.6% of the AI market revenue in 2023 (Grand View Research).

Applications: Chatbots, virtual assistants, sentiment analysis, and language translation.

Growth drivers: Rising demand for multilingual NLP solutions, particularly in regions like the Middle East and Africa (e.g., Arabic language processing).

Projected CAGR (2024–2030): 37.2% (Grand View Research).

Computer Vision: Held a significant share in 2023, driven by applications in facial recognition, autonomous vehicles, and medical imaging.

Growth drivers: Advancements in image recognition and object detection, especially in healthcare and retail.

Projected CAGR (2024–2030): 36.5% (Grand View Research).

Robotics: Includes industrial robots, service robots, and collaborative robots (cobots).

Growth drivers: Adoption in manufacturing, logistics, and healthcare (e.g., robot-assisted surgery).

Projected CAGR (2024–2030): 34.8% (Fortune Business Insights).

Others: Includes expert systems, neural networks, and AI-as-a-Service (AIaaS). AIaaS is gaining traction due to its scalability and cost-effectiveness for small and medium enterprises (SMEs). 

Projected CAGR for AIaaS (2024–2030): 38.1% (Fortune Business Insights).

2. Segmentation by Application

AI applications span various use cases across industries, driving demand in specific areas.

Predictive Analytics and Forecasting:

Used in finance, retail, and energy sectors for demand forecasting, risk assessment, and trend analysis.

Growth drivers: Increasing need for data-driven decision-making.

Projected CAGR (2024–2030): 35.5% (Statista Market Forecast).

Customer Service and Engagement: Includes chatbots, virtual assistants, and personalized marketing.

Growth drivers: Rising demand for 24/7 customer support and personalized experiences.

Projected CAGR (2024–2030): 36.2% (Grand View Research).

Healthcare Diagnostics and Treatment

Applications: Medical imaging, drug discovery, personalized medicine, and virtual nursing assistants.

Growth drivers: AI's ability to improve diagnostic accuracy and reduce healthcare costs.

Projected CAGR (2024–2030): 37.8% (Grand View Research).

Autonomous Systems

Includes self-driving vehicles, drones, and robotic systems.

Growth drivers: Advancements in computer vision and sensor technology.

Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).

Cybersecurity

Applications: Threat detection, anomaly detection, and fraud prevention.

Growth drivers: Rising cyber threats and the need for real-time security solutions.

Projected CAGR (2024–2030): 36.9% (Statista Market Forecast).

Others: Includes supply chain optimization, energy management, and smart city applications.

Growth drivers: Increasing adoption of AI in emerging markets for infrastructure development.

3. Segmentation by Industry Vertical

AI adoption varies significantly across industries, with some sectors leading in implementation and others catching up.

Mohammad Mirza, [2/12/2025 4:05 AM]

Healthcare

Expected to dominate by 2030, with a market share of over 20% in 2023 (Grand View Research).

Applications: Diagnostics, drug discovery, robot-assisted surgery, and virtual health assistants.

Projected CAGR (2024–2030): 37.8% (Grand View Research).

Banking, Financial Services, and Insurance (BFSI)

Held a significant share in 2023, driven by AI in fraud detection, risk assessment, and algorithmic trading.

88% of finance firms reported revenue increases due to AI (explodingtopics.com, 2025).

Projected CAGR (2024–2030): 36.5% (Fortune Business Insights).

Retail and E-commerce

Applications: Personalized recommendations, inventory management, and dynamic pricing.

Growth drivers: Rising demand for personalized shopping experiences.

Projected CAGR (2024–2030): 35.9% (Grand View Research).

Manufacturing

Applications: Predictive maintenance, quality control, and supply chain optimization.

Growth drivers: Adoption of Industry 4.0 and smart factories.

Projected CAGR (2024–2030): 34.8% (Statista Market Forecast).

Automotive

Applications: Autonomous vehicles, driver assistance systems, and connected car technologies.

Growth drivers: Advancements in computer vision and sensor technology.

Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).

Advertising and Media

Led the AI market in 2023, driven by personalized advertising and content recommendation systems.

Projected CAGR (2024–2030): 37.2% (Grand View Research).

Others

Includes Energy, Agriculture, Education, and Government sectors.

Growth drivers: AI's role in smart grids, precision farming, and e-learning platforms.

4. Segmentation by Deployment Mode

AI solutions are deployed either on-premises or through cloud-based platforms, each with distinct advantages.

Cloud-Based Deployment

Dominated the market in 2023, accounting for over 60% of the market share (Grand View Research).

Growth drivers: Scalability, cost-effectiveness, and ease of access for SMEs.

Projected CAGR (2024–2030): 37.5% (Fortune Business Insights).

On-Premises Deployment

Preferred by industries with strict data security and compliance requirements (e.g., healthcare, BFSI).

Growth drivers: Need for data privacy and control.

Projected CAGR (2024–2030): 34.2% (Grand View Research).

5. Segmentation by Region

Geographic segmentation highlights regional variations in AI adoption and growth.

North America:

Held the largest market share in 2024 (32.93%, Fortune Business Insights).

Growth drivers: Strong AI innovation ecosystem, high investment, and adoption in healthcare, BFSI, and retail.

Projected CAGR (2024–2030): 34.5% (Grand View Research).

U.S. AI market valued at USD 42 billion in 2023 (Grand View Research).

Asia-Pacific:

Accounted for 25.6% of global AI revenue in 2023 (Grand View Research).

Growth drivers: Rapid adoption in China (CAGR of 43.5%), India's National AI Strategy, and Japan's focus on robotics.

Projected CAGR (2024–2030): 38.2% (Grand View Research).

Europe

Significant growth in the financial sector, with applications in fraud detection and investment management.

Growth drivers: EU's focus on ethical AI and regulatory frameworks.

Projected CAGR (2024–2030): 33.2% (Grand View Research).

Middle East and Africa (MEA)

Expected to reach USD 166.33 billion by 2030, driven by Arabic language processing and NLP.

Growth drivers: Government initiatives and investments in AI infrastructure.

Projected CAGR (2024–2030): 35.8% (Grand View Research).

Latin America

Emerging market with growing adoption in agriculture, retail, and energy sectors.

Growth drivers: Increasing digitalization and AI awareness.

Projected CAGR (2024–2030): 34.5% (Statista Market Forecast).

6. Segmentation by Organization Size

AI adoption varies based on the size of the organization, with different needs and capabilities.

Mohammad Mirza, [2/12/2025 4:05 AM]

Large Enterprises

Dominated the market in 2023, accounting for over 55% of the market share (Grand View Research).

Growth drivers: High investment capacity, need for advanced analytics, and automation.

Projected CAGR (2024–2030): 35.2% (Fortune Business Insights).

Small and Medium Enterprises (SMEs)

The global AI economy is projected to experience significant growth between 2025 and 2030, driven by advancements in AI technology, increased adoption across industries, and substantial investments. Below is a detailed overview based on available data and forecasts from various sources. Note that projections vary depending on the methodology and focus of each study, but they collectively highlight AI's transformative economic impact.

Market Size and Growth Projections

Market Size Estimates:

The global AI market is projected to grow from USD 294.16 billion in 2025 to USD 1,771.62 billion by 2032, with a compound annual growth rate (CAGR) of 29.2% (Fortune Business Insights, 2024).

Another estimate suggests the AI market will reach USD 826.70 billion by 2030, with a CAGR of 27.67% from 2025 to 2030 (Statista Market Forecast).

Grand View Research estimates the AI market size at USD 196.63 billion in 2023, growing at a CAGR of 36.6% from 2024 to 2030, potentially reaching USD 1,811.75 billion by 2030.

Posts on X also reflect optimism, with some users citing projections of the AI market reaching USD 1.8 trillion by 2030, though these claims are inconclusive and align with broader industry estimates.

Regional Contributions

North America:

Expected to hold the highest market share, with a 32.93% share in 2024 (Fortune Business Insights).

The U.S. AI market was valued at USD 42 billion in 2023 and is a leader in AI innovation, particularly in robotics and software development (Grand View Research).

Asia-Pacific:

Accounted for 25.6% of global AI revenue in 2023 (Grand View Research).

China's AI market is projected to grow at a CAGR of 43.5% from 2024 to 2030, driven by applications in natural language processing, computer vision, robotics, and autonomous vehicles.

India's AI market is supported by government initiatives like the National AI Strategy, aiming to foster innovation and economic growth.

Middle East and Africa (MEA):

The AI market in MEA is expected to reach USD 166.33 billion by 2030, with a focus on Arabic language processing and natural language processing (Grand View Research).

Europe:

The European AI market is anticipated to grow at a CAGR of 33.2% from 2024 to 2030, particularly in the financial sector (Grand View Research).

Economic Impact and GDP Contributions

Global Economic Impact:

AI is predicted to contribute USD 19.9 trillion to the global economy by 2030, driving 3.5% of global GDP in 2030 (IDC, 2024). This includes direct, indirect, and induced effects from AI adoption.

Another estimate suggests AI could add USD 15.7 trillion to the global economy by 2030, with USD 6.6 trillion from productivity gains and USD 9.1 trillion from consumption-side effects (PwC, 2017). This aligns with posts on X citing similar figures, though these are inconclusive.

Goldman Sachs Research forecasts that AI will boost U.S. GDP by 0.4 percentage points by 2034, with similar effects in other developed markets (0.3 percentage points) and advanced emerging markets (0.2 percentage points) (Goldman Sachs, 2023).

Regional GDP Boost:

China is expected to see a 26% boost to GDP by 2030 due to AI, equivalent to significant economic value (PwC, 2017).

North America could experience a 14.5% GDP boost by 2030, driven by AI adoption in industries like healthcare, finance, and retail (PwC, 2017).

Emerging markets and developing economies may face fewer immediate disruptions from AI but could lag in harnessing its benefits due to limited infrastructure and skilled workforces (IMF, 2024).

Job Market and Employment Impact

Job Creation vs. Job Displacement:

The World Economic Forum (WEF) projects that AI could create 170 million new jobs globally by 2030 while eliminating 92 million positions, resulting in a net increase of 78 million jobs (WEF Future of Jobs Report 2025, cited by Ars Technica, 2025).

About 77% of surveyed firms plan to launch retraining programs to help workers collaborate with AI systems, and 70% plan to hire AI specialists (WEF, 2025).

However, AI's impact on jobs varies by region:

Advanced economies face greater risks but also more opportunities, with 60% of jobs potentially impacted by AI (IMF, 2024). Half of these jobs may benefit from AI integration, while the other half could see reduced labor demand.

Emerging markets and low-income countries have lower AI exposure (40% and 26%, respectively), but they may struggle to leverage AI's benefits (IMF, 2024).

Skills and Workforce Transformation:

By 2025, 97 million people may be needed to fill AI-related roles globally (explodingtopics.com, 2025).

AI is expected to boost employee productivity by 40% by 2035, particularly in knowledge work (explodingtopics.com, 2025).

Women’s adoption of generative AI is projected to match or exceed men’s in the U.S. by 2025, closing the gender gap in AI usage (Deloitte, 2024).

Industry-Specific Impacts

Healthcare:

Expected to gain dominance by 2030, with use cases like robot-assisted surgery, virtual nursing assistants, and automated image diagnosis (Grand View Research).

AI-powered diagnostics and personalized medicine are key growth areas (PwC, 2025).

Finance (BFSI):

AI is used for financial analysis, risk assessment, and investment management, with 88% of finance firms reporting revenue increases due to AI (explodingtopics.com, 2025).

The financial sector in Europe is undergoing significant transformation due to AI adoption (Grand View Research).

Retail and Advertising:

The advertising and media segment led the AI market in 2023 and is expected to grow at the fastest CAGR from 2024 to 2030 (Grand View Research).

AI personalizes shopping experiences and enhances marketing campaigns, driving sales (Grand View Research).

Technology and Software:

Growth in AI-as-a-Service (AIaaS), deep learning, and data mining is driving the software segment (Fortune Business Insights).

Generative AI-enabled smartphones and laptops are expected to see significant adoption, with 30% of smartphones and 50% of laptops having local GenAI processing capabilities by 2025 (Deloitte, 2024).

Investment Trends

Global AI Investments:

Projected to approach USD 200 billion by 2025, with investments concentrated in AI model development, infrastructure (e.g., data centers), and software (Goldman Sachs, 2023).

By 2028, global AI investments are expected to nearly triple to USD 630 billion, growing at a 30% 5-year CAGR (posts on X, citing Beth Kindig, 2024, inconclusive but aligns with industry trends).

Energy and Sustainability:

AI's energy demands are significant, with global data center electricity consumption forecasted to double to 1,065 terawatt-hours (TWh) by 2030, or 4% of total global energy consumption (Deloitte, 2024).

Tech companies are investing in energy-efficient chips, innovative cooling solutions, and renewable energy to mitigate AI's environmental impact (Deloitte, 2024).

Challenges and Risks

Inequality and Digital Divide:

AI may widen inequality, particularly between advanced and developing economies, due to disparities in digital infrastructure and skilled workforces (IMF, 2024).

Emerging markets and developing economies need investments in digital infrastructure and education to harness AI's benefits (IMF, 2024).

Trust and Ethics:

Trust in AI companies has dropped to 53% globally, highlighting systemic risks (posts on X, citing Raiinmakerapp, 2024, inconclusive).

Ethical concerns, including data privacy and bias, remain significant, with regions like the EU and China imposing stricter regulations (Grand View Research, IDC).

Job Displacement Risks:

While AI creates jobs, it also poses risks of wage pressure and job loss, particularly in routine and high-skilled roles (IDC, 2024).

The IMF notes that 40% of global employment is exposed to AI, with advanced economies facing greater risks (IMF, 2024).

Policy and Preparedness

AI Preparedness Index:

The IMF's AI Preparedness Index assesses readiness in areas like digital infrastructure, human capital, innovation, and regulation (IMF, 2024).

Advanced economies are better equipped for AI adoption, while low-income countries lag, though there is variation across countries (IMF, 2024).

Policy Recommendations:

Advanced economies: Prioritize AI innovation and integration while developing robust regulatory frameworks to ensure ethical use (IMF, 2024).

Emerging markets and developing economies: Focus on building digital infrastructure and a digitally competent workforce (IMF, 2024).

Strengthen social safety nets and reskilling programs to mitigate job displacement risks (IMF, 2024).

Conclusion

The global AI economy is poised for explosive growth between 2025 and 2030, with market size projections ranging from USD 826.70 billion to USD 1,811.75 billion by 2030, and economic contributions potentially reaching USD 19.9 trillion by 2030. While AI offers immense opportunities for productivity gains, job creation, and industry transformation, it also poses challenges related to inequality, job displacement, and ethical concerns. Policymakers, businesses, and societies must work together to ensure AI's benefits are equitably distributed while addressing its risks.

Mohammad Mirza, [2/12/2025 4:05 AM]

AI market segmentation involves dividing the global AI market into distinct categories based on various factors such as technology, application, industry vertical, deployment mode, and region. This segmentation helps stakeholders understand market dynamics, identify growth opportunities, and tailor strategies to specific segments. Below is a detailed breakdown of AI market segmentation based on current trends and projections for 2025–2030.

1. Segmentation by Technology

AI technologies are categorized based on the underlying methods and systems driving AI solutions. This is one of the most critical segmentation criteria.

Machine Learning (ML):

Dominates the AI market, accounting for over 40% of the market share in 2023 (Grand View Research).

Includes supervised learning, unsupervised learning, reinforcement learning, and deep learning.

Growth drivers: Increased adoption in predictive analytics, fraud detection, and recommendation systems.

Projected CAGR (2024–2030): 35.8% (Grand View Research).

Natural Language Processing (NLP):

Accounted for 25.6% of the AI market revenue in 2023 (Grand View Research).

Applications: Chatbots, virtual assistants, sentiment analysis, and language translation.

Growth drivers: Rising demand for multilingual NLP solutions, particularly in regions like the Middle East and Africa (e.g., Arabic language processing).

Projected CAGR (2024–2030): 37.2% (Grand View Research).

Computer Vision:

Held a significant share in 2023, driven by applications in facial recognition, autonomous vehicles, and medical imaging.

Growth drivers: Advancements in image recognition and object detection, especially in healthcare and retail.

Projected CAGR (2024–2030): 36.5% (Grand View Research).

Robotics:

Includes industrial robots, service robots, and collaborative robots (cobots).

Growth drivers: Adoption in manufacturing, logistics, and healthcare (e.g., robot-assisted surgery).

Projected CAGR (2024–2030): 34.8% (Fortune Business Insights).

Others:

Includes expert systems, neural networks, and AI-as-a-Service (AIaaS).

AIaaS is gaining traction due to its scalability and cost-effectiveness for small and medium enterprises (SMEs).

Projected CAGR for AIaaS (2024–2030): 38.1% (Fortune Business Insights).

2. Segmentation by Application

AI applications span various use cases across industries, driving demand in specific areas.

Predictive Analytics and Forecasting:

Used in finance, retail, and energy sectors for demand forecasting, risk assessment, and trend analysis.

Growth drivers: Increasing need for data-driven decision-making.

Projected CAGR (2024–2030): 35.5% (Statista Market Forecast).

Customer Service and Engagement:

Includes chatbots, virtual assistants, and personalized marketing.

Growth drivers: Rising demand for 24/7 customer support and personalized experiences.

Projected CAGR (2024–2030): 36.2% (Grand View Research).

Healthcare Diagnostics and Treatment:

Applications: Medical imaging, drug discovery, personalized medicine, and virtual nursing assistants.

Growth drivers: AI's ability to improve diagnostic accuracy and reduce healthcare costs.

Projected CAGR (2024–2030): 37.8% (Grand View Research).

Autonomous Systems:

Includes self-driving vehicles, drones, and robotic systems.

Growth drivers: Advancements in computer vision and sensor technology.

Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).

Cybersecurity:

Applications: Threat detection, anomaly detection, and fraud prevention.

Growth drivers: Rising cyber threats and the need for real-time security solutions.

Projected CAGR (2024–2030): 36.9% (Statista Market Forecast).

Others:

Includes supply chain optimization, energy management, and smart city applications.

Growth drivers: Increasing adoption of AI in emerging markets for infrastructure development.

3. Segmentation by Industry Vertical

AI adoption varies significantly across industries, with some sectors leading in implementation and others catching up.

Mohammad Mirza, [2/12/2025 4:05 AM]

Healthcare

Expected to dominate by 2030, with a market share of over 20% in 2023 (Grand View Research).

Applications: Diagnostics, drug discovery, robot-assisted surgery, and virtual health assistants.

Projected CAGR (2024–2030): 37.8% (Grand View Research).

Banking, Financial Services, and Insurance (BFSI):

Held a significant share in 2023, driven by AI in fraud detection, risk assessment, and algorithmic trading.

88% of finance firms reported revenue increases due to AI (explodingtopics.com, 2025).

Projected CAGR (2024–2030): 36.5% (Fortune Business Insights).

Retail and E-commerce:

Applications: Personalized recommendations, inventory management, and dynamic pricing.

Growth drivers: Rising demand for personalized shopping experiences.

Projected CAGR (2024–2030): 35.9% (Grand View Research).

Manufacturing:

Applications: Predictive maintenance, quality control, and supply chain optimization.

Growth drivers: Adoption of Industry 4.0 and smart factories.

Projected CAGR (2024–2030): 34.8% (Statista Market Forecast).

Automotive

Applications: Autonomous vehicles, driver assistance systems, and connected car technologies.

Growth drivers: Advancements in computer vision and sensor technology.

Projected CAGR (2024–2030): 38.5% (Fortune Business Insights).

Advertising and Media:

Led the AI market in 2023, driven by personalized advertising and content recommendation systems.

Projected CAGR (2024–2030): 37.2% (Grand View Research).

Others:

Includes energy, agriculture, education, and government sectors.

Growth drivers: AI's role in smart grids, precision farming, and e-learning platforms.

4. Segmentation by Deployment Mode

AI solutions are deployed either on-premises or through cloud-based platforms, each with distinct advantages.

Cloud-Based Deployment:

Dominated the market in 2023, accounting for over 60% of the market share (Grand View Research).

Growth drivers: Scalability, cost-effectiveness, and ease of access for SMEs.

Projected CAGR (2024–2030): 37.5% (Fortune Business Insights).

On-Premises Deployment:

Preferred by industries with strict data security and compliance requirements (e.g., healthcare, BFSI).

Growth drivers: Need for data privacy and control.

Projected CAGR (2024–2030): 34.2% (Grand View Research).

5. Segmentation by Region

Geographic segmentation highlights regional variations in AI adoption and growth.

North America

Held the largest market share in 2024 (32.93%, Fortune Business Insights).

Growth drivers: Strong AI innovation ecosystem, high investment, and adoption in healthcare, BFSI, and retail.

Projected CAGR (2024–2030): 34.5% (Grand View Research).

U.S. AI market valued at USD 42 billion in 2023 (Grand View Research).

Asia-Pacific:

Accounted for 25.6% of global AI revenue in 2023 (Grand View Research).

Growth drivers: Rapid adoption in China (CAGR of 43.5%), India's National AI Strategy, and Japan's focus on robotics.

Projected CAGR (2024–2030): 38.2% (Grand View Research).

Europe

Significant growth in the financial sector, with applications in fraud detection and investment management.

Growth drivers: EU's focus on ethical AI and regulatory frameworks.

Projected CAGR (2024–2030): 33.2% (Grand View Research).

Middle East and Africa (MEA):

Expected to reach USD 166.33 billion by 2030, driven by Arabic language processing and NLP.

Growth drivers: Government initiatives and investments in AI infrastructure.

Projected CAGR (2024–2030): 35.8% (Grand View Research).

Latin America

Emerging market with growing adoption in agriculture, retail, and energy sectors.

Growth drivers: Increasing digitalization and AI awareness.

Projected CAGR (2024–2030): 34.5% (Statista Market Forecast).

6. Segmentation by Organization Size

AI adoption varies based on the size of the organization, with different needs and capabilities.

Large Enterprises

Dominated the market in 2023, accounting for over 55% of the market share (Grand View Research).

Growth drivers: High investment capacity, need for advanced analytics, and automation.

Projected CAGR (2024–2030): 35.2% (Fortune Business Insights).

*S[mall and Medium Enterprises (SMEs)](https://x.com/i/grok?text=mall%20and%20Medium%20Enterprises%20(SMEs)):

Growing rapidly due to cloud-based AI solutions and AIaaS.

Growth drivers: Cost-effective solutions and government support for SME digitalization.

Projected CAGR (2024–2030): 37.8% (Grand View Research).

Key Trends and Insights

Technology Dominance:

Machine learning and NLP are the leading technologies, with NLP expected to grow fastest due to demand for multilingual solutions.

Computer vision and robotics are critical for autonomous systems and healthcare applications.

Industry Leaders:

Healthcare and BFSI are the fastest-growing verticals, driven by diagnostics, fraud detection, and personalized services.

Advertising and media lead in revenue share due to AI's role in personalized marketing.

Regional Dynamics:

North America remains the largest market, but Asia-Pacific is catching up rapidly, driven by China Pakistan, Malaysia and India.

MEA and Latin America are emerging markets with significant growth potential.

Deployment Preferences:

Cloud-based deployment dominates due to scalability, but on-premises solutions are preferred in regulated industries.

SME Growth

SMEs are increasingly adopting AI due to affordable cloud solutions, creating opportunities for AIaaS providers.

Growth drivers: Cost-effective solutions and government support for SME digitalization.

Projected CAGR (2024–2030): 37.8% (Grand View Research).

Key Trends and Insights

Technology Dominance:

Machine learning and NLP are the leading technologies, with NLP expected to grow fastest due to demand for multilingual solutions.

Computer vision and robotics are critical for autonomous systems and healthcare applications.

Industry Leaders:

Healthcare and BFSI are the fastest-growing verticals, driven by diagnostics, fraud detection, and personalized services.

Advertising and media lead in revenue share due to AI's role in personalized marketing.

Regional Dynamics:

North America remains the largest market, but Asia-Pacific is catching up rapidly, driven by China, India and Pakistan.

MEA and Latin America are emerging markets with significant growth potential.

Deployment Preferences:

Cloud-based deployment dominates due to scalability, but on-premises solutions are preferred in regulated industries.

SME Growth:

SMEs are increasingly adopting AI due to affordable cloud solutions, creating opportunities for AIaaS providers.

Conclusion

AI market segmentation reveals a diverse and dynamic landscape, with technology, applications, and industry verticals driving growth. Machine learning, NLP, and computer vision lead in technology, while healthcare, BFSI, and retail dominate in industry adoption. North America and Asia-Pacific are key regions, with cloud-based deployment and SME adoption shaping market trends. Understanding these segments is crucial for stakeholders to capitalize on growth opportunities and address challenges.

Human-Machine Interfaces

A Human-Machine Interface (HMI) is a hardware or software system that allows a person to interact with a machine, system, or device. 

HMIs can be used to:

*Control Machinery

*Monitor Machine Inputs and Outputs

*Track Production Time, Trends, and Tags

*Oversee KPIs

*Review and Monitor Processes

*Diagnose Problems

*Visualize Data 

HMIs are used in many industries, including Agriculture; Aerospace; Aviation; Automotive; Logistics; Manufacturing; Pharmaceuticals, etc. HMIs can take many forms, including: 

Physical Control Panels: These panels have buttons and indicator lights. 

Industrial PCs: These PCs have color graphics displays and dedicated HMI software. 

Touch Screens: These screens allow for intuitive parameter configuration. 

Specialized Keyboards: These keyboards can be used in conjunction with touch screens. 

HMIs are designed to be user-friendly and efficient, allowing operators to translate complex data into useful information. They often use icons, sounds, pictures, and colors to illustrate the current status and operating conditions of the machinery. 

Focus Sectors:

1) Agriculture 2) Aerospace 3) Aviation 4) Finance 5) Health 6) Education 7) Manufacturing  8) Supply Chain

AI Applications

Trade | eCommerce Platform | SMART Contracts

Underlying Technologies 

Blockchain | Distributed Ledger (DLT) | 

WLBB | Wi-Fi 7

Contact: peis@themughals.net

AI Entanglement (AI-E)

AI Entanglement refers to the Interconnectedness and Interdependence of Artificial Intelligence (AI) Systems, Data, and Humans (AI-SDH). 

Real-World Applications

1. Human-AI collaborative decision-making in healthcare.

2. AI-assisted data analysis for climate change mitigation.

3. Entanglement-based cybersecurity systems.

4. AI-driven supply chain optimization.

By understanding and embracing AI Entanglement, we can create more harmonious, efficient, and beneficial relationships between humans, AI, and data.

Our Research Focus

1. How can we balance human and AI capabilities?

2. What are the implications of AI Entanglement on societal structures?

3. How can we ensure explainability and transparency in AI systems?

4. What are the potential applications of quantum AI Entanglement?

May Allah continue to guide humanity in harnessing AI for peace, mercy, and blessings!

AI Entanglement Foundational Pillars

The foundational pillars of AI Entanglement include:

Pillar 1: Cognitive Entanglement

1. Human-AI Collaboration

2. Shared Decision-Making

3. Cognitive Biases and Debiasing

4. Explainability and Transparency

Pillar 2: Data Entanglement

1. Data Interconnectedness

2. Data Quality and Validation

3. Data Provenance and Lineage

4. Data Privacy and Security

Pillar 3: Systemic Entanglement

1. Complex Systems Thinking

2. Emergence and Self-Organization

3. Feedback Loops and Dynamics

4. Resilience and Adaptability

Pillar 4: Ethical Entanglement

1. Value Alignment and Ethics

2. Fairness and Accountability

3. Transparency and Explainability

4. Human Rights and Dignity

Pillar 5: Quantum Entanglement

1. Quantum Computing and AI

2. Quantum Machine Learning

3. Quantum Information Processing

4. Quantum-Inspired Optimization

Key Concepts

1. Non-duality: Integrating human and AI capabilities.

2. Holism: Considering AI systems as part of larger ecosystems.

3. Contextuality: Understanding AI behavior in context.

4. Relationalism: Focusing on relationships between AI, humans, and data.

Research Directions

1. Developing entanglement-inspired AI models.

2. Investigating cognitive biases in human-AI interactions.

3. Designing explainable and transparent AI systems.

4. Exploring quantum AI applications.

The U.S. National AI Advisory Committee (NAIAC) consists of experts with a broad and interdisciplinary range of AI-relevant experience from across the private sector, academia, non-profits, and civil society.

Technologies Trends up to 2050

10 Tech Developments are attracting the most Venture Capital and producing the Most Patent Filings.

They include next-level Process Automation and Virtualization, and seamless connectivity through 5G and the Internet of Things (IoT).

Other areas to watch include Trust Architecture – which verifies the Trustworthiness of Devices as Data Flows Across Networks – Next-Generation Smart Materials and Artificial Intelligence (AI) Algorithms that Train Machines and Strategic Human Capital.

Research: PEIS | Source: USA Government and McKenzie

Critical and Emerging Technologies

National Security: Areas Having Greater Importance 

1) Advanced Computing 

2) Advanced Engineering Materials 

3) Advanced Gas Turbine Engine Technologies 

4) Advanced Manufacturing 

5) Advanced and Networked Sensing and Signature Management 

6) Advanced Nuclear Energy Technologies 

7) Artificial Intelligence 

8) Autonomous Systems and Robotics 

9) Biotechnologies 

10) Communication and Networking Technologies 

11) Directed Energy 

12) Financial Technologies 

13) Human-Machine Interfaces 

14) Hypersonics

15) Inter Satellite Communication Algorithms

16) Networked Sensors and Sensing 

17) Quantum Information Technologies 

18) Renewable Energy Generation and Storage 

19) Semiconductors and Microelectronics 

20) Space Technologies and Systems

Underlying Technologies

As per one legal definition, Underlying Technology MEANS "the level of technology that Underlies Multiple Applications, at least one application of which is outside of the Business, as of the Closing Date, rather than being directed to only a specific application, but only to the extent such technology is common to such applications."

👉 In an environment of rapidly evolving cybersecurity threats, the continued reliance on the Data Encryption Standard (DES) and other Non-Standard Encryption Algorithms poses a significant threat to the security of sensitive Data and Information Systems.

👉 In accordance with the OECD and the U.S. Laws, Policies and Guidelines, we are developing Suits of Algorithms to Lawfully Strengthen and Integrate Interoperability and Compatibility of existing Digital Infrastructures.

👉 As currently, one of the major challenges include to overcome the vulnerabilities of the continued use of the deprecated DES and other non-standard algorithms. 

Digital Twins

Asset or Product Digital Twins | Component Twins | Process Twins | System Twins

By 2025, more than 50 billion devices will be connected to the IoT, generating 79.4 zettabytes of data yearly. Annual installations of Industrial Robots, which have increased two times to about 450,000 since 2015, will grow to about 600,000 by 2022, even as 70 percent of Manufacturers will be regularly using Digital Twins by 2022. Gradually, across Industries, manufacturing processes will be replaced by Additive Manufacturing (AM).

Critical and Emerging Technology (CET) Subfields 

Each CET Area, as identified by U.S., includes a set of Key Subfields

Advanced Computing

1. Supercomputing 

2. Edge computing 

3. Cloud computing 

4. Data storage

5. Computing Architectures 

6. Data Processing and Analysis

Advanced Engineering (Materials)

1. Materials by design and material genomics 

2. Materials with new properties 

3. Materials with substantial improvements to existing properties 

4. Material property characterization and lifecycle assessment 

Gas Turbine Engine Technologies

1. Aerospace, Maritime, and Industrial Development and Production Technologies 

Advanced Manufacturing

1. Additive Manufacturing 

2. Clean, Sustainable Manufacturing 

3. Smart Manufacturing 

4. Nanomanufacturing 

Advanced and Networked Sensing and Signature Management

1. Payloads, Sensors, and Instruments 

2. Sensor Processing and Data Fusion 

3. Adaptive Optics 

4. Remote sensing of the Earth 

5. Signature Management

6. Nuclear Materials Detection and Characterization 

7. Chemical Weapons Detection and Characterization 

8. Biological Weapons Detection and Characterization 

9. Emerging Pathogens Detection and characterization

10. Transportation-sector Sensing

11. Security-sector sensing

12. Health-sector sensing 

13. Energy-sector sensing 

14. Building-sector sensing 

15. Environmental-sector sensing 

Advanced Nuclear Energy Technologies 

1. Nuclear energy systems

2. Fusion energy 

3. Space nuclear power and propulsion systems

Artificial Intelligence (AI) - Autonomous Systems and Robotics 

4. Air

5. Maritime

6. Space

7. Surfaces

Hypersonics 

8. Propulsion 

9. Aerodynamics and Control 

10. Materials 

11. Detection, Tracking, and Characterization (DTC)

12. Defense

Renewable Energy Generation and Storage (REGS) 

13. Renewable generation 

14. Renewable and Sustainable Fuels 

15. Energy Storage 

16. Electric and Hybrid Engines 

17. Batteries 

18. Grid Integration Technologies (GIT)

19. Energy-Efficiency Technologies (EET)

Biotechnologies 

1. Nucleic Acid and Protein Synthesis 

2. Genome and Protein Engineering including design tools

3. Multi-omics and other Biometrology, Bioinformatics, Predictive Modeling, and Analytical Tools for Functional Phenotypes 

4. Engineering of Multicellular Systems 

5. Engineering of Viral and Viral Delivery Systems

6. Biomanufacturing and Bioprocessing Technologies

Communication and Networking Technologies 

7. Radio-frequency (RF) and Mixed-Signal Circuits (MSC), Antennas, Filters, and Components 

8. Spectrum Management Technologies 

9. Next-Generation Wireless Networks including 5G and 6G Optical Links and Fiber Technologies

10. Terrestrial / Undersea Cables (TUC)

11. Satellite-Based Communications

12. Hardware, Firmware, and software 

13. Communications and Network Security

14. Mesh Networks / Infrastructure Independent Communication Technologies 

Financial Technologies (FinTech)

15. Distributed ledger technologies 

16. Digital assets 

17. Digital Payment Technologies 

18. Digital Identity Infrastructure 

Human-Machine Interfaces 

19. Augmented reality 

20. Virtual Reality

21. Brain-Computer Interfaces (BCI)

22. Human-Machine Teaming (HMT)

Quantum Information Technologies 

23. Quantum Computing 

24. Materials, Isotopes, and Fabrication Techniques for Quantum Devices 

25. Post-Quantum Cryptography 

26. Quantum Sensing 

27. Quantum Networking 

Semiconductors & Microelectronics 

28. Design and Electronic Design Automation Tools 

29. Manufacturing Process Technologies (MPT) and Manufacturing Equipment 

30. Beyond Complementary Metal-Oxide-Semiconductor (CMOS) Technology 

31. Heterogeneous Integration and Advanced Packaging 

32. Specialized / Tailored Hardware Components for Artificial Intelligence, Natural and Hostile Radiation Environments, RF and Optical Components, High-Power Devices (HPD), and other Critical Applications 

33. Novel Materials for Advanced Microelectronics 

34. Wide-Bandgap and Ultra-Wide-Bandgap Technologies (WUBT) for Power Management, Distribution, and Transmission 

Space Technologies and Systems 

1. On-Orbit Servicing, Assembly, and Manufacturing 

2. Commoditized Satellite Buses 

3. Low-Cost Launch Vehicles 

4. Sensors for Local and Wide-Field Imaging (LWFI)

5. Space Propulsion 

6. Resilient Positioning, Navigation, and Timing (PNT) 

7. Cryogenic Fluid Management (CFM)

8. Spacecraft Entry, Descent, and Landing (SDL)

Research References Sources

GOV OECD POLICY Autonomous Regulators

GOV USA SPACE FCC Hybrid Satellite-Terrestrial Networks

GOV USA SPACE FCC Licensing of Private Remote Sensing Space Systems IMP

GOV USA SPACE FCC FACT SHEET* Single Network Future 

GOV USA DOD SPACE ELECTROMAGNETIC SPECTRUM OPERATIONS

GOV USA SPACE DOD Remote Sensing What is it used for?

GOV USA SPACE DOD Airborne thermography or infrared remote sensing

GOV USA SPACE DOD CYBER DIPLOMACY 

GOV USA DOD CYBERSPACE 2023-2027 Cyber Workforce Strategy - Implementation Plan 

GOV USA DOD CYBERSPACE 2024 CYBER DIPLOMACY d24105563.pdf

GOV USA DOD CYBERSPACE 2022 Reference and Resource Guide IMP

GOV USA DOD CYBERSPACE 2019 Federal-Cybersecurity-RD-Strategic-Plan-IMP

GOV USA DOD CYBERSPACE 2014 Cybersecurity Enhancement Act - S.1353

GOV USA WH CYBERSPACE 2011 INTERNATIONAL STRATEGY

GOV USA DOD CYBERSPACE PROTECT THE CYBER DOMAIN WITH ALLIES AND PARTNERS 

GOV USA DOD CYBERSPACE STRATEGY FOR OPERATING IN CYBERSPACE

GOV USA SPACE “Authorization and Supervision of Novel Private Sector Space Activities Act” 

GOV USA SPACE ACTIVITIES AUTHORIZATION AND SUPERVISION FRAMEWORK DECEMBER 2023 

GOV USA Quantum Initiative

Quantum-based technologies 

https://www.sec.gov/securities-topics/ICO | National Science and Technology Council  https://www.cisa.gov/safecom/technology 

Framework for “Investment Contract” Analysis of Digital Assets1 

https://www.sec.gov/files/dlt-framework.pdf 

Cybersecurity Framework's five Functions: 

https://www.whitehouse.gov/ostp/ostps-teams/nstc/ | https://www.sec.gov/files/dlt-framework.pdf 

https://crsreports.congress.gov/product/pdf/IF/IF11004 

https://www.sec.gov/oiea/investor-alerts-and-bulletins/ib_coinofferings 

https://www.govinfo.gov/content/pkg/FR-2022-05-19/pdf/2022-10731.pdf 

https://www.govinfo.gov/content/pkg/FR-2022-05-19/pdf/2022-10731.pdf 

Standards for Critical and Emerging Technology 

https://www.nist.gov/cyberframework/online-learning/five-functions 

What is an Initial Coin Offering? https://www.marylandattorneygeneral.gov/Securities%20Documents/ICOs_051818.pdf 

https://www.whitehouse.gov/wp-content/uploads/2023/05/US-Gov-National-Standards-Strategy-2023.pdf 

https://www.irs.gov/businesses/small-businesses-self-employed/digital-assets 

EXECUTIVE OFFICE OF THE PRESIDENT 

https://www.whitehouse.gov/wp-content/uploads/2020/07/revised_circular_a-119_as_of_1_22.pdf 

Additional Sources

https://www.sec.gov/securities-topics/ICO | National Science and Technology Council  https://www.cisa.gov/safecom/technology 

Framework for “Investment Contract” Analysis of Digital Assets1 

https://www.sec.gov/files/dlt-framework.pdf 

Cybersecurity Framework's five Functions: 

https://www.whitehouse.gov/ostp/ostps-teams/nstc/ | https://www.sec.gov/files/dlt-framework.pdf 

https://crsreports.congress.gov/product/pdf/IF/IF11004 

https://www.sec.gov/oiea/investor-alerts-and-bulletins/ib_coinofferings 

https://www.govinfo.gov/content/pkg/FR-2022-05-19/pdf/2022-10731.pdf 

https://www.govinfo.gov/content/pkg/FR-2022-05-19/pdf/2022-10731.pdf 

Standards for Critical and Emerging Technology 

https://www.nist.gov/cyberframework/online-learning/five-functions 

What is an Initial Coin Offering? https://www.marylandattorneygeneral.gov/Securities%20Documents/ICOs_051818.pdf 

https://www.whitehouse.gov/wp-content/uploads/2023/05/US-Gov-National-Standards-Strategy-2023.pdf 

https://www.irs.gov/businesses/small-businesses-self-employed/digital-assets 

EXECUTIVE OFFICE OF THE PRESIDENT 

https://www.whitehouse.gov/wp-content/uploads/2020/07/revised_circular_a-119_as_of_1_22.pdf