Inclusive Prosperity: Artificial Intelligence – Insights from Technology and Innovation Report 2025

Executive TL;DR

AI will generate $4.8T in market value by 2033, but benefits are heavily concentrated among a few players and nations. Deliberate action on infrastructure, skills, governance, and inclusion is essential to avoid deepening inequality.

• Impact: Job polarization and productivity divergence
• Modal leaders: Nvidia, Microsoft, Apple
• Emerging tech: Generative AI
• Key frameworks: Five As, AI Disclosure Mechanism, Worker-Centric Strategies
• Upside: Inclusive industrial growth if strategic gaps close

Executive Overview

Artificial Intelligence (AI) is reshaping economic structures at unprecedented speed, creating both immense opportunity and acute risks of deepening global inequality. Two forces stand out: the concentration of AI capabilities among a handful of technology giants and the widening infrastructure and skills gaps between countries. While developing economies are accelerating AI adoption to gain productivity, they face systemic barriers — limited data ecosystems, scarce compute resources, and chronic skills shortages — that could leave them permanently behind.

Policymakers have an urgent mandate: treat AI as a general-purpose technology (GPT), akin to electricity, capable of pervasive impact across sectors and society. Without proactive strategies, robust governance, and equitable access, AI’s benefits will remain concentrated, compounding disparities across regions and industries.

Top 10 Strategic Insights

1. AI Market Surge By 2033, AI will create an estimated $4.8 trillion in market value, accounting for nearly 30% of frontier technologies.
   Implication: Executives must prepare for intensified competition and rapid innovation cycles.
2. Dominance of Few Players Five U.S. companies control most AI investments, each exceeding $2 trillion in market capitalization. The private sector now outpaces academia and government in AI research output and production of models.
   Implication: Smaller firms and public institutions will need alliances and shared infrastructure to avoid marginalization.
3. Innovation Concentration China and the U.S. together hold nearly two-thirds of patents in frontier technologies and over one-third of AI publications since 2000. The Revealed Technology Advantage (RTA) index shows deep specialization — Germany in wind energy, Japan in EVs, India in nanotechnology, Korea in 5G.
   Implication: Companies and nations should leverage local strengths while building broader innovation capacity.
4. Slow Technology Diffusion The gap between frontier firms and laggards is widening, particularly in digital and skill-intensive sectors. This slowdown makes it harder for latecomer economies to catch up.
   Implication: Active diffusion policies and investment in digital readiness are essential.
5. Infrastructure Divide The U.S. controls ~33% of the world’s top supercomputers. Cloud-based models and Digital Public Infrastructure (e.g., “CERN for AI”) could narrow this chasm.
   Implication: Shared resources and open innovation ecosystems are key enablers.
6. Generative AI Acceleration Generative AI could scale from $137B in 2023 to $900B by 2030. It represents the third wave of AI — following rule-based systems and statistical learning — and offers significant augmentation potential, especially for less-skilled workers.
   Implication: Workforce strategies should emphasize human-AI complementarity.
7. IP and Regulatory Uncertainty Most jurisdictions prohibit naming AI systems as inventors. South Africa stands as an exception. Meanwhile, copyright policies (e.g., Singapore’s Copyright Act 2021) are evolving to balance innovation with protection.
   Implication: Legal clarity on AI-generated IP and data rights will shape investment and product strategies.
8. Fragmented Governance 118 countries, mostly in the Global South, are excluded from the seven major AI governance initiatives. Major economies’ policies generate spillovers that influence other nations’ choices and can restrict their development paths.
   Implication: Inclusive multilateral frameworks and representation are vital.
9. Cross-Sector Applications AI is driving impactful pilots — from disease diagnosis tools in agriculture (e.g., Tumaini, MkulimaGPT) to smart welding robots and predictive maintenance in manufacturing, and AI-powered healthcare solutions in emerging economies.
   Implication: Scaling proven use cases requires targeted investment and context-specific adoption models.
10. Anticipatory Policy and Disclosure The report calls for an AI Public Disclosure Mechanism, akin to ESG reporting, and an anticipatory approach to regulation to avoid the “timing dilemma” where intervention comes too late.
    Implication: Transparent impact assessments and early policy engagement are strategic necessities.

Supporting Themes and Strategic Frameworks

Maturity and Evolution AI has evolved in three waves:

• Rule-based systems (1950s–60s)
• Statistical learning (1990s–present)
• Generative AI (2020s–) This progression reinforces AI’s status as a GPT — pervasive, dynamic, and highly complementary to other technologies.

Five As of AI Adoption Success depends on:

1. Availability (infrastructure and data)
2. Affordability (access costs)
3. Awareness (knowledge diffusion)
4. Ability (skills and capabilities)
5. Agency (empowerment and governance)

Fifth Industrial Revolution The report frames AI as a catalyst for the “fifth industrial revolution,” blending human-machine collaboration, sustainability, and personalization.

Worker-Centric Approaches AI’s impact is nuanced: while 40% of jobs face disruption risk, generative AI also enables augmentation and upskilling, particularly for lower-skilled roles. Policies should integrate reskilling, labor-friendly incentives, and union participation.

Competitive Landscape Snapshot

• United States: Dominates AI infrastructure, compute power, and foundational models.
• China: Leads in 5G, drones, solar PV integration.
• European Union: Advances ethical AI frameworks.
• India & Brazil: Progressing in data readiness but trailing in compute resources.
• Startups: Critical drivers of innovation, yet face structural funding asymmetries.

Risk Radar

Monopoly Concentration
(High Impact / High Likelihood)
A handful of dominant tech companies control the bulk of AI investments and infrastructure, posing serious risks to competition, innovation diversity, and equitable access.

Job Displacement
(High Impact / High Likelihood)
Automation and generative AI threaten up to 40% of global employment, with particularly acute exposure in finance, IT, and administrative roles.

Data and Algorithmic Bias
(Medium Impact / High Likelihood)
Models trained on skewed datasets risk perpetuating discrimination and eroding trust, with significant compliance and reputational consequences.

Infrastructure Inequity
(High Impact / Medium Likelihood)
Developing economies lack affordable access to compute power and data resources, creating systemic barriers to adoption and innovation.

Regulatory Fragmentation
(Medium Impact / Medium Likelihood)
Divergent national policies and limited Global South participation in governance frameworks are increasing complexity and compliance burdens.

Intellectual Property and Legal Ambiguity
(Medium Impact / High Likelihood)
Uncertainty around AI-generated inventions and data usage rights complicates investment decisions and could trigger litigation or policy delays.

Executive Action Checklist

• Forge cross-sector and cross-border partnerships in AI R&D.
• Invest in cloud-based shared infrastructure and Digital Public Infrastructure.
• Develop clear IP and data governance strategies.
• Prioritize worker-centric reskilling and augmentation pathways.
• Implement ethical AI disclosure frameworks.
• Embed sustainability and inclusion as core strategic pillars.

Source Attribution

Insights derived from Technology and Innovation Report 2025: Inclusive Artificial Intelligence for Development. Contact [email protected]  if you have trouble accessing.