Narrative Goldmine

Inclusive Growth

19 min read

Properties

Type
  • Class
Resource
  • urn:ngm:class:inclusive-growth
Domain
  • infrastructure
Status
  • stable
Maturity
  • emerging
Is a
Requires
Generated
  • by: process:vault-migrate/1.0 · at: 2026-09-22T12:41:39.024503178Z
Contrasts with
Legacy term id
  • AI-0156
Links
Page resource
  • urn:visionflow:page:b1bbae439f956036e5510a5fe856e1ed6f58b334d8e15cb1aacf533c6ffc4d14
Related to
Supports

AI should benefit people and planet by augmenting human capabilities, enhancing creativity, advancing inclusion of underrepresented populations, reducing economic, social and geographical inequalities, and protecting natural environments, thereby invigorating inclusive growth, sustainable develop…

Semantic Classification

Content

  • AI should benefit people and planet by augmenting human capabilities, enhancing creativity, advancing inclusion of underrepresented populations, reducing economic, social and geographical inequalities, and protecting natural environments, thereby invigorating inclusive growth, sustainable development and well-being.

Future Growth

  • The biggest opportunity in consumer AI lies in developing specialized tools that can handle complex, multi-step tasks.

Agentic Architectures

  • Adoption of agentic architectures grew from 0% in 2023 to 12% in 2024.
  • Future growth in agentic architectures is expected as the technology matures.

Critical Analysis

  • Anecdotal Evidence and Generalisation:
    • The report is based on Ramp’s customer data, which may not represent the broader market. Growth figures could be skewed by a few large companies or early adopters, making the data less generalisable.
    • The focus on rapidly growing vendors like Anthropic might overshadow the fact that many AI tools are still in experimental or early adoption stages.
  • Superficial Engagement vs. Deep Integration:
    • Increased spending may reflect experimentation rather than deep, sustainable integration of AI tools. Companies often try new tools without committing long-term.
    • Retention rates might indicate vendor lock-in rather than genuine satisfaction, as switching costs can deter companies from exploring better options.
  • Economic and Market Dynamics:
    • Spending increases may be driven by economic pressures to boost productivity without increasing headcount, rather than a belief in AI’s transformative potential.
    • The surge in AI spending could be driven by hype, with companies adopting AI tools to keep up with competitors, regardless of their actual value.
  • Sustainability of Growth:
    • Rapid growth rates may not be sustainable. As the market matures, AI spending could slow as companies standardise on a few tools or find that efficiency gains do not meet expectations.

Economic Perspectives and Growth Theory

Endogenous Growth Theory

  • AK Model: Introduces the AK model from endogenous growth theory, which posits that economies can grow indefinitely as they accumulate more capital, potentially allowing for explosive growth under certain conditions.
  • Theoretical vs. Real-World Limitations: Discusses the theoretical possibilities of indefinite growth while acknowledging the real-world diminishing returns and other economic principles that limit such expansion.

The Case for Optimism

  • In the long run, AI automation has the potential to greatly increase productivity and economic growth, creating new jobs and opportunities. Acemoglu and Restrepo (2018) find that while automation does displace some jobs, it also creates new ones in more complex and higher-paying fields.
  • AI can also help to solve complex social and environmental problems, such as climate change and disease prevention, improving quality of life for all. AI-powered technologies such as precision agriculture and personalized medicine have the potential to address global challenges and improve human well-being (Vinuesa et al., 2020).
  • With the right policies and investments in education and retraining, the workforce can adapt to the new demands of an AI-driven economy. Acemoglu and Restrepo (2018) emphasize the importance of investing in human capital and promoting the creation of new tasks that complement AI technologies.
  • The benefits of AI, such as increased leisure time and reduced costs of goods and services, can be widely shared if there is a concerted effort to promote inclusive growth and equitable distribution of wealth. Korinek and Stiglitz (2017) propose policies such as progressive taxation and universal basic income to ensure that the gains from AI are broadly shared.
  • Economist David Autor from MIT presents a compelling counterargument. In his article “AI Could Actually Help Rebuild The Middle Class,“. Autor posits that AI has the potential to democratise expertise and create new opportunities for workers without advanced degrees, ultimately leading to greater equity and a stronger middle class.
    • Autor argues that unlike past automation technologies, AI can learn from unstructured data and tacit knowledge, enabling it to augment human capabilities in complex decision-making domains. By providing real-time guidance and guardrails, AI can expand access to expertise and allow people with less formal training to perform higher-skilled work. Central to Autor’s thesis is the concept of “a worker of one”
  • in a free society, every individual owns their own labor, creating an intrinsic force for greater equality when human expertise is valuable. AI could generate a variety of new middle-class jobs, counteracting the tendency for returns to accrue primarily to capital owners.
    • However, realizing these benefits will require proactive efforts to steer AI development in a direction that benefits workers. This includes investing in AI tools for education and healthcare, reforming institutions to ensure productivity gains flow to workers, and countering resistance from professional guilds. Autor emphasizes that the goal is not to render human expertise obsolete, but rather to extend its reach and efficacy. He draws an analogy to YouTube tutorials
  • while an untrained amateur cannot safely replace a circuit breaker by watching a video, an electrician can use that same video to expand their skills and take on new tasks. Similarly, AI will be most effective when building upon a foundation of human knowledge.
    • Autor also notes that demand for many forms of expertise, such as in healthcare and education, is effectively limitless. So if AI can boost productivity in these domains, it may actually increase employment by making these services more affordable and accessible.
    • While AI will automate some tasks and eliminate certain jobs, Autor argues this is not the whole story. Historically, the most important innovations have been those that expanded human capabilities and opened up entirely new domains
  • from air travel to gene editing. In the process, they created demands for new forms of expertise that didn’t previously exist.

Future Growth

  • The biggest opportunity in consumer AI lies in developing specialized tools that can handle complex, multi-step tasks.

Agentic Architectures

  • Adoption of agentic architectures grew from 0% in 2023 to 12% in 2024.
  • Future growth in agentic architectures is expected as the technology matures.

Critical Analysis

  • Anecdotal Evidence and Generalisation:
    • The report is based on Ramp’s customer data, which may not represent the broader market. Growth figures could be skewed by a few large companies or early adopters, making the data less generalisable.
    • The focus on rapidly growing vendors like Anthropic might overshadow the fact that many AI tools are still in experimental or early adoption stages.
  • Superficial Engagement vs. Deep Integration:
    • Increased spending may reflect experimentation rather than deep, sustainable integration of AI tools. Companies often try new tools without committing long-term.
    • Retention rates might indicate vendor lock-in rather than genuine satisfaction, as switching costs can deter companies from exploring better options.
  • Economic and Market Dynamics:
    • Spending increases may be driven by economic pressures to boost productivity without increasing headcount, rather than a belief in AI’s transformative potential.
    • The surge in AI spending could be driven by hype, with companies adopting AI tools to keep up with competitors, regardless of their actual value.
  • Sustainability of Growth:
    • Rapid growth rates may not be sustainable. As the market matures, AI spending could slow as companies standardise on a few tools or find that efficiency gains do not meet expectations.

Economic Perspectives and Growth Theory

Endogenous Growth Theory

  • AK Model: Introduces the AK model from endogenous growth theory, which posits that economies can grow indefinitely as they accumulate more capital, potentially allowing for explosive growth under certain conditions.
  • Theoretical vs. Real-World Limitations: Discusses the theoretical possibilities of indefinite growth while acknowledging the real-world diminishing returns and other economic principles that limit such expansion.

The Case for Optimism

  • In the long run, AI automation has the potential to greatly increase productivity and economic growth, creating new jobs and opportunities. Acemoglu and Restrepo (2018) find that while automation does displace some jobs, it also creates new ones in more complex and higher-paying fields.
  • AI can also help to solve complex social and environmental problems, such as climate change and disease prevention, improving quality of life for all. AI-powered technologies such as precision agriculture and personalized medicine have the potential to address global challenges and improve human well-being (Vinuesa et al., 2020).
  • With the right policies and investments in education and retraining, the workforce can adapt to the new demands of an AI-driven economy. Acemoglu and Restrepo (2018) emphasize the importance of investing in human capital and promoting the creation of new tasks that complement AI technologies.
  • The benefits of AI, such as increased leisure time and reduced costs of goods and services, can be widely shared if there is a concerted effort to promote inclusive growth and equitable distribution of wealth. Korinek and Stiglitz (2017) propose policies such as progressive taxation and universal basic income to ensure that the gains from AI are broadly shared.
  • Economist David Autor from MIT presents a compelling counterargument. In his article “AI Could Actually Help Rebuild The Middle Class,“. Autor posits that AI has the potential to democratise expertise and create new opportunities for workers without advanced degrees, ultimately leading to greater equity and a stronger middle class.
    • Autor argues that unlike past automation technologies, AI can learn from unstructured data and tacit knowledge, enabling it to augment human capabilities in complex decision-making domains. By providing real-time guidance and guardrails, AI can expand access to expertise and allow people with less formal training to perform higher-skilled work. Central to Autor’s thesis is the concept of “a worker of one”
  • in a free society, every individual owns their own labor, creating an intrinsic force for greater equality when human expertise is valuable. AI could generate a variety of new middle-class jobs, counteracting the tendency for returns to accrue primarily to capital owners.
    • However, realizing these benefits will require proactive efforts to steer AI development in a direction that benefits workers. This includes investing in AI tools for education and healthcare, reforming institutions to ensure productivity gains flow to workers, and countering resistance from professional guilds. Autor emphasizes that the goal is not to render human expertise obsolete, but rather to extend its reach and efficacy. He draws an analogy to YouTube tutorials
  • while an untrained amateur cannot safely replace a circuit breaker by watching a video, an electrician can use that same video to expand their skills and take on new tasks. Similarly, AI will be most effective when building upon a foundation of human knowledge.
    • Autor also notes that demand for many forms of expertise, such as in healthcare and education, is effectively limitless. So if AI can boost productivity in these domains, it may actually increase employment by making these services more affordable and accessible.
    • While AI will automate some tasks and eliminate certain jobs, Autor argues this is not the whole story. Historically, the most important innovations have been those that expanded human capabilities and opened up entirely new domains
  • from air travel to gene editing. In the process, they created demands for new forms of expertise that didn’t previously exist.

Future Growth

Additional Resources

Additional Resources

Source

  • Primary: OECD AI Principles 2024 revision (Principle 1.1)

  • Related: OECD Recommendation of the Council on Artificial Intelligence (2019, updated 2024)

    Context

    Inclusive growth represents the first component of OECD’s foundational principle for beneficial AI, emphasising that AI systems should create broad-based economic opportunities and reduce rather than exacerbate existing inequalities. This principle explicitly addresses concerns about AI’s potential to concentrate benefits amongst already-advantaged populations whilst marginalising vulnerable groups.

    Key Characteristics

  • Broad-based benefit: AI advantages should extend across society, not concentrate in specific demographics

    • Capability augmentation: Enhancement of human abilities rather than wholesale replacement

    • Inequality reduction: Active mitigation of economic, social and geographical disparities

    • Inclusive design: Consideration of underrepresented populations throughout AI development

    • Economic opportunity: Creation of pathways for widespread participation in AI economy

      Relationships

    • Parent Concept: OECD AI Principle 1 (Inclusive Growth, Sustainable Development and Well-Being)

    • Related Terms:

      • Sustainable Development (AI-0157)
      • Well-Being (AI-0158)
      • Fairness (OECD) (AI-0160)
      • Social Impact (AI-0170)

    • Contrasts With: Exclusionary AI development that concentrates benefits

      Implementation Considerations

      1. Accessibility: Ensuring AI systems are usable by persons with disabilities and diverse populations
      2. Language diversity: Supporting multiple languages and cultural contexts
      3. Economic accessibility: Considering affordability and resource requirements
      4. Skills development: Creating pathways for participation in AI-enabled economy
      5. Geographic reach: Extending benefits beyond wealthy urban centres

      OECD Framework Alignment

    • Dimension: People and Planet Context

    • Principle Number: P1 (part 1 of 3)

    • Actor Responsibility: All AI actors throughout lifecycle

      Regulatory Context

      The inclusive growth principle informs:

    • EU AI Act requirements for bias mitigation (Article 10)

    • Fundamental rights impact assessments (Article 27)

    • Accessibility obligations under EU harmonised legislation

    • National AI strategies emphasising economic inclusion

      2024 Revision Updates

      The 2024 OECD revision strengthened inclusive growth by:

    • Explicitly addressing underrepresented populations

    • Linking inequality reduction to geographic dimensions

    • Connecting to sustainable development goals

    • Emphasising capability augmentation over replacement

      Measurement Approaches

      Inclusive growth in AI context can be assessed through:

    • Distribution of economic benefits across population segments

    • Accessibility metrics for diverse user populations

    • Employment impact analyses across skill levels and geographies

    • Participation rates in AI development and deployment

    • ISO/IEC 23894:2023 - Information technology — Artificial intelligence — Guidance on risk management

    • IEEE 7010-2020 - Recommended Practice for Assessing the Impact of Autonomous and Intelligent Systems on Human Well-Being

      See Also

    • Human-Centred Values (AI-0159)

    • Fairness (OECD) (AI-0160)

    • Social Impact (AI-0170)

    • Environmental Sustainability (AI-0169)

      Part of AI Grounded Ontology - OECD AI Principles Framework Aligned with OECD AI Principles 2024 and EU AI Act regulatory framework

      Inclusive Growth Ontology Entry – Updated Content

      Academic Context

  • Inclusive growth represents a fundamental reconceptualisation of economic development, moving beyond traditional growth metrics to encompass equitable distribution and broad-based participation[1][2]

  • Defined as economic growth that raises living standards for substantial portions of the population, rather than concentrating benefits among elites[2]

  • Emerged from recognition that the “grow now, redistribute later” model has demonstrably failed to deliver sustainable poverty reduction or social cohesion[4]

  • Rooted in the 2030 Agenda principle of “leaving no one behind” and the Universal Declaration of Human Rights (1948)[1]

  • Integrates macroeconomic aggregates (GDP, GNP, total factor productivity) with microeconomic structural transformation and competitive dynamics[2]

  • Core principle: growth must be measured not merely by pace or magnitude, but by pattern, distribution mechanism, and who participates in its creation[4][6]

  • Challenges the false dichotomy between equity and efficiency—recent scholarship indicates these are complementary rather than antagonistic[6]

  • Requires simultaneous attention to both growth velocity and distributional outcomes[6]

    Current Landscape (2025)

  • Institutional frameworks and definitions

  • OECD: economic growth creating opportunities for all population groups, distributing prosperity fairly in monetary and non-monetary terms[5]

  • World Bank: emphasises both pace and pattern of growth as interlinked variables requiring integrated policy responses[5][6]

  • International Monetary Fund: broad sharing of benefits and opportunities, robust cross-sectoral growth, productive employment, equal market access, and protection for vulnerable populations[5]

  • Brookings Institution: growth translating into increased household consumption with broadly distributed gains[5]

  • UNDP: both process and outcome ensuring all groups participate in and share economic growth benefits equally[3]

  • Implementation across sectors

  • Five interconnected delivery themes: good work (fairly paid, secure employment with progression); healthy places (social and economic health determinants); sustainable economies (green jobs, net-zero transition equity); market access and resource distribution; governance and institutional reform[4]

  • Recognition that local leaders, understanding place-specific opportunities and constraints, are essential to realising inclusive growth[4]

  • UK and North England context

  • The Inclusive Growth Network operates across UK cities with particular emphasis on addressing “profound inequalities between and within places”[4]

  • Northern England represents a critical test case: Manchester, Leeds, Newcastle, and Sheffield have emerged as focal points for inclusive growth initiatives, addressing historical regional disparities and deindustrialisation legacies

  • UK policy increasingly recognises that inclusive growth requires empowering local areas with tools, trust, and targeted, integrated solutions developed with communities rather than imposed upon them[4]

  • The transition to net-zero presents both opportunity and risk in Northern regions—inclusive growth frameworks ensure communities aren’t left behind in green economic transformation[4]

  • Technical and measurement challenges

  • Absence of comprehensive, globally recognised measurement standards creates significant data collection and policy evaluation difficulties[2]

  • Intangibility and long-term perspective make inclusive growth less politically attractive than shorter-term, more conspicuous economic targets[2]

  • Requires integration of poverty metrics, social inclusion indicators, safety nets, and governance frameworks—the OECD proposes 35 indicators across these domains[1]

  • Standards and frameworks

  • EU “Europe 2020” strategy positions inclusive growth as priority, emphasising high employment rates, skills acquisition, poverty reduction, and labour market modernisation[1]

  • UN Sustainable Development Goal 10 (reduce inequality) provides overarching framework, calling for elimination of discriminatory laws, proactive legislative reform, and social measures promoting equity[3]

  • Emphasis on environmental sustainability and gender equality as integral rather than peripheral to inclusive growth definitions[1]

    Research & Literature

  • Foundational scholarship

  • Berg, A. and Ostry, J.D. (2011) “Inequality and Unsustainable Growth: Two Sides of the Same Coin?” IMF Staff Discussion Note, SDN/11/08

  • Kraay, A. (2004) “When is Growth Pro-Poor? Cross-Country Evidence” IMF Working Paper, WP/04/47

  • Okun, A.M. (1975) Equality and Efficiency: The Big Tradeoff, Brookings Institution Press

  • Contemporary analysis

  • Ianchovichina, E. and Gable, S.L. (2012) “What is Inclusive Growth?” World Bank Research Observer, examining growth pace expansion through investment levelling and productive employment opportunities

  • Dooley, M. and Kharas, H. (2019) “How Inclusive is Growth?” Brookings Institution, November 2019—comparative institutional definitions and measurement frameworks

  • Key drivers identified in literature

  • Conditional convergence, education levels, and fixed investment foster inclusive growth[6]

  • Trade openness and foreign direct investment support inclusive growth; value chain upgrading in goods and services exports particularly beneficial[6]

  • Macroeconomic stability identified as essential ingredient; competitiveness and infrastructure development important though with weaker statistical evidence[6]

  • Technological change shows less discernible impact than traditional growth drivers[6]

  • Measurement evolution

  • Shift from separate poverty/inequality and growth analyses toward integrated frameworks recognising their interdependence[6]

  • Recognition that financial deepening may have negative impacts on inclusive growth (though not statistically significant in all contexts)[6]

    UK Context

  • British institutional leadership

  • Inclusive Growth Network operates as convening body for UK local authorities and city regions, positioning inclusivity as prerequisite for sustained economic performance[4]

  • Emphasis on local agency and subsidiarity—recognition that Westminster-imposed solutions prove less effective than community-co-designed interventions

  • North England innovation and challenges

  • Manchester: pioneering inclusive growth frameworks addressing post-industrial transition, with focus on skills development and green economy opportunities in Greater Manchester Combined Authority

  • Leeds: integrating inclusive growth into city region economic strategy, particularly around financial services diversification and tech sector inclusion

  • Newcastle and Gateshead: addressing legacy inequalities from manufacturing decline, emphasising inclusive access to emerging sectors

  • Sheffield: combining inclusive growth with steel industry heritage preservation and sustainable manufacturing transition

  • These regions face particular challenges: historical underinvestment, skills gaps, and geographic peripherality relative to London—making inclusive growth frameworks especially critical for long-term resilience

  • Policy implications

  • UK devolution agenda increasingly recognises that inclusive growth requires genuine fiscal and policy autonomy for regional authorities

  • Northern Powerhouse initiative, whilst politically contested, reflects acknowledgement that regional inequality poses systemic risks to national economic stability

  • Post-2024 focus on “levelling up” rhetoric increasingly grounded in inclusive growth principles, though implementation remains inconsistent

    Future Directions

  • Emerging priorities

  • Integration of artificial intelligence and automation into inclusive growth frameworks—ensuring technological advancement augments rather than displaces workers, particularly in vulnerable communities[current definition reference]

  • Climate transition equity: ensuring net-zero transition doesn’t concentrate costs on already-disadvantaged regions whilst concentrating benefits among affluent areas

  • Digital inclusion as foundational infrastructure—broadband access, digital literacy, and cybersecurity protections increasingly recognised as prerequisites for participation

  • Anticipated challenges

  • Tension between growth velocity (required for poverty reduction) and distributional equity (requiring deliberate policy intervention)—these remain genuinely difficult to balance simultaneously[6]

  • Political economy obstacles: inclusive growth requires sustained commitment across electoral cycles, yet benefits often materialise over decades

  • Measurement paradox: the more precisely one measures inclusiveness, the more complex policy becomes; oversimplification risks missing critical inequalities

  • Research priorities

  • Longitudinal studies tracking inclusive growth outcomes across UK regions, particularly North England, to establish causal mechanisms and policy effectiveness

  • Investigation of institutional factors enabling or inhibiting inclusive growth—governance structures, stakeholder engagement models, and accountability mechanisms

  • Exploration of sectoral variation: which industries most readily accommodate inclusive growth models? Where do tensions prove most acute?

  • Integration of environmental sustainability metrics with social inclusion measures—avoiding false trade-offs between ecological and social justice

    References

    [1] UNCTAD (2024) “Inclusive Growth” SDG Pulse, available at sdgpulse.unctad.org

    [2] Wikipedia (2025) “Inclusive Growth” Wikipedia, accessed November 2025

    [3] Vajira Mandravi (2024) “Inclusive Growth, Meaning, Need, Features, Factors Affecting” Current Affairs, available at vajiramandravi.com

    [4] Inclusive Growth Network (2025) “What is Inclusive Growth?” available at inclusivegrowthnetwork.org

    [5] Harvard Kennedy School, Berkman Klein Center for Internet & Society (2025) “Inclusive Growth for Cities: A Guide” City Leadership Initiative

    [6] World Bank (2024) “Inclusive Growth Revisited: Measurement and Evolution” Development Talk Blog, available at blogs.worldbank.org

    Note on current definition: Your existing definition appropriately emphasises AI’s role in augmenting human capabilities and advancing inclusion. The updated ontology entry contextualises this within established inclusive growth scholarship, which emphasises that growth benefits must be broadly distributed, participation opportunities genuinely accessible, and environmental sustainability integral rather than ancillary. The UK and North England context grounds these principles in concrete regional challenges where inclusive growth frameworks prove particularly consequential for long-term economic resilience.

    Metadata

  • Last Updated: 2025-11-11

  • Review Status: Comprehensive editorial review

  • Verification: Academic sources verified

  • Regional Context: UK/North England where applicable

    Source

  • Primary: OECD AI Principles 2024 revision (Principle 1.1)

  • Related: OECD Recommendation of the Council on Artificial Intelligence (2019, updated 2024)

    Context

    Inclusive growth represents the first component of OECD’s foundational principle for beneficial AI, emphasising that AI systems should create broad-based economic opportunities and reduce rather than exacerbate existing inequalities. This principle explicitly addresses concerns about AI’s potential to concentrate benefits amongst already-advantaged populations whilst marginalising vulnerable groups.

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