AI’s hidden recession: How fewer jobs and cultural backlash create a governance crisis – Fortune

Report on the Impact of Artificial Intelligence on Workforce Dynamics and Sustainable Development Goals

Introduction: AI, Productivity, and Emerging Risks to Sustainable Development

The rapid integration of Artificial Intelligence (AI) into the global economy is creating a significant paradigm shift in labor markets. While U.S. companies report record productivity gains with minimal payroll expansion, this trend signals a potential disruption to global employment that directly challenges the achievement of key Sustainable Development Goals (SDGs). Projections from Goldman Sachs indicate that AI automation could impact the equivalent of 300 million full-time jobs worldwide. This report analyzes the implications of this technological shift, with a specific focus on its potential to undermine progress on SDG 5 (Gender Equality), SDG 8 (Decent Work and Economic Growth), and SDG 10 (Reduced Inequalities).

Historical Precedents and the Threat to SDG 5: Gender Equality

History provides critical lessons on how societies respond to labor scarcity. During periods of economic distress, opportunities have often been rationed along gender lines, leading to significant setbacks for gender equality. The current AI-driven transformation risks repeating this pattern, directly threatening the principles of SDG 5.

A Pattern of Exclusion in Past Economic Crises

• The Great Depression: Numerous U.S. states and school districts implemented “marriage bars,” which prohibited the employment of married women to prioritize jobs for male breadwinners.
• Post-World War II: Governments actively encouraged women to leave factory jobs to make way for returning soldiers, closing wartime child-care centers that had enabled their participation.
• Post-War Compacts: In nations like Japan and Australia, the “male breadwinner compact” institutionalized a system where men were guaranteed lifetime employment while women were directed towards part-time or unpaid caregiving roles.

Modern Parallels and the Risk of Regression

As AI enables “headcount-light” corporate structures and automates knowledge-based roles, a similar dynamic of exclusion may emerge. The displacement of mid-career professionals, combined with inadequate retraining programs, creates economic anxiety. This anxiety can fuel rhetoric that recasts gender equity as a societal problem, undermining the foundational goal of SDG 5 to achieve gender equality and empower all women and girls.

Economic Implications for SDG 8 and SDG 10

The short-term efficiency gains celebrated by investors present a long-term economic paradox that conflicts with the objectives of SDG 8 (Decent Work and Economic Growth) and SDG 10 (Reduced Inequalities).

The Conflict Between Short-Term Efficiency and Long-Term Growth

While reducing headcount may boost immediate profits, sustainable economic prosperity depends on broad income distribution and consumption. Excluding a significant portion of the workforce, particularly women, has severe negative consequences:

• Reduced GDP: The International Monetary Fund estimates that raising women’s labor force participation to match men’s could increase GDP by as much as 35% in some economies. Excluding women actively works against this potential for growth.
• Shrinking Markets: A smaller base of employed consumers leads to diminished market size, reduced innovation, and lower economic resilience.
• An Aging, Less Dynamic Workforce: In most advanced economies, women aged 25-54 represent the majority of new entrants into the prime-age labor force. Pushing them out results in a workforce that is smaller, older, and less adaptable to technological change, directly hindering the “full and productive employment” objective of SDG 8.

Recommendations for Corporate Governance Aligned with the SDGs

The challenges posed by AI are not merely social issues but are core governance concerns for corporate boards and investors. Proactive measures are required to ensure that technological adoption aligns with the principles of sustainable and inclusive development.

Integrating SDG Principles into Corporate Strategy

Directors and management must address the human capital implications of AI to mitigate risks and ensure their social license to operate. The following actions are recommended:

1. Quantify and Disclose AI Impact: Management should be required to quantify how AI will alter headcount, skill requirements, and pay equity over the next five years. This data is essential for transparently addressing impacts on SDG 5 and SDG 8.
2. Audit for Algorithmic Bias: Companies must rigorously examine algorithmic HR tools to identify and eliminate hidden biases that could perpetuate discrimination based on gender or age, thereby ensuring compliance with the spirit of SDG 10.
3. Enhance Human-Capital Reporting: Disclosures should explicitly detail how automation affects opportunities for different demographic groups, providing stakeholders with a clear view of the company’s commitment to inclusive growth.
4. Acknowledge Interdependence: Leadership must recognize that corporate success is inextricably linked to the economic health of the societies in which they operate. A short-term efficiency strategy that fuels long-term unemployment and inequality is unsustainable.

Conclusion: Aligning Technological Advancement with Sustainable Development

AI will fundamentally redefine how human value is created. The critical choice facing corporate and policy leaders is whether this redefinition will be inclusive or exclusive. Optimizing solely for short-term efficiency risks creating a society that is economically brittle and socially fractured, moving further away from the Sustainable Development Goals. To build a future that is both prosperous and equitable, leaders must ensure that the deployment of AI promotes decent work, gender equality, and reduced inequalities for all.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

• SDG 5: Gender Equality

  The article is fundamentally about the threat that AI-driven economic shifts pose to gender equality. It draws historical parallels, such as the “marriage bars” during the Great Depression and post-WWII policies, where women were pushed out of the workforce during times of job scarcity. The author warns that AI could trigger a “similar re-ordering” and that “when work becomes scarce, societies ration opportunity, and women often pay the price.” This directly connects to the goal of achieving gender equality and empowering all women and girls.

• SDG 8: Decent Work and Economic Growth

  The article discusses the core tenets of SDG 8 by highlighting the tension between technological advancement and employment. It notes that AI is driving “record productivity,” yet “payrolls barely rise,” and that automation could affect “300 million full-time jobs worldwide.” The central theme revolves around the challenge of maintaining “full employment” and ensuring that economic growth is inclusive, rather than creating a “headcount-light” economy where prosperity is not shared. The article questions whether AI will lead to decent work for all or exacerbate job insecurity.

• SDG 10: Reduced Inequalities

  This goal is addressed through the article’s focus on how the economic disruption from AI could widen societal inequalities. The author warns that as the “labor market polarizes,” there is a risk of exclusion, particularly based on gender. The historical examples provided show how policies can institutionalize inequality by favoring one group (male breadwinners) over another. The article argues that choices made by leaders now will determine “who is allowed to create value,” directly implicating the goal of reducing inequality within and among countries by ensuring equal opportunity.

2. What specific targets under those SDGs can be identified based on the article’s content?

1. Target 5.5: Ensure women’s full and effective participation and equal opportunities for leadership in political, economic and public life.

   The article directly relates to this target by warning of a potential regression in women’s economic participation. The discussion of historical “marriage bars” and the current risk of women being “pushed out” of the workforce highlights a direct threat to their “full and effective participation” in economic life. The article cites an IMF statistic that “raising women’s labor force participation to men’s levels could expand GDP by up to 35%,” reinforcing the economic importance of achieving this target.

2. Target 8.5: By 2030, achieve full and productive employment and decent work for all women and men… and equal pay for work of equal value.

   This target is central to the article’s argument. The estimate that AI could affect “300 million full-time jobs” poses a significant challenge to achieving “full and productive employment.” The article’s concern that AI will create “headcount-light” companies where productivity rises but jobs do not, speaks to the challenge of ensuring decent work for all. Furthermore, the recommendation for corporate boards to “quantify how AI will change headcount, skill mix, and pay equity” explicitly connects the issue to the goal of equal pay and opportunity.

3. Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices…

   The article emphasizes how societal choices, not just technology, create inequality. It references past discriminatory policies like “marriage bars” and the “male breadwinner compact” that were designed to exclude women from the workforce. The author warns that a similar “gender regression” could occur today, framed as “cultural renewal” or “moral restoration.” This directly engages with the need to prevent discriminatory practices that deny equal opportunity and create inequalities of outcome.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

• Women’s labor force participation rate

  This indicator is explicitly mentioned. The article cites the International Monetary Fund (IMF), stating that “raising women’s labor force participation to men’s levels could expand GDP by up to 35%.” This metric is a direct way to measure progress towards women’s full economic participation (Target 5.5).

• Pay equity by gender

  This indicator is implied in the recommendation that corporate directors “press management to quantify how AI will change… pay equity over the next five years.” Tracking the gender pay gap, especially in industries heavily impacted by AI, would be a key measure of progress towards Target 8.5.

• Employment and opportunity metrics by gender and age

  The article implies the need for this indicator by urging boards to “ensure that human-capital disclosures explain how automation affects opportunity by gender and age.” This suggests tracking metrics such as the proportion of women and older workers in roles being automated versus roles being created, which would measure progress on ensuring equal opportunity (Target 10.3).

• Number of jobs displaced or affected by automation

  This is an explicit indicator mentioned in the article: “Goldman Sachs estimates that AI automation could affect the equivalent of 300 million full-time jobs worldwide.” Monitoring this figure provides a scale of the challenge to achieving full employment (Target 8.5).

4. Table of SDGs, Targets, and Indicators

Source: fortune.com