Companies are rapidly scaling up their use of artificial intelligence (AI) to improve efficiency and control expenses—an especially pressing goal amid high inflation and slow economic growth. This pace of adoption makes close scrutiny essential for investors. From our perspective, the environmental and social consequences of AI—including its energy consumption, effects on employment, and approaches to data governance—carry significant financial relevance for companies globally.
AI represents the most transformative technological breakthrough since the internet and is expected to penetrate every sector, fundamentally reshaping economies and societies in the years ahead. Corporate uptake accelerated sharply after late 2022, following the emergence of generative AI tools. Early adoption has been strongest in areas like IT, finance, supply chains, and manufacturing, while functions including marketing, sales, product development, and human resources are expected to see widespread implementation by 2025.
As AI becomes embedded across a broader range of business activities, it is important to evaluate its implications from multiple perspectives. One useful approach is to assess environmental and social risks and opportunities—along with broader systemic effects, business and investment considerations, and impacts on daily life—through an AI impact framework.
From an environmental standpoint, energy use stands out as the most significant issue, making it a logical starting point.
Rising Energy Needs—Alongside Efficiency Gains
The most substantial environmental challenge associated with AI stems from its energy demands, particularly as hyperscalers—firms that provide vast, scalable computing infrastructure—expand data-center capacity at scale. Electricity consumption is expected to continue rising for the foreseeable future.
Data centers accounted for roughly 2% of global electricity usage in 2022, and projections suggest this figure could climb to as much as 7% by 2030. In the US alone, six states already see data centers consuming over 10% of their total electricity supply, with Virginia leading at approximately 25%.
This surge in demand is creating opportunities in two key ways: it is accelerating investment in renewable energy, and it is pushing hyperscalers to improve energy efficiency in their facilities—changes that are rippling through supply chains and opening potential avenues for investors. The downside risk is that many newly built AI data centers rely on natural gas, which could undermine long-term commitments to carbon neutrality.
For fixed-income investors, AI’s heavy energy and capital requirements present both challenges and opportunities. Companies that fail to adapt may encounter credit deterioration, tighter access to financing, and higher borrowing costs. Conversely, firms involved in developing or financing AI-driven data centers—as well as utilities upgrading grids and expanding renewable capacity—are increasingly turning to green bond issuance to mitigate AI’s growing energy footprint.
From an active investment standpoint, success hinges on identifying issuers with credible transition plans, competitive strengths linked to AI, prudent capital management, and diversified funding sources. Such companies are more likely to improve credit profiles over time and offer compelling investment prospects.
Growing electricity demand is also straining broader infrastructure systems. Beneficiaries of necessary upgrades may include manufacturers of high- and medium-voltage cables, providers of energy-efficient cooling solutions, producers of gas turbines (particularly those enhanced with AI-based controls), and suppliers of on-site power generation technologies such as fuel cells.
Water availability represents another major environmental concern. Poor water management can materially harm both business performance and investment returns, while firms that proactively address water risks may gain strategic advantages.
Beyond these challenges, AI can play a constructive role in environmental monitoring—enhancing the tracking and measurement of carbon emissions through satellite imagery, refining estimates of wildfire emissions, improving oversight of carbon sequestration projects, and strengthening the credibility of carbon markets.
Employment Risks Hinge on Effective Adaptation
Within the social dimension of the AI impact framework, three issues stand out: workforce disruption, misinformation, and bias.
Large-scale job displacement is one of the most prominent social risks. The World Economic Forum’s Future of Jobs Report 2025 indicates that AI and information technology are expected to drive the most profound operational changes by 2030. Even so, the net effect on employment could be relatively neutral.
Past technological shifts suggest that disruption is not solely about job losses or gains, but also about changes in job composition and the nature of work itself. While AI may reduce demand for clerical and administrative roles, it is also likely to create new positions in AI-related fields. Whether employment grows overall will depend on the effectiveness of reskilling efforts and how well organizations and workers adjust.
This transition poses risks for many companies but also creates opportunities for others, such as firms specializing in AI education and workforce training. Investors should keep a close watch on developments in this area.
The spread of misinformation and disinformation—already prevalent on social platforms and now intensified by AI—poses significant threats to trust in businesses, governments, and institutions. A widespread erosion of confidence could result in substantial social and economic consequences.
Bias embedded in AI systems presents another challenge. If training data reflect existing human biases, AI applications—such as facial recognition—may perform unevenly across different demographic groups. Such outcomes can expose companies to reputational harm and legal liabilities, with potential knock-on effects for investors.
Remaining Vigilant as AI Advances
Governance forms the next critical layer in evaluating AI’s environmental and social implications. To support this, we have outlined ten core principles for responsible AI use, along with guiding questions that investment teams can raise when engaging with corporate leadership.
While asset managers play a role in corporate oversight, investors must also stay highly alert. Distinguishing future AI leaders from laggards is becoming increasingly important as the technology reshapes businesses at speed. In our view, sustained vigilance is essential to navigating both the risks and the opportunities that will emerge as AI continues to evolve.
AI represents the most transformative technological breakthrough since the internet and is expected to penetrate every sector, fundamentally reshaping economies and societies in the years ahead. Corporate uptake accelerated sharply after late 2022, following the emergence of generative AI tools. Early adoption has been strongest in areas like IT, finance, supply chains, and manufacturing, while functions including marketing, sales, product development, and human resources are expected to see widespread implementation by 2025.
As AI becomes embedded across a broader range of business activities, it is important to evaluate its implications from multiple perspectives. One useful approach is to assess environmental and social risks and opportunities—along with broader systemic effects, business and investment considerations, and impacts on daily life—through an AI impact framework.
From an environmental standpoint, energy use stands out as the most significant issue, making it a logical starting point.
Rising Energy Needs—Alongside Efficiency Gains
The most substantial environmental challenge associated with AI stems from its energy demands, particularly as hyperscalers—firms that provide vast, scalable computing infrastructure—expand data-center capacity at scale. Electricity consumption is expected to continue rising for the foreseeable future.
Data centers accounted for roughly 2% of global electricity usage in 2022, and projections suggest this figure could climb to as much as 7% by 2030. In the US alone, six states already see data centers consuming over 10% of their total electricity supply, with Virginia leading at approximately 25%.
This surge in demand is creating opportunities in two key ways: it is accelerating investment in renewable energy, and it is pushing hyperscalers to improve energy efficiency in their facilities—changes that are rippling through supply chains and opening potential avenues for investors. The downside risk is that many newly built AI data centers rely on natural gas, which could undermine long-term commitments to carbon neutrality.
For fixed-income investors, AI’s heavy energy and capital requirements present both challenges and opportunities. Companies that fail to adapt may encounter credit deterioration, tighter access to financing, and higher borrowing costs. Conversely, firms involved in developing or financing AI-driven data centers—as well as utilities upgrading grids and expanding renewable capacity—are increasingly turning to green bond issuance to mitigate AI’s growing energy footprint.
From an active investment standpoint, success hinges on identifying issuers with credible transition plans, competitive strengths linked to AI, prudent capital management, and diversified funding sources. Such companies are more likely to improve credit profiles over time and offer compelling investment prospects.
Growing electricity demand is also straining broader infrastructure systems. Beneficiaries of necessary upgrades may include manufacturers of high- and medium-voltage cables, providers of energy-efficient cooling solutions, producers of gas turbines (particularly those enhanced with AI-based controls), and suppliers of on-site power generation technologies such as fuel cells.
Water availability represents another major environmental concern. Poor water management can materially harm both business performance and investment returns, while firms that proactively address water risks may gain strategic advantages.
Beyond these challenges, AI can play a constructive role in environmental monitoring—enhancing the tracking and measurement of carbon emissions through satellite imagery, refining estimates of wildfire emissions, improving oversight of carbon sequestration projects, and strengthening the credibility of carbon markets.
Employment Risks Hinge on Effective Adaptation
Within the social dimension of the AI impact framework, three issues stand out: workforce disruption, misinformation, and bias.
Large-scale job displacement is one of the most prominent social risks. The World Economic Forum’s Future of Jobs Report 2025 indicates that AI and information technology are expected to drive the most profound operational changes by 2030. Even so, the net effect on employment could be relatively neutral.
Past technological shifts suggest that disruption is not solely about job losses or gains, but also about changes in job composition and the nature of work itself. While AI may reduce demand for clerical and administrative roles, it is also likely to create new positions in AI-related fields. Whether employment grows overall will depend on the effectiveness of reskilling efforts and how well organizations and workers adjust.
This transition poses risks for many companies but also creates opportunities for others, such as firms specializing in AI education and workforce training. Investors should keep a close watch on developments in this area.
The spread of misinformation and disinformation—already prevalent on social platforms and now intensified by AI—poses significant threats to trust in businesses, governments, and institutions. A widespread erosion of confidence could result in substantial social and economic consequences.
Bias embedded in AI systems presents another challenge. If training data reflect existing human biases, AI applications—such as facial recognition—may perform unevenly across different demographic groups. Such outcomes can expose companies to reputational harm and legal liabilities, with potential knock-on effects for investors.
Remaining Vigilant as AI Advances
Governance forms the next critical layer in evaluating AI’s environmental and social implications. To support this, we have outlined ten core principles for responsible AI use, along with guiding questions that investment teams can raise when engaging with corporate leadership.
While asset managers play a role in corporate oversight, investors must also stay highly alert. Distinguishing future AI leaders from laggards is becoming increasingly important as the technology reshapes businesses at speed. In our view, sustained vigilance is essential to navigating both the risks and the opportunities that will emerge as AI continues to evolve.