Making Space, a talent acquisition and learning platform dedicated to closing the disability employment gap, has received a $500,000 grant from the GitLab Foundation. The funding will support the expansion of its Ascend Fellowship and accelerate the development of its responsible AI tools, aimed at improving employment access and long-term economic mobility for Disabled professionals.

The grant will enable a hybrid initiative that combines fellowship programming, systemic change within employers, and national deployment of Making Space’s AI tools. These tools include skill translation, benefits continuity support, and guidance for navigating ABLE accounts.

Disabled individuals continue to face significant disparities, being twice as likely to be unemployed and twice as likely to live in poverty due to systemic obstacles such as inaccessible workplaces, hiring bias, and fear of losing critical benefits like Medicaid or housing support. Making Space addresses these challenges holistically by providing a safe community, accessible learning, benefits navigation, and preparing employers to be more inclusive.

This new funding builds on the success of the Ascend Fellowship in partnership with the GitLab Foundation, which produced measurable economic mobility results for participants. In its first cohort, the program generated $111 million in projected lifetime earnings for 100 participants, achieving a 123:1 ROI and training roughly 5,000 hiring managers within partner companies.

With the grant, Making Space will scale its fellowship and pilot the next generation of AI tools with participants before a broader platform rollout. Once fully deployed, these tools and the accompanying curriculum are expected to benefit Disabled adults nationwide by supporting job placement, wage growth, and pathways to earn safely without risking essential benefits.

“Most platforms focus on placement. Making Space focuses on sustainability, which transforms the experience for Disabled workers navigating a system often designed to hold them back,” said Matt Zieger, Chief Programs and Partnerships Officer at GitLab Foundation. “This initiative builds employer partnerships that open doors—and keep them open.”

Reflecting on the program, Cat Contillo, a Making Space Ascend Fellow, shared, “Ascend taught me that my lived experience as a disabled person is a form of expertise, not a limitation. Over the past year, Making Space connected disabled, chronically ill, and neurodivergent professionals through webinars, hands-on projects exploring accessible work, and individualized guidance that truly met me where I am. The emphasis on peer connection over hierarchy was invaluable—some of the most meaningful moments came from learning alongside other disabled professionals and creating career paths that fit our lives.”

The project will also expand Making Space’s employer-focused efforts, integrating disability inclusion training into corporate systems, enhancing accessible hiring practices, and ensuring that Disabled professionals are supported to thrive and advance in their careers.

The GitLab Foundation’s mission is to boost lifetime earnings through access to opportunity, prioritizing initiatives that deliver measurable economic mobility outcomes.

“Disabled professionals represent an untapped pool of talent—qualified, capable, and ready for the next generation of work. What needs to evolve is the system around them. With the support of the GitLab Foundation, we’re helping employers rethink hiring, dismantle systemic barriers, and create opportunity at scale,” said Keely Cat-Wells, CEO & Founder of Making Space.

FedEx Corp has introduced a company-wide AI Education and Literacy initiative that delivers customized, role-specific learning experiences to employees across its global workforce. The initiative reflects FedEx’s ongoing dedication to developing its people and aligns with the organization’s values and culture.

As part of its long-term transformation strategy, FedEx is leveraging artificial intelligence to create more intelligent and efficient supply chains. The new AI learning program is designed to establish a strong foundation of AI knowledge and capabilities throughout the organization, enabling employees to use AI tools responsibly and productively in their roles.

“Data and AI are playing an increasingly central role in shaping the future of business,” said Raj Subramaniam, President and Chief Executive Officer of FedEx Corporation. “As we continue evolving into an AI-enabled enterprise, our team members remain our greatest strength. By investing in AI education, we’re helping our people build skills, work more safely and intelligently, and uncover new opportunities across FedEx.”

Alongside its internal AI training initiatives, FedEx has partnered with Accenture to deliver tailored learning experiences. Using Accenture’s AI-driven LearnVantage platform, employees will receive scalable, personalized training that includes interactive live sessions and skill validation to encourage engagement and adoption. Customized learning pathways will support employees at various stages of their career development as they build AI proficiency.

The expanded AI training program aims to create a common technological understanding across the organization. Employees will gain the knowledge required to apply AI responsibly within their specific functions, while leaders will learn how to integrate AI into team operations and cross-functional workflows to support meaningful transformation.

“Our goal is to equip our workforce with AI knowledge and practical digital skills that will help propel our business forward,” said Vishal Talwar, Chief Digital and Information Officer at FedEx Corporation. “By building a shared understanding of AI, we’re giving team members access to learning modules, certifications, and advanced opportunities that support long-term career growth.”

“By prioritizing its people and equipping them with essential AI skills, FedEx is redefining what smarter supply chains look like,” said Julie Sweet, Chair and CEO of Accenture. “We’re proud to collaborate with FedEx on delivering role-based AI training that empowers employees to work responsibly, explore new ways of operating, and confidently lead transformation across the organization.”

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.

Artificial intelligence (AI) and machine learning (ML) are rapidly reshaping industries and daily life, yet this progress comes with a rising environmental cost. Large-scale data centers require immense amounts of electricity and water, and the production of advanced hardware demands significant natural resources.

To counter these impacts, materials scientists are developing new solutions that enhance durability, improve energy efficiency and increase the resilience of the infrastructure that supports AI. Dow, for instance, uses advanced materials science to create technologies that improve thermal management in data centers. These solutions allow cooling systems to run more efficiently than conventional air-cooled systems, reducing energy use and helping cut greenhouse gas (GHG) emissions.

These advancements demonstrate how focused innovation can mitigate AI’s environmental footprint and encourage a more responsible, sustainable digital landscape.
 
What Is the AI Ecosystem?
The AI ecosystem refers to the expanding, interconnected system of technologies, data centers, hardware, organizations, and operational processes that enable the development, deployment and use of artificial intelligence.
 
Key Points

 
Environmental Impacts of AI
AI’s environmental effects span the entire lifecycle of its systems—from production to operation to disposal. Data centers, which supply the computing power for AI, consume enormous amounts of electricity. By 2026, these facilities may use around 1,050 terawatt-hours of power—enough to rank them as the world’s fifth-largest energy consumer if they were a country. When this energy comes from fossil fuels, it significantly increases GHG emissions.

Water consumption is another major concern. Cooling technologies used in data centers and power plants require vast amounts of water, with global usage projected to reach up to 6.6 billion cubic meters annually by 2027. The rapid development cycle of AI hardware also accelerates the generation of electronic waste; by 2030, as much as 5 million tons of e-waste could result from upgrading devices and components to support more advanced AI models.
Addressing these impacts is crucial to ensuring AI’s growth aligns with global sustainability goals.
 
Key Areas of Environmental Impact
Energy Use
Training and operating modern AI systems demands extensive electricity. Cooling alone can represent up to 40% of a data center’s consumption in dense computing environments. Solutions include developing more efficient algorithms, improving hardware, and relying on renewable or lower-carbon energy sources.

Water Use
Water is heavily used in cooling processes and in power generation that supports AI infrastructure. As AI adoption accelerates, reducing water usage and finding water-efficient cooling technologies will be increasingly important.

Land Use
Expanding data center infrastructure often requires large areas of land, potentially disrupting ecosystems and increasing resource extraction in previously undeveloped zones.

Climate Impact
AI can support climate resilience by helping optimize resource use and forecast environmental conditions. However, without sustainable development strategies, the energy demands of AI systems may contribute to global emissions.
 
Advancing Sustainable AI
As the demand for AI grows, so does the need to make its development more environmentally responsible. High-performance computing strains power grids and increases emissions unless efficient cooling, responsible sourcing, and circular design principles are used.

Manufacturing components like processors and semiconductors often involves intensive mining and large volumes of water and energy. Taking a full lifecycle approach—from material sourcing to end-of-life disposal—is essential to minimizing environmental harm.

According to Brendy Lange, President of Performance Materials & Coatings, customers increasingly recognize that long-term sustainability requires innovation at the materials level, motivating deeper collaboration to design solutions that can withstand the demands of advanced technologies, including AI.
 
Material Innovations Driving a Sustainable AI Future
Advanced materials are essential to supporting next-generation AI systems and communication networks.

Consumer Devices
AI-enabled devices—from smartphones to wearables—depend on materials designed to manage heat, enhance durability and reduce waste. Dow’s innovations help improve thermal performance and extend device lifespan. Reusable or recyclable materials, such as DOWSIL™ TC-3015 Re-workable Thermal Gel, support circularity in consumer electronics.

Cloud and Data Centers
Overheating reduces data center efficiency and accelerates equipment failure. Dow provides heat transfer fluids, thermal interface materials and silicone encapsulants that help manage heat effectively. These solutions support liquid cooling, immersion cooling and long-lasting thermal performance, enabling operators to reduce energy consumption and extend hardware life.

Telecom Infrastructure
Telecom systems must withstand extreme environmental conditions. Silicone-based adhesives, sealants, and protective coatings protect components from moisture, dust, and thermal stress. Dow also offers cable innovations—such as AXELERON™ and ENDURANCE™ compounds—that improve power delivery and data transmission while supporting circularity through materials like REVOLOOP™ recycled resins.
 
Additional Sustainable AI Strategies
Organizations committed to sustainability are implementing:

These practices create a foundation for an ethical and resilient AI-driven future.
 
Meeting AI’s Energy Demand With Advanced Nuclear Power
Major technology companies are beginning to invest in small modular nuclear reactors to supply clean, reliable energy for large data operations. These reactors offer near-zero emissions and fill energy gaps where renewables may not be sufficient. Dow is similarly exploring advanced nuclear technology with X-energy to reduce emissions at its Seadrift Operations site on the U.S. Gulf Coast.
 
Building a Sustainable AI Future
Sustainable development must be embedded throughout the AI ecosystem. By adopting clean energy sources, using efficient materials and designing systems with circular principles in mind, organizations can reduce AI’s environmental footprint while maintaining high performance.

As AI becomes integral to everything from personal electronics to communications networks and cloud computing, sustainable design will be essential to balancing technological advancement with planetary well-being.

These collective efforts highlight the critical role sustainability plays in shaping AI’s future—creating solutions that support both the environment and the accelerating digital world.