AI competitiveness and legitimacy will hinge not just on technical advances but on how companies secure clean energy, demand transparent environmental and social data from suppliers, and ensure fair rights across AI's human labor supply chains
Key takeaways:
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Clean energy takes center stage in corporate AI initiatives — Access to cheap, low‑carbon power will become a core driver of AI competitiveness, especially in the US, where electricity costs are on the rise.
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Corporate buyers of AI will exert new leverage over suppliers — Corporate buyers will increasingly use their purchasing power to push data center operators to align AI build‑outs with local climate, water, and community expectations — not just to supply more metrics.
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AI’s human labor layer enters mainstream due diligence — AI labor supply chains will be brought into the mainstream supply chain and require human rights due diligence.
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As we enter 2026, there are three main themes that many corporations will need to manage around issues of renewable energy, AI supplier behavior, and labor.
Theme 1: Renewables move to the center of corporate AI strategies
In 2026, AI competitiveness and energy policy will be tightly fused. With AI workloads driving up electricity demand amid datacenter buildouts, particularly in the United States, access to renewable energy sources in the form of abundant, cheap, low‑carbon power becomes a decisive factor in AI pricing and availability. Countries and companies that lock in this advantage early will shape AI deployment patterns for the rest of the decade.
“The economics of renewable energy are what is causing it to accelerate, even in the US,” says John Friedman, an expert in sustainability and business. “Despite the political winds, the fact is that wind and solar are growing faster… because it is cheaper, better energy.”
In addition, countries and firms with large, subsidized renewable energy capabilities and flexible grids, such as China’s massive solar, wind, and hydro infrastructure, will have a low-cost advantage. (However, countries’ push for AI may counteract this by prompting governments to prioritize domestic AI stacks over purely cost‑optimized ones.) Yet, combining this asset with China’s increasingly sophisticated large language models, such as Kimi K2 and DeepSeek, it is not outside the realm of possibility that the country could emerge in the top spot in AI development and innovation.
Corporate pressure to increase AI adoption for efficiency combined with stakeholder expectations of investing in a low-carbon future will make renewables the center of corporate AI strategies. Increasingly, companies will be asked where their computers run, what energy mix powers them, how cost effective that energy mix is, and whether companies are effectively endorsing environmentally and socially harmful projects in host communities.
Theme 2: Local backlash forces suppliers and companies to confront AI’s impact
Over the last few years, big names among AI infrastructure providers have tried to take advantage of the AI revolution, investing hundreds of billions of dollars in AI-related data centers, cloud systems, and other infrastructure with no end in sight over the next few years.
Despite the demand, local communities in which large data center construction projects are planned are pushing back. According to Data Center Watch, $64 billion of data center projects in the US have been blocked or delayed amid local opposition since 2025. This opposition comes in part because of concerns regarding local communities’ increasing utility bills, strains on local water and natural resources, and the reduction of working farmland from data center rezoning attempts in rural communities.
In fact, AI data centers are pushing up electricity demand and fueling higher electricity prices for many US households. And, as retail electricity price increases over the next couple of years are likely to continue, it will be in part because of these current and future data centers consuming more electricity.
As a result, the demand from stakeholders — in particular, those from local communities including local and state politicians — for increased transparency on the environment and social impacts of corporate AI services is likely to surge. In turn, corporate buyers of AI services will put pressure on the big AI service suppliers to provide more precision in the locations of such data systems as well as disclose more associated sustainability data, such as energy sources, grid impacts, and their level of community engagement where large AI infrastructure is based.
To deal with these competing priorities, boards of companies using AI services will need to reconcile AI cost‑cutting with their transition commitments by ensuring that cost advantages are not built on externalizing environmental and social harms.
Not surprisingly, in 2026, more boards will be drawn into explicit debates about whether AI‑driven cost savings justify exposure to higher community, political, and regulatory risk. This turns questions about data center locations and power contracts into mainstream agenda items.
Theme 3: The human layer of AI emerges as a centerpiece of the supply chain
The idea that AI is automating everything will sit uncomfortably alongside a growing recognition that large‑scale AI depends on a largely invisible workforce. Across the full AI life cycle of products — some of which rely on models that utilize labor in data collection, curation, annotation, labeling, evaluation, and content moderation — there are thousands of workers performing the tasks that make models safe, accurate, and usable.
As AI systems scale across sectors, demand for this human labor increases in volume and complexity, according to Keri Lloyd, a human rights expert at Article One Advisory. Indeed, much of it remains outsourced, precarious, or gig‑based (often in the Global South), with low pay, weak protections, and exposure to psychologically harmful content rampant. Civil society, unions, and regulators are beginning to connect AI innovation with labor rights and occupational health; and this reality makes the human layer of AI a frontline human rights issue rather than a technical detail.
The decent work agenda for AI‑related labor is likely to move from a niche concern to a mainstream pillar of corporate human rights due diligence. Companies will be under pressure to know what subcontractors and suppliers are doing to ensure human rights for individuals doing AI data enrichment and moderation work, under what conditions, and through which intermediaries.
Following the evolution of how conflict minerals or modern slavery have been integrated into supplier management, a shared view of AI labor supply chains by corporate procurement, legal, product management, and sustainability teams will materialize.
Forward into 2026
As AI becomes embedded in the infrastructure of daily life, companies will face mounting pressure to demonstrate that their AI strategies align with human rights and environmental commitments, not just efficiency gains. The convergence of these three themes signals that transparency in AI governance in 2026 will be inseparable from broader corporate governance and responsibility. And those organizations that treat these themes as compliance checkboxes rather than fundamental design principles will risk both reputational damage and operational disruption in an increasingly scrutinized landscape.
“Companies that fear the exaggerated risk of attracting the ire of activists are underestimating the greater risk of losing the goodwill of customers, investors, and employees that they need,” Friedman adds.
You can find out more about how companies are managing issues of sustainability here
