Once positioned as leaders in sustainability and setting ambitious net-zero goals that align with global environmental efforts, organizations are taking a turn on their climate and carbon reduction commitments. However, this rapid rise of energy-hungry artificial intelligence is forcing companies to reconsider or abandon their commitments as they struggle to balance environmental responsibility.
Energy consumption: AI vs. Blockchain
The debate over energy consumption across sectors is not new. Blockchain technology, especially cryptocurrencies like Bitcoin, has faced heavy criticism for its excessive energy use, leading to calls for regulation. In contrast, AI—despite having greater energy demands—has escaped such scrutiny. Data centers account for almost 1-1.3% of global electricity consumption.
This disparity in sentiment between the energy consumption of blockchain and AI is perplexing. However, when considered, it makes sense that AI is a helpful tool for incumbent industry players.
Unlike blockchain, which is a disruptive force challenging the status quo, AI’s ability to drive economic growth and reinforce existing power structures makes it a preferred technology among tech giants. Given that investments in AI are expected to reach $200 billion by 2025, the impact of AI on the environment is often downplayed, fitting the commercial and broader agendas of large corporations.
In contrast, blockchain’s decentralized nature threatens traditional centralized systems, leading to more scrutiny, criticism, and calls for regulation of energy use. The silence around AI’s environmental impact could, therefore, be more about the selective emphasis on technologies that support or challenge its existing power dynamics.
Yet, major tech companies are shifting away from purchasing carbon offsets due to the burgeoning energy consumption of their AI operations. There is also a growing emphasis on the need for genuine emissions reductions and greater transparency in energy use.
The Role of Blockchain in the AI Revolution
With AI expansion costs soaring, new doors for blockchain-based marketplaces are also opening. These platforms present a decentralized privacy-focused solution where user data is not stored or used to train the AI models. However, to truly compete with centralized systems, these decentralized models need to prove that they can deliver high-quality outputs, not just privacy.
While centralized AI is better quality as user data is used to train the models faster, decentralized AI is exploring new ways to enhance performance by training AI models using data that cannot be seen in raw form
AI models are trained across multiple devices, leveraging local computational resources without compromising on accuracy and data privacy. Distributed training approaches integrate diverse data sources to build more robust and scalable AI systems.
In harnessing this vast, distributed network of mobile devices, the solution taps into a more energy-efficient computing resource that doesn't require the cooling systems needed in data centers but also democratizes access to computational power.
Read more: How is AI & Climate Tech Spearheading the Race to Net Zero?
How Is AI Affecting Companies’ Goals?
Training and running large AI models requires substantial computational power typically sourced from data centers in regions with lower energy costs. This is because renewable energy sources are not always available around the clock, making them less reliable for constant computing needs. Due to the rising demands of AI and manufacturing sectors, enterprises are rushing to expand their generating capacity to meet their energy transition goals.
Further research indicates that the electricity consumption of AI and global data centers is set to double by 2026. Regional transmission capacity needs to double by 2035 to accommodate this growing demand, and interregional capacity will need to increase over five-fold. This expanded capacity is critical to link wind and solar energy. The rapid growth in energy demand is pushing tech companies to choose between advancing AI technology as well as adhering to their net-zero climate commitments.
Reasons Excluding AI for the Rollback
Although many corporations have made ambitious pledges initially to adopt sustainable practices, these promises frequently to fall short. A major obstacle is the lack of cohesion across leadership teams. A recent research highlighted that 58% of executives face significant disagreements in balancing immediate business demands with environmental, social, and governance (ESG) goals. This issue is further aggravated by the absence of effective measurement tools. Without robust metrics, tracking progress or linking executive bonuses to ESG performance often becomes challenging, making it difficult to stay committed to sustainability objectives.
Read more: Disclosure v. Diversity Washing: Reflecting on the Critical Components in Reporting
Another challenge is the external alignment of set goals with stakeholders. Many organizations often face internal cultural issues as a barrier to achieving their ESG goals. Decision-making and accountability are further complicated due to the shortage of essential skills and mindset within the organization. Additional challenges include insufficient progress in understanding climate-related financial risks and difficulties in integrating ESG factors into capital allocation. Despite significant investments in sustainable solutions, many companies are struggling to fulfill their sustainability promises.
Major corporations are reevaluating their sustainability strategies, especially around the use of carbon offsets. The growing concerns around the effectiveness of carbon offsets and the risk of reputational damage if commitments are not met have prompted several companies to shift focus. Many are moving away from reliance on carbon offsets and prioritizing the reduction of actual emissions within their operations. This exemplifies how innovative solutions, combined with the privacy and cost efficiency offered by decentralized infrastructure, can help address both the growing demand for computational power and more equitable access to technology at a reasonable price. Having more local compute-for-AI offerings will further help in providing a more enduring pathway for AI to expand without the flip-flopping seen by the tech giants.
Key Takeaways
- Companies concerned about the sustainability of their businesses, customer base, and employees are quietly backing out of their ESG commitments.
- They have discovered that meeting net-zero goals is both difficult and expensive as well as threatens their sustainability.
- Due to these concerns, they are moving away from previously declared goals.
- Today, only 4 percent of companies are on track.
Read more: Building a Sustainable Tomorrow: Solutions to Climate Change
Final Thoughts
Today, big corporations are pulling back on their climate commitments.
Even before the rise of the generative AI wave, software consumed a considerable amount of energy. But now, it is booming. A survey published by the International Energy Agency evaluated that a Google search query requires 0.3 watt-hours of electricity on average, while a ChatGPT request consumes 2.9 watt-hours.
Behind chatbots and AI-infused software lies a vast network of power-hungry data centers. This growing energy needs are only set to go up as competitive pressure drives companies worldwide to invest in building larger AI models and data centers. Enterprises are working on technological methods to use less power or balance their demand on the grid more efficiently. This includes squeezing more efficiency from chips and servers, laying out equipment that requires less cooling, and shifting loads across different areas based on where green energy is available. This requires greater transparency from enterprises to establish how much energy AI products consume.
A leader in ESG Services, SG Analytics offers bespoke sustainability consulting services and research support for informed decision-making. Contact us today if you are searching for an efficient ESG (Environmental, Social, and Governance) integration and management solution provider to boost your sustainable performance.
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