Equities

Will AI help or hinder the transition to a greener economy?

June 23, 2025 - 4 min read

AI promises to make significant contributions to accelerate the transition to a greener economy. But this technological revolution is still in its early days and already represents a significant immediate risk for the Earth’s resources. As demand for AI continues to grow, the energy intensity and carbon footprint of the data centers that support this technology also increase. In light of these challenges, how can sustainable investors effectively navigate and engage with the transformative theme of AI while addressing its environmental impact?

AI & Climate Change Opportunities and challenges for a sustainable future

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AI is adopted at the fastest pace of any technology in history

AI is being adopted by corporates and individuals at the fastest pace of any technology in history. OpenAI’s ChatGPT, has reached 100m users in record time, only 2 months after launching in November 2022, and now boasts 180m users with 23% of US adults¹ and 92% of Fortune 500 companies² reportedly using the service. AI solutions and the foundation models that power them have advanced rapidly in just the past 18 months. Over the next 5-10 years, AI should transform the global economy with significant efficiency and productivity gains, as well as our lives.

ChatGPT reached mainstream adoption in record time

Time required for various apps to reach 100mn users

Source: Visual Capitalist, Bank of America

AI already permeates virtually all aspects of our lives, revolutionizing industries and transforming the way we work, live and interact. While we are still in the early stages of developing AI solutions, a wide array of potential use cases is already emerging in sectors such as healthcare (AI powered diagnostic tools), manufacturing (automation, predictive maintenance), retail (optimized supply chains and personalized shopping), finance (fraud detection), home (virtual assistants and smart devices), education (personalized learning and automation) and many others. Software companies are enhancing their offering by creating generative AI applications for business and consumer use cases, accelerating the adoption of the technology. The potential from agentic³ and physical AI⁴ will only expand the use cases and proliferate AI throughout society.

The AI ecosystem is vast and expanding quickly

The AI infrastructure layer includes hardware and cloud infrastructure necessary to store and process vast amounts of data to train AI models and run real time inference tasks.

AI hardware forms the backbone of AI computation, providing specialized chips designed with the processing power needed for both training and inference tasks: Nvidia is the undisputed leader here with its Hooper and Blackwell GPUs⁵. Several contenders such as AMD and most Big Tech companies are offering or developing their own AI chips. TSMC, as the leading semiconductor foundry and with its unique CoWoS⁶ advanced packaging technology, is critical to manufacturing AI chips. This hardware is paired with a software layer (such as Nvidia’s CUDA⁷) with pre-built libraries to help developers of AI models.
Cloud Service Providers (CSPs): Hyperscalers (such as Amazon Web Services, Google Cloud, Microsoft Azure) enable businesses to rent hardware and software for AI as a service, allowing them to store data and utilize AI tools without investing in their own GPUs, infrastructure, and data centers.
Infrastructure: AI data centers need specialized hardware, networking equipment, cooling systems, and robust energy infrastructure to handle the growing computational demands of AI. This necessitates significant investments in energy infrastructure, including power generation, transmission, distribution, and backup power solutions. Additionally, effective power management solutions are essential to ensure reliable power availability and stability.

Quartely capex by hyperscaler in US$bn

Record-high capex spend well expected, particularly Meta

Source: Bank of America, companies - Microsoft data adjusted for CY basis

Information and Communication Technology (ICT) investment has grown to around 5.5% of US GDP in recent years, higher than at any other time since the 2000 dotcom bubble."

– International Energy Agency (IEA) Creative Commons by 4.0

AI infrastructure is seeing unprecedented levels of investments and developments driven by technology giants like Alphabet, Meta, Microsoft and Amazon who are quickly expanding their capital expenditures (each estimated between USD40-75bn for 2024) into servers and data centers to meet demand for generative AI, large language models and cloud services. In 2024, total spend from these hyperscalers could reach around USD240bn (according to Jefferies, more than a fifth of total S&P500 capex), a significant increase from USD148bn in 2023. These investments are expected to continue to accelerate near term with the combined capex of tech giants reaching around USD300bn in 2025 with a focus on data centers equipped with high performance GPUs (like the new Nvidia Blackwell chips), their own custom silicon development and energy infrastructure to support AI workloads. In 2025, Microsoft alone is on track to invest approximately USD80bn to buildout AI-enabled datacenters to train AI models and deploy AI and cloud-based applications around the world.

Source: Bank of America & Alphabet, Meta, Microsoft and Amazon’s websites

Investment in ICT-related assets and infrastructures as a share of GDP

United States, 1995-2023

Source: IEA analysis based on data from US Bureau of Economic Analysis (2024)

Foundational models and GenAI services: The building, testing and refining of AI models is central to creating pre trained intelligent systems such as foundational models (the leader being GPT from OpenAI) that can then be fine-tuned for various applications (natural language processing, genAI etc). The famous ChatGPT is the leader in GenAI services today.⁸

¹ Source: Pew Research Center

² Source: OpenAI

³ Agentic AI refers to artificial intelligence systems that possess the ability to act autonomously and make decisions based on their understanding of the environment, goals, and available information.

⁴ Physical AI re fers to artificial intelligence systems that are embodied in physical forms, such as robots or smart devices, enabling them to interact with and manipulate the physical world.

⁵ GPUs stands for Graphics Processing Units. GPUs are specialized electronic circuits designed to speed up the creation of images and video. According to Jon Peddie Research as of 5th June 2025, Nvidia has a 92% market share of the graphics card market.

⁶ CoWos stands for Chip-on-Wafer-on-Substrate. Developed by TSMC, the platform provides best-in-class package technology for ultra-high performance computing applications.

⁷ CUDA stands for Compute Unified Device Architecture. Developed by NVIDIA, the platform provides a development environment for creating high-performance, GPU-accelerated applications.

⁸ As of 5th June 2025, ChatGPT had a 60% market share of AI search in the US making it the leading Generative AI Chatbot.

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