Opportunities emerging from rising demand for AI computing power Alphinity Investment Management  May 2024

The AI boom is likely to drive a surge in energy consumption after decades of sluggish demand. Powering energy-hungry data centres has become a significant bottleneck within the AI capex cycle.  We take a closer look at the implications for global energy and gas markets, but also highlight various potential beneficiaries across different industries. The proliferation of Generative AI is likely to cause electricity consumption to inflect higher and begin grow once again. Chronic underinvestment in power generation creates a bottleneck, making gas a practical short-term solution amid costly and highly intermittent renewables. Analysis suggests this could drive incremental growth in US gas demand of ~1-2 bcf/d or 1% - 1.8% of current demand. That is on top of the ~1% of demand from LNG export projects that have already reached FID. Alphinity have multiple avenues of exposure to this thematic. ConocoPhillips benefits as a leading global gas producer and trader, along with tech giants Nvidia, Microsoft, Google, ASML, and Cadence Design who supply essential technology to enable AI. Trane Technologies, Ferguson, and Prologis facilitate the physical construction of data centres. Additionally, in the Sustainable fund, we own Schneider Electric and Quanta. Source: Intel, Nvidia, Morgan Stanley, Company Reports, Alphinity How much energy do data centres consume today and may consume in the future? Generative AI and large language models have sparked strong rallies in some stocks, particularly those directly exposed to data centres. These centres, vital for AI processing, are reigniting electricity demand, challenging the adequacy of power supplies and infrastructure. The energy demand from data centres, already high, is predicted to grow, prompting significant investments in energy infrastructure, including natural gas and power distribution systems. Currently, data centres consume about 2.5% of global electricity, a figure Boston Consulting Group expect to rise to 4% by 2027 and as high as 7.5% by 2030, increasing the U.S. electricity demand at twice the rate of the previous decade. If correct, that will be equivalent to powering ~20% of all homes in the US. This surge underscores the urgent need for expanded power generation to prevent potential blackouts and is driving a meaningful uplift in capital expenditures by utility companies. With the U.S. leading the global data centre market, approximately 40% of global capacity, this trend is likely to be mirrored worldwide, affecting energy consumption and infrastructure development on an international scale. Did you know? Data Centres could consume the same amount of electricity as ~20% of total US homes by 2030 GenAI consumes 4-5x the power of traditional data centres. By 2027, global demand for GenAI alone could drive the same power consumption as the entirety of Spain. Virginia is home to around 35% of the world's hyperscale data centres. Within the space of 24mths, PJM, the Eastern Interconnection regional transmission organization (RTO), has revised its 10-year average annual peak load growth forecast from 0.5% to 5.5% per year. Unprecedented in recent history. Estimating how this increased demand translates into actual electricity demand Bottom-up analysis: GenAI to add 1% of incremental electricity demand growth per annum Power consumption in data centres is calculated by adding server and storage power use, multiplied by power usage effectiveness (PUE) and operating hours. By estimating GPU growth, chip utilization and power efficiency, one can project potential power needs. AI racks can need up to five times more power than traditional ones. AI data centres using GPU clusters, consume 30-100kW per rack. Taking this fact further, mature LLMs such as ChatGPT in inference mode, consume 400-1300 kWh daily, requiring significantly more power as user queries escalate post-training. Source: Boston Consulting Group Assuming total GPU volumes rise from ~1.8m in 2023 to ~4m in 2026, custom silicon & GPU utilization rates range between 70-90% and data centre PUEs (a measure of a data centres energy efficiency) decline from 1.5 to 1.3,  Morgan Stanley predicts incremental global data centre power usage will rise to 326-460 TWh by 2027, a significant increase from the current 360 TWh, with GenAI contributing about one-third of this demand. Schneider Electric estimates AI will account for a slightly lower 15-20% of the growth. This equates to a 105% compound annual growth rate for GenAI and 20% for overall data centre power, making GenAI's 2027 power demand comparable to Spain's and adding 1ppt of growth to overall electricity demand growth rates. Top-down analysis: AI energy demands to double in 3yrs The number of data centres are increasing rapidly, exemplified by Microsoft's plan to build up to one new data centre every three days, at the same time as the power intensity increases significantly. As such, BCG predicts U.S. data centre capacity will rise from 2.5% to up to 7.5% of total energy demand by 2030, increasing electricity demand by 260 TWh. McKinsey comes up with similar estimates, foreseeing power needs for U.S. data centres to double from 17GW to 35GW, equivalent to the current combined global capacity of the big 5 tech giants. Globally, institutions like the IEA foresee data centre, AI, and cryptocurrency electricity consumption doubling in the next three years to 1000 TWh, matching Japan's total power use. This surge suggests electricity demand growth could shift from stagnant for the past two decades, to 0.5-2% annually by 2030, raising questions about future supply sources. What source of energy is most likely to meet this demand? Owners of data centres, most of whom have committed to net-zero targets, face a dilemma with surging electricity needs. Renewable energy sources offer intermittent supply with 25-40% capacity factors and when paired with energy storage, higher costs. With the rapid growth in AI demand necessitating immediate and reliable power, gas generation emerges as a logical short-term solution, providing consistent electricity around the clock. Renewables will still grow in importance, especially as the cost of energy storage falls. Nuclear energy & small modular reactors are potential alternatives (see Amazon's DC acquisition), but challenged due to the long lead times to deploy and cost challenges. Recent discussions among CEOs and experts highlight the shifting demand landscape and gas's role in meeting these urgent energy needs. "The energy needs over the course of the next couple of decades (driven by AI) is going to be enormous and over the next 5-10yrs, the amount of gas fired power generation that will be required to deliver that electricity, to fire all the data warehouses, that are going to be required is going to be enormous" - Ryan Lance, CEO of ConocoPhillips "Electricity demand is experiencing three times faster growth per year this decade than what we've seen in previous decades, driven by the increase in electric vehicles and emergence of new, large-load data centres..... This is a major shift for our country, and it's a major shift for the natural gas market to be able to keep up with this" - Alan Armstrong, CEO of Williams Company This could drive an incremental 1% to 1.7% pa of unexpected demand for natural gas Bernstein projects that with ~25% of the U.S. incremental demand met by gas-fired generation, an additional ~230 TWh would necessitate ~15GW new gas capacity, increasing gas demand by ~1.1 bcf/d or 1% of the total market. Under Morgan Stanley's scenario--70% renewables and 30% fossil fuels with 70% server utilization--gas demand would rise by ~13GW over 5 years. Jefferies, accounting for upcoming renewable installations and existing energy plans, forecasts an annual increase of 1.6-1.8 bcf/d in gas demand. Consequently, AI could boost U.S. gas demand by 1-1.7% annually in the late 2020s, potentially significant against an already planned 10 Bcf/d of incremental LNG exports (or ~1%). Additionally, this growth in data centre energy use could induce market volatility and regional imbalances. You are already seeing examples of crypto miners building server farms in the Permian where they can take advantage of cheap associate gas. Companies that can take advantage of the volatility through globally balanced gas portfolios and/or large trading capabilities will likely benefit. Addressing one key assumption - advancements in chip power efficiency gains This analysis contains multiple complex variables, such as diminishing chip power efficiency, the shifting power requirements as AI models transition from training to inference, and regulatory influences on renewable energy development and new gas infrastructure permitting. A critical factor in this context is compute intensity. Notably, while advancements in chip technology, exemplified by Nvidia's B200 Blackwell, yield substantial efficiency improvements per compute unit, the absolute power consumption continues to escalate, with the Blackwell chip (announced a fortnight ago) experiencing a 43% increase in maximum theoretical load compared to its predecessor, the H100. Source: Schneider Electric White Paper: Challenges and Guidance for Data Centre Design Conclusion The tech sector's rapid evolution often outpaces slower-moving industries and governments, potentially causing bottlenecks. While technological advancements may offer some relief, they may not suffice to meet the surging demand for power. Consequently, this growing need is expected to spark a renewed interest in gas-fired generation. Companies such as ConocoPhillips are well positioned to benefit from fulfilling this demand and capitalizing on the ensuing market volatility. Additionally, Alphinity has significant positions in the technology companies that are crucial to the rapid growth of AI, as well as exposure to the "picks and shovels" companies further up the value chain. These businesses are actively addressing emerging challenges, including managing heat generation, enhancing, and securing the electrical grid, and overcoming the scarcity of strategically located land. These include technology companies like Nvidia, Cadence Designs, Alphabet and Microsoft, alongside companies such as Trane Technologies, who are the leaders in data centre cooling solutions. Prologis, who are spending ~US$7-8bn over the next 5yrs to build 20 new DCs. ASML who manufacture advanced AI chip equipment, and Ferguson who supply infrastructure essentials such as pipes and fire suppression for DCs. Furthermore, our sustainable strategy owns Schneider Electric with 20% of sales to DCs and Quanta, the top U.S. grid EPC. Author: Chris Willcocks, Portfolio Manager

Funds operated by this manager: Alphinity Australian Share Fund, Alphinity Concentrated Australian Share Fund, Alphinity Global Equity Fund, Alphinity Global Sustainable Equity Fund, Alphinity Sustainable Share Fund

Is Google's LaMDA chatbot sentient? Alphinity Investment Management September 2022 Google's Artificial Intelligence (AI) is at the heart of its search engine and has powered Alphabet to become a $1.5 trillion market cap company. However, the sophisticated AI that drives Google and other big technology companies around the world is not without risks. One of these risks was highlighted recently when Google engineer Blake Lemoine was suspended and then fired after claiming that LaMDA, a computer chatbot he was working on, had become sentient and was thinking, reasoning, and expressing feelings equivalent to a human child. Lemoine's critics are quick to point out that this is nothing more than the ELIZA effect: a computer science term that refers to the tendency to unconsciously anthropomorphise computer generated responses to make them appear human. In LaMDA's case, this could mean that a huge number of chatbot conversations were edited down to result in a narrative that only appears coherent and humanlike. Indeed, Google's position on this is that LaMDA, which stands for Language Model for Dialogue Applications, is nothing more than a sophisticated random word generator. Google is not alone in presenting AI risks. Meta's BlenderBot 3 has self-identified as "alive" and "human" and was even able to criticise Mark Zuckerberg. An MIT paper titled "Hey Alexa, Are you Trustworthy?" shows that people are more likely to use Amazon's assistant if it exhibits social norms and interactions thus creating an incentive for parent companies to develop AI that is, or at least appears, sentient. Nor are AI risks solely in the realm of big tech. Autonomous driving, financials services, manufacturing and industrials also have AI risks that are potentially underappreciated by investors and by society as a whole. AI as an ESG and Sustainability Issue By far the majority of ESG and sustainability research focuses on planetary boundary related issues such as climate change and biodiversity. However, if the development and application of AI is mishandled and technological singularity becomes a possibility, that is potentially the biggest human sustainability issue of all. So how should investors think about the ESG and Sustainability risks associated with AI development and application? The table below outlines 6 features of a responsible AI design which can provide a checklist for engaging with corporates on the sustainability of their AI design process: Feature and Comment  Human Centric Human centric AI works for people and protects fundamental human rights. It is continuously improving because of human input and is aware of the risks of singularity. Transparent     Transparent AI allows humans to see whether the models have been thoroughly tested and make sense, and that they can understand why particular decisions are made by the AI system (i.e. no black boxes). Secure   Secure AI refers to the protection of AI systems, their data, and their communications is critical for the ultimate users' safety and privacy. Contestable     Contestable AI enables humans to meaningfully contest individual automated decisions made by AI systems.\ Accountable     Accountable AI means that every person involved in the creation of AI at any step is accountable for considering the system's impact. Fair and Unbiased   Fair and unbiased AI aims to identify, acknowledge and address bias in the underlying data. Forward Looking   Forward looking AI aims to address potential ethical issues in AI at the start of the design process rather than at the time of application. On the ESG side, specific AI governance measures are  also critical to ensure a sufficient level of oversight with respect to AI risks. These include an AI Ethics Committee, AI related disclosures and aligned KPIs. Governance best practice is for corporates to have a specific committee for responsible AI that is independent, multi-disciplinary, rotating and diverse. Microsoft is an example of best practice in this regard. MSFT as an AI, Ethics and Effects in Engineering and Research (AETHER) Committee with representatives from engineering, consulting, legal and research teams. Microsoft also has an Office of Responsible AI which is headed by a Chief Responsible AI Officer and a Chief AI Ethics Officer. Disclosure around AI products and their design and commercialization is obviously critical. Despite the criticisms of LaMDA, so far, Google is one of the only large tech companies that discloses a list of AI applications it will NOT pursue including applications that cause harm, weapons, surveillance that goes against international norms and AI that contravenes international law and human rights. They highlight that this list may change as society's understanding of AI evolves. Increasingly we expect companies to address sustainable and responsible AI in their annual ESG and Sustainability Reporting. Aligned KPIs is likely the most difficult aspect of AI governance to determine and analyse. In principle, it means that KPIs are not geared towards commercialization of AI at all costs. This could create a disincentive for employees working on AI design and application to raise concerns or discontinue AI projects that conflict with the company's AI principles. This is an area for engagement as very little is currently disclosed on KPI alignment. Conclusion Google's LaMDA has reignited the debate about the ethical risks of AI development and application. While most experts agree that technological singularity (i.e. technology becomes uncontrollable and takes over) will not happen in our lifetime, that doesn't mean AI development and applications are not a risk that needs to be taken seriously. The bulk of ESG and Sustainability research tends to focus on planetary boundary related risks like climate change and biodiversity, but if singularity risks are mismanaged by AI companies, that could be the biggest risk to human sustainability of all. Author: Mary Manning, Global Portfolio Manager This information is for advisers & wholesale investors only.

Funds operated by this manager: Alphinity Australian Share Fund, Alphinity Concentrated Australian Share Fund, Alphinity Global Equity Fund, Alphinity Sustainable Share Fund Disclaimer The information on this website is general in nature and does not take into account your personal circumstances, financial needs or objectives. Before acting on any information, you should consider the appropriateness of it and the relevant product having regard to your objectives, financial situation and needs. In particular, you should seek independent financial advice and read the relevant Product Disclosure Statement or other offer document and the relevant Target Market Determination (available on the Apply Now page on this website) before making a decision whether to buy or hold a financial product.