“The secret of change is to focus all of your energy not on fighting the old, but on building the new.”
-Dan Millman
Key Takeaways
AI infrastructure demand seems to have shifted from a conventional cyclical setup toward a potential supercycle, as indicated, in our view, by renewed GPU rental rate inflation, accelerating earnings, and sustained enterprise AI adoption
Physical bottlenecks in data center construction, combined with rising customer prepayments and broadening neocloud demand, have been extending supply constraints across the semiconductor and compute ecosystem
We have rebuilt our exposure across the AI value chain as we encountered new data, but we continue to monitor valuations, free cash flow pressure, leverage, and the eventual returns on AI capital investment
We do not seek to be contrarian for the sake of being contrarian. We have consistently let the data dictate our actions, leading us at times to look very contrarian to the market and at other times to align with consensus. This approach has helped us in the past, but it is not a flawless process—especially when we encounter supercycles like the one we think we see today in AI.
One of the most frustrating aspects of the last 18 months or so has been our relative performance. While our philosophy, process, and focus on adaptability remain unchanged, this period has no doubt been challenging. Having been in this business for as long as we have, we know it is not the first time we have made the wrong call, nor will it be the last.
At the end of the day, we are not in the business of making excuses. We are in the business of pursuing performance. Given our focus on downside-risk management, we know we may underperform our benchmarks in frothy markets. Having said that, our view is that context is always key, and we do believe there were valid reasons to be concerned about the current cycle, which ultimately did not materialize in the way we expected.
As a reminder, we have never been perma-bears or bulls on technology or any other sector. Our views have, and always will, evolve depending on the environment and data. We mention that because we do not want to be mistaken for a dogmatic manager who has never owned—or would never own—most cyclical parts of the semiconductor industry. Nor do we want to be grouped with the type of aggressive growth managers who will ride out and defend expensive names irrespective of market conditions.
With that in mind, we will quickly recap where we have been over the last couple of years and how we arrived at our current positioning.
EX-ANTE EXPECTATIONS MEET EX-POST REALITIES
If we rewind the clock a couple of years to the end of 1Q 2024, all our core strategies were overweight the information technology sector, including the semiconductor industry. However, throughout 2024 and into early 2025, we began to reduce exposure to semis (and tech broadly) due to valuation concerns as price-to-revenue multiples were hovering meaningfully above peak levels that were seen in 2021. In our view, the industry was overearning and priced at a peak multiple, which is a setup that has historically offered poor go-forward returns.
Global Semiconductors
Source: GQG Partners LLC (chart). AllianceBernstein, World Semiconductor Trade Statistics, Bloomberg LP (data). Data as of 5 October 2026. Global semiconductors Next Twelve Months (NTM) price-to-revenue based on the MSCI ACWI Semiconductor and Semiconductor Equipment Index. It is not possible to invest directly in an index. PAST PERFORMANCE MAY NOT BE INDICATIVE OF FUTURE RESULTS.
In addition, throughout 2025, concerns about rising CapEx, ROI on AI investments, the easy availability of the latest GPUs, and declining GPU rental rates made us wary of revisiting these exposures. During this time, fundamentals were not yet deteriorating, but company valuations were, in our view, extremely aggressive. Any reversion to the mean would have been very painful and could have created not just relative downside but absolute downside as well. This methodology has helped us navigate cyclical tech in prior cycles reasonably well, with 2022 being the most recent example.
However, in 2Q 2026, the data began to shift. GPU rental rates began to surge and semiconductor earnings accelerated, driven by continued AI CapEx and increased corporate willingness to experiment and spend on AI projects. As we revisited and refreshed our view on semis, we realized that what seemed like peak-cycle dynamics were starting to look like a supercycle.
While our current view is that this is not a normal cycle, we believe we should be long these names until the data tells us otherwise. We believe this cycle could last much longer than most seem to expect as AI adoption is still in its infancy, despite all the various issues and concerns we discussed in previous white papers.
WHAT CHANGED?
Based on our research, from AI startups to data center construction, we were concerned about overcapacity. Our channel checks in the fall of 2025 indicated wide availability of Nvidia GPUs across all vintages, and this was reflected in declining rental rates throughout 2025, as shown in the charts below.
GPU Availability Is Tight
Source: GQG Partners LLC (chart). Bloomberg LP, Silicon Data (data). Data as of 30 September 2026. The NVIDIA A100 is a data center GPU built on the Ampere architecture, powering AI, data analytics, and high-performance computing (HPC). The NVIDIA H100 is an enterprise data center GPU based on the Hopper architecture, purpose-built for massive AI training, Large Language Models (LLMs), and high-performance computing (HPC). The NVIDIA B200 is a data center GPU built on the Blackwell architecture and includes 208 billion transistors in a dual-die design.
Additionally, on the cloud side, we were concerned about margin deterioration. We observed Oracle, the fourth major US player in cloud, ramping up its operations and gaining market share, most likely at the expense of the category leader, Amazon (AWS), whose margins for the bulk of 2025 were below 2024 levels.
Margins Came In Better Than Expected
Despite this, what is quite clear in both charts is that something meaningfully changed in 2026. GPU rental rates began to rise again, and AWS margins continued to march higher, eventually returning to early 2025 levels.
In our view, it was a perfect storm of increased token generation for media usage and software development combined with the continued maturation and adoption of AI products from Anthropic, OpenAI, and others by enterprises. At the same time, headwinds to data center buildouts, an issue we initially thought was a bug in the supply chain, turned out to be more of a feature from an investment perspective.
Data centers are not coming online fast enough to meet growing demand. This is due to various reasons, including permitting issues, shortages of turbines and racks, and Not in My Backyard (NIMBY) activism. Ironically, this has eased fears of compute oversupply while demand continues to soar. This is highly important, in our view, because as supply remains low, it acts as a tailwind for tech hardware as companies scramble to buy whatever they can, and it allows GPU rental rates to remain elevated as customers are willing to pay up for deployed compute.
Once all of these things hit, and they did so pretty quickly, our view was that a lot of the fears we had around CapEx, ROI on AI investments, etc., moderated. As we looked at the data and started to hear shifting anecdotes in our research, we questioned whether this was indeed a supercycle, where patterns and logic from past booms do not always apply.
That does not mean that our concerns have fully abated. Free cash flow deterioration is real, and there is no doubt that debt issuance within the tech sector is rising. And the question of what the returns on invested capital will be for many AI use cases remains unanswered. However, we believe that the current backdrop provides sufficient mitigating factors to those concerns, at least for now, but these are factors to monitor as the data may change.
Take the memory space, for example. We were cautious to invest in these companies because of our experiences from previous cycles, but as we did more work, we became more open to the notion that this cycle is far more nuanced than prior ones. And with demand increasing, brownfield expansions that have normally taken four to five quarters to come online no longer appear to be sufficient to meet demand. We believe the environment now needs greenfield expansions that can take more than three years to come to fruition.
One other big change is that companies are now receiving prepayments for buildouts, something that was not happening at all not that long ago. For example, Oracle and Nebius are now receiving 30% to 50% advances from their customers to build out data centers.1,2 Neocloud business models such as Nebius, in our view, were unproven 12-18 months ago when they had only a handful of clients. Fast forward to today, and now they have meaningfully broadened their customer bases and appear to have sustained demand.
Taken together, these dynamics help explain why our exposures have come to span the full ecosystem: supply remains constrained by physical limitations, while indicators of demand had accelerated.
Few things are static in this business or in markets. Of course, our aim is to continually improve on navigating whatever challenges come our way, and for that reason, our views are always subject to change. We believe this positioning supports our goal of building portfolios with competitive compounding potential along with downside risk management.
As always, thank you for your support.
Rajiv Jain
Chairman & Chief Investment Officer
GQG Partners LLC
END NOTES
1Q1 2027 Oracle Corporate Earnings Report. 10 September 2026.
2Q2 2026 Nebius Group NV Corporate Earnings Report. 12 August 2026.
DEFINITIONS
Artificial Intelligence (AI): Technology that enables computers to perform tasks associated with human intelligence, such as understanding language, recognizing patterns, learning from data, reasoning, and making decisions.
Brownfield: An existing site, facility, or system that is being upgraded, expanded, or repurposed. In infrastructure and technology, a brownfield project builds on existing assets—for example, converting an existing data center into an AI-capable facility.
Capital Expenditures (CapEx): Money a company spends to buy, maintain, or upgrade physical assets like buildings, machinery, and technology. CapEx is a long-term investment in the business—what a company spends today to keep growing or running efficiently tomorrow.
Graphics Processing Unit (GPU): A specialized processor designed to handle many calculations simultaneously. GPUs power graphics rendering and accelerate workloads such as AI model training, scientific computing, and data processing.
Greenfield: A project built from scratch on a new site or within a new environment, without relying on existing infrastructure or systems. A greenfield data center is designed and constructed specifically for its intended workload, such as AI computing.
Free Cash Flow (FCF): The cash a company generates from its operations after paying for capital expenditures, including maintenance and growth investments. It is the money left over that could be returned to shareholders, used to pay down debt, or reinvested.
High-Performance Computing (HPC): the use of powerful, often parallel computer systems to process complex calculations and large datasets at very high speed. Common applications include scientific research, engineering simulations, weather forecasting, and AI training.
Large Language Model (LLM): an AI model trained on vast amounts of text to understand and generate human language. LLMs can answer questions, summarize content, draft text, translate languages, write code, and analyze documents.
Neocloud: A cloud-computing provider built primarily to deliver large-scale GPU and AI infrastructure. Neoclouds typically offer high-performance compute capacity, storage, and networking optimized for training and running AI models.
Next Twelve Months (NTM): In finance, it refers to expected financial results—such as revenue, earnings, or valuation multiples—over the coming 12 months.
Return on Investment (ROI): A measure of how much profit or loss an investment generates relative to its original cost, expressed as net gain divided by the amount invested. For example, an ROI of 20% means the investment earned $0.20 for every $1.00 invested.
Supercycle: An extended period of unusually strong, self-reinforcing growth in demand, investment, or prices within an industry or commodity market. These cycles often last several years and are driven by structural forces such as technological change, supply constraints, or major shifts in global demand.
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The MSCI All Country World Index (MSCI ACWI) captures large and mid cap representation across 23 Developed Markets (DM) and 24 Emerging Markets (EM) countries.
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CIOPR 3Q26-RTL
