A decade of delivery: infrastructure's changing world

4D Infrastructure

April 2026

(10-minute read)

The 4D Global Infrastructure Fund (Unhedged) recently passed its tenth anniversary, delivering annualised returns of over 10% since inception^[1]. Global Portfolio Manager Sarah Shaw reflects on what's changed in the infrastructure world over that decade, what the next chapters look like, and why the core investment case remains as compelling as ever.

What's changed over the past ten years

The investment universe has grown -- and matured

When we launched the Fund in 2016, the global listed infrastructure universe was narrower and less sophisticated than it is today. The pool of investable companies has expanded substantially, as sub-sectors and individual companies have repositioned themselves to capitalise on the structural characteristics that define quality infrastructure: underpinned by contract or regulation, earnings visibility and long asset lives.

Market capitalisation across the asset class has grown at a compound annual growth rate (CAGR) of approximately 10% since the late 1990s^[2], and the number of specialist global infrastructure managers has grown from a handful to more than 60.^[3] The asset class is better understood, better researched, and more institutionally accepted than it was a decade ago.

The demand for infrastructure investment has become structurally larger

Ten years ago, three core investment growth themes were gaining traction: the catch up from chronic under-investment in developed market infrastructure, global population growth, and the rise of the emerging middle class. Each remains significant today, but two additional themes have since emerged with a scale that few anticipated.

The first is energy transition. The decarbonisation of the global economy requires an unprecedented build-out of cleaner generation, networks/grids (transmission & distribution), and storage capacity. The International Energy Agency (IEA) estimates that annual global clean energy investment, which was $1.5 trillion in 2022 and $2.2 trillion in 2025, needs to reach $3.5 trillion annually by 2030 and over $4.5 trillion by 2046. This isn't a short-term investment cycle - it's a multi-decade structural opportunity.

The second is AI and its insatiable demand for electricity, abundant water and digital infrastructure. The major hyperscalers are forecast to collectively deploy more than USD 600 billion in capital expenditure in 2026 alone^[4], with the vast majority directed at data centre expansion and AI compute infrastructure. Global data centre electricity demand is projected to more than double by 2030, reaching nearly 1,000 TWh. This is roughly equivalent to Japan's entire annual electricity consumption.^[5] The infrastructure that carries, stores, and powers the digital economy - fibre, towers, substations, and transmission grids - is being recognised as the critical backbone it is.

Taking the full picture together - energy transition, digital transformation, population growth, replacement of ageing developed-market assets, and the emergence of the middle class - the McKinsey Global Institute estimates that over USD 100 trillion of global infrastructure investment is needed through to 2040 (~USD 7 trillion per annum). The world is not close to spending at that rate._[6] That gap between need and delivery is the opportunity and critically this investment must happen regardless of geopolitics, trade wars, wars, or economic cycles.

Geopolitical complexity has intensified

Managing country and political risk has always been central to 4D's investment process. Over the past decade, that discipline has been tested by Brexit, a global pandemic and related fallout, the Russia-Ukraine conflict and its impact on energy markets, China's property sector stress and longer-term demographic pressures, the resurgence of resource nationalism, and more recently the Middle East conflict. Infrastructure assets sit at the intersection of economics and politics and understanding the regulatory and sovereign environment in which each asset operates is, in our view, non-negotiable for a disciplined global approach.

Sustainability has evolved from environmental screening to systemic integration

A decade ago, ESG in infrastructure was largely synonymous with carbon footprint and environmental compliance. The conversation has since broadened substantially. Gas and nuclear were written off under early green mandates but have undergone a significant re-evaluation as policymakers grappled with the realities of energy security and transition sequencing. Responsible investment is now respected as a system-level discipline, encompassing social licence, regulatory relationships, community resilience, and governance quality. 4D's country-level ESG analysis, integrated into our bottom-up stock selection, has always reflected this more complete picture.

Physical climate risk is now a core investment variable

The increasing frequency and severity of weather events such as floods, wildfires and extreme temperatures directly affect the value and viability of infrastructure assets. Our analysis now incorporates asset-level climate exposure as a material risk consideration, not a secondary one.

What will change

Looking ahead, we expect at least six forces to continue to shape the infrastructure universe over the next decade.

The energy transition will continue to drive substantial investment needs

While the speed of ultimate decarbonisation remains unclear, there appears to be a real opportunity for multi-decade investment as every country moves towards a cleaner environment. Energy transition and decarbonisation of the power sector is an obvious thematic and is expected to have the greatest impact on countries looking for Net Zero. However, it is not just the energy sector with other forms of infrastructure, namely transportation and technology, also having a key role to play. There will be no Net Zero without significant new infrastructure spend.

The 2022 energy crisis across Europe highlighted that this transition must be managed in a socially responsible way with security of supply a priority. This realisation has fast-tracked the build of new renewables sources, increased investment in grids to enable its distribution to end users and reaffirmed the importance of transition fuels such as gas in the shift.

Electricity demand linked to AI will rise sharply

AI and data centres are the defining technological story of this decade. And data centres are, fundamentally, an infrastructure demand story. They require enormous amounts of secure base-load energy -- preferably green -- and an abundance of water for cooling. IEA projects that data centres will drive more than 20% of electricity demand growth in advanced economies to 2030, and in the US, data centres could account for almost half of the growth in electricity demand between now and 2030.

Further to this, data centres, fibre networks, subsea cables, and satellite systems are increasingly being treated by governments as strategic infrastructure assets. We expect this will accelerate regulatory framework development and investors will need to navigate a more complex approval and operating environment for digital assets, particularly in geopolitically sensitive jurisdictions.

Emerging markets will become an increasingly active part of the opportunity set

Emerging market (EM) economies are expected to grow rapidly over the next 30 years, altering the current world economic order which has been in place for much of the post-WWII era. This growth will be driven by an evolving middle class. Given the potential size of the middle class in EMs (which make up over 85% of the global population) changes in spending and consumption patterns will have significant implications for global business opportunities and infrastructure investment for decades to come.

The infrastructure investment need in these regions is huge. For example, EMs currently have one-third of the energy consumption per capita of the developed world. As incomes rise, that gap closes -- and the infrastructure to support it must continue to be built with over two thirds of the forecast clean energy investment needed in the emerging world.

As governance frameworks mature and institutional capital deepens in key markets, we expect the investable EM infrastructure universe to expand, and for active managers with dedicated and experienced analytical capability to be well positioned to capture that opportunity.

Water security is the underappreciated dimension of the investment imperative

Chronic underinvestment in ageing water networks across developed markets -- where pipe replacement cycles routinely run decades behind schedule and leakage rates in some systems exceed 30% of total supply -- is creating a huge capital deployment opportunity. In the emerging world, the imperative is more acute still: more than two billion people lack access to safely managed drinking water, and the UN's Sustainable Development Goal 6 -- universal access to clean water and sanitation by 2030 -- is materially off track, redirecting political will and multilateral capital toward the sector.

The convergence of climate stress such as droughts, floods, and rising water scarcity is intensifying the physical risk to water assets and the cost of inaction. And the AI theme compounds the need for secure water sources as it is essential for cooling. Water infrastructure is transitioning from an afterthought in infrastructure portfolios to an important opportunity.

Deglobalisation will reshape transport, logistics, and industrial infrastructure

The fragmentation of global supply chains - driven by geopolitical tensions, economic nationalism, and the strategic imperative for greater self-sufficiency - is redirecting trade flows and creating new infrastructure investment requirements that did not exist a decade ago.^[7] The reshoring of manufacturing to the US, Europe, and strategic allies is generating demand for industrial facilities, regional logistics networks, port upgrades, and rail and road connections that support new trade corridors. This, coupled with increasing desire for energy independence, is driving even more demand for domestic energy infrastructure.  For infrastructure investors, this represents a structural shift in the composition of the investable universe and underscores the importance of active, geopolitically-informed portfolio management.

Defence and security spending will drive a new wave of dual-use infrastructure

Governments across Europe, Asia-Pacific, and North America are materially increasing defence budgets in response to a more fractured global security environment. EU member states spent €343 billion on defence in 2024, a 19% increase year-on-year, with further increases expected.^[8] The implications for infrastructure are substantial: hardened communications networks, secure energy supply, resilient transport corridors, and cyber-capable digital infrastructure are all critical components of modern defence readiness. Much of this investment will be channelled through, or create demand for, listed infrastructure companies.

What hasn't changed

Infrastructure fundamentals have proven true to label

Over the past decade, the global listed infrastructure universe has navigated some of the most severe macro shocks in living memory: a global pandemic, supply chain collapse, a surge in inflation not seen since the 1980s, a European land war, and an energy crisis that redrew assumptions about how the world powers itself. Through all of it, infrastructure earnings have done what the asset class promised: they have held up.

That resilience reflects the structural characteristics that define infrastructure: long-duration contracted or regulated earnings streams, essential service monopolies, and pricing mechanisms with inflation linkage. More importantly, over the last decade the annualised earnings CAGR of the listed infrastructure universe has significantly exceeded that of general equities - a record that speaks to the quality of the underlying cash flows, not simply market conditions. Yield has also been a consistent differentiator. In every year of the Fund's ten-year life, the income component of listed infrastructure has exceeded that of broad global equities.^[9]

Infrastructure is still not well understood as a standalone asset class

The persistent perception of listed infrastructure as a 'bond proxy': reactive to interest rate movements and little else. This continues to understate the earnings dynamism, diversification benefits, and structural growth potential of the asset class. The reality is that infrastructure companies generate earnings growth through capital investment cycles, pricing power, and volume growth, not simply by the level of rates. That mischaracterisation has, if anything, created ongoing opportunities for active managers prepared to look through it.

Allocations remain sub-optimal

Despite the asset class's risk-adjusted return profile - delivering broadly comparable long-term returns to global equities with materially lower volatility and downside capture - institutional and retail allocations to listed infrastructure remain modest. Many global equity managers hold minimal infrastructure exposure, often concentrated in a small number of large-cap utility names. The diversification and income benefits of a dedicated, actively managed global infrastructure allocation continue to be under-utilised.

The listed-versus-unlisted valuation gap persists

Institutional capital continues to flow disproportionately to unlisted infrastructure, tolerating illiquidity premiums and elevated entry valuations. Listed infrastructure offers access to the same underlying asset with daily liquidity, lower transaction costs, and, at current valuations, what we consider a compelling relative entry point. As listed infrastructure fundamentals continue to prove themselves and the asset class benefits from increased institutional recognition, we believe this discount represents an embedded source of potential value for long-term listed infrastructure investors.

A global portfolio must genuinely be global

The temptation to treat 'global' infrastructure through a developed-market lens - principally North America and Western Europe - remains common. At 4D we have always believed, and our experience over ten years has reinforced, that a portfolio which ignores the scale, diversity, and growth potential of Asia, Latin America, and evolving emerging markets is accepting a significant constraint on both return potential and portfolio construction quality.

Not just a defensive allocation -- a structural growth story

Ten years ago the 4D Global Infrastructure Fund (Unhedged) launched with the conviction that a rigorously managed, genuinely global portfolio of listed infrastructure assets could deliver reliable, macro-aware returns through market cycles. A decade of annualised returns exceeding 10%, through some of the most challenging market conditions in modern history, has validated that conviction.

What has changed is the scale of the opportunity. The energy transition, the digital economy, the reshoring of supply chains, the emerging middle class evolution, and decades of under-investment in developed markets have created a pipeline of infrastructure investment need that will define capital allocation for the next generation.

What hasn't changed is our approach. Disciplined country and sector analysis. Integrated and rigorous bottom-up/top down stock selection. A focus on quality assets with superior management teams, visible earnings, genuine competitive moats, and appropriate valuations.
 

^{[1] The 4D Global Infrastructure Fund (Unhedged) returned 10.36% per annum net of fees and expenses for the ten years to 31 March 2026. Past performance is not indicative of future performance.}

^{[2] GLIO (Global Listed Infrastructure Organisation), GLI101: Why Invest in GLI.}

^{[3] GLIO, GLI101: Why Invest in GLI.}

^{[4] Multiple sources. CreditSights (November 2025) projected aggregate capex for the five largest hyperscalers (Amazon, Alphabet, Microsoft, Meta, Oracle) at approximately USD 602 billion in 2026. Subsequent guidance from individual companies following Q4 2025 earnings calls suggests aggregate 2026 capex could exceed USD 650 billion.}

^{[5] International Energy Agency (IEA), Energy and AI Special Report, April 2025.}

^{[6] 'The Infrastructure Moment', September 2025.}

^{[7] IFM Investors, Infrastructure Horizons 2025 report.}

^{[8] European Commission, ReArm Europe / SAFE programme, 2025. Carnegie Endowment for International Peace, December 2025; European Commission, October 2025. commission.europa.eu}

^{[9] GLIO, GLI101: Why Invest in GLI. On average since 2003, global listed infrastructure has yielded approximately 3.6% versus 2.6% for global equities. glio.org}

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Artificial Intelligence: The Rise of Agentic AI

Ox Capital (Fidante Partners)

March 2026

(15-minute read)

1. What is Agentic AI?

For the past two years, most people have experienced artificial intelligence as a chatbot. You type a question, it answers. Then it sits there, cursor blinking, waiting for your next instruction. This model--sometimes called co-pilot AI--is useful, but fundamentally passive. It is a clever assistant that never takes initiative.

Agentic AI is qualitatively different. Give it a goal--"Find the cheapest flights to Tokyo for these dates, cross-reference with hotel availability near Shibuya, and book the best combination under $3,000"-- and it breaks the problem into steps, uses multiple tools (flight databases, hotel sites, a calendar), backtracks if a lead is a dead end, and delivers a finished result. The human sets the destination; the agent drives the car.

How It Works Under the Hood

Three innovations have converged to make this possible.

The brain: large language models. Models like ChatGPT, Claude, and Gemini are the reasoning engines. They are akin to extremely well-read graduates who have absorbed most of the internet. They can process, plan, write, and make judgements. A brain alone is not enough. We also need hands and a way to interact with the world.

The plumbing: a universal connector called Model Context Protocol (MCP). MCP enables the AI engine to connect to systems where work can get done. With MCP, agents can access customer databases, accounting software and logistics platforms, for instance. Until recently, every connection had to be custom-built, like wiring a new plug for every appliance in your house.

In late 2024, Anthropic introduced MCP which is essential one standardised socket that enables AI agents to plug into any data source or tool. By February 2026, MCP had crossed 97 million monthly downloads and adopted by major AI providers. It is the industry standard and is now governed by the Linux Foundation, co-founded by OpenAI, Google, Microsoft, Amazon, and others.

The training leap: Autonomous learning. In the early days, AI was trained by having humans grading its outputs. This was slow and expensive. A newer approach - Reinforcement Learning with Verifiable Rewards (RLVR) - gives the model automatic, objective feedbacks. The model iterates thousands of times at machine speed, teaching itself through trial and error. This is why coding agents have improved at a pace that has stunned even the people building them.

Where Things Stand Today

The industry crossed a "chasm" in late 2025. The landmark event was the launch of the latest update of Claude Code from Anthropic. Claude Code was then capable of compressing a year's development work into hours or days, according to one of its senior software engineers. In early 2026, NVIDIA reported 64 per cent of organisations actively deploying AI in operations, with a vast majority seeing productivity gains. PwC found 79 per cent of 300 company executives they surveyed in 2025 were already leveraging agentic AI, with two-thirds reporting measurable productivity improvements.

The agent market is projected to surge from ~US$8 billion in 2025 to over US$50 billion by 2030, according to market research firm MarketsandMarkets. Agentic AI is no longer a laboratory curiosity.

2. What Agents Can Do: Real Examples, Real Results

Tools like Claude Code, OpenAI Codex, and Cursor now build entire software features autonomously. A developer provides a specification (prompt), such as to "Build a secure login system with encrypted tokens and full test coverage", and the agent will then design the architecture, write codes across multiple files, generate tests, fix what breaks, and submit the finished product. A task that would take two days previously can now be delivered in minutes.

Another field where agentic AI is making wave is customer service. Salesforce's Agentforce 3.0 automates 85 per cent of tier-1 support inquiries and 60 per cent of routine sales follow-ups for enterprise clients. The agents don't just answer questions, they proactively identify upsell opportunities in existing accounts without human prompt. Cost per interaction: ~US$0.40, versus $2.70-$5.60 for a human agent.

Oracle reports customers have reduced invoice processing cycles by 80 per cent using predictive agents that reroute shipments based on real-time logistics data. PepsiCo, working with Siemens and NVIDIA, converted US factories into high-fidelity 3D digital twins, virtual replicas where AI agents simulate operations before any physical changes are made. This resulted in 20% throughput increase and 10-15% reductions in capital expenditure.

Amazon used its coding agent to modernise thousands of legacy Java applications in weeks rather than years. Anthropic's Claude can now interact with software that has no modern programming interface. The AI "sees" the screen and operates mouse and keyboard like a human, bridging the gap to old enterprise systems that companies have been unable to upgrade for decades.

The Cost Equation

The unit cost of running AI, measured in tokens, has plummeted. In 2022, a million tokens cost around US$20. By late 2025, tokens from lightweight models hit US$0.15, a 99% decline. Three forces are driving down the cost of tokens:

1. NVIDIA's ever more efficient new AI chips
2. Improvement in models and algorithms
3. Aggressive competition from new entrants undercut prices

A basic coding assistant like GitHub Copilot costs just US$10 per month which is trivial relative to a developer's salary, yet capable of making developers far more productive. Noticeably, full agentic coding tools cost as little as US$20 per month. Every quarter, agents get smarter and cheaper. For high-wage, services-heavy economies, the incentive to deploy agents is acute.

3. Why Agentic--Not Co-Pilot--Is the Killer App

A co-pilot is like a knowledgeable colleague helping us. We ask, they answer, then stop and wait. We still manage the workflow, decide what's next, copy information between systems. The human is the engine; the co-pilot is a mere productivity boost.

An agentic system is more like hiring a competent employee. You say: "Resolve all outstanding customer complaints. Anything under $500, handle it. Anything above, escalate with a summary." The agent will then read tickets, pull up history via MCP, draft responses, process refunds, flag high-value cases, and report back at day's end. We only need to define the goal and do not need to prompt each step (if the agent is trained properly).

Another example is supply chain management. When we ask, "What's the status of shipment #4471?", a co-pilot will only return an answer as of that point in time. In comparison, an agent will monitor the shipment continuously, when it detect that a road closure will delay #4471, it will find an alternative route, rebook the freight, update the customer portal, and send another notification, before you know there is a problem.

The co-pilot answers questions about the world, the agent manages and reacts to forces in the world.

The architecture enabling this at scale is the multi-agent system: teams of specialised agents coordinated by an orchestrator, like a project manager assigning tasks to specialists. One agent generates code, another tests it, a third checks for security vulnerabilities, and the orchestrator coordinates. Gartner predicts that by the end of 2026, 40 per cent of enterprise applications will feature task-specific agents, up from less than 5 per cent in 2025. Among companies measuring returns on generative AI, average ROI is 49 per cent, with 92 per cent of early adopters reporting positive results, according to recent report from Snowflake (an AI Cloud company).

4. Broader Implications

i. Why Coding Is Just the Beginning

The fact that coding is verifiable makes it the first area in which agentic AI has made an impact. An agent writes a program, runs tests, and gets an instant pass-or-fail verdict, the perfect training loop for RLVR.

Other domains are going to be disrupted by AI. Legal compliance (does this clause satisfy regulation X?), financial reconciliation (do these numbers match the audited figures?), A/B-tested marketing copy (does this version increase conversions?), and diagnostic imaging (does this scan match established clinical criteria?) all provide objective quality measures. As verification tools and domain-specific benchmarks mature across industries, agents will be disruptive and offer significant productivity uplift.

Revenue per employee in AI-exposed industries has surged 27 per cent since 2022--over three times the growth elsewhere. According to a PWC report, workers with AI skills earn a 56 per cent wage premium, more than double the prior year. Meanwhile, 69 per cent of workers expect agents to take over parts of their job within 12 months.

ii. Software Industry: Moats Under Siege

For two decades, SaaS companies bill their customers based on number of seats per month. The economic moats were strong as they are around user habits, interface design and data lock-in. Part of the economic moats is a result of switching costs of user habit and interface familiarity.

Agentic AI inverts this. When an agent can operate a customer relationship management software or manage a project board without a human logging in, companies can cut back their software subscriptions sharply.

A leaked memo from a Fortune 50 company revealed plans to cut its Salesforce and ServiceNow licence spend by 60 per cent, opting instead to use raw API credits from AI model providers.

The issue is that agents are becoming a new operating layer between users and software, potentially changing the way we interact with software. An agent orchestrates actions across different software behind the scene with users entering natural language commands. When natural language is the interface, software interface design matters less. When MCP lets any agent plug into any system, proprietary integrations matter less. When an agent can learn to operate any software in minutes by "watching" the screen, switching costs evaporate. Pre-AI agents, getting humans to do their jobs inside your software was a powerful moat. If agents are doing the work, who cares about human workflow?

This extends to the broader internet economy. If agents increasingly handle research, shopping, and service interactions on behalf of users, the advertising-supported models that underpin much of the web face a fundamental question: who sees the ad if an agent is doing the browsing? When an agent researches a purchase, compares options, and completes a transaction without a human ever visiting a website, the entire value chain of digital advertising, from impression to click to conversion, is potentially disrupted.

This does not mean these businesses disappear overnight, but their economic moats are under pressure from a direction few anticipated even two years ago.

iii. Workforce Reorganisation: Unpredictable Ripple Effects

If agents absorb entire job functions, corporate structure changes fundamentally. The future may be one of skilled humans orchestrating specialised AI agent teams!

The implications cascade in ways that are difficult to predict. Fifty per cent fewer customer service staff means fewer HR managers, smaller offices, fewer software licenses, different training programs, and reduced demand for the SaaS tools those employees used to rely on.

Each layer of workforce reduction triggers second- and third-order effects on enterprise spending that ripple through the entire software supply chain. This is precisely why the impact on SaaS software stock valuations is so hard to forecast. The magnitude of change depends not just on how good agents get, but on how radically companies are willing to redesign their operating models.

Gartner predicts 35 per cent of point-product SaaS tools will be replaced or absorbed by 2030, but the remaining 65 per cent will need to reinvent themselves around outcomes rather than seats. Another future vision is individuals commanding AI workforces rather than human staff. That is, solo founders deploying agent fleets to build products, analyse markets, and launch companies.

The World Economic Forum's "Future of Job Report 2025" projected a net gain of 78 million jobs by 2030 (170 million created, 92 million displaced). The transition will be uneven and the ultimate shape of the global workforce remains uncertain.

Conclusion

Agentic AI is not another incremental software upgrade. It is a structural shift in how economic output is produced, from that of humans using tools to humans directing autonomous systems that execute work on their behalf.

The technology is real and maturing fast. Agentic AI economics are compelling. Real-world applications, from coding to claims processing to supply chain orchestration, are already in production at scale.

For investors, the second-order effects may matter most. Will agents become the primary users of digital services? Will attention-based business models erode? Are per-seat SaaS pricing models facing an uncertain future? Will corporations fundamentally re-organise their workforces to incorporate AI agents?

The direction of travel is clear, the pace is faster than any prior technology cycle, and the time to understand agentic AI is not next year. It is now.

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Important Information: This material has been prepared by Ox Capital Management Pty Ltd (Ox Cap) (ABN 60 648 887 914) Ox Cap is the holder of an Australian financial services license AFSL 533828 and is regulated under the laws of Australia. This document does not relate to any financial or investment product or service and does not constitute or form part of any offer to sell, or any solicitation of any offer to subscribe or interests and the information provided is intended to be general in nature only. This should not form the basis of, or be relied upon for the purpose of, any investment decision. This document is not available to retail investors as defined under local laws. This document has been prepared without taking into account any person's objectives, financial situation or needs. Any person receiving the information in this document should consider the appropriaten