AI Infrastructure vs Applications: Where Capital Is Actually Going

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Welcome to Unlocking Liquidity, the podcast from PrimaryMarkets that brings the dynamic world of private capital to life. Each week, we dive into the trends, opportunities and challenges shaping today’s investment landscape, from emerging asset classes and market innovation through to strategies for navigating liquidity in unlisted markets. Whether you’re an experienced investor, a dealmaker, or simply curious about private markets, Unlocking Liquidity offers analysis and real-world insights to help you make sense of complexity and stay ahead of what’s next.

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Artificial intelligence has become one of the most powerful investment themes of the modern era, but where is capital actually flowing? While headlines focus on AI chatbots and productivity tools, the biggest investments are often happening behind the scenes, in the infrastructure that powers the entire ecosystem.

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In this episode, we explore the growing divide between AI infrastructure and AI applications. We examine why investors are pouring billions into data centres, semiconductor technology and cloud computing, while simultaneously backing a new generation of AI-enabled businesses transforming industries from healthcare and mining to finance and agriculture.

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We also look at Australia's role in the AI landscape, where specialised applications and industry expertise may present compelling opportunities for private market investors. Most importantly, we discuss where value is likely to accrue over the coming decade and what wholesale and sophisticated investors should be looking for as AI moves from technological innovation to economic necessity.

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Whether you're evaluating private capital opportunities, emerging technology companies or the broader future of artificial intelligence, this conversation provides a framework for understanding where capital is going today and where the next generation of value creation may emerge tomorrow.

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Artificial intelligence has become one of the defining investment themes of the decade. Yet as capital continues to flood into the sector, investors are increasingly asking a critical question: where is the real value being created?

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While public attention often focuses on consumer-facing applications such as AI assistants, image generators and productivity tools, much of the investment activity occurring behind the scenes is directed toward the infrastructure that powers these systems. The distinction between AI infrastructure and AI applications is becoming increasingly important for investors seeking exposure to the sector.

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The AI ecosystem can broadly be divided into two categories. Infrastructure encompasses the foundational technologies that enable AI to operate, including semiconductor chips, data centres, cloud computing platforms, networking equipment, model development frameworks and data management systems. Applications sit on top of this infrastructure and use AI capabilities to solve specific problems for businesses or consumers.

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Understanding where capital is flowing between these two segments provides valuable insight into how investors are assessing both risk and opportunity in the AI economy.

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The extraordinary rise of generative AI has created unprecedented demand for computing power. Training and operating large language models requires vast quantities of specialised hardware, particularly graphics processing units (GPUs), advanced networking systems and highly sophisticated data centres. This demand has resulted in a global race to build AI infrastructure. Major technology companies are investing hundreds of billions of dollars into AI-related capital expenditure. Cloud providers are constructing new data centres at a pace rarely seen in the technology sector, while semiconductor manufacturers continue to expand production capacity to meet demand.

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From an investor's perspective, infrastructure offers several attractive characteristics. First, infrastructure businesses often sit at the centre of multiple AI value chains. Rather than relying on the success of a single application, they benefit from growth across thousands of AI products and services.

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Second, infrastructure providers can develop significant competitive advantages through scale, technical expertise and capital intensity. Building advanced semiconductor fabrication facilities or hyperscale data centres requires enormous investment, creating substantial barriers to entry.

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Third, infrastructure businesses frequently generate recurring revenue streams. Cloud computing providers, data storage operators and AI platform companies often benefit from long-term customer relationships and increasing utilisation as AI adoption grows.

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These characteristics have attracted both institutional and private market capital, with investors viewing infrastructure as a relatively lower-risk method of participating in the AI revolution.

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Despite the attention surrounding infrastructure, the application layer remains where many of the most visible innovations are occurring.

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AI applications are transforming industries ranging from healthcare and financial services to manufacturing, logistics and education. Companies are using AI to automate workflows, improve decision-making, reduce costs and create entirely new products and services.

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The barriers to building AI applications have fallen dramatically over the past several years. Developers can now access powerful foundation models through application programming interfaces (APIs), allowing them to focus on solving industry-specific problems rather than developing their own AI models from scratch.

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This has triggered an explosion of startup activity. Thousands of AI-enabled businesses have emerged globally, targeting everything from legal document review and software development to customer service and scientific research.

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For investors, the application layer offers the potential for extraordinary returns. Successful applications can scale rapidly with relatively modest capital requirements compared with infrastructure businesses. Software companies can achieve significant operating leverage, particularly when AI enhances productivity and reduces labour intensity.

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However, the application segment also presents challenges. Competition is fierce, customer switching costs can be low and product differentiation may prove difficult as underlying AI capabilities become more widely available. As a result, investors are becoming increasingly selective, focusing on businesses that possess proprietary data, industry-specific expertise or deeply integrated customer relationships.

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Recent venture capital activity provides an important window into where sophisticated investors believe value will emerge.

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During the initial wave of generative AI investment, capital flowed heavily into foundation model developers and infrastructure providers. Investors recognised that computing power and model development capabilities would be essential enablers of the AI ecosystem.

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More recently, however, there has been a noticeable broadening of investment activity into applications. Venture investors are increasingly backing companies that use AI to address specific business problems rather than attempting to build foundational technologies.

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This evolution mirrors previous technology cycles. During the early internet era, significant capital was directed toward network infrastructure and telecommunications. Once the infrastructure matured, value creation increasingly shifted toward applications and services built on top of that foundation.

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A similar pattern appears to be emerging within AI. Infrastructure remains essential, but investors are increasingly searching for businesses capable of translating AI capabilities into measurable commercial outcomes.

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The key distinction is that AI applications must demonstrate tangible economic value. Investors are looking beyond technological novelty and focusing on productivity gains, revenue enhancement, cost reductions and customer retention.

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Australia occupies an interesting position within the global AI landscape. While the country is unlikely to become a dominant producer of advanced semiconductors or hyperscale cloud infrastructure, it is developing strengths in several important areas of the AI value chain.

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Australia's growing data centre sector is attracting substantial investment as demand for AI computing infrastructure increases. Companies such as NEXTDC have expanded capacity to support cloud computing and AI workloads, while international technology firms continue to invest in Australian digital infrastructure.

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Australia also possesses significant advantages in renewable energy generation, which may become increasingly important as AI-related power consumption rises globally. The intersection of AI infrastructure and energy infrastructure is emerging as a strategic investment theme, with access to reliable and cost-effective electricity becoming a competitive differentiator.

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At the application level, Australia has produced a growing number of AI-focused businesses addressing industry-specific challenges. Areas such as mining technology, agriculture, healthcare, financial services and education have proven fertile ground for AI innovation.

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Companies including Harrison.ai have demonstrated how Australian businesses can build globally relevant AI applications by leveraging domain expertise and proprietary datasets. Similarly, AI-driven mining technology, agritech and enterprise software companies continue to attract investor attention.

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For private market investors, Australia's strength may ultimately lie less in foundational infrastructure and more in specialised applications that solve complex industry problems.

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One reason infrastructure continues to attract enormous amounts of capital is that AI remains constrained by computing resources.

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Demand for processing power has consistently exceeded supply, creating favourable economics for infrastructure providers. Companies supplying GPUs, networking equipment and data centre capacity have enjoyed strong pricing power as customers compete for limited resources.

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However, history suggests that infrastructure economics eventually normalise. Capacity expands, competition increases and margins often compress over time.

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Applications follow a different economic model. While they may face greater competitive pressure, successful software businesses can achieve substantial margins once they reach scale. They are typically less capital intensive and can grow rapidly without requiring billions of dollars of physical infrastructure investment.

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This dynamic creates an interesting tension for investors. Infrastructure may offer more predictable near-term growth, while applications may provide greater long-term upside.

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As AI technology becomes increasingly commoditised, the businesses that own customer relationships, proprietary data and industry workflows may capture a larger share of economic value.

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For wholesale and sophisticated investors, private markets are likely to play an increasingly important role in AI investment.

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Many of the most innovative AI companies remain privately held for longer periods than previous generations of technology businesses. As a result, significant value creation is occurring before public market listings.

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This trend is particularly relevant in Australia, where emerging AI companies often seek growth capital through private funding rounds rather than immediate public market access.

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Investors evaluating AI opportunities should consider where a company sits within the broader AI ecosystem. Infrastructure businesses may benefit from long-term demand growth but often require substantial capital investment. Application businesses may offer faster growth potential but require strong competitive differentiation.

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The most compelling opportunities may emerge at the intersection of the two. Companies that combine proprietary infrastructure, specialised datasets and industry-specific applications can create powerful competitive advantages that are difficult to replicate.

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Examples include enterprise AI platforms, industry-specific operating systems and workflow solutions deeply embedded within customer operations. These businesses are often positioned to capture recurring revenue while benefiting from the ongoing advancement of underlying AI technologies.

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The AI investment narrative is often presented as a choice between infrastructure and applications. In reality, the ecosystem is highly interconnected.

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Infrastructure enables applications, while applications drive demand for infrastructure. The relationship is symbiotic rather than competitive.

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Nevertheless, the distribution of investment capital provides important signals about where investors believe risk-adjusted returns can be achieved. To date, infrastructure has captured a disproportionate share of funding because it represents the foundation upon which the entire AI economy depends.

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As the market matures, however, value creation is likely to become more evenly distributed. The winners may not necessarily be those building the largest models or operating the largest data centres. Instead, they may be the companies that use AI most effectively to solve meaningful commercial problems.

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For investors, this suggests a more nuanced approach is required. Rather than simply seeking exposure to AI as a theme, it is increasingly important to understand where within the value chain a business operates, how it generates competitive advantage and whether it can sustain that advantage as the technology evolves.

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The debate between AI infrastructure and AI applications is ultimately a debate about where value will accrue within one of the most transformative technological shifts in modern history.

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Today, much of the capital continues to flow toward infrastructure because AI's rapid growth requires enormous computing resources, data storage capacity and network capability. These foundational assets remain critical bottlenecks in the ecosystem.

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Over time, however, investors are likely to place increasing emphasis on applications that deliver measurable business outcomes and establish durable customer relationships. As infrastructure becomes more abundant and accessible, differentiation may increasingly shift toward proprietary data, domain expertise and workflow integration.

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For wholesale and sophisticated investors exploring opportunities in private markets, the key is not choosing one side of the equation over the other. Rather, it is understanding how infrastructure and applications interact, where competitive advantages are emerging and which businesses are positioned to capture value as AI adoption moves from experimentation to economic necessity.

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The AI revolution is not simply about technology. It is about the creation of entirely new markets, business models and investment opportunities. The investors who understand where capital is going today—and where value is likely to emerge tomorrow—will be best positioned to benefit from the next phase of AI-driven growth.

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And that brings us to the end of this episode of Unlocking Liquidity. Thanks for spending your time with us, we hope today’s conversation gave you a fresh perspective on private markets and how liquidity is evolving. If you enjoyed the episode, please follow or subscribe wherever you listen, and feel free to share it with someone who’d get value from it. For more insights, opportunities and episodes, visit PrimaryMarkets.com. Until next time, thanks for listening, and we’ll see you in the next conversation.